JP6427640B2 - Activity analysis system, activity analysis method, and program - Google Patents

Description

  The present invention relates to an activity analysis system, an activity analysis method, and a program.

  In order to execute business efficiently, it is necessary that an office environment suitable for business is provided. In creating a new office environment, the current activity status of a user who is an office worker in the environment before creating a new office environment may be investigated. For example, methods such as activity-based costing and action measurement using a carrying tag are known as methods of investigating the activity status. As one method of the above-mentioned action measurement, the technology of fixed point observation which uses a RFID tag or detects a work state using image information is disclosed (for example, refer to patent documents 1). According to Patent Document 1, it is possible to analyze the movement of the user of the facility based on the image information captured in the actual facility.

JP, 2012-218861, A

  However, by analyzing the movement of the user of the facility based on the image information and the like captured at the actual facility by the method as described in Patent Document 1, a plan for suitably using the facility is to be formulated. I can not In addition, there is a problem that a method of quantitatively verifying whether the created office plan is as expected is not established.

  The present invention has been made in view of such circumstances, and it is an object of the present invention to provide an activity analysis system, an activity analysis method, and a program that support the creation of a plan for suitably using a facility. is there.

The present invention has been made to solve the above problems, a first activity analysis system is an aspect of the present invention, in order to analyze the user's activities in the facility, the facility activities of the user An activity analysis system that models and simulates an action of an agent in a related numerical model space, comprising: first data defining a numerical model space related to the facility; and an action characteristic of the agent corresponding to the user A second data defining, a third data defining an action scene as a model of an action continuing for a predetermined time in the numerical model space, and a third data for correcting the behavior characteristic of the agent defined And various data including four data are stored in the storage unit, and the second data is stored based on the fourth data. The action characteristic of the agent, and selecting a specific action scene from action scenes defined by the third data based on the corrected action characteristic of the agent; According to the first data, an arithmetic processing unit that executes a simulation that causes the agent to act in the numerical model space of the facility, and the user's behavior such that the agent's behavior characteristic approaches the user's behavior characteristic and a data generation unit for generating the second data and the fourth data based on historical, the data generating unit, when the data of the analyzed behavior scene of the agent to the second data are present in advance Following the fourth data corresponding to the change in the action scene of the agent to be analyzed. If the data of the action scene to be analyzed by the agent does not exist in advance in the second data, the fourth data corresponding to the action scene newly provided as the action scene to be analyzed is added, It is an analysis system .
In the above-described activity analysis system, the data generating unit corrects the second data for determining the behavioral characteristics of the agent based on the behavioral characteristics of the user obtained by the measurement, the behavioral characteristics of the agent the Generate fourth data.
Further, in the above-described activity analysis system, the data generation unit generates the fourth data for correcting the behavior characteristic of the agent so as to be the behavior characteristic of the user derived from the analysis result of the subjective survey.
The above-mentioned activity analysis system also includes an analysis unit that analyzes the user's activity history and generates the fourth data for correcting the agent's activity characteristic.
Further, in the above activity analysis system, the analysis unit analyzes the usage state of the facility from behavior history data on the behavior history of the user, and from the data including behavior history data on the user behavior history, the analysis unit Perform at least one of analyzing space fullness status.
In the activity analysis method according to the second aspect of the present invention, the computer of the activity analysis system comprises: first data defining a numerical model space related to the facility; and an activity characteristic of the agent corresponding to the user A second data defining, a third data defining an action scene as a model of an action continuing for a predetermined time in the numerical model space, and a third data for correcting the behavior characteristic of the agent defined And storing various data including four data in the storage unit, correcting the behavior characteristic of the agent based on the second data based on the fourth data, and based on the corrected behavior characteristic of the agent, A specific action scene is selected from action scenes defined by the third data, and according to the selected action scene, When you run a simulation to action the agent in serial numerical model space of the facility defined by the first data, behavioral characteristics of the agent based on the user's behavior history so as to approach the behavior characteristics of the user the The second data and the fourth data are generated, and when data of an action scene to be analyzed by the agent is present in the second data in advance, the second data corresponding to a change in the action scene to be analyzed by the agent 4 data is added, and when the data of the action scene to be analyzed by the agent does not exist in the second data in advance, the fourth data corresponding to the action scene newly provided as the action scene to be analyzed is An additional step of performing an activity analysis method.
In addition, the program according to the third aspect of the present invention is an activity of modeling and simulating the user's activity into the agent's behavior in the numerical model space related to the facility in order to analyze the user's activity in the facility In the computer of the analysis system, first data defining a numerical model space related to the facility, second data defining an action characteristic of the agent corresponding to the user, and the agent in the numerical model space are defined. Storing, in a storage unit, various data including third data that defines an action scene as a model of an action that continues for a long time and fourth data for correcting the behavior characteristic of the agent that is defined; Complementing the agent's behavior characteristic based on the second data based on the fourth data And selecting a specific action scene from the action scenes defined by the third data based on the corrected action characteristic of the agent, and defining by the first data according to the selected action scene Executing a simulation for causing the agent to act in the numerical model space of the facility, and the second data and the second data based on the user's action history such that the agent's action characteristic approaches the user's action characteristic Generating fourth data; and if data of an action scene to be analyzed by the agent is present in the second data in advance, the fourth data corresponding to a change in the action scene to be analyzed by the agent Data of the action scene to be analyzed by the agent added to the second data If not previously exist is a program for executing the steps of: adding the fourth data corresponding to the action scenes newly provided as the action scene of the analysis.
Further, in the office activity analysis system related to the above activity analysis system, there is provided an office activity analysis system for analyzing office activity in a facility configured such that a plurality of types of tasks are respectively executed by each user, An analysis unit that analyzes information including any of a history of communication between users and a user's action history, and data indicating the status of the office activity of the user based on the result of the analysis, And a data generation unit that generates data for correction.
Further, in the office activity analysis system related to the above activity analysis system, there is provided an office activity analysis system for analyzing office activity in a facility configured such that a plurality of types of tasks are respectively executed by each user, An analysis unit that analyzes information including any of a history of communication between users and a user's action history, and data indicating the status of the office activity of the user based on the result of the analysis, And a data generation unit that generates data for correction.

 Further, in the above-mentioned office activity analysis system, the analysis unit is a status of the office activity of the user including at least one of history information of communication between the users and user activity history information indicating activity history of the user. And a first analysis unit configured to analyze office activity of the user from activity history information indicating.

 Further, in the above-described office activity analysis system, the analysis unit includes a second analysis unit that analyzes the usage status of the facility from information including the activity history information.

 Further, in the above-described office activity analysis system, the analysis unit is characterized by comprising a third analysis unit that analyzes the fullness of the space of the facility from the information including the activity history information.

 Further, the office activity analysis system described above is characterized by comprising a fourth analysis unit that generates a proximity request degree between a group to which the user belongs and another group based on a result of the analysis.

In the office activity analysis system described above, the user belongs to any one of a plurality of groups disposed in the facility, and the analysis unit includes history information of the communication and the user behavior history information. A fifth analysis unit analyzes the degree of association between a group to which the user belongs and other groups from information.

 Further, in the above-described office activity analysis system, the analysis unit generates, based on the activity history information, correction information for correcting the office plan of the facility generated based on the proximity request degree. Do.

  Further, in the office activity analysis system described above, the history information of the communication includes history information in which information is transmitted and received using communication between the user and another user who uses the facility. I assume.

 Further, in the above-described office activity analysis system, the analysis unit detects a specific keyword included in the communication amount or communication of communication determined that users belonging to different organizations are communicating using communication. The number of times or frequency is counted, and the degree of proximity between the tissues is calculated according to the counted result.

  Also, the office activity analysis method related to the above activity analysis system is an office activity analysis method for analyzing office activity in a facility configured such that a plurality of types of tasks are respectively performed by each user, Analyzing the information including any of the history of communication between users and the user's action history, and data indicating the status of the office activity of the user based on the result of the analysis, And D. generating data for correcting the office.

  In addition, the program related to the above activity analysis system communicates between the users on a computer of an office activity analysis system that analyzes office activity in a facility configured such that a plurality of types of tasks are respectively performed by each user. Analyzing the information including any of the user's history and the user's action history, and based on the result of the analysis, data indicating the status of the office activity of the user, the office of the user is corrected And a step of the data generation unit generating data to be executed.

  According to the present invention, it is possible to provide an activity analysis system, an activity analysis method, and a program that support the creation of a plan for suitably using a facility.

FIG. 1 is a block diagram of an office planning support system according to an embodiment of the present invention. It is an explanatory view showing a data flow of an office planning support system concerning this embodiment. It is explanatory drawing which shows the relationship of the actual activity and action scene which concern on this embodiment. BRIEF DESCRIPTION OF THE DRAWINGS It is explanatory drawing which shows an example of a structure of the subjective research system 100 which investigates the office environment in the plant | facility where several structure | tissues (tenant) enter a plant | facility based on this embodiment. It is a block diagram which shows the structure of the objective investigation system 200 which concerns on this embodiment. It is an explanatory view about the specification of the place where users have approached concerning this embodiment. It is a flowchart which shows the procedure of the process which concerns on the objective investigation of the office plan assistance system which concerns on this embodiment. It is an explanatory view shown about the degree of proximity demand concerning this embodiment. It is explanatory drawing shown about the degree of inter-tissue proximity which concerns on this embodiment. It is explanatory drawing shown about the degree of inter-tissue proximity which concerns on this embodiment. It is a block diagram of the plan requirement extraction system in this embodiment. It is a flowchart which shows the procedure of the process which produces | generates the plan requirement of the basic plan in this embodiment. It is explanatory drawing explaining the process which extracts the division with a high degree of association between each division based on the degree of closeness between organizations in this embodiment. It is explanatory drawing which shows the table used for calculation of the utilization equivalent area based on the analysis result of the action log in this embodiment. It is a flowchart which shows the procedure of the process which selects the form of a specific action scene based on the result of the action log in this embodiment. It is a radar chart which shows the sufficiency degree for every action scene by the subjective survey result in this embodiment. It is a radar chart which shows the fulfillment degree for every environment by the subjective survey result in this embodiment. It is explanatory drawing which shows the table used for calculation of the required area in this embodiment. It is explanatory drawing which shows the calculation method of the required window surface length in this embodiment. It is explanatory drawing shown about the stacking process in this embodiment. It is explanatory drawing shown about the blocking process in this embodiment. It is a block diagram of the simulator in this embodiment. It is a flowchart which shows the procedure of the process in the office plan assistance system of this embodiment. It is explanatory drawing which shows the structural example of the plant | facility data storage part in this embodiment. It is an explanatory view showing an example of composition of group configuration information TBL in this embodiment. It is an explanatory view showing an example of composition of action definition TBL in this embodiment. It is an explanatory view showing an example of composition of common action definition TBL in this embodiment. It is an explanatory view showing an example of composition of function definition TBL in this embodiment. It is an explanatory view showing an example of composition of move destination distribution TBL in this embodiment. It is an explanatory view showing an example of composition of start-of-work condition setting TBL in this embodiment. It is an explanatory view showing an example of composition of history information TBL in this embodiment. It is explanatory drawing which shows an example which displays the simulation result in this embodiment as animation. It is explanatory drawing which supplements description of FIG. 31A in this embodiment. It is explanatory drawing which shows the example of the display which visualized the log | history which the agent crawled by simulation shown to FIG. 31A in this embodiment. It is explanatory drawing which shows an example which makes the number of persons who exist in an action scene as a simulation result in this embodiment a graph, and displays it. It is a flow chart which shows an outline procedure of processing in simulator 300 in this embodiment. It is a flowchart which shows the procedure of the simulation process in the simulator 300 in this embodiment.

  Hereinafter, embodiments of the present invention will be described with reference to the attached drawings.

  FIG. 1A is a block diagram of an office planning support system according to an embodiment of the present invention. FIG. 1B is an explanatory view showing a data flow of the office planning support system according to the embodiment of the present invention.

The office planning support system 1 (planning support system) shown in FIG. 1A includes a subjective survey system 100, an objective survey system 200 (activity analysis system), a simulator 300, a plan requirement extraction system 400, a storage unit 500, and a basic data input system 800. And a data output system 700.
Subjective investigation system 100 collects opinions of users (users) who use facility 2 (existing facilities) of an existing office, and based on the collected opinions, each user feels in using facility 2 Visualize the data and output the data.
The objective survey system 200 (office activity analysis system) analyzes office activity in the facility 2 configured such that a plurality of types of tasks are respectively performed by each user. Such an objective investigation system 200 collects information indicating the usage status of the facility 2 without intervention of the user. The objective investigation system 200 analyzes the utilization state of the facility 2 based on the collected data, and outputs the analyzed data. For example, the objective investigation system 200 analyzes information including any of communication history between users and user behavior history. Alternatively, the objective investigation system 200 analyzes information including history information of communication of the user and user behavior history information indicating the behavior history of the user. For example, information including the communication history information of the user and the user behavior history information indicating the user behavior history is treated as the user activity history information.
The basic data input system 800 acquires basic data used for processing in a simulator 300 described later, and stores the basic data in the storage unit 500.
The simulator 300 assumes that work is performed in an existing facility 2 or an office (facility) at the design stage as the business is performed in a virtual facility corresponding to the facility 2 or an office (facility) at the design stage. Simulate. By this simulation, the simulator 300 generates data that estimates the office environment in the facility corresponding to the virtual facility. The simulator 300 verifies the office plan designed by the designer.
The plan requirement extraction system 400 helps the created office plan to function effectively and to create an optimal office plan in the facility. For example, the planning requirement extraction system 400 is a facility where a plurality of types of tasks are performed by each user, and supports creation of an office plan of a facility used by the user. The plan requirement extraction system 400 generates a plan requirement for designing an office plan based on various survey data stored in the storage unit 500. For example, such a planning requirement extraction system 400 performs various processes in cooperation with the subjective research system 100, the objective research system 200, the simulator 300, and the like.
The data output system 700 outputs data generated by the subjective research system 100, the objective research system 200, the simulator 300, and the plan requirement extraction system 400, respectively. For example, the data output system 700 includes data relating to the plan requirements of the basic plan generated by the plan requirement extraction system 400, survey data including subjective survey data and objective survey data visualized by the plan requirements creation unit 411, The data including the verification result verified by the simulator 300 is output.
The storage unit 500 includes various data generated and output in each unit of the subjective investigation system 100, the objective investigation system 200, the simulator 300, and the plan requirement extraction system 400, and various data used for processing of each of these systems. Remember. For example, the storage unit 500 may be provided with subjective survey data including either a subjective survey result of the user or an analysis result based on the subjective survey result, and an objective survey result for an activity of the user or an analysis result based on the objective survey result. Either or both of the objective survey data including any is included in the survey data, and the survey data is stored. When the above-mentioned objective survey data includes information obtained from the result of objective survey on the activity of the user in the used facility that the user is using, the objective survey data includes each function of each facility in the used facility. It contains information indicating the usage status. The information indicating the usage status of each function in the used facility may include information such as user's (worker's) behavior information, schedule information, communication information and the like by a hand-held or installed-type sensor. Further, both the user targeted for the subjective survey and the user targeted for the objective survey are users of existing facilities.
Hereinafter, details of the subjective research system 100, the objective research system 200, the plan requirement extraction system 400, and the simulator 300 will be described in order.

[1. Subjective survey system 100]
The subjective research system 100 will be described with reference to FIGS. 2 and 3.
The subjective research system 100 obtains the opinion of the user who uses the facility 2 of the existing office as subjective research data, and carries out a process of analyzing the subjective research data.

<Analysis of subjective survey data>
Analysis of subjective survey data refers to the process of collecting the opinions of the users who are actually using the facility and analyzing what the user feels when using the facility based on the collected opinions. is there. In this analysis, the subjective research system 100 performs statistical processing by quantifying the extracted result by extracting the potential feeling of each user by a method such as a questionnaire or hearing. The subjective survey system 100 can statistically organize what the user individually felt from the results of the questionnaire and the hearing, and can obtain data that can be handled as the consensus of the user. As a method of performing such analysis, for example, the method of Japanese Patent No. 4500846 is known.

  According to Japanese Patent No. 4500846, the user's action is analyzed according to the action scene (work scene). Hereinafter, the process of the subjective research system 100 will be described which classifies the user's action into any of a plurality of types of action scenes and analyzes the user's action. For example, as an example of a plurality of types of action scenes, there are action scenes such as actions which a user personally carries out and actions which are carried out with a plurality of users. Note that different types of actions may be assigned to those defined as the same type of action scene. For example, different types of actions may be assigned according to constraints.

The relationship between an actual activity and an action scene will be described with reference to FIG. The figure is an explanatory view showing a relation between an actual activity and an action scene.
The facility currently occupied by the resident P11 forming an organization is referred to as a facility 2-1. The actual activities of the resident 11 include a single operation W11A performed on an individual basis, a communication operation W11C sharing information using a communication means, and a cooperative operation W11B performed with other persons in the facility. The contact work W11C such as processing of a telephone or an email is not limited to a specific action scene, and can be performed on any action scene unless the use of the communication device is restricted. The communication work W11C includes a case where the target address is outside the facility and a case where the destination is addressed to another department in the facility.

<Configuration Example of Subjective Investigation System 100>
(First Configuration Example of Subjective Investigation System 100)
Moreover, the figure shows an example of a structure of the subjective survey system 100 which surveys the office environment in each plant | facility.
The subjective research system 100 shown in the figure investigates the opinion of the resident P11 about the office environment in the facility 2-1. The subjective research system 100 includes an individual research unit 110 and a general research unit 120.
The individual investigation unit 110 obtains information on the office environment in the facility 2-1 which is the office environment Y111. The individual investigation unit 110 compiles the survey results of the office environment Y 111 and outputs the compiled survey results.

