JP7507579B2 - Behavioral characteristic quantification device, simulator device, design support device, behavioral characteristic quantification method, and program - Google Patents

Description

The present invention relates to a behavioral characteristic quantification device, a simulator device, a design support device, a behavioral characteristic quantification method, and a program.

When designing a new facility or changing the layout, it is desirable to make the facility easy for users to use. When areas for users to work are arranged within such a facility, the users move within the facility from any position within the arranged area or from the user's seat assigned for work to perform the desired tasks. Analysis techniques are known for analyzing the behavior of users that involves movement within such facilities. User movement trends may be analyzed based on the area of areas arranged within the facility and with different usage purposes.

JP 2016-115004 A

However, even when analyzing areas with different usage purposes based on their area, it can be difficult to represent user movement trends.

The present invention has been made in consideration of the above-mentioned circumstances, and an object of the present invention is to provide a behavioral characteristic quantification device, a simulator device, a design support device, a behavioral characteristic quantification method, and a program that can estimate user movement trends with greater accuracy.

The present invention has been made to solve the above problems, and a first aspect of the present invention is a behavioral characteristic quantification device in which a plurality of areas, each having a predetermined area, are defined within a range in which a user can move in real space, the plurality of areas including a first area and a second area, and the behavioral characteristic quantification device is equipped with a calculation unit that quantifies the tendency of a user in the first area to attempt to move to the second area using an analysis result of how the first area and the second area are connected.

In addition, in the behavioral characteristic quantification device, the calculation unit quantifies the user's tendency to attempt the movement by using the area of the range visible within the second area when the second area is viewed from a specific point within the first area as the degree of visibility.

In addition, in the behavioral characteristic quantification device, the calculation unit quantifies the user's tendency to attempt the movement using a first index value based on the area of the second region and the area of the visible range.

In addition, in the behavioral characteristic quantification device, the calculation unit quantifies the tendency of the user to attempt the movement using a second index value based on the area of the second area, the area of the movable range, and the area of the first area.

In addition, in the behavioral characteristic quantification device, the calculation unit quantifies the tendency of the user to attempt the movement using the second index value based on the ratio between the area of the second area and a reference area obtained by subtracting the area of the first area from the area of the movable range.

In addition, in the behavioral characteristic quantification device, the calculation unit quantifies the user's tendency to attempt the movement using a third index value based on the positional relationship between a specific point in the first area and the second area.