Furthermore, as shown in the figure, in the case where organizations having high degrees of association are dispersed among a plurality of facilities, the individual investigation unit 110 sets information on the office environment in the facility 2-2 as the office environment Y112, It is acquired separately from the above office environment Y111. The individual investigation unit 110 acquires information on the office environment in the facility 2-3, which is the office environment Y113, separately from the office environment Y112. The individual investigation unit 110 sums up the investigation results for the office environment Y112 and the office environment Y113, and outputs the respective investigation results.
The integrated research unit 120 obtains integrated research results based on the results compiled by the individual research units 110.
As shown in the figure, different facilities 2-1, 2-2, 2-3 (wherein the facilities 2 are simply referred to in the following description when the tenants with high degree of business relevance move in) If there is, each individual investigation section 110 divides the investigation about the office environment in each of the facilities according to each of the facilities.

(Second Configuration Example of Subjective Investigation System 100)
Moreover, FIG. 3 is explanatory drawing which shows an example of a structure of the subject matter investigation system 100 which investigates the office environment in the plant | facility where several structures (tenant) move in one plant | facility. As shown in the figure, in the case where a plurality of organizations (tenants) move into one facility (for example, the facility 20), it is preferable to conduct a survey in units of organizations.
The individual investigation part 110 puts together the investigation result about office environment Y121, office environment Y122, and office environment Y123, and outputs each investigation result. Generate survey results for each organization (resident). The integrated research unit 120 integrates the survey results of each organization (tenant) to obtain a comprehensive survey result.

  The subjective research system 100 described above receives the opinion of the user (resident, the organization to move in) using the existing facility 2 as the subjective research data, and carries out a process of analyzing the received subjective research data. For example, the subjective survey system 100 collects the user's opinion by a questionnaire, and statistically processes and quantifies the questionnaire result. Thereby, the subjective research system 100 generates and outputs subjective research result data summarizing the user's opinion. The subjective research system 100 writes and stores the output subjective research result data in the subjective research result data TBL of the storage unit 500 (FIG. 1).

[2. Objective Survey System 200]
The objective investigation system 200 will be described with reference to the drawing.

<Configuration of Objective Survey System 200>
FIG. 4 is a block diagram showing the configuration of the objective investigation system 200. As shown in FIG. In the figure, in addition to the objective survey system 200, a subjective survey system 100, a simulator 300, a plan requirement extraction system 400, and a storage unit 500 are shown together.

  The objective investigation system 200 includes an action measurement unit 210, an action measurement data acquisition unit 220, an analysis unit 230, a communication history measurement unit 260, and a communication history data acquisition unit 270.

  The behavior measurement unit 210 in the objective investigation system 200 measures the behavior of each user in the facility 2. The action measurement data acquisition unit 220 acquires data (user action history information) indicating the user's action obtained by the measurement by the action measurement unit 210 from the action measurement unit 210, and the history information storage unit 521 of the storage unit 500. Remember to Here, the history information storage unit 521 of the storage unit 500 will be described. The history information storage unit 521 associates and stores identification information of a group and user action history information indicating an action of a user belonging to the group.

The communication history measurement unit 260 in the objective investigation system 200 measures communication behavior using the communication means of each user in the facility 2. For example, the communication history measuring unit 260 detects communication using the communication means, and sets the detected communication history as communication history data (communication history information). The communication history data acquisition unit 270 acquires communication history data (communication history information) and stores the communication history data in the history information storage unit 522 of the storage unit 500. Here, the history information storage unit 522 of the storage unit 500 will be described. The history information storage unit 522 associates and stores identification information of a group and history information of communication of users belonging to the group. The history information storage unit 522 stores the group identification information and the communication history information of the communication of the user belonging to the group in association with the communication history information between the groups detected from the history information of the communication. You may configure it.
Note that the information including at least one of the history information of communication between users obtained as described above and the user behavior history information indicating the behavior history of the user is an activity history showing the status of the office activity of the user Described as information.

  The analysis unit 230 in the objective investigation system 200 analyzes data acquired from at least one of the behavior measurement data acquisition unit 220 and the communication history data acquisition unit 270, and outputs an analysis result. The analysis unit 230 obtains user action history information from the action measurement data acquisition unit 220, and obtains communication history information from the communication history data acquisition unit 270. The analysis unit 230 analyzes the degree of association between groups from information including at least one of user action history information indicating actions of users belonging to different groups and / or detected communication history information.

  For example, the analysis unit 230 includes a first analysis unit 231, a second analysis unit 232, a third analysis unit 233, a fourth analysis unit 234, and a fifth analysis unit 235. The first analysis unit 231 analyzes the activity of the user from information including at least one of the detected history information of communication and the user behavior history information. For example, the first analysis unit 231 may use the history information of communication between users stored in the history information storage unit 522, and the user behavior history information indicating the user's behavior history stored in the history information storage unit 521. The user's office activity is analyzed from activity history information indicating the status of the user's office activity including at least one of them. The second analysis unit 232 analyzes the use situation of the facility from the information including the activity history information stored in the history information storage unit 521 or the history information storage unit 522. For example, the analysis of the usage condition of the facility is performed by extracting the total usage time for each function provided in the facility, and comparing the predetermined reference usage time (threshold) with the total usage time of the function provided in the facility. The third analysis unit 233 compares the above-mentioned analysis with the magnitude relationship or calculates the usage rate per unit period, and outputs the result. The third analysis unit 233 analyzes the sufficiency situation of the space of the facility from the information including the activity history information stored in the history information storage unit 521 or the history information storage unit 522. For example, the analysis of the space fullness condition of the facility is performed by extracting the number of users per floor area and comparing the number of persons (threshold value) determined in advance with the number of users per floor area. The third analysis unit 233 carries out the above analysis by comparing the magnitude relationship or calculating the ratio, and outputs the result. The fourth analysis unit 234 generates proximity request data based on information including at least one of communication history information and user action history information detected by the analysis result of the first analysis unit 231, and outputs the proximity request data. The proximity request data indicates a group that can be determined to be effective to be placed in proximity among the groups when placing different groups in a common facility. The fifth analysis unit 235 generates information for determining constraint conditions of the simulator 300 based on the data acquired from the action measurement data acquisition unit 220, and outputs the generated data. The analysis unit 230 writes and stores data output from each unit in the analysis unit 230 in the storage unit 500.

  The objective investigation system 200 comprised as mentioned above implements the process which analyzes the objective investigation data shown below, for example.

<Process to analyze objective survey data>
In the process of analyzing the objective survey data, information indicating the use state of the facility 2 is collected without intervention of the user, and the use state of the facility 2 is analyzed based on the collected data. The objective investigation system 200 collects necessary data and carries out an analysis process based on the collected data without imposing a burden on the user on the investigation.

  The behavior measurement unit 210 collects objective survey data. When collecting objective survey data, it is better to survey without giving the user a sense of "being surveyed" or without letting the user perform a specific action for survey. For example, since the user is conscious of being "investigated", he or she may take a different action from the action when he was not conscious, or may limit the action. By collecting the objective survey data such that the behavior measurement unit 210 does not receive such an influence, it is possible to obtain data of the user at normal times.

  The data based on the objective survey collected without giving the user the awareness of "investigated" can be roughly divided into two. The first data relates to the survey results of the user's behavior. The second data relates to the survey result of the user's communication.

(About "investigation about user's action")
The "investigation of the user's behavior" is to investigate the user's behavior accompanied by movement in the facility. For example, by conducting this survey, it is possible to obtain as a data the situation in which interviews and meetings with users of related departments moving into the same facility are conducted, and the situation in which the facility in the facility is used. .

  The following method may be mentioned as a method of the “survey about the user's action” by the action measurement unit 210. For example, as a specific method of behavior survey, one that collects information using a camera, a seating detection sensor, a portable identification device, a room entry / exit gate, a microphone, etc., self-reported collection, fixed-point observation by a third party, etc. Those to be implemented are listed. The behavior measurement unit 210 may include various sensors corresponding to the above measurement method, and may be configured to obtain information from the outside of the behavior measurement unit 210. Also, any medium can be used to record the survey results regardless of the type, and analysis is performed based on the collected survey results.

For example, when information is collected using a camera (imaging device) and an image processing device as sensors, the situation of the site obtained by the camera is recorded as a moving image or a still image. When recorded as a moving image, movement and behavior of a person detected as a subject can be continuously recorded, and movement (action) of a person can be extracted by performing image processing based on information of the moving image. . On the other hand, when a still image is recorded, it can be used as information sampled at the imaging time point by performing image processing based on the information of the still image imaged intermittently. For example, a region where there is a change between images can be extracted from the images by differential processing between still images.
Furthermore, by identifying a person imaged as a subject from the image, it is possible to detect that the person was present in the imaging range of the camera. The camera may use a camera installed at a fixed point, or a camera carried by a third party may be used. Even in the case of using a portable camera, it is possible to detect an approach of a person carrying the camera and a person imaged as a subject, and the implementation status of communication.

The information of the seating detection sensor can be obtained from the information from the sensor provided according to the chair, the operation history information of the terminal device used in the seat, etc., and the seating state of a specific seat is detected from the information can do.
For example, when information is collected using a seating detection sensor, the sensor provided according to the chair may be provided on any of the seat surface of the chair, the backrest, the armrest, the leg, etc. As long as the state of sitting on the chair can be detected, there is no limitation on the installation position, installation method, and the like. Alternatively, a sensor provided according to the chair may be provided above the seat (for example, the ceiling) or at a table. Note that seating information may be obtained by applying the image processing method described above. In the case of these detection methods, it is possible to detect the state as to whether or not sitting. On the other hand, when detecting using the operation history information of the terminal device, the user who used the seat is specified from the authentication information for using the terminal device or the network, or based on the information, sitting in the seat It can also be used as information indicating the state of presence or absence.

When collecting information using a portable identification device as a sensor, obtain position information of the portable identification device from information detected using a portable identification device carried by the user, or from detection history information recording the information. Can. Further, the position of the portable identification device detected using the portable identification device and the information corresponding to the detected time are associated with the identification information of the portable identification device and stored as a detection history, and the detection history information is stored. You may use it. The information corresponding to the detected time does not necessarily have to be the detected time itself. For example, the time may be quantized according to the detection accuracy, and information indicating the quantized time may be used.
There are the following two methods of collecting information using a portable identification device.
For example, a terminal (base station) is installed at a predetermined predetermined position, and the terminal detects the proximity of the portable identification device to detect the position of the portable identification device from the position of the terminal at which the portable identification device is detected. You can get information on Furthermore, if a plurality of terminals are provided, and at each of the different terminals, a portable identification device to which specific identification information is given is detected at different times, respectively, the user carrying the portable identification device can It can be estimated that it moved between the terminals.
Further, the approach of the portable identification devices carried by the user may be detected, and the approach of the users carrying the respective portable identification devices may be detected.
If the portable identification device to which specific identification information is given is detected at different times, it can be estimated that the user carrying the portable identification device has moved between the terminals.
Alternatively, a terminal (base station) may be installed at a predetermined position, and the position of the terminal may be acquired by detecting a signal transmitted by the terminal by the portable identification device. Furthermore, a plurality of terminals are provided, and the portable identification device detects signals from different terminals, whereby identification information of each terminal included in the signal detected by the portable identification device and the received signal strength of the same signal (RSSI ) May be used to detect the position of the portable identification device carried by the user. The proximity of the portable identification devices carried by the user may be detected to detect that the users carrying the respective portable identification devices are approaching.
When the portable identification device to which specific identification information is given detects signals from different terminals at different times, it can be estimated that the user carrying the portable identification device has moved between the above-mentioned terminals.
As the portable identification device, various devices such as RFID (radio frequency identifier) tags, RFID tag readers, mobile phones, smart phones, watches having wireless communication functions, tablet-type terminal devices, game machines, etc. can be used. As the terminal described above, various wireless transmitters, various wireless communication devices, and the like corresponding to the above-described portable identification device can be used.

  When collecting information using an entry / exit gate, a gate management device that manages entry / exit of the user is provided as a sensor in the entry / exit gate. As information on entry and exit gates, history information can be obtained that the user has entered and exited through the gates. For example, the number of occupants in a specific room, or the persons or groups who are in occupancy may be specified from the information on entry / exit gates.

In the case of collecting information on the sound of the site, the sound generated by the user's activity or the sound of the site due to the interaction between the users can be collected by the microphone serving as the sensor to obtain information on the sound of the site.
When collecting the information of the vibration of the site, the vibration generated by the user's activity can be detected by the vibration sensor which is a sensor to obtain the information of the vibration of the site.

Self-reporting is a method of registering its own action history as data from a terminal device or the like. For example, there is a system (scheduler) that registers scheduled actions to perform schedule management. The historical data of the behavior is accumulated in order by registering the planned behavior or registering the performance of the behavior actually performed using such a system. Data registered in this manner may be used as self-reported data.
The fixed point observation by a third party is a method of observing and recording the situation in accordance with the detection condition at a predetermined position.

In the “investigation of the user's action” by the objective investigation system 200, for example, the action measurement unit 210 includes detection means corresponding to any one or more of the above detection methods, and each user in the facility 2 Measure the behavior of The action measurement unit 210 generates data (user action history information) indicating the action of the user detected by each detection means provided.
The action measurement data acquisition unit 220 acquires data (user action history information) indicating the user's action generated by the action measurement unit 210 and stores the data in the history information storage unit 521.
For example, in the history information storage unit 521, the time, the position, and the information of the group to which the approaching user belongs are stored in the user action history information TBL in association with each other at the time when the user approach is detected. The action measurement data acquisition unit 220 writes the acquired data in the user action history information TBL of the history information storage unit 521 and stores the data. In the user action history information TBL, the time, position, sound pressure level, vibration magnitude, etc. at which the user's approach was detected may be stored in association with information of groups to which adjacent users belong. Good.
A general method other than the above can be applied to the above detection method, the type of information to be recorded, and the method of storing the information.

(About "survey about user's communication")
The "investigation of the user's communication" is to investigate the state of communication between the departments using the facility using the communication means without accompanying the movement in the facility. For example, by conducting this survey, it is possible to obtain, as data, the situation in which the users of the related departments in the facility communicate with the users of the departments using the facility using communication means.
For example, as a method of investigating the status of communication, desired information may be obtained from a communication log of a communication management apparatus (hereinafter, simply referred to as “communicator (office communicator)”) that stores the communication status. A communicator (office communicator) is a telephone by IP (Internet Protocol) phone, a voice conference or video conference by a teleconference system, a web conference, a chat using short message service, an electronic mail, a screen sharing of a display screen of a terminal device, etc. It has a communication management processing function that records the history of communication between individuals and organizations. According to this investigation method, the telephone by IP (Internet Protocol) phone, the voice conference and the video conference by the remote conference system, the WEB conference, the chat using short message service, the electronic mail, the screen sharing of the display screen of the terminal device etc. Enables analysis based on communication surveys between individuals and organizations.

  In addition to the above, a telephone (IP telephone), a facsimile (FAX), an e-mail service server (POP (Post) that is subjected to connection processing via a circuit-switched local telephone exchange (PBX) or by a connection processing server. Office Protocol)) server, Internet Message Access Protocol (IMAP) server, e-mail via SMTP (Simple Mail Transfer Protocol), etc., short message communication using SNS (social networking service), bulletin board etc. Posting may also be included in the analysis. Furthermore, it may be analyzed including information such as self-reporting on the communication history and fixed-point observation by a third party. In addition, the medium which records each information can be used for analysis regardless of the type.

  In the “investigation of the user's communication” by the objective investigation system 200, for example, the communication history measurement unit 260 is a device for recording the communication history of a communicator (office communicator) or the like provided corresponding to the network equipment in the facility 2. Get the communication log recorded by The communication history measurement unit 260 detects the history of communication using the communication means from the acquired communication log, and generates the history of the detected communication as communication history data (communication history information). The communication history data acquisition unit 270 acquires communication history data (communication history information) and causes the communication history information TBL of the history information storage unit 522 to store the communication history data.

  As described above, by investigating the status of communication between the departments using the facility, it is possible to investigate the activity status by the communication of the user without moving the user.

("About the analysis method of the user's activity")
In the process of analyzing the user's activity based on the collected objective survey data, at least one of the processes according to the following analysis method is performed. The processing to be performed is as follows.
・ Analytical processing when detecting situations where users are in close proximity to each other ・ Analytical processing when detecting situations where communication is taking place using communication ・ General analytical processing of office activities The above-described processing to be performed as processing to be analyzed will be described in order.

(1) Analysis Processing when Detecting Situation in which Users are Close to Each Other When detecting the situation in which users are close to each other, the analysis unit 230 performs the following analysis process.
-Identification of places where users are close to each other-Identification of users when users are close to each other-Identification of users where users are close to each other Organizations to which users belong-Users are close to each other Identification of Proximity Between Tissues to which the User in the Case belongs In the following, each analysis process described above will be described in order.

(1-1) Identification of a place where users are in proximity With reference to FIG. 5, the identification of a place in which users are close will be described. The figure is an explanatory view for specifying a place where users are close to each other.
For example, the analysis unit 230 of the objective investigation system 200 identifies the location where the users are in proximity based on the information acquired by any of detection means such as a camera, sitting, a portable identification device, an entrance / exit gate, or a microphone. To carry out.
The analysis unit 230 outputs the information of the position where it is determined that the users are close to each other based on the respective information. The position may be a grid determined based on coordinates determined based on latitude / longitude, azimuth, etc. or coordinates determined based on the shape of the facility or site, an area divided at a specific floor of the facility, etc. Based on at least one of

For example, the facility shown to the figure (a) is mentioned as an example, and is demonstrated. The organization shown in FIG. 6A is divided into two floors, and the organizations A to F move in. In the illustrated state, the tissues A to C are arranged on the second floor, and the tissues D to F are arranged on the first floor.
The figure (b) shows the example which defined the grid according to the shape of the floor of a plant | facility.
The same figure (c) shows the example which defined several area according to the shape of the floor according to the function allocated to the floor of a plant | facility.
The figure (d) shows the example which defines a position by the grid defined according to the shape of the floor of a plant | facility, and several areas defined according to the function allocated to the floor of a plant | facility. As shown in (d) of the figure, by defining both a simple grid and an area indicating the allocated function, it is easy to specify the position.

(1-2) Identification of users when users are close to each other For example, the analysis unit 230 specifies the users when the users are close to each other by a camera, sitting, carrying RFID, a room entry / exit gate, Implement based on the information acquired using a microphone or the like.
As described above, the position of the user can be specified by at least one of the above methods. Therefore, the analysis unit 230 specifies that there are users who are close to each other by at least one of the following methods from the information of the specified position of the user.
The analysis unit 230 detects that the positions of at least two users remain in the same zone for at least a predetermined time after the positions of at least two users are specified. , Identifying that there are users who are close to each other. Here, the zone indicates an area determined based on the unit grid, and indicates an area corresponding to any unit grid.
-After the positions of at least two users are specified, the analysis unit 230 keeps the state in which at least two users continue to be in proximity and stay in the corresponding zone for a predetermined time or more. When it is detected, it is identified that there are users who are close to each other.

(1-3) Identification of an organization to which a user belongs when the users are close to each other For example, the analysis unit 230 refers to the group configuration information TBL 532 in which the information of the user is stored, and the user Get information about the organization to which you belong. Details of the group configuration information TBL 532 will be described later.

(1-4) Regarding Specification of Proximity (Relational Degree) Between Tissues to Which the Users Belong When Users are Close to Each Other For example, the analysis unit 230 specifies users when the users are close to each other In accordance with the result of the above, referring to the group configuration information TBL 532 in which the information of the user is stored, the information of the organization to which the user belongs is acquired. The analysis unit 230 counts frequencies when it is determined that users belonging to different tissues are in proximity. The analysis unit 230 specifies the degree of proximity between the tissues according to the frequency of the counting result. The analysis unit 230 writes and stores the calculation result in the proximity detection TBL of the storage unit 500. When determining the degree of proximity based on the above-described degree, the analysis unit 230 can determine that the degree of proximity (degree of association) between tissues is higher as the degree is higher.
Note that, instead of the frequency, using the frequency of occurrence of an event in which it is determined that users belonging to different tissues are close to each other, the proximity (relevancy) between the tissues is identified according to the frequency. May be As described above, when the proximity is determined based on the frequency of occurrence of an event in which it is determined that users belonging to different tissues are close to each other as described above, the analysis unit 230 determines the proximity between tissues as the frequency increases. It can be determined that the degree of association) is high.

The description will be supplemented with respect to the function of enhancing detection accuracy by combining with analysis processing in the case where users belonging to different tissues are in proximity to each other.
The analysis unit 230 may use the sound collected by the microphone to estimate the proximity state from the interaction between the users. It may not be possible to determine whether or not a dialogue is actively performed only by detecting the proximity of the users. In such a case, by using the sound pressure level of the sound collected by the microphone or the like, it is possible to detect the exchange of the conversation of the target user. If the sound pressure level in the unit time changes frequently, it can be estimated that the user's conversation is frequent. Furthermore, at that time, the sound pressure level of a series of syllables is detected. There is a difference between the sound pressure level of one series of syllables and the sound pressure level of another series of syllables and another series of syllables, the sound pressure level of said series of syllables and the sound pressure level of other series of syllables When it is detected repeatedly, it can be estimated that the target user is in conversation.
Note that instead of detecting a situation in which users belonging to different tissues are in proximity to each other, a process similar to the above can be used to detect a situation in which users belonging to the same tissue are interacting with each other. For example, when a user representing an organization belongs to the same organization, the analysis unit 230 can derive the degree of association between members of the organization based on the result of the above detection, and the calculation result is stored in the storage unit 500. The association degree detection TBL is written and stored.

As described above, the analysis unit 230 can increase the determination accuracy by using the sound collected by the microphone in combination with the determination of the degree of proximity.
In addition to the detection of the sound pressure level, by performing the process of specifying the speaker in addition, it is possible to obtain even a situation where conversations between users representing tissues are performed.
As described above, the analysis unit 230 can increase the determination accuracy by performing the process of specifying the speaker by using the sound collected by the microphone in combination with the determination of the degree of proximity.

A more specific example of processing by the analysis unit 230 is shown.
The information of the sound of the site can be obtained from the sound collected by the microphone as the information of the sound generated by the activity of the user, the sound of the dialogue between the users, and the like. For example, the sound pressure level of the collected sound may be applied as the sound information of the site, the conversation included in the acquired sound may be extracted, and a specific keyword included in the conversation may be acquired.
For example, when applying the sound pressure level of the collected sound as the sound information of the site, the analysis unit 230 regards a busy place exceeding a predetermined sound pressure level as a place where communication is performed. The analysis unit 230 may regard a place where a sound exceeding a predetermined sound pressure level is continuously detected as a place where communication is actively performed. Furthermore, the analysis unit 230 uses the feature that the difference in detection time (or phase difference) is caused by the propagation delay of sound based on the difference or phase difference in detection time of sounds arriving at two different positions. The position of the speaker may be specified to specify the position of the speaker.
Also, when acquiring a specific keyword from the conversation included in the acquired sound, the analysis unit 230 uses the purpose of the conversation or the purpose of using the place where the conversation is being performed, based on the frequency of occurrence of the specific keyword. And so on. For example, as a keyword, a word or a clause used when requesting a requirement is set. In this case, keywords such as "please" and "cooperation" may be mentioned. Analyze the relevance and plan the arrangement by acquiring these keywords. As described above, a set of tissues may be selected in which more or more frequently detected words or phrases are used in requesting requirements. As described above, the analysis unit 230 identifies the proximity (degree of association) between the tissues to which the user who faces and interacts belongs, and identifies the identified result as the proximity detection TBL of the storage unit 500. It writes in (relevance degree detection TBL) and makes it memorize | store.

(2) Analysis processing when detecting a situation in which communication is performed using communication In the case of detecting a situation in which communication is performed using communication, the analysis unit 230 may at least one of the following: The analysis process is performed, and the result specified by the analysis process is written and stored in the proximity detection TBL (relevance detection TBL) of the storage unit 500.
・ Analytical processing to identify the user who is communicating using communication • Communication using the analytical processing and communication to identify the organization to which the user who is communicating belongs using communication Analysis processing for identifying proximity between organizations to which the user belongs, analysis processing for specifying the degree of association between members in the organization to which the user who is communicating belongs using the following analysis processing described above Will be described in order.

(2-1) Identification of the user who is in communication using communication For example, the analysis unit 230 is information (telephone number, address, ID, etc.) for identifying the user in communication using communication. , From the communication log of the communicator (office communicator). The information obtained from the communication log of the communicator (office communicator) includes the information of the user who requested the start of the communication service and the information of the other user who is the other party of the communication service.

(2-2) Identification of an organization to which a user who is communicating using communication belongs To, for example, the analysis unit 230 refers to the group configuration information TBL 532 in which the information of the user is stored, and the user The information (group identification information etc.) of the organization (group) to which the user belongs is acquired from the information specifying the user, such as the ID of. An example of the group configuration information TBL 532 will be described later.

(2-3) Identification of proximity between organizations to which a user who is communicating using communication belongs. For example, the analysis unit 230 allows users belonging to different organizations to communicate using communication. The number of times of communication or the number or frequency of detecting a specific keyword included in the communication is counted. The analysis unit 230 calculates the degree of proximity between the tissues according to the counting result. The analysis unit 230 stores the calculation result in the proximity detection TBL (relevance detection TBL) of the storage unit 500. For example, in the case of determining the degree of proximity based on the result of the communication survey as described above, the analysis unit 230 indicates that the specific keyword is included in the communication more frequently as the communication amount increases and the ratio of the communication amount increases. It is determined that the degree of proximity between tissues is high as the frequency of the specific keyword included in the communication or the frequency of communication is high.

(2-4) About specifying degree of association between members in an organization to which a user who is communicating using communication belongs. For example, the analysis unit 230 allows a user belonging to the same organization to communicate using communication. The number of communications or the number or frequency of detection of a specific keyword included in the communications is counted. The analysis unit 230 calculates the degree of association between members of the tissue according to the counting result. The analysis unit 230 stores the calculation result in the association degree detection TBL of the storage unit 500. For example, in the case of determining the degree of association based on the result of the communication survey as described above, the analysis unit 230 indicates that the specific keyword is included in the communication more frequently as the communication amount increases and the ratio of the communication amount increases. It is determined that the degree of association between members belonging to the same organization is high as the frequency of the specific keyword included in the communication or the frequency with which the communication includes the specific keyword is high.

(3) Comprehensive analysis processing for office activities based on objective survey results For example, the analysis unit 230 performs at least one of the following processes as comprehensive analysis processing for office activities based on objective survey results Then, the result specified by the analysis processing is written and stored in the proximity detection TBL (relevance detection TBL) of the storage unit 500.
Calculation process of proximity request degree based on action survey result (action measurement data) Calculation process of proximity request degree based on communication history survey correction correction to correct analysis result based on the number of times Calculation processing of total proximity request degree calculated based on processing and (proximity request degree based on action measurement data) and (proximity request degree based on communication history measurement data) Hereinafter, each calculation processing described above will be described in order.

(3-1) Calculation of proximity request degree based on the result of action survey (action measurement data) From action measurement data of the subject, action with interaction of the subject is detected, and action with detected interchange is Calculate the corresponding proximity.

(Method of calculating proximity based on total time of interaction with other departments)
Referring to FIG. 7, the method of calculating the degree of proximity based on the total time exchanged with other departments will be described. FIG. It is.
Temporarily, we assume exchange between departments by multiple target people. For example, an example of calculation of the degree of proximity when members of department A and department B meet is shown. Suppose that members b, c, and f participated in the meeting from the A section. From department B, it is assumed that their members k and m participated in a one-hour meeting.

  Since Mr. b who belongs to A department participates in the meeting in which a person other than his own department participates for one hour, it is calculated from the following formula (1) as a point by Mr. b to the point between A department and B department Accumulate values.

Points by b:
+1 (h) x 1 (person) = 1 point (1)

  As shown in the above equation (1), the point is determined by the product of the number of hours and the point per person. For example, in the above equation (1), the point per person is 1.

  Since c and f who belong to the A department also participated for 1 hour in a meeting in which a person other than the own department participates, in the case of the b and b as a point by the c and f between the A department and the B department Similarly, each point is integrated.

  In addition, k and m who belong to department B each participated in a meeting in which a person other than their own department participates for 1 hour, so points between department A and department B are points by k and m, As in the case of Mr. b, each point is integrated.

  As described above, the time (parts) involved in the meeting is integrated with the value obtained according to the number of people participating in the meeting, and the value (point) of the integrated result is made the proximity between the relevant departments.

(3-2) Calculation of proximity request degree based on communication history survey result (communication history data) From the communication history data of the object person, the object person detects the history of having interacted with other departments using communication means And calculate the degree of proximity according to the detected history of alternating current.

(How to calculate the degree of proximity when communicating with other departments using communication means)
In case 1 of this case, it is assumed that communication with other departments is made using communication means. When using communication means, there may be only one person requesting communication. For example, the method of calculating the degree of proximity will be exemplified when Mr. g of A division sends an e-mail to i of B division.
Extract the addressee (to) and the sender (from) from the e-mail header information. For example, the case where the mail attached with the following header information is sent is illustrated.

To i@m.jp
From g@m.jp

In the above case, it can be understood from the "To i@m.jp" portion specifying the addressee that the addressee of this mail is Mr. i. This i's e-mail address identifies that the e-mail address is for department A. Also, from the part of "From g@m.jp" that designates the sender, it is known that the sender of this mail is Mr. g. From this mail address of Mr. g, it is identified that the sender of this mail is department B.
From the result of the above identification, one point is added to each of the division B as the transmission source and the division A as the destination. In the above case, Mr. i and Mr. g both belong to the division for which the degree of proximity is determined.

  On the other hand, emails that do not belong to the department for which proximity is to be determined are also included in the communication history. In these cases, points may be added only to the division corresponding to either the transmission source or the destination. As described above, for points to be added to emails that do not belong to the department for which the email transmission source or destination is a target for which proximity is to be determined, both the transmission source or destination The points may be weighted when they belong to the target department. For example, in the above case, the weighting may reduce the points to be integrated.

For example, one unit of points is accumulated to the division to which the user described in “to”, “cc”, “bcc” and “from” belongs. However, when “to” “cc” “bcc” includes a plurality of users belonging to the same department, integration is not performed for each user, but one unit of points is integrated per department.
If all the user's divisions described in “to”, “cc”, “bcc”, and “from” are the same, two units of points are accumulated in the divisions.
As described above, the degree of proximity can be calculated from the information exchanged between the departments according to the above-described integration method also for the mail specifying a plurality of destinations.
When “to” “cc” “bcc” includes a user who belongs to the same department as the sender (“from”), the point to which the sender belongs belongs one unit, The above integration (proximity between departments) may be performed separately. By this integration, the degree of association between members in the same department can be calculated.
Note that according to the above processing, processing is performed according to the above calculation method even when transmitting mail to one-to-multiple destinations, as in the case where the sender transmits a mail for making it known to the department. be able to.

(3-3) Method of correcting the analysis result based on the number of times In the case of the method of calculating the degree of proximity based on the number of times described above, the target of integration is determined depending on whether or not a specific person actively acts. There may be a bias in the counting result of the number of events. In such a case, the number of events to be excluded from integration is also noted. For example, the total number of the number of events to be integrated and the number of events to be out of integration is calculated, and the number of events to be excluded from integration is shown as a ratio to the total number. The degree of proximity is calculated using the ratio calculated in this manner.

(3-4) Calculation processing of total proximity demand degree calculated based on (proximity demand degree based on action measurement data) and (proximity demand degree based on communication history measurement data) Above (proximity degree based on action measurement data) The calculation of the inter-tissue proximity (total proximity demand) based on (proximity based on communication history measurement data) and (communication proximity measurement data) will be described.
First, for each of (proximity based on action measurement data) and (proximity based on communication history measurement data), the ratio (proximity ratio) for each tissue is calculated.
For example, based on (proximity based on action measurement data), the proximity ratio based on the action log between department A, department B, and department C is calculated. The ratio of department A is indicated by “Amove”, the proportion of department B by “Bmove”, and the proportion of department C by “Cmove”. The proximity ratio based on the action log between department A, department B and department C is It can be shown as (Amove: Bmove: Cmove).
Similarly, based on (proximity based on communication history measurement data), the proximity ratio based on the communication log between department A, department B and department C is calculated. The ratio of the A division is indicated by “Atele”, the division of the B division by “Btele”, and the division of the C division by “Ctele”, the proximity ratio based on the communication log between the A division, It can be shown as (Atele: Btele: Ctele).
By defining as described above, the inter-tissue proximity is calculated based on the proximity ratio based on the action measurement data (action log) and the proximity ratio based on the communication history measurement data (communication log) Can. For example, the proximity ratio based on the action log and the proximity ratio based on the communication log are respectively multiplied as shown in the following equation (2) to calculate the proximity between tissues.

(Inter-organizational proximity) = (Amove * Atele: Bmove * Btele: Cmove * Ctele) (2)

It will become like FIG. 9 when making it table | surface and show the proximity degree between tissues calculated by said Formula (2). 8 and 9 are explanatory diagrams showing the degree of proximity between tissues.
The numerical values shown in the column where the row and the column shown in the table of FIG. 8 intersect indicate the degree of inter-organizational proximity between the departments. In the table, values are not shown in almost half of the columns, but it is because calculation of inter-tissue proximity is performed without specifying the direction of information between departments. It is FIG. 9 which is rewritten to the figure which shows the degree of proximity between tissues using the value shown in FIG.

  The process related to the objective investigation will be described with reference to FIG. The figure is a flowchart which shows the procedure of the process which concerns on an objective investigation.

The behavior measuring unit 210 measures the behavior of each user in the facility 2. The action measurement data acquisition unit 220 acquires data (user action history information) indicating the user's action obtained by the measurement by the action measurement unit 210 as action measurement data (step S21).
Further, the communication history data acquisition unit 270 acquires communication history data (communication history information) indicating communication behavior using the communication means of the user obtained by measurement by the communication history measurement unit 260 (step S22). Note that step S21 and step S22 may be performed in parallel, and the order of the processing to be performed does not matter.
The analysis unit 230 analyzes the user's activity based on at least one of the acquired behavior measurement data and communication history data (step S23).
The analysis unit 230 generates proximity request data based on an analysis result obtained by analyzing a user's activity from at least one of acquired behavior measurement data and communication history data, and acquires various data, analysis results, proximity request data Are stored in the storage unit 500 (step S24).
Further, the analysis unit 230 generates data for correcting the action of the agent in the later-described simulation based on the acquired action measurement data and / or the communication history data (step S25). The process in step S25 can be omitted depending on conditions such as whether or not correction is required. Moreover, when implementing step S25, step S24 and step S25 may be performed in parallel, and the order of the process to implement each does not matter.

  In the above step S23, the analysis unit 230 further analyzes the use situation of the facility from the information including the activity history information, and analyzes the fullness situation of the facility space from the information including the user action history information. May be

Here, for comparison, a general method of the office activity survey so far is described. In the general method of office activity survey so far, self-reported or fixed-point observation has been used. The self-reporting method could not be analyzed in place of the above-mentioned objective survey, because the activity of the recording act itself causes a change in the user's activity. Moreover, in fixed-point observation, it was difficult for a third party to conduct an investigation to identify an individual, and it was impossible to record who and whom were communicating. Furthermore, communication using communication was excluded from the survey.
Even if you plan a survey on communications using communications, it is often necessary to introduce a new survey environment. Therefore, the economic burden is large to carry out a survey on communication using communication, and it has not been easy to take a practical configuration.

On the other hand, in the method shown in the present embodiment, desired information required for designing an office plan by subjecting the user's activity in a real office to quantitative analysis with low load by the objective survey system 200 You can get The survey results obtained by the objective survey system 200 can be reflected in a basic plan such as business process, volume, stacking, zoning and the like when the maintenance is performed according to the office plan.
According to the above method, analysis is possible including the result of the communication survey between individuals and organizations by IP phone, chat, mail, etc. using communication log of communicator (office communicator).

[3. Planning requirement extraction system 400]
The planning requirement extraction system 400 will be described below.

<Configuration of Planning Requirement Extraction System 400>
The plan requirement extraction system according to the present embodiment will be described with reference to FIG. The figure is a block diagram of the plan requirement extraction system 400 in this embodiment.

The plan requirement extraction system 400 includes a plan requirement generation unit 411 and a correction unit 413.
The plan requirement generation unit 411 generates subjective survey data including either the subjective survey result of the user or the analysis result based on the subjective survey result, and the objective survey result for the user's activity or the analysis result based on the objective survey result. Objective survey data including any is acquired as survey data. For example, the objective investigation data acquired by the plan requirement generation unit 411 includes proximity request information between organizations.
The plan requirement generation unit 411 generates a plan requirement of the basic plan based on survey data including the acquired subjective survey data and objective survey data. The plan requirement generation unit 411 visualizes survey data including the acquired subjective survey data and objective survey data, and the generated basic plan. The plan requirement generation unit 411 generates various data for supporting the design of the basic plan using these functions.

  The correction unit 413 corrects the constraint condition when generating the plan requirement of the basic plan based on the verification result by the simulator 300.

  The plan requirement extraction system 400 configured as described above includes the subjective survey data of the survey result by the subjective survey system 100 and the objective survey data of the survey result by the objective survey system 200 in the survey data. The plan requirement extraction system 400 generates a plan requirement of the basic plan of the target office based on the survey data by the plan requirement generation unit 411, for example. In addition, the planning requirement extraction system 400 can also correct the constraint conditions when generating the planning requirements based on, for example, a verification result of the simulator 300 described later.

  A process of generating a plan requirement of the basic plan in the plan requirement extraction system 400 will be described with reference to FIG. The figure is a flowchart showing a procedure of processing of generating a plan requirement of the basic plan in the plan requirement extraction system 400. For example, the process shown in the figure is a process in the case of generating a plan requirement of the basic plan based on the subjective survey result and the objective survey result.

The subjective survey data which is the survey result by the subjective survey system 100 includes either the user subjective survey result or the analysis result based on the subjective survey result. The objective investigation data which is the investigation result by the objective investigation system 200 includes either the objective investigation result for the user's activity or the analysis result based on the objective investigation result.
The plan requirement generation unit 411 acquires survey data including the subjective survey data and the objective survey data from the storage unit 500. The above-mentioned objective investigation data includes the degree of proximity between tissues (step S141).
The correction unit 413 acquires correction request information, and generates a correction amount according to the correction request information (step S142). For example, the correction request information is information that can be determined to require correction based on the subjective survey data, or is information generated by a simulator 300 described later. When the correction based on the subjective survey data is not required or when the same information is not generated by the simulator 300, the process of step S142 is not performed and the process proceeds to the next process.
The plan requirement generation unit 411 calculates the required area based on survey data including the acquired subjective survey data and objective survey data. Further, the plan requirement generation unit 411 corrects the calculation result of the required area according to the correction amount generated in step S142 (step S143).
The plan requirement generation unit 411 performs the stacking process based on the inter-tissue proximity and the required area. The stacking process is a process of allocating floors for arranging each organization (step S144).
The plan requirement generation unit 411 performs blocking processing based on the result of the stacking processing. The blocking process is a process of arranging the tissue allocated by the stacking process in each floor, and may be called a zoning process (step S145).
By the above process, the plan requirement extraction system 400 can generate the plan requirement of the basic plan based on survey data including subjective survey data and objective survey data.
In addition, although it is as above when producing | generating the plan requirement of a basic plan based on a subjective survey result and an objective survey result, an objective survey result can not be used when not carrying out an objective survey. As described above, when the objective survey result can not be used, the plan requirement generation unit 411 may calculate the required area on the basis of the survey data including the acquired subjective survey data in step S143. On the other hand, when the subjective survey is not conducted, the subjective survey results can not be used. As described above, when the result of the subjective survey can not be used, the plan requirement generation unit 411 may calculate the required area on the basis of the survey data including the acquired objective survey data in step S143. In addition, when any survey result of a subjective survey result and an objective survey result can not be used, you may implement said process based on the data which the user defined. For example, the above process may be performed based on the number of people planning to use the facility and the recommended value of the area allocated to each user.
As described above, although either the subjective survey result or the objective survey result or the case where the survey result of the method is not available is shown, a part of the subjective survey result or a part of the objective survey result is The above processing may be performed by the user compensating for the missing subjective survey data or the objective survey data.

The following process is performed to generate the planning requirements of the above basic plan.
・ About processing to extract division with high degree of association between each department based on proximity between organizations ・ About calculation of required area of each division ・ About calculation of required window length ・ About stacking processing ・ About blocking processing

  Hereinafter, the above-described process will be described by taking a more specific example.

(1) Process of Extracting Divisions with High Degree of Association between Divisions Based on Inter-organizational Proximity First, with reference to FIG. The process of extracting will be described. The figure is explanatory drawing explaining the processing which extracts the division with a high degree of association between each division based on the degree of closeness between organizations.
For example, as shown in the figure, the target divisions are arranged concentrically. The order of arranging the divisions follows the order of the degree of proximity between tissues based on the degree of proximity between tissues described above. The process of arranging the respective departments may apply a process such as an algorithm for solving the traveling salesman problem.
By arranging as described above, for example, target divisions are arranged in a circle, and a polygon having the target divisions at the top is formed. The sides and diagonals of the polygon indicate the degree of association between each department. Here, the divisions connected by the polygon edge are arranged in order so as to be divisions with high degree of association between the divisions. The numbers shown along with each side are the values of inter-organizational proximity between the aforementioned departments.
In the example shown in this figure, the departments of department A, department B, department D, department C, and department E are arranged in the following order.

  Division A-Division B-Division D-Division C-Division E-(A Division)

  For example, the proximity between the divisions A, B, D, D, C, C, E, E, and A is 10, 8, 6, 4, 3 points, respectively. It becomes (point). Here, the sides of the E department and the A department having the smallest inter-organizational proximity value are solved, and the sides are arranged in a line. By this, it is possible to obtain the alignment of each department as shown below.

Sections A-B-D-C-E In the above description, the order of inter-organizational proximity is the order, but the sum of the sides of the convex polygon is the largest. You may arrange it so that

(2) Calculation of Required Area of Each Division The calculation of the required area of each division will be described with reference to FIGS. 13 to 17.
The calculation of the required area of each department is carried out according to a plurality of processes shown below in order.
-Calculation of usage equivalent area based on analysis result of action log-Calculation of correction factor based on subjective survey result-Calculation of required area

(2-1) Calculation of Usage Equivalent Area Based on Analysis Result of Action Log With reference to FIG. 13, a method of calculating the usage equivalent area based on the analysis result of the action log will be described. The figure is explanatory drawing which shows the table used for calculation of the utilization equivalent area based on the analysis result of an action log. The table shown in the figure is provided with items of action scene ID, maximum usage rate, unit area, total number of users, stay rate, usage equivalent area, and unit area, total number of users, stay percentage, usage equivalent Each data of the area is associated with the action scene ID.

The action scene ID corresponds to identification information for identifying the above-mentioned "action scene". Here, action scenes of scenes 1 to 7 are illustrated.
The maximum usage rate is the maximum value of the usage rate in each action scene calculated based on the result of the action survey.
The unit area corresponds to the corresponding action scene (action scene ID), and indicates an area recommended as an area when the user performs the action of the action scene. For example, the value can be predetermined according to the lowest planned area standard, and can be treated as a fixed value.
The total number of users indicates the total number of users who execute the action scene corresponding to the action scene ID.
The stay rate indicates the stay rate of each action scene derived from the result of the action log.
The usage equivalent area is calculated according to the following equation (3) based on the unit area, the number of all users, and the value of the staying rate.

  (Use equivalent area) = (unit area) x (total number of users) x (stay rate) ... (3)

In the calculation of the usage equivalent area using the above equation (3), the precondition of the action scene may be changed. For example, any of fixed seat type, free address type, or semi-free type can be selected as the form of the action scene (station) that performs the main task. The fixed seat type indicates a usage form in which a seat to be used is designated. The free address type indicates a usage form in which the user can freely select and use a seat that is not in use. The semi-free address type indicates a usage form in which a user who is permitted to use a seat in the range is freely selected and made available by using a seat in a range that is not in use.
In addition, when the objective survey result can not be used, the stay rate shown in the said Formula (3) may be made into the value defined based on the subjective survey result.

  Here, with reference to FIG. 14, a process of selecting a form of a specific action scene based on the result of the action log will be described. The figure is a flowchart showing a procedure of processing for selecting a form of a specific action scene based on the result of the action log.

The plan requirement generation unit 411 acquires the maximum usage rate of each action scene with reference to the table (storage unit 500) illustrated in FIG. 13 that holds the result of the action log. The plan requirement generation unit 411 determines whether or not the maximum utilization rate does not reach a predetermined threshold value TH1 which is determined in advance (step S1421).
If it is determined in step S1421 that the maximum utilization rate is greater than or equal to the threshold TH1 (step S1421: NO), the process proceeds to step S1425.
On the other hand, if it is determined in step S1421 that the maximum usage rate is less than the above threshold TH1 (step S: Yes), the plan requirement generation unit 411 performs the work of the customer according to a predetermined determination rule. It is determined whether the form is a business form in which the form of the free address and the semi-free address can be proposed. Also, when selecting which of the free address and the semi-free address is to be proposed, the plan requirement generation unit 411 may select based on the degree of intra-organizational relevance of the department. For example, when it is determined that the intra-organizational relevance of the department is equal to or greater than a predetermined threshold TH2, the plan requirement generation unit 411 selects a semi-free address form, and the intra-organization relevance of the department is If it is determined that the value is less than the predetermined threshold value TH2, the form of the free address is selected (step S1422).

  If it is determined in step S1422 that the business form of the customer is not a business form in which the form of the free address and semi-free address can be proposed (step S1421: No), the process proceeds to step S1425.

  On the other hand, if it is determined in step S1422 that the business form of the customer is a business form in which the form of the free address can be proposed (step S1421: free address), the process proceeds to step S1423. In this case, the form of the free address is selected according to the determination in step S1422, and the plan requirement generation unit 411 sets the maximum usage rate of the action scene to the threshold value TH1 and ends the process of selecting the form of the action scene. (Step S1423).

  If it is determined in step S1422 that the business form of the customer is a business form in which the form of the semi-free address can be proposed (step S1421: semi-free address), the process proceeds to step S1424. In this case, the form of the semi-free address is selected according to the determination in step S1422, and the plan requirement generation unit 411 sets the maximum usage rate of the action scene to the instructed value, and the form of the specific action scene. Finish the process of selecting. The value to be set as the maximum usage rate may be a predetermined value, or a value designated by the designer may be acquired and set (step S1424).

  When it is determined in step S1421 that the maximum usage rate is equal to or higher than the threshold TH1 (step S1421: No), or according to the determination in step S1422, the business type of the customer is free address or semi free address. When it is determined that the work form is not a business form that can be proposed (step S1421: No), the plan requirement generation unit 411 calculates the number of seats according to the number of users. The plan requirement generation unit 411 selects one of the types of seats as candidates, and ends the process of selecting the form of the specific action scene (step S1425).

(2-2) Calculation of Correction Rate Based on Subjective Investigation Result Calculation of the correction rate based on the subjective investigation result will be described with reference to FIGS. 15 to 17. FIG. 15 is a radar chart showing the degree of satisfaction for each action scene according to the result of the subjective survey. The calculation results of the satisfaction level of each action scene shown in scene 1 to scene 7 are shown. Here, the fulfillment degree of each action scene is used in the process described below.

  FIG. 16 is a radar chart showing the degree of satisfaction of each environment according to the result of subjective survey. The calculation results for the degree of satisfaction of each element indicated by space, tool, rule, system, and atmosphere are shown. Here, the degree of space satisfaction is used in the processing described below.

  FIG. 17 is an explanatory view showing a table used to calculate the required area. In the table shown in the figure, items of action scene ID, action scene fullness rate, scene shortness rate, dissatisfaction rate, spatial factor fullness rate, spatial factor dissatisfaction rate, scene shortness rate including spatial factor, and required area Each data of action scene fullness rate, scene shortness rate, dissatisfaction rate, spatial factor fullness rate, spatial factor dissatisfaction rate, scene shortness rate including space factor, required area is associated with action scene ID ing. The table shown in this figure is associated with the table shown in FIG. 13 described above. The numerical values shown in the figure show an example.

In the above table, the action scene ID corresponds to identification information for identifying the above-mentioned "action scene". Here, action scenes of scenes 1 to 7 are illustrated.
The action scene sufficiency rate indicates the sufficiency degree for each action scene according to the result of the subjective survey shown in FIG. 15 described above as a percentage (the sufficiency rate).
The scene shortage rate indicates the degree of deficiency with respect to the action scene sufficiency degree expressed as a percentage, and indicates the value as a percentage.
The dissatisfaction distribution rate may not reach 100% simply by adding the above-mentioned scene shortage rate. The dissatisfaction distribution rate indicates a value normalized to a total of 100 insufficiency rates of each action scene as a percentage.
The spatial element fullness rate indicates the fullness of the space shown in FIG. 16 described above as a percentage (full rate). Here, a uniform value is set for each action scene.
The spatial factor dissatisfaction rate indicates the degree of deficiency with respect to the degree of space sufficiency, and indicates the value as a percentage.
The scene shortage rate in which the spatial factor is taken into account indicates the product of the aforementioned dissatisfaction distribution rate value and the spatial factor dissatisfaction rate value as a percentage.
As described above, based on the survey data stored in the storage unit 500, the plan requirement generation unit 411 derives the proximity association between the organizations based on the degree of association (fading) between the organizations using the facility. Note that the related proximity between organizations may include information by face-to-face interaction across organizations such as individuals, departments, departments, and departments, or information by remote communication and the like.
The plan requirement generation unit 411 extracts, from the subjective survey data, a situation in which the function allocation in the office is insufficient, based on the user's subjective request for the office function. The plan requirement generation unit 411 generates the plan requirement of the office in such a way as to eliminate the situation where the function allocation in the office that has been extracted is insufficient.
The plan requirement generation unit 411 may further extract a situation in which the function allocation in the office is excessive among the use states of the functions in the office. The plan requirement generation unit 411 generates a plan requirement of the office based on the related proximity such that the extracted function distribution in the office eliminates an excessive situation. Note that the plan requirement generation unit 411 is configured to use each of the facilities based on the survey data including at least one of the information including the usage status of each function in the used facility used by the user and the objective survey data. The allocation to each office function may be adjusted so that the function allocation in the facility is eliminated from the usage states of functions, and the planning requirements of the office may be generated.

(2-3) Calculation of Required Area The calculation of the required area will be described with reference to FIGS. 13 and 17.
The required area indicates the area required for each action scene. The plan requirement generation unit 411 calculates the required area according to the equation (4). That is, an area obtained by compensating for the scene shortage rate in which the space element is added to the utilization equivalent area shown in FIG. 13 described above is taken as the required area.

(Required area)
= (Use equivalent area) x (100 + (value of scene shortage rate with space factor added)) / 100
... (4)

  The required area of each action scene can be obtained by the procedure described above.

(3) Calculation of Necessary Window Surface Length Next, calculation of the necessary window surface length will be described with reference to FIG. The figure is explanatory drawing which shows the calculation method of required window surface length. As shown in the figure, sections E to A are shown in the order shown below.

  E department-C department-D department-B department-A department

This alignment is calculated based on the above-described inter-tissue proximity. Generally, the depth dimension from the window surface is often made uniform because of the structure of the building and securing of the flow line. Here, let the depth from the window surface be a depth dimension D. As described above, by dividing the required area of each tissue by the depth dimension D, the required window length required for each tissue can be calculated. The total required window length required for each tissue is shown as required window length L.
For example, the plan requirement generation unit 411 calculates the entire necessary window surface length by the planner designating the office room depth dimension in advance. A more specific numerical value is mentioned and demonstrated. For example, when the total required area is 410 m ² and the depth dimension D of the office is set to 10 m, the total required window length L is 41 m.

(4) Stacking Process Next, the stacking process will be described with reference to FIG. The figure is an explanatory view showing a stacking process.
For example, as shown in this figure, the plan requirement generation unit 411 allocates sections E to A to two floors, the 14th and 15th floors. Here, on the 14th floor, all of the E and C divisions and part of the D division were allocated, and on the 15th floor, part of the remaining D division and all of the B division-A division were allocated. The case is illustrated.

(5) Blocking Process Next, the blocking process will be described with reference to FIG. The figure is an explanatory view showing the blocking process.
As a result of the above-described stacking process, sections E to A are allocated to two floors, the 14th and 15th floors. In this blocking process, the plan requirement generation unit 411 arranges each department in each of the 14th and 15th floors. As a result, as shown in the same figure, the result that each division is arranged along the window surface is obtained.
Although division D shown in FIG. 20 is arranged across two floors according to the result of stacking processing, even if it is the same division (organization), if the degree of association between members of the division is low, division D is arranged on different floors Even if it is done, the decrease in convenience can be reduced. In such a case, the tissue set as the unit of analysis may be divided into sub-tissues and analyzed based on the degree of association in the tissue.
In addition, when performing the above-mentioned blocking process, each section can be arranged more easily by referring to the position of the vertical flow line such as a stair, an elevator or the like. In the case where tissues having a relatively high degree of proximity are placed on different floors, the tissues having a relatively high degree of proximity are placed so as to be close to vertical flow lines.
The D departments shown in FIG. 20 are divided into two floors, and the results of the blocking process are also arranged apart from each other in the direction of the window surface. If it is set that the degree of association between members of the D department is high, the plan requirement generation unit 411 reverses the order of arrangement on any of the 14th and 15th floors, and further adds the D department. It is good to arrange on the vertical flow line side.

  The plan requirement extraction system 400 generates a plan requirement as a recommendation for the arrangement of the organization using the facility according to the procedure described above.

[4. Simulator 300]
The simulator 300 (analyzer) will be described below.

<Configuration of Simulator 300>
The simulator 300 in the present embodiment will be described with reference to FIG. The figure is a block diagram of the simulator 300 in this embodiment.
As described above, the simulator 300 estimates the situation in which the work is performed in a facility configured to perform a plurality of types of work respectively by each user.
As a target of simulation of the simulator 300, a plurality of virtual areas corresponding to the plurality of areas are provided in a virtual facility imitating the facility so that a plurality of areas corresponding to each task are provided in the facility. For example, the simulator 300 performs multi-agent type analysis that causes agents that virtualize a plurality of users to act in a virtual space.

  The simulator 300 (simulation system) includes a setting condition acquisition unit 311, a characteristic data acquisition unit 312, an action data generation unit 313, a calculation unit 314, a determination unit 315, a usage totaling unit 316, an effectiveness verification unit 317, and a parameter information setting unit. And 318.

The setting condition acquiring unit 311 regards an agent in a virtual facility as a user in the facility, and sets an action scene that defines the agent's action so as to simulate the user's action. The setting condition acquiring unit 311 stores data on each set action scene in the action definition TBL 533.
For example, the setting condition acquiring unit 311 may perform subjective survey data including either a subjective survey result of the user or an analysis result based on the subjective survey result, and an analysis based on an objective survey result for the user's activity or the objective survey result. Objective survey data including any of the results are acquired as survey data. The setting condition acquiring unit 311 determines the action of the agent in the virtual area as an action scene so as to simulate the action of the user based on the acquired investigation data. The defined action scene is characteristic data that defines the agent's action. The objective investigation data acquired by the setting condition acquisition unit 311 may include mutual proximity between a plurality of departments using the facility. In this case, the setting condition acquiring unit 311 is a virtual area so as to simulate the user's behavior based on the mutual proximity between a plurality of departments using the facility, or in combination with subjective survey data, objective survey data, etc. The action of the agent in may be defined as an action scene.

  The characteristic data acquisition unit 312 acquires characteristic data of the agent. The characteristic data of the agent includes data corresponding to the result of the subjective survey of the user using the facility or the objective survey of the user. Also, as different privileges are set to the actual user, the agent may also be configured to have different privileges depending on users of different privileges. The property data of the agent in such a case includes data corresponding to the authority of the agent. The characteristic data acquisition unit 312 determines the action of the agent such that the agent belonging to the virtual area takes the same action pattern as another agent belonging to the same virtual area. The characteristic data acquisition unit 312 stores data defining the behavior of the agent in the group configuration information TBL 532 as the characteristic data of the agent.

The action data generation unit 313 generates action data indicating an agent's action based on the action scene stored in the action definition TBL 533 and the facility data stored in the facility data storage unit 531. The facility data is predetermined and stored in the facility data storage unit 531. The behavior data is data indicating an agent's behavior. In this manner, an agent in a virtual facility is regarded as a user in the facility, and an action scene that defines the agent's action is set so as to simulate the user's action. The action data generation unit 313 generates action data indicating the action of the agent based on the action scene set as described above and the stored facility data.
The action data generation unit 313 determines the action of the agent so that the agent belonging to the virtual area takes the same action pattern as the other agents belonging to the same virtual area, and the action data generation unit 313 Behavior data of each agent may be generated.
In addition, the behavior data generation unit 313 may adjust the characteristics of the agent according to the characteristic data of the agent. The action data generation unit 313 may generate action data of each agent based on the characteristic data of each agent stored in the group configuration information TBL 532.

The calculation unit 314 performs arithmetic processing of a variable whose value changes according to the action of each agent. The variables include the production volume of the intelligent production of the agent, the heat quantity of the heat load generated, and the usage status of various places in the facility.
The calculation unit 314 includes a production amount counting unit 3141, a heat load counting unit 3142, a facility usage status counting unit 3143, and a movement history counting unit 3144.

The production amount tabulation unit 3141 defines a first evaluation index that the intellectual production is performed by the action of the agent, and the production of the intellectual production increases according to the increase of the time existing in the virtual area, Summarize the output of the intelligent production.
It is assumed that the production amount tabulation unit 3141 assumes that the intelligent production is performed by the action of the agent, and that the production amount of the intelligent production decreases according to the increase of the time when the agent is not present in the virtual area. An index may be determined, and the output of the intelligent production may be totaled.
The heat load totaling unit 3142 calculates the amount of heat generated according to the generated action of the agent, and sets it as a heat load according to the action scene.
The facility usage status tabulation unit 3143 calculates the usage status (utilization rate) of each place in the facility according to the action of the generated agent.
The movement history aggregation unit 3144 aggregates movement times between action scenes associated with the agent's action. For example, the movement history totaling unit 3144 assumes that intelligent production is performed by the action of the agent at the time when the agent exists in a certain virtual area. The movement history aggregation unit 3144 aggregates movement trajectories when the agent moves from the first virtual area to the second virtual area, and counts the number and frequency of crawling with other agents during movement, the number of steps, and the like. Do.

The judgment unit 315 is the production amount of intellectual production aggregated by the production volume aggregation unit 3141, the heat quantity of the generated heat load calculated by the heat load aggregation unit 3142, each place in the facility calculated by the facility usage condition aggregation unit 3143. Summarize the usage status (utilization rate) and the opportunity loss rate (probability that you want to use but can not use). In addition, according to each aggregation value such as the movement time according to the agent's action, the number or frequency of wrinkles between agents, and the total number of steps, the result of the arrangement of the area arranged in the facility collected by the movement history aggregation unit 3144 Determine the quality of the
Further, the determination unit 315 may determine that an opportunity loss has occurred in the action of the agent due to the action of the agent being restricted by the action of another agent. In addition, the determination unit 315 may combine the individual determination results described above, and may determine the quality of the result of the arrangement of the areas arranged in the facility based on the combined result.

The usage counting unit 316 counts the number of times the action of the agent is restricted by the authority given to the agent or by the action of another agent, or the number of times restricted or the waiting time generated by the restriction. In this case, the determination unit 315 may determine the degree of the opportunity loss in the action of the agent based on the result of the counting by the usage counting unit 316.
Note that the usage counting unit 316 may count the usage instead of the degree of opportunity loss. In this case, the usage counting unit 316 counts the usage time using the function from the action of the agent. In this case, the determination unit 315 may determine the degree of use in the action of the agent based on the result of counting by the degree of use counting unit 316.

  The effectiveness verification unit 317 quantitatively verifies the effectiveness of the office plan. For example, based on the acquired survey data, the planning requirement generation unit 411 generates a planning requirement. An office plan is designed by a designer based on the generated planning requirements. The effectiveness verification unit 317 verifies the effectiveness of the designed office plan based on the planning requirements generated based on the survey data. A more specific example is shown. From the office plan generated based on the acquired survey data, a virtual facility corresponding to a specific facility (for example, the facility 20 in FIG. 3) is determined, and the effectiveness of the office plan is determined by the simulation result in the determined virtual facility Have to verify. In this case, the effectiveness verification unit 317 verifies the effectiveness of the office plan based on the ratio of the value of the item in the designed office plan to the value of the item of the corresponding plan requirement. By the above-described process, the effectiveness of the office plan can be quantitatively verified.

The effectiveness verification unit 317 in the present embodiment can further verify the effectiveness of the office plan by the following method.
For example, in order to provide a plurality of areas corresponding to work in the facility, a plurality of virtual areas corresponding to the plurality of areas are provided in a virtual facility imitating the facility 20. The effectiveness verification unit 317 regards an agent in the virtual facility as a user in the facility, and sets an action scene that defines the agent's action so as to simulate the user's action. The effectiveness verification unit 317 generates action data indicating the action of the agent based on the set action scene and the facility data of the virtual facility. Further, the validity verification unit 317 may verify the validity of the office plan based on the generated behavior data. Details of the simulation in the virtual facility and verification of the effectiveness of the office plan will be described later.

  The parameter information setting unit 318 generates parameter information for setting the constraint condition of the simulator 300 based on the analysis result by the subjective research system 100 and the objective research system 200. The parameter information setting unit 318 generates parameter information that corrects the behavior characteristic of the agent in the simulator 300 so as to be close to the behavior characteristic of the actual user. For example, the parameter information setting unit 318 sets parameter information as information for correcting the agent's action based on the action characteristic data derived from the analysis result by the objective investigation system 200 to be close to the user's action characteristic. It is also good. Alternatively, the parameter information setting unit 318 corrects the agent's behavior based on the behavior characteristic data derived from the analysis result by the objective survey system 200 to be the user behavior characteristic derived from the analysis result by the subjective survey system 100. Parameter information may be set as the information. For example, as the latter case, it may be determined that the behavior characteristic of the user derived from the analysis result by the objective survey system 200 is not the ideal behavior characteristic from the analysis result by the subjective survey system 100. In such a case, parameter information for setting behavior characteristic data may be set to be an ideal behavior characteristic.

  The storage unit 500 includes a facility data storage unit 531, group configuration information TBL 532, action definition TBL 533, common action definition TBL 534, function definition TBL 535, move destination distribution TBL 538, start condition setting TBL 539, history information TBL 536 (movement history data storage unit). Prepare. Examples of the facility data storage unit 531, group configuration information TBL532, action definition TBL533, common action definition TBL534, function definition TBL535, destination allocation TBL538, start condition setting TBL539, and history information TBL536 will be described later.

  The simulator 300 configured as described above performs a verification process on the designed office basic plan.

  The process in the simulator 300 will be described with reference to FIG. The figure is a flowchart showing the procedure of processing in the simulator 300.

The setting condition acquiring unit 311 sets the behavior characteristic of the agent in the later stage simulation based on the subjective survey result by the subjective survey system 100 or the objective survey result by the objective survey system 200 (step S41).
Based on the proximity requirement generated from the survey result by the subjective survey system 100 or the objective survey system 200, a layout plan of each function in the facility is defined. The setting condition acquiring unit 311 acquires information related to the arrangement of each function in the facility, and stores the information related to the acquired arrangement in the facility data storage unit 531 and the action definition TBL 533 (step S42).

  The parameter information setting unit 318 corrects the behavior characteristic of the agent in the subsequent simulation based on the objective investigation result by the objective investigation system 200 (step S43). The calculation unit 314 carries out a simulation based on the set conditions (step S44).

  The validity verification unit 317 verifies the validity of the office plan based on the above simulation result (step S45). The validity verification unit 317 outputs the verification result (step S46).

  By the process described above, the simulator 300 carries out the verification process of the basic plan based on the above verification result.

<About the simulation of the action scene assumed in a facility>
In the present embodiment, the facility to be simulated is configured such that each type of task is performed by each user. The simulator 300 simulates the situation in which the work is performed in such a facility, as the work is performed in the virtual facility. The results of this simulation predict the occurrence of events that may occur in a real facility through the events that occur in a virtual facility.

The simulator 300 provides a plurality of virtual areas corresponding to a plurality of areas in an actual facility in a virtual facility imitating the facility so that a plurality of areas corresponding to each task are provided in an actual facility. The provided virtual area is stored in the facility data storage unit 531 as facility data of a virtual facility.
The simulator 300 regards an agent in the virtual facility as a user in the facility, and sets an action scene that defines the agent's action so as to simulate the user's action. The simulator 300 generates action data indicating the action of the agent based on the set action scene and the facility data stored in the facility data storage unit.

<Main Features of Simulator 300>
Hereinafter, main features of the simulator 300 will be organized.

(A) "Multi-agent" The simulator 300 of this embodiment defines a plurality of agents (multi-agents) corresponding to a plurality of users in the virtual space, and reproduces the activity in the office. For example, the simulator 300 is configured such that a plurality of virtual users (agents) perform a task in the virtual space so that a plurality of existing users perform their tasks in parallel. For example, the agent is provided with a plurality of types, and by setting individual behavior characteristics for each type, it is possible to reproduce the difference in behavior due to the difference in the task in charge or the like. For example, as the plurality of types, types such as Salesperson, Engineer, and Manager may be set.

(A) "Action Characteristic" The simulator 300 individually sets, for each agent, an action characteristic according to the type of agent as the agent's action characteristic. For example, the behavior characteristics include a standard attendance time, a closing time, a ratio for performing overtime, a walking speed, and a ratio for selecting various action scenes. Further, the simulator 300 sets a plurality of behavior characteristics as the behavior characteristics of each agent so that the behavior characteristics of the agent can be switched according to the conditions. By switching the plurality of behavior characteristics in this manner, for example, each agent can select different behavior characteristics according to the conditions within one day, and can act according to the selected behavior characteristics.
As the simplest example, the case where the simulator 300 simulates the normal behavior and the non-normal behavior, and switches the normal behavior and the non-normal behavior to each other will be described. For example, the non-ordinary time is assumed to be a break time zone such as a lunch break, an overtime work time zone, an absence time of a manager, or the like, from normal user's attendance to leaving work.
The behavior characteristic of normal time measures the behavior of a plurality of existing users regarding each position about the behavior from the actual user's attendance at work to the leaving work, the probability that the user selects the action scene, the movement probability between action scenes, the stay Count time etc. and digitize it. The behavior characteristics of unusual behavior are also measured in the same manner as usual, by measuring behavior during lunch which is set as anomalous, etc., and counting the movement probability between behavior scenes in unusual behavior, stay time, etc. Quantify. The data quantified in this way is used as the agent's behavior characteristics. Furthermore, as the unusual behavior, in addition to the behavior during the break time including lunch, the behavior during the overtime work, the behavior when the manager is absent, etc. may be set. For example, as the behavior characteristic of the overtime work period, the ratio between the user who performs overtime work in the overtime work period and the user who does not work overtime is set. In addition, with regard to the user who substitutes for the work of the manager in the absence of the manager, etc., the behavior characteristic of when the manager is away and when the manager is present (normal) is set and managed. Depending on whether the job is present or not, the behavior characteristic of the user is adjusted depending on which one of the case where the manager is away and the case where the manager is present is selected. This setting may be set separately as different numerical data such as data for determining companion behavior to be described later, or as a variation from a behavior characteristic of normal, a rate of change, etc. The difference may be defined based on the behavior characteristic. The data for determining companion behavior and the like may include data to be applied to activities such as waiting for waiting for an administrator's idle time, and companionship to be taken by the manager and moving. In the following description, companion behavior will be described as a representative.

(C) "Action Scene" In the situation where the actual work is being performed, the situation where the work is being performed in the virtual space is modeled, focusing on the fact that there are different kinds of action scenes. When modeling, set multiple action scenes. For example, seven action scenes shown below are set as a plurality of modeled action scenes. Seven action scenes are “Think”, “Station”, “Community”, “Review”, “Reception”, “Academy”, “Break” (Break). For a description of each of the above action scenes, reference is made to Japanese Patent No. 4500846. In view of the above, the value of the intellectual productivity of each action scene is set.

The simulator 300 may be configured to perform the following settings so as to reproduce the actual user's behavior more faithfully.
For example, without fixing the seat of the user, it may be operated so that the user can select a seat to work when needed. In such an operation, in the setting of the simulator 300, either the fixed seat or the free address can be selected, and is set independently for each range.

  For example, in the case of instructing the members of the organization in a command or command in the organization, or in order to share information required for the group, such as know-how, skills, etc. It may act in common. When taking such actions, members of the organization often move together. In order to apply to such a situation, the simulator 300 may be set to take an action for transmitting instructions to members, a companion action, or the like in a specific action scene. As described above, in the case of an action scene in which an action for transmitting instructions to members and an accompanying action are set, members of the organization are placed in a common place for the members according to the action scene. It can be set to move.

  In the setting, the simulator 300 sets the standard number of people and the usage time to each action scene, and the agent sets the standard deviation random number to generate a staying action each time the agent is used.

(D) "Evaluation method" of the effectiveness of the office plan The effectiveness verification unit 317 verifies the effectiveness of the designed office plan based on the planning requirements generated based on the survey data. This verification includes at least one of the following two verifications.

  The first verification verifies how well the designed office plan meets the planning requirements of the office plan. For example, in this first verification, for the arrangement allocated to each scene, the ratio of each area (area allocation ratio) is calculated in relation to the plan requirement of the office plan and the designed office plan. Verify the effectiveness of the designed office plan based on the calculated ratio. The above verification can quantitatively verify whether or not the designed office plan meets the planning requirements of the office plan.

In the second verification, a virtual facility is determined from the designed office plan, and the effectiveness of the designed office plan is verified from the result of simulation in the defined virtual facility. By this second verification, it is possible to calculate the sufficiency of the area and the like set for each function in the designed office plan and the effectiveness of the organization arrangement. The effectiveness of the designed office plan can be quantitatively verified from this verification result.
For example, the simulator 300 can calculate office activities on multiple days, and the results show that the amount of intellectual production in each action scene, the heat load of heat load, the usage status (usage rate), the opportunity loss rate It is possible to output (probability of not being able to), movement time between agent action scenes, movement locus, place and number and frequency of crawling, total number of steps, etc. Office activities on multiple days may be calculated, the average or variance of the results may be calculated, and the calculated results may be evaluated. In the simulation shown in the present embodiment, since random numbers are used so that the behavior of the agent becomes natural behavior, the results of the simulation on a daily basis often do not always have the same result. Therefore, by performing the evaluation based on the results of simulations on a plurality of days as described above, it is possible to reduce the influence of using the above-mentioned random numbers and to evaluate.
Hereinafter, a specific configuration method of the above verification will be described.

<Various information tables>
A configuration example of the various information table will be described with reference to FIGS. 23 to 30.

(Facilities data storage unit)
FIG. 23 is an explanatory view showing a configuration example of a facility data storage unit.
The facility data storage unit 531 stores data including items such as the floor of the facility, the function, and the area allocated to the function as basic information on the facility. The item of function is associated with data indicating the function assigned to each floor. For example, as data indicating a function, data indicating an area (ZA, ZB, ZC) divided into zones, each conference room, a core unit, etc. is included. For example, the basic information on the above facilities includes data based on the results designed by the designer. In addition, the item of the area allocated to each function may be added with an item for storing the result calculated as the planning requirement of the office plan, in contrast to the result designed by the designer. In this case, the data of the plan requirement generated by the plan requirement generation unit 411 described above is written as the data of the area calculated as the plan requirement of the office plan.

(Group configuration information TBL)
FIG. 24 is an explanatory view showing a configuration example of the group configuration information TBL.
Group configuration information TBL 532 defines basic information of users (agents) belonging to each group.
Group configuration information TBL 532 associates data of items such as group, ID, type, attribute, attendance time, closing time, walking speed, common action 1, common action 2, overtime rate 1, overtime rate 2 with ID. Remember.

  Data (group identification information) for identifying an organization (group) to which the user belongs is associated with the item of the group. Data for identifying a user is associated with the item of ID. The type item is associated with the job type of the user, and the attribute item is associated with the role in the organization. Data indicating the time of arrival at work and the time of closing work is associated with the items of time of arrival at work and the time of closing work. For example, the data is data such as data corresponding to the time determined by the actual results or work rules, or data corresponding to the time on a day on which overtime work is not scheduled. The item of walking speed (moving speed) is associated with data indicating the speed of moving in the facility. For example, the data indicates walking speed and the like. The items of common action 1 and common action 2 are associated with data indicating that there is an action that causes the same kind of action according to the action of a representative of the whole group or a part of the group members such as a leader. The items of overtime work rate 1 and overtime work rate 2 are data indicating the ratio of the number of days for performing overtime work to the predetermined first time to the working days, and the number of days for performing overtime work until the second predetermined time for the working days Data indicating the ratio is associated. For example, it is assumed that the first time is after the closing time and reaches earlier than the second time.

  For example, there are groups A to F as groups. Each member belonging to the group A has an action characteristic of performing WCA 1 defined as common action 1 and WCA 2 defined as common action 2 along with the leader (identification information A 01) .

(Action definition TBL)
FIG. 25 is an explanatory view showing a configuration example of the action definition TBL.
The action definition TBL 533 defines standard actions of users belonging to each group.
The action definition TBL 533 identifies data of an item such as a group, an ID, a zone, a seat, a plurality of action scenes (scene 1, scene 2, scene 3A, scene 3B,..., Scene N) It stores it in association with information (group) and ID.
Data (group identification information) for identifying an organization (group) to which the user belongs is associated with the item of the group. Data for identifying a user is associated with the item of ID. As a result of allocation (stacking, zoning) in the facility, data for identifying the allocated area is associated with the item of the zone. The item of the seat is associated with data that defines the operation mode of the seat provided in the zone where stacking (stacking, zoning) is performed in the facility.
Moreover, the probability at the time of selecting a specific action pattern out of a plurality of action patterns is associated with each item of a plurality of action scenes. For example, the probability is based on the ratio of the time, based on the survey result on the user's behavior, classifying the time that was actually acting into any of a plurality of action patterns according to the content of the action. May be determined.

For example, from the items of the group configuration information TBL 532 of groups A and B, it can be understood that each is a technical job group and a sales job group. From this behavior definition TBL 533, in the zone ZA to which the group A is assigned, the seats are fixedly determined such that specific members dedicate each seat, while in the zone ZB to which the group B is assigned, each seat is The seat is set free so that members can share it.
In addition, the agent corresponding to the user with the identification number A01 belonging to the group A selects a normal action scene according to the ratio defined in scene 1, scene 2, scene 3A, scene 3B, ..., scene N. Do. The above ratios can be set independently for each user. Note that the ratio of all action scenes may be set as a predetermined value set in advance as an initial value, and may be changed as necessary. The agent is processed to act in accordance with the selected action scene according to the above ratio. The duration of the action in each action scene is defined as the standard working time, and is defined as performing one action in that time.
As described above, “scene 3A” and “scene 3B” indicate action scenes that can be roughly classified as scene 3. Here, the method of dividing into “scene 3A” and “scene 3B” may be used when the functions corresponding to the respective scenes are different. For example, when there are a plurality of conference rooms that correspond to functions, and simulation is performed by using a plurality of conference rooms, the accuracy of the simulation result can be improved.

(Common action definition TBL)
FIG. 26 is an explanatory view showing a configuration example of the common action definition TBL.
The common action definition TBL 534 stores data of items of function, common action, time zone, companion, action scene, incidence rate in association with group identification information (group) for identifying an organization.
The item of function is associated with data identifying the function provided in the facility. The item of common action is associated with data indicating the same kind of action in which another user (agent) takes an action according to the action of the representative. The time zone item is associated with data indicating a time zone in which common actions occur. For example, the time zone can be classified into an opening time (time to work), during work, lunch break, overtime work, and the like. The companion item is associated with a flag that determines whether another user (agent) takes an action according to the action of the representative. The items of the action scene are associated with data indicating the action scene as the goal of the common action. The occurrence rate item is associated with data indicating the probability of taking a common action. By setting the item of this incidence rate, it not only defines the case (probability: 100%) that always causes common action every day, but also changes it to any probability (0 to 100%) depending on the day be able to. The time at which the common action occurs is determined based on the generated random number.
The people affected by the companion behavior may include not only members in the group but also members of other groups.

(Function definition TBL)
FIG. 27 is an explanatory view of a configuration example of the function definition TBL.
The function definition TBL 535 stores data of items of action scene, function, by time, exclusion, standard usage time, number of users, INPUT, OUTPUT, and heat load in association with function identification information for identifying a function.
Data for identifying various scenes defined as action scenes are associated with items of action scenes. The item of function is associated with data identifying the function provided in the facility. The hourly items are associated with data specifying time zones in which functions provided in the facility can be used. For example, it is possible to set an operation to open a room used as a meeting room during work as a room for lunch on lunch break.

  The item of exclusion is associated with data identifying whether the authority to use the function provided in the facility is set in the action scene. For example, in the scene 1, an area which can be occupied exclusively can be used during business hours. There is no exclusion setting in this area, and users in each group can use the same area. In scene 2, the assigned station carries out the task in charge during business hours. The station is set for exclusion, and use of the action scene is permitted according to the authority set for each user. There is a conference room A as one function of the scene 3. When this conference room A is used during business hours, it can be used only by authorized users. For example, the authorized user is set to allow the use of the meeting room A during business hours. On the other hand, other users except for authorized users have exclusive settings for restricting their use. With such settings, only authorized users can use the meeting room within a predetermined time, and even if there is a vacant seat in the meeting room for which exclusive restrictions are set, the situation can be used by unauthorized users Can be simulated. As one function of the scene N, there is a conference room A. When this meeting room A is used for lunch break, unlike the above case, there is no exclusive setting for restricting the use of an unauthorized user, and it is possible to simulate a situation that anyone can use. In the meeting rooms B and C in the scene 3 and the meeting room B in the scene N, settings according to the above description are made.

  In the item of standard usage time, data indicating the standard usage time when using the function is associated. For example, in the item of standard usage time, it is a standard that the user stays in the action scene and performs work after starting the action scene until starting the movement to the next action scene after finishing the action scene. Correspond to the data showing the time. The standard usage time is a representative value determined for each action scene, but in order to simulate the behavior of the agent randomly occurring, the usage time is dispersed around the standard usage time. For example, the above-mentioned stay time sets the use time of the length which multiplied the random use time to the standard use time. The standard usage time indicates the standard value of the staying time in which the agent stays in the same action scene. The item of number of users is associated with data indicating the maximum number of people using the function. In the items of INPUT and OUTPUT, associate data indicating productivity per unit time that can be expected when using the function. For example, the item of INPUT indicates the amount of data available to the user. The item of OUTPUT indicates the amount of data provided by the user. The heat load item is associated with data indicating the amount of heat generated by the user in the action scene. For example, the value of the data is the value of the amount of heat when one user carries out the action of the action scene. In the scene 1, the sum of the amount of heat generated by the user himself and the amount of heat generated by the tool used by the user is set to 300 (for example, 300 W / h of heat generated per hour). In the scene 3, the amount of heat generated by the user (for example, the amount of heat generated per hour is 100 W / h). For example, the equipment used by the above user includes various equipment such as lighting equipment, air conditioning equipment, and ventilation equipment. The above values can be determined based on the rated capacities of various facilities. Alternatively, as in the case of simulating the case where each facility can be controlled individually for each device, the above values are based on the actual usage form, and based on the turn-off rate, the dimming rate, and the operation rate. You may set it to the value evaluated.

(Movement destination distribution TBL)
FIG. 28 is an explanatory view showing a configuration example of the movement destination distribution TBL.
The movement destination distribution TBL 538 stores data of items of an action scene and “movement destination and movement probability” in association with scene identification information for identifying the action scene.
Data for identifying various scenes defined as action scenes are associated with items of action scenes. This item indicates the action scene in which the user is currently placed. In the item "destination and probability of movement", each movement destination when moving from the action scene in which the user is currently placed to another action scene is associated with data indicating a standard probability that causes the movement.

(Opening condition setting TBL)
FIG. 29 is an explanatory view showing a configuration example of the start condition setting TBL.
The opening condition setting TBL 539 defines an opening operation of a user who belongs to each group.
The start condition setting TBL 539 includes group identification information (group) for identifying tissue, data of an item such as a group, an ID, a plurality of action scenes (scene 1, scene 2, scene 3,..., Scene N). Store in association with
Data (group identification information) for identifying an organization (group) to which the user belongs is associated with the item of the group. Data for identifying a user is associated with the item of ID. The items of the plurality of action scenes are associated with data indicating the action scenes corresponding to the action that the user initially performs at the start of work. For example, if the user who arrives at the beginning of the work with the same behavior pattern every day, one behavior scene corresponding to the behavior is set to be selected every day. If the probability that can be selected is indicated as a percentage, in the above case, the value of a specific action scene is set to 100, and the values of other action scenes are set to 0.
On the other hand, in the case of the user who arrives at the start of the work with different action patterns depending on the day, values corresponding to the ratio of the action that can be selected are set for the plurality of action scenes corresponding to each of the plurality of actions that can be selected. If the probability that can be selected is indicated as a percentage, in the above case, the value of non-selected action scenes is set to 0 so that the sum of the values of specific action scenes that can be selected is 100.
By setting the starting conditions as described above, even for users who take actions that go directly to customers, such as salespeople, without going to work at the beginning of the work, the scene corresponding to "going out" as the action scene By setting, it is possible to simulate also in the above case.
In addition, in an office provided with a wardrobe and a personal locker, it is observed that the user drops in the assigned wardrobe and the personal locker at the time of leaving work. When a bookshelf or the like containing materials shared by the group is provided, an action is observed in which members of a specific group drop by where the bookshelf or the like exists. In the case of simulating the behavior as described above, it is possible to simulate the use situation by defining the position of the wardrobe, locker, bookshelf, etc.

(History information TBL)
FIG. 30 is an explanatory view showing a configuration example of the history information TBL.
The history information TBL 536 stores the simulation result. The history information TBL 536 shown in the figure stores a set of data of a specific evaluation timing among a series of history information recorded as time-series data. For example, a specific evaluation timing is indicated as timing k. The timing k is assumed to be associated with the month, day, and time. The history information TBL 536 is configured to store data of each timing as time series data such as k + 1, k + 2,..., K + n following the timing k.
History information TBL 536 identifies tissue, data of items such as group, ID, position (X, Y), action scene, link, productivity (INPUT, OUTPUT), heat load, next position (NX, NY) Group identification information (group) and ID in association with each other and stored as time-series data.
Data (group identification information) for identifying an organization (group) to which the user belongs is associated with the item of the group. Data for identifying a user is associated with the item of ID. The item of position (X, Y) is associated with data indicating the current position of the agent. For example, the position is defined using orthogonal coordinates indicated by the X axis and the Y axis. The item of the action scene is associated with data indicating the action scene set for the current agent. The link item is associated with data indicating the passage in which the agent is moving. Here, the passage is regarded as a link, and the branch point of the passage is regarded as a node and modeled. By modeling the path in this manner, when the agent moves on the link and reaches the node, the link is switched to the next agent. The item of link is set to data according to the above switching. The items of productivity (INPUT, OUTPUT) are associated with data indicating the amount of intellectual products produced in the action scene set for the current agent. The item of heat load is associated with data indicating the amount of heat generated in the action scene set for the current agent. The next position (NX, NY) item is associated with data indicating a position to which the agent is to be moved at the next simulation timing. The next position (NX, NY) is defined using the same coordinate axes as the items of position (X, Y).

<About the process of simulator 300>
The simulator 300 refers to the above information TBL, associates the information TBL, and performs the following processing.

(A) Formation Process of Facility Model The setting condition acquiring unit 311 acquires basic information on a target facility, and generates a candidate of a route through which an agent moves in a virtual facility. For example, the setting condition acquisition unit 311 selects the shortest route from among the plurality of routes that are candidates. In addition, the method of selecting a route can use a general algorithm.

(A) Process of Configuring Behavior Model The characteristic data acquisition unit 312 sets a standard behavior of the user belonging to each group in the behavior definition TBL 533.

(C) Process of simulating the action of an agent (user) The action data generation unit 313 simulates the action of each agent based on the action characteristic set in the action definition TBL 533. The values set in the action definition TBL 533 are standard values of the respective values. The behavior data generation unit 313 causes the agent to act so that the actual user acts by fluctuating a command that causes the agent to act on the basis of the standard value using a random number.

(D) Process of generating an action to be processed preferentially using a simplified action model As shown in the action definition TBL 533, the action of each agent at a specific time is made a single action, and each action is The definition is simplified. However, in order to simulate performing another action by interrupting the action being performed, the action data generation unit 313 performs an action to be processed prior to the action being performed or the action scheduled next. Generate behavioral data to indicate. The action data generated by the action data generation unit 313 controls the agent instructed to perform the action to be processed preferentially so that the instructed action is processed with priority over the action being performed according to the instruction. . For example, as such action to be preferentially processed, there may be a companion action which is influenced by the action of another member. A plurality of processes including those to be prioritized as described above are selected as candidates, and one process is selected from those candidates and made to be executed by the agent (see step S3603 etc. in FIG. 34 described later) ).

(E) Process of Calculating Evaluation Values in Accordance with Various Evaluation Criteria The simulator 300 calculates evaluation values in accordance with various evaluation criteria from the history of behavior of each agent. The simulator 300 obtains the following evaluation value from the history of actions of each agent.

  For example, it is assumed that an agent's action causes intellectual production according to the action, and the evaluation index (the first evaluation that the production of the intellectual production increases according to the increase of time that the agent exists in the virtual area) Determine the indicator). The behavior data generated by the behavior data generation unit 313 includes the production amount (values of INPUT and OUTPUT) of intellectual production at each evaluation timing. The production amount aggregation unit 3141 aggregates the production amount (INPUT, OUTPUT value) of intellectual production at each evaluation timing for each agent, for each group, for each action scene, and uses the result of aggregation as an evaluation value.

  Also, for example, an evaluation index (second evaluation index) in which a heat quantity corresponding to an action is generated by an action of an agent, and an amount of heat generation increases according to an increase in time in which the agent is present in the virtual area Determine The heat load totaling unit 3142 calculates the amount of heat according to the generated action of the agent, and sets it as a heat load according to the action scene. The heat load totaling unit 3142 totals the heat quantity at each evaluation timing for each agent, for each group, and for each action scene, and uses it as an evaluation value of heat load.

  Also, for example, it is assumed that the use situation of each place in the facility changes according to the action according to the action of the agent, and the use rate of each place in the facility increases according to the increase of the time when the agent exists in the virtual area Establish an evaluation index (third evaluation index) The facility usage status tabulation unit 3143 calculates the usage status of each place in the facility according to the action of the generated agent. The heat load totaling unit 3142 counts the use status of each place in the facility at each evaluation timing for each agent, for each group, and for each action scene, and uses it as the evaluation value of the use status for each place in the facility.

(Display of simulation result)
An example of display of simulation results will be described with reference to FIGS. 31A, 31 B, 31 C, and 32.

FIG. 31A is an explanatory view showing an example of displaying a simulation result as an animation. 31B is an explanatory view supplementing the description of FIG. 31A. 31C is an explanatory view showing an example of a display in which the history of the agent crawling is visualized by the simulation shown in FIG. 31A.
Reference numeral 600 in FIG. 31A indicates the display range of the screen. In the screen 600, a part of one floor of a virtualization facility virtualizing the facility 20 is shown. The floor is divided into a plurality of areas, and functions are defined for each of the divided areas. For example, in the area ZS, work desks are arranged side by side, and the action scene of this area is defined as a "station". Area ZR is a meeting room in which desks for a meeting are arranged side by side, and the action scene of this area is defined as "review". In the area ZC, desks and chairs capable of simple meetings are arranged, and the action scene of this area is defined as "community". The area ZB includes the core area of the facility 20 and is designated as an area where a break or the like can be performed, and the action scene of this area is defined as "break". Thus, functions and action scenes are defined in each area of the virtualization facility.

  In the screen 600, reference numerals 601 to 606 indicate agents. The code | symbol 607 shows the agent in a companion action. Reference numerals 611 to 616 indicate seats for agents to work respectively. For example, agents 601 to 605 arrive at seats 611 to 615 and are in a situation where they are working according to their respective action scenes. On the other hand, the agent 606 is moving from the seat 616 toward the area ZR. Reference numerals 621 to 623 indicate paths used by the agent 606 for movement.

Here, movement of each agent will be described with reference to FIG. 31B. Each agent moves along the aisle from the current seat to the destination seat. The agent is set so that the part designated in the wall or core area or the part where a desk, fixtures and the like are arranged is not a passage. As a result, the candidate for the passage becomes a shaded part.
For example, as the agent 606 moves from the seat 616 toward the area ZR, candidates of a plurality of routes as indicated by a dashed dotted line may be mentioned. For example, the behavior data generation unit 313 selects a route of the shortest moving distance from among these candidates, and sets the selected route as a moving route. The action data generation unit 313 calculates the position to be moved by each agent at the next evaluation timing based on the current position according to the selected movement route, and stores the position in the history information TBL 536. The data output system 700 advances and displays the position of each agent at the position of the next evaluation timing in the image corresponding to the next evaluation timing. By the above process, the agent 606 is displayed to move from the seat 616 toward the area ZR. For example, although each agent is shown using a face symbol, it indicates that the agent with parentheses near the face is in motion. The number of parentheses indicates the movement speed, and the direction with respect to the face indicates the movement direction. In the example shown in this figure, parentheses are displayed behind the agent as if it indicates the position where the agent was present immediately before.

Although not shown, the trajectory of the agent may be displayed on the main display screen. Further, the position where the agents roam may be displayed according to the timing at which the agent roams, or as illustrated in FIG. 31C, the position where the agents roam may be displayed in an overlapping manner. In FIG. 31C described above, the concentration is displayed as the frequency of drooping or the frequency of occurrence increases. For example, when the concentration is displayed in a high concentration, the vicinity of the entrance of the conference room, the vicinity of the entrance of the elevator provided in the core unit, the place corresponding to the scene provided for activating communication between users Be The form of such display facilitates the determination of whether or not the display of the above-mentioned gradation matches the one originally intended.
In addition, when displaying as mentioned above, unlike displaying the locus | trajectory which moved simply, it can display without a screen being filled with the locus | trajectory being filled up, and it becomes easy to read that it is an overwhelmed position and it is overwhelmed. In the above display example, gray scale is displayed by monochrome display, but may be displayed according to a color wheel by color display, or gradation may be applied. Moreover, although the circle centering on the crawling point is displayed, the size of the circle may be adjusted according to the number and frequency of the wrinkles. For example, the circle is made larger according to the increase in the number and frequency of moxibustion. The above shows an example, and the shape of the figure showing the droop point is not limited to a circle, and a shape suitable for display can be selected from various shapes, and the size can be appropriately selected. .
Note that the displays separately described in FIG. 31A and FIG. 31C may be displayed on a common screen.

  Referring back to FIG. 31A, the case where there is no seat even if the agent arrives at the destination will be described. In such a case, after the simulator 300 leaves history information indicating that the target action could not be performed in the history information TBL 536, the simulator 300 selects and selects the next movement destination (action scene) of the agent. Start moving the agent to move to the destination.

The simulator 300 generates the state of the virtual facility at a timing determined by the cycle of a predetermined unit time T by simulation. Although each screen 600 is generated as a still image, the agent is moving in the virtual facility by sequentially switching and displaying the state of the virtual facility generated by the simulator 300 as time passes. Is displayed on.
In the screen 600, the agent is displayed to move smoothly along the aisle by setting the distance for the agent to move to the unit time T to be smaller than the displayed size of the agent itself. .

FIG. 32 is an explanatory view showing an example of displaying the number of persons present in the action scene as a simulation result as a graph. The figure shows a line graph showing the change in the number of people present in each action scene as time passes, and the horizontal axis indicates time, and the vertical axis indicates the number of people.
Each of the broken lines shown in the figure corresponds to each action scene. As shown in the figure, it is possible to read the situation in which the number of people in each action scene changes as time passes.

An outline of processing in the simulator 300 will be described with reference to FIG. The figure is a flowchart showing an outline procedure of processing in the simulator 300.
The setting condition acquiring unit 311 acquires information indicating the target range in the virtual facility corresponding to the target facility from the facility data storage unit 531, and sets the target range in which the simulation is to be performed (step S31).
The setting condition acquiring unit 311 acquires data defining each action scene from the function definition TBL 535, sets an area where the agent can enter in the virtual facility, and the agent removes the area excluding the above enterable area in the virtual facility. It sets as an entry impossible area which can not enter (Step S32).
The characteristic data acquisition unit 312 acquires group configuration information from the group configuration information TBL 532, acquires agent characteristic data from the action definition TBL 533, determines start conditions of each agent from the start condition setting TBL 539, and acquires data. The characteristics of the agent are set (step S33).
The setting condition acquiring unit 311 acquires data on the moving destination and the moving probability from the moving destination distribution TBL 538, and sets the assignment of the action scene (step S34).
The characteristic data acquisition unit 312 acquires companion information from the common behavior definition TBL 534, and sets an agent's behavior model (step S35).
The behavior data generation unit 313 simulates the behavior of the agent according to the set behavior model of the agent, and stores the history data generated in each cycle of the simulation that is repeatedly performed in the history information TBL 536 (step S36).
The simulator 300 outputs the action of the agent stored in the history information TBL 536 as a result of simulation through the data output system 700 (step S37).

  By the process described above, the simulator 300 can set the characteristics of the agent to the desired characteristics to simulate the agent's activity in the virtual facility.

  An example of the simulation process in the simulator 300 will be described with reference to FIG. The figure is a flowchart showing a procedure of simulation processing in the simulator 300. The process shown below is a process of simulating one day's behavior of each agent, and is performed for each agent.

The behavior data generation unit 313 refers to the group configuration information TBL 532 to determine whether the time in the simulation space has passed the time when the agent has arrived at work (step S3601). If it is determined that the time in the simulation space has not passed the agent's office arrival time as a result of the determination in step S3601 (step S3601: No), the behavior data generation unit 313 sets the time in the simulation space as the agent's office arrival time Let the agent wait until it is If it is determined from the determination result in step S3601 that the time in the simulation space has passed the agent's arrival time (step S3601: YES), the action data generation unit 313 sets the target action scene (the move destination of the agent) Hereinafter, the action of the agent is simply extracted from the action definition TBL 533 as “the target scene”. However, when coming to work, instead of the action definition TBL 533, the action of the agent is extracted from the start condition setting TBL 539 (step S3602).
The action data generation unit 313 determines whether or not there is a plurality of target action scenes (target scenes) (step S3603). If it is determined that there is a plurality of target scenes according to the determination result in step S3603 (step S3603: YES), the action data generation unit 313 selects one target scene from the plurality of target scenes. For example, the action data generation unit 313 may select a target scene closest to the current position from a plurality of target scenes (step S3604).
If it is determined that there is not a plurality of target scenes according to the determination result in step S3603 (step S3603: No), or after the process of step S3604 is finished, the action data generation unit 313 refers to the common action TBL 533 Then, it is determined whether or not it is a target scene to which a plurality of agents relate, that is, a time zone in which a so-called companion action is set (step S3605).

If it is determined that the agent is in the time zone in which the agent acts alone (step S3605: No), the behavior data generation unit 313 moves the agent to the target scene of the agent (step S3606). .
The behavior data generation unit 313 determines whether the use of the target scene of the agent is restricted (step S3607). If it is determined from the determination result in step S3607 that the use of the target scene of the agent is not restricted (step S3607: No), the action data generation unit 313 uses the action scene selected as the target scene of the agent It is determined whether the situation is within the number of people who can use the action scene (step S3608).
If it is determined from the determination result in step S3608 that the usage status of the action scene selected as the target scene is within the number of people who can use the action scene (step S3608: YES), the process proceeds to step S3614.

  If it is determined from the result of the determination in step S3605 that the target scene to which a plurality of agents relate is in a time zone in which a so-called companion action is set (step S3605: Yes), the action data generation unit 313 It is determined whether the agent is restricted from using the related target scene together (step S3611).

If it is determined from the determination result in step S3611 that the use of the target scenes related to a plurality of agents is not restricted (step S3611: No), the behavior data generation unit 313 responds to the determination result. The number of people using the target scene is determined (added) (step S3612).
The behavior data generation unit 313 moves the agent to the target scene (step S3613), and proceeds to step S3614.

If it is determined that the target scene is within the usable number of people according to the determination result in step S3608 (step S3608: YES), or after the process of step S3613 is completed, the behavior data generation unit 313 performs the above determination The agent causes the agent to perform the task of the target scene (step S3614).
The behavior data generation unit 313 refers to the group configuration information TBL 532 to determine whether the time in the simulation space is over the working time of the agent (step S3615).
If it is determined that the time in the simulation space does not exceed the working time of the agent according to the determination result in step S3615 (step S3615: NO), the process proceeds to step S3602.
On the other hand, if it is determined that the time in the simulation space has passed the working time zone of the agent based on the result of the determination in step S3615 (step S3615: YES), the work of the agent of the day is ended. The end time of the above-mentioned working time zone is either the time set as the set time or the work end time of the day for carrying out the overtime calculated based on the overtime rate.

  Note that the action data generation unit 313 determines whether there is another target scene in any of the following cases according to the result of the determination in step S3607 (step S3609). For example, when it is determined that the use of the target scene is restricted as the above case (step S3607: Yes), the use situation of the action scene selected as the target scene is the action scene according to the result of the determination in step S3608. Is determined not to be within the usable number of people (step S3608: No), and it is determined from the result of the determination in step S3611 that the use of the target scenes involving multiple agents is restricted. One of the cases (step S3611: Yes) may be mentioned. In the determination of step S3609, when there is another target scene, the action data generation unit 313 determines that the distance from the current position to the other target scene is longer than a predetermined distance determined in advance. In this case, it may be determined that there is no other target scene. As described above, by not selecting a target scene farther than a predetermined distance, it is possible to simulate even a real user limiting the selection range in consideration of the efficiency.

If it is determined from the determination result in step S3609 that there is another target scene (step S3609: YES), the process proceeds to step S3603.
If it is determined that there is no other target scene as a result of the determination in step S3609 (step S3609: NO), the process proceeds to step S3602.

  By the process described above, the simulator 300 can simulate an activity in a virtual facility using an agent, thereby facilitating verification of an office plan in the facility.

[5. About processing of office planning support]
A process of office planning support by the office planning support system 1 of the present embodiment will be described.
The office plan support system 1 combines the processes performed by the subjective survey system 100, the objective survey system 200, the simulator 300, and the plan requirement extraction system 400 to perform the following office plan support process.

(About the process related to the condition setting for creating office plan)
In order to set conditions for office planning, it is necessary to collect information on what the planned office environment should be. However, the office environment changes under the influence of the configuration of the facility and the behavior of the resident user.
For example, as shown in FIG. 2 described above, the facility currently occupied by the resident P11 forming the organization is taken as the facility 2-1. The facility 2-1 is provided with a function F11. The behavior characteristic of the resident P11 is taken as a behavior characteristic M11. The office environment Y11 in this case is determined by the configuration status of the function F11 and the behavior characteristic M11 of the resident P11.
The tenant P11 evaluates the office environment Y11 in the facility 2-1 according to the respective evaluation criteria (subjective evaluation), and each tenant P11 individually judges the quality of the environment. Therefore, the office planning support system 1 investigates the judgment result for each tenant P11 using the subjective survey system 100, and collects information indicating the deviation between the current environment and the environment desired by each tenant P11. .
In addition, the office planning support system 1 investigates the behavior of the resident P11 in the facility 2-1 or the amount of communication in which the tenant P11 shares information using communication by the objective research system 200. Investigate the basic activities of tenants.

  In addition, there is a facility 2-2 different from the facility 2-1 and a facility 2-3, a resident P12 moves in the facility 2-2, and a move P13 moves in the facility 2-3. As in the case of the facility 2-1, the facility 2-2 is provided with the function F12, and the facility 2-3 is provided with the function F13. Assuming that the behavior characteristic of the resident P12 is the behavior characteristic M12, the office environment Y12 is determined by the configuration status of the function F12 and the behavior characteristic M12 of the tenant P12. Further, assuming that the behavior characteristic of the resident P13 is the behavior characteristic M13, the office environment Y13 is determined by the configuration state of the function F13 and the behavior characteristic M13 of the resident P13.

Next, the case where each resident who has respectively entered the different facilities as described above becomes a resident of the new facility 20 will be examined. In the above case, the resident P11, the resident P12 and the resident P13 move to the facility 20 and become a resident of the facility 20.
If it thinks simply, if the facilities scale of each of the facilities 2-1 before moving, the facilities 2-2, and the facilities 2-3 is prepared for the facility 20, and each tenant acts with the same action characteristic as before moving By providing the same office environment as before relocation, there is no change in usage.

  However, if a function with the same scale as that of the facilities 2-1, 2-2, and 2-3 before relocation is prepared as the function F20 in the facility 20, the tenant shares the prepared function with the function F20. The function may be excessive or lacking. For example, a conference room will be described as an example. The number and size of the conference rooms should be planned so that they will not be used properly. Unless time is not properly used, a situation may occur where the reservation is full and can not be used when it is desired to use it. Therefore, it is desirable that the conference room be configured so that there is room for use.

  When merging facilities as described above, for example, if there is room in the use situation of the meeting room in each facility before relocation, the degree of margin in each facility is added, and the extra transfer in the facility after relocation May be excessive. On the other hand, if there is no room in any facility or all the facilities before relocation, even if the margin of those facilities is added, there may occur a case where the margin transfer is insufficient.

  Furthermore, when tenants who were in different facilities entered the same facility, they can face each other when exchanging information. For example, in the case where information is shared using the communication means before relocation, it is possible to face and share information after the residence without using the communication means.

  Therefore, in order to effectively use the above-mentioned opportunity, it is necessary to provide a facility for newly entering a facility to perform an act of sharing information in a face-to-face manner. Here, the scale of the function for performing the act of facing and sharing information will differ depending on the degree of association between the newly entering tenants. In short, even if tenants who are in close relationship with each other need to have a larger scale, even if tenants with a relatively low degree of association live in the same facility, The size to be added for the above purpose can be underestimated.

  Therefore, the office planning support system 1 according to the present embodiment collects opinions on behavior patterns predicted by the tenants from the tenants who will newly use the facility by the subjective survey system 100, and the opinions are collected. Prepare the required scale based on. The validity of the prepared scale is verified by simulation under different conditions.

  According to the method of design support shown in the present embodiment, even if it is difficult to design as described above, the office plan support system 1 can perform the simulation based on the quantitative calculation method by the simulator 300. Excess and deficiency of facilities (functions) in facilities can be eliminated at the design stage of office planning.

(About the design method to reduce the excess and deficiency of the function arrangement in the office environment)
Hereinafter, a design method for reducing the excess or deficiency of the function arrangement in the office environment will be described. In the design method shown below, first, the office planning support system 1 detects excess or deficiency of the function arrangement in the office environment by any of the following methods.

  First, the behavior measurement unit 210 of the objective survey system 200 specifies the position at which the user is in a meeting, time, and the like by the behavior survey. When it is determined that the identified position corresponds to the position corresponding to the specific function in the facility, the analysis unit 230 of the objective investigation system 200 determines that the user uses the function. The analysis unit 230 of the objective investigation system 200 adds up the results of the above determinations, and determines whether the use rate of the function is out of the range of the use rate set in advance.

  Secondly, the determination unit 315 of the simulator 300 determines whether or not the utilization rate of a specific function (action scene) in the facility deviates from the predetermined utilization rate range according to the simulation result.

  The office planning support system 1 detects the excess or deficiency of the function arrangement in the office environment based on any of the above determination results.

Next, a method of adjusting the excess and deficiency of the function arrangement in the office environment will be described.
The correction unit 413 of the planning requirement extraction system 400 adjusts the excess or deficiency of the function arrangement in the office environment by any of the following methods according to the excess or deficiency information indicating the excess or deficiency of the function arrangement in the office environment.

First, when the utilization rate of a specific function is lower than a predetermined utilization rate range, the correction unit 413 of the planning requirement extraction system 400 reduces the allocation to that function.
Second, if the utilization rate of a particular function is higher than the predetermined utilization rate range, the correction unit 413 of the planning requirement extraction system 400 increases the allocation to that function.
Thirdly, if the allocation of functions is increased by performing the second method described above and the functions are accumulated, the correction unit 413 of the plan requirement extraction system 400 will exceed the initially planned facility size. Review to increase the size of the facility.
After performing any or a plurality of adjustments described above, the validity verification unit 317 of the simulator 300 determines again about the adjusted office environment. In addition, when adjusting with the above-mentioned first method, when facilities of the scale assigned by the second and third methods can not be prepared, or when reviewing the placement efficiency in the same facility without relocation, etc. The adjustment may be made by reducing the allocation amount by prioritizing the function of low importance and the function of low influence by reduction.

(About the evaluation method of function and productivity in office environment)
Hereinafter, an evaluation method of functions and productivity in an office environment by the office planning support system 1 will be described. In the evaluation method shown below, the layout design in the office is performed first, and it is evaluated based on the layout design whether it is possible to efficiently use the respectively arranged functions.

First, the simulator 300 of the office planning support system 1 sets an evaluation value of productivity when each function is used. For example, the setting condition acquisition unit 311 defines the evaluation value of productivity as a total value of the amount of information that enables generation and sharing of information per unit time, and the amount of information that supplies the generated information. For example, the amount of information to be targeted is investigated to share information, and the amount of information obtained from the result of the investigation, the design information in the case of using the designed result as a product, the conference Includes the amount of information that could be shared and shared.
The setting condition acquisition unit 311 defines, for each action scene, the value of productivity of the action scene, and generates an amount of information per unit time and supplies the information amount that can be shared, and the generated information. The set information amount is set in the item of INPUT and OUTPUT of the function definition TBL 535, respectively.

Next, based on the result of simulation by the simulator 300, the effectiveness verification unit 317 counts the usage time of the action scene for the action scene used by each agent, and also counts the productivity.
The office planning support system 1 causes the setting condition acquisition unit 311 to adjust any of the conditions that determine the office environment, based on the above-described result of counting the productivity for each user. After the adjustment of the conditions is made, the validity verification unit 317 determines again about the adjusted office environment.

(About evaluation method of function and energy consumption in office environment)
Hereinafter, an evaluation method of functions and energy consumption in an office environment by the office planning support system 1 will be described. In the evaluation method shown below, the layout design in the office is performed first, and the energy consumption by using the respectively arranged function is evaluated based on the layout design.

First, the simulator 300 of the office planning support system 1 sets the consumption of energy when each function is used in a facility as an evaluation value. For example, the setting condition acquisition unit 311 defines the amount of energy consumption as the amount of energy generated per unit time. The energy amount to be targeted includes the amount of heat generated when the user (agent) is in the action scene corresponding to the function, the amount of heat generated from the device operated by the user (agent), the air conditioner and lighting equipment It includes the amount of heat generated when it is operated.
The setting condition acquisition unit 311 defines the energy consumption of the action scene in association with each action scene.

Next, based on the result of simulation by the simulator 300, the effectiveness verification unit 317 counts the usage time of the action scene and also counts the energy consumption according to the action scene used by each agent.
The office planning support system 1 causes the setting condition acquisition unit 311 to adjust any of the conditions that affect energy consumption based on the above-described aggregation result of the energy consumption for each user. After the adjustment of the conditions is made, the validity verification unit 317 determines again the adjusted office environment.

(About evaluation method of flow line in office environment)
Hereinafter, the evaluation method of the flow line in the office environment by the office planning support system 1 will be described. In the evaluation method described below, the office planning support system 1 first performs the layout design in the office, and based on the layout design, the flow line for moving between the arranged behavior scenes (functions) evaluate.

  First, the simulator 300 of the office planning support system 1 verifies the movement of the user between the action scenes based on the result of simulation using an agent. In the simulation by the simulator 300, a time interval capable of capturing the movement of the agent is set as a unit time, and the position of the agent in the facility per unit time is recorded.

  In this simulation, the simulator 300 moves each agent in a virtual facility according to the set conditions. For example, the simulator 300 generates a movement route based on the result of the layout design. The simulator 300 extracts the shortest route from the current position to the position designated as the next action scene as the movement route. The simulator 300 selects the moving route which becomes the extracted shortest route as the flow line of each agent.

  By such simulation, the agent moves along the selected flow line. During the move, there is a problem (す) with other agents. Also, other agents may be overtaken or overtaken by other agents. When the frequency of occurrence of these cases is high, the simulator 300 estimates that there is a time zone in which agents concentrate on the route, and the route is crowded.

  On the other hand, there is an idea that communication between users can be achieved because there are many wrinkles. Depending on how you set up the flow line, the occurrence situation of the drought changes. In addition, evaluation of flow lines can be applied to verification of the effect from the number and frequency of occurrence of wrinkles, as in the case of actively arranging functions that activate communication triggered by wrinkles.

Also, the simulator 300 records the movement route of the agent, and the validity verification unit 317 acquires the recorded data. Thus, the validity verification unit 317 can obtain the movement amount of the agent from the movement route of the agent acquired from the simulator 300. The effectiveness verification unit 317 can estimate the distance traveled by the user at the actual facility and the number of steps from the movement amount of the agent.
It can be verified by the effectiveness verification unit 317 that the movement distance of the agent in the simulation is relatively small, and accordingly the time required for the movement is reduced, based on the result of the simulation in the planning stage of the office. The simulator 300 thus makes it possible to estimate the expected effects of the improvement at the planning stage. As a result, by relocating or relocating the office, the user can obtain time from the simulator 300 for reducing the time required to move in the office. This allows the user to proceed with planning from the planning stage prior to relocation or relocation of the office so that time may be reduced for traveling within the office for other purposes.

(About the simulation method when changing office environment)
Hereinafter, a simulation method in the case of changing the office environment (office plan) using the office plan support system 1 will be described. For example, the office environment may be changed by reviewing the office environment at the stage where the objective survey was conducted. A more specific example is given. It is assumed that as a result of soliciting the user's opinion about the dissatisfaction point of the current office environment, an opinion such as “the environment for taking a break is insufficient” is received. In such a case, it is determined that the current office lacks an environment for taking a break, and the office plan is reviewed to improve the environment for taking a break. Such an opinion is derived from the result of the subjective survey of the user, and even if the user is dissatisfied as above, it does not appear in the result of the objective survey in which the user's activity (action) is surveyed There are many things.

  In the above case, it is assumed that a break room is newly provided to correct the office environment to eliminate the dissatisfaction of the office environment. Even if a chat room is newly provided in the office plan after correction, there is no action history that the user uses the chat room. In such a situation, when simulation is performed using an agent that acts based on the action history of the stage before improving the office environment, the phenomenon that the agent does not use the newly provided environment (action scene) Can occur.

Therefore, in the office planning support system 1, the agent's behavior characteristic is corrected so that the agent uses the newly provided environment (action scene). For example, as shown in FIG. 1, the simulator 300 of the office planning support system 1 obtains survey data of a subjective survey from the subjective survey system 100. The simulator 300 corrects the agent's behavior characteristic based on the survey data of the subjective survey obtained from the subjective survey system 100. By this correction, the agent's action pattern (action characteristic) set based on the investigation data of the objective investigation system 200 can be corrected by the investigation data of the subjective investigation system 100.
It should be noted that such a correction amount of the action characteristic may be determined according to the correction amount of the action scene. For example, when the ratio of the action scene is changed, the correction rate of the action characteristic is determined according to the difference between the ratio before and after the change or the change rate of the ratio before and after the change.
As described above, when there is no action scene, the simulator 300 takes a value corresponding to the amount of the newly provided action scene as the value of the action characteristic of the agent corresponding to the newly provided action scene. Set The simulator 300 corrects the value of the action characteristic of the agent corresponding to the other action scene according to the above setting according to the value of the action characteristic of the agent corresponding to the newly provided action scene.

As described above, the simulator 300 can simulate the behavior characteristic of the agent by correcting it based on the survey data of the subjective survey system 100 without causing a contradiction.
By correcting in this way, even if the amount of change in the office environment is large, it is possible to correct the agent's behavior characteristics, and to estimate the change in the user's behavior pattern accompanying the change in the office environment. Can.

(1) The above-mentioned objective survey system 200 (office activity analysis system) analyzes office activity in an office (facility) configured such that a plurality of types of tasks are respectively performed by each user. The analysis unit 230 analyzes information including any of communication history between users and user behavior history. The fourth analysis unit 234 (data generation unit) generates data for correcting the office of the user, which is data indicating the status of the office activity of the user based on the analysis result.
In the objective investigation system 200, the analysis unit 230 analyzes information including any of the history of communication between users and the user's action history, and the fourth analysis unit 234 analyzes the information based on the analysis result, Generate data for correcting the user's office. This enables the objective survey system 200 to facilitate verification of the office plan at the facility.

(2) Further, in the above-mentioned objective investigation system 200, the first analysis unit 231 in the analysis unit 230 is at least one of history information of communication between the users and user behavior history information indicating the behavior history of the user. And analyzing the user's office activity from activity history information indicating the status of the user's office activity.
Such an objective investigation system 200 can analyze the office activity of the user from the information including at least one of the detected history information of communication and the user behavior history information.

(3) Moreover, in the above-mentioned objective investigation system 200, the 2nd analysis part 232 in analysis part 230 analyzes the use situation of the above-mentioned institution from information including the above-mentioned user action history information.
Such an objective survey system 200 makes it possible to analyze the use situation of a facility from information including user action history information.

(4) Moreover, in said objective investigation system 200, the 3rd analysis part 233 in the analysis part 230 analyzes the sufficiency situation of the space of the said facility from the information containing the said user action historical information.
Such an objective survey system 200 makes it possible to analyze the space fullness of a facility from information including user behavior history information.

(5) Further, in the above-mentioned objective investigation system 200, the fourth analysis unit 234 in the analysis unit 230 generates the degree of proximity request between the group to which the user belongs and the other group based on the result of the analysis.
The objective research system 200 analyzes the degree of association between the group to which the user belongs and the other group from the information including the detected history information of communication and the user action history information, as described above. It becomes possible to generate proximity requirements between a group to which a user belongs and other groups.

(6) Further, in the above-described objective investigation system 200, the user belongs to any one of a plurality of groups arranged in the facility. The fifth analysis unit 235 in the analysis unit 230 analyzes the degree of association between the group to which the user belongs and the other group from the information including the history information of the communication and the user action history information.
Such an objective investigation system 200 makes it possible to analyze the degree of association between a group to which the user belongs and other groups from information including the detected communication history information and user action history information.

(7) Further, in the above-mentioned objective investigation system 200, the analysis unit 230 generates correction information for correcting the office plan of the facility generated based on the proximity request degree, based on the user action history information. .
Based on the user action history information, correction information for correcting the office plan of the facility generated based on the proximity request degree between the group to which the user belongs and the other group is generated by the objective research system 200 as described above. It becomes possible.

(8) Further, in the above-mentioned objective investigation system 200, the history information of the communication includes history information of transmitting and receiving information using communication between the user and another user who uses the facility. . For example, the history information of the communication includes a telephone call between the user and another user who uses the facility, a voice conference or video conference by a teleconference system, a web conference, a chat using a short message service, an electronic mail, a terminal A history of telecommunications is included, including any of screen sharing of the display screen of the device and sending and receiving of facsimiles and the like.
With such an objective survey system 200, the group to which the user belongs and the other group are based on the history information in which information is transmitted and received using communication between the user and other users who use the facility. It is possible to generate proximity requirements.

(9) Further, in the above-described objective investigation system 200, the analysis unit 230 determines the amount of communication or the specific keyword included in the communication when it is determined that users belonging to different organizations communicate using communication. The number of times or frequency of detection is counted, and the degree of proximity between the tissues is calculated according to the counted result.
The objective investigation system 200 counts the number or frequency of detection of the amount of communication of communication determined that users belonging to different organizations communicate using communication or the specific keyword included in the communication. It is possible to calculate the degree of proximity between the tissues according to the counted result.

  The embodiment of the present invention has been described above, but the subjective survey system 100, the objective survey system 200, the simulator 300, and the plan requirement extraction system 400 in the office plan support system 1 shown in FIG. There is. Then, the process of the series of processes related to the process described above is stored in a computer readable storage medium in the form of a program, and the above process is performed by the computer reading and executing this program. Here, the computer readable storage medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, and the like. Alternatively, the computer program may be distributed to a computer through a communication line, and the computer that has received the distribution may execute the program. The term "computer system" as used herein also includes an OS and the like.

  That is, the subjective survey system 100, the objective survey system 200, the simulator 300, and the plan requirement extraction system 400 in the office planning support system 1 internally include a computer system. The central processing unit such as CPU reads out the above program to the main storage device such as ROM and RAM to process all or part of the processing performed by the computer system in the office planning support system 1 and process the information It is realized by executing the process. In addition, each process part which comprises the subjective survey system 100, the objective survey system 200, the simulator 300, and the plan requirement extraction system 400 may be implement | achieved by a dedicated hardware.

As mentioned above, although one embodiment of the present invention was described, an embodiment of the present invention is not limited to the above. The embodiment of the present invention can be, for example, a modification of the above embodiment as follows.
For example, in the above embodiment, the office planning support system 1 is divided into the subjective survey system 100, the objective survey system 200, the simulator 300, and the plan requirement extraction system 400 for the sake of convenience of the configuration related to the present invention. The division of the subjective survey system 100, the objective survey system 200, the simulator 300, and the plan requirement extraction system 400 may be changed from those illustrated above, or the respective systems may be integrated. In addition, a part of the configuration included in each system may be included in the configuration of another system.

  In the above description of the group configuration information TBL 532 in the simulator 300, the opening time and closing time have been described as constants, but may be variables. For example, the simulator 300 simulates the opening time and closing time of the workplace where the flextime system and the core time are applied to the opening time and closing time as variables. The simulator 300 may set the values of the opening time and closing time based on random numbers so as to have a distribution of opening times and closing times based on the result of the objective survey. By making the opening time and closing time variable in this way, it is possible to reproduce the change in opening time and closing time of the office to which the flextime system is applied.

  Also, in the above explanation, the number of users to be set as the target of examination was described as the number of members of the organization, but in fact, since there are employees who take vacations, it is assumed that all members go out of business There is no limit. In such a case, a vacation acquisition rate may be obtained from the result of a subjective survey, etc., the number of people acting based on the vacation acquisition rate may be reduced, and a simulation based on the reduced number of people may be performed.

  Note that the part described as “office” in the above description may be read as “workplace”.

  In addition, although the data structure shown to the figure has shown what was made into the form of the table in order to demonstrate easily, the form of a data structure may be another form.

1 Office Planning Support System (Planning Support System),
100 Subjective Survey System, 200 Objective Survey System (Activity Analysis System),
300 simulator (usage situation estimation unit), 400 plan requirement extraction system, 500 storage unit

Claims (7)

An activity analysis system that models and simulates the activity of the user in the behavior of an agent in a numerical model space related to the facility, in order to analyze the activity of the user in the facility,
A first data defining a numerical model space related to the facility, a second data defining an action characteristic of the agent corresponding to the user, and a model of an action for which the agent continues for a defined time in the numerical model space Storage unit storing various data including third data defining a scene of action as a subject and fourth data for correcting the behavior characteristic of the defined agent;
The behavior characteristic of the agent based on the second data is corrected based on the fourth data, and a particular one of the behavior scenes defined by the third data is based on the behavior characteristic of the agent that has been corrected. An operation processing unit that selects an action scene and executes a simulation that causes the agent to act in the numerical model space of the facility defined by the first data according to the selected action scene;
A data generation unit that generates the second data and the fourth data based on the user's action history so that the agent's action characteristic approaches the user's action characteristic ;
Equipped with
The data generation unit
When the data of the action scene of the analysis target of the agent is present in the second data in advance, the fourth data corresponding to the change of the action scene of the analysis target of the agent is added,
When the data of the action scene to be analyzed by the agent does not exist in advance in the second data, the fourth data corresponding to the action scene newly provided as the action scene to be analyzed is added.
Activity analysis system. The data generation unit
Generating the second data for determining the behavior characteristic of the agent based on the behavior characteristic of the user obtained by measurement, and the fourth data for correcting the behavior characteristic of the agent;
The activity analysis system according to claim 1. The data generation unit
Generating the fourth data for correcting the behavior characteristic of the agent so as to be the behavior characteristic of the user derived from the analysis result of the subjective survey;
The activity analysis system according to claim 1 or 2. The activity analysis system according to any one of claims 1 to 3, further comprising: an analysis unit that analyzes the behavior history of the user and generates the fourth data for correcting the behavior characteristic of the agent. The analysis unit
At least one of analyzing the use situation of the facility from action history data relating to the action history of the user and analyzing a fullness situation of the space of the facility from data including the action history data;
The activity analysis system according to claim 4. An activity analysis method of an activity analysis system which models and simulates the activity of the user in the behavior of an agent in a numerical model space related to the facility in order to analyze the activity of the user in the facility,
The computer of the activity analysis system
A first data defining a numerical model space related to the facility, a second data defining an action characteristic of the agent corresponding to the user, and a model of an action for which the agent continues for a defined time in the numerical model space Storing various data including the third data defining the action scene as the second action and the fourth data for correcting the action characteristic of the defined agent in the storage unit;
The behavior characteristic of the agent based on the second data is corrected based on the fourth data, and a particular one of the behavior scenes defined by the third data is based on the behavior characteristic of the agent that has been corrected. Selecting an action scene, and executing a simulation that causes the agent to act in the numerical model space of the facility defined by the first data, according to the selected action scene;
Generating the second data and the fourth data based on the user's action history such that the agent's action characteristic approaches the user's action characteristic ;
When the data of the action scene of the analysis target of the agent is present in the second data in advance, the fourth data corresponding to the change of the action scene of the analysis target of the agent is added,
Adding the fourth data corresponding to the action scene newly provided as the analysis target action scene, when the data of the analysis target action scene of the agent does not exist in the second data in advance;
How to perform activity analysis. A computer of an activity analysis system that models and simulates the user's activity in the agent's behavior in the numerical model space related to the facility to analyze the user's activity in the facility,
A first data defining a numerical model space related to the facility, a second data defining an action characteristic of the agent corresponding to the user, and a model of an action for which the agent continues for a defined time in the numerical model space Storing various data in the storage unit, including third data defining a scene of action as a subject, and fourth data for correcting the behavior characteristic of the agent as defined above;
The behavior characteristic of the agent based on the second data is corrected based on the fourth data, and a particular one of the behavior scenes defined by the third data is based on the behavior characteristic of the agent that has been corrected. Selecting an action scene, and executing a simulation for causing the agent to act in the numerical model space of the facility defined by the first data according to the selected action scene;
Generating the second data and the fourth data based on the user's action history such that the agent's action characteristic approaches the user's action characteristic ;
When the data of the action scene of the analysis target of the agent is present in the second data in advance, the fourth data corresponding to the change of the action scene of the analysis target of the agent is added,
Adding the fourth data corresponding to the action scene newly provided as the analysis target action scene, when there is no data of the analysis target action scene of the agent in the second data;
A program to run a program.

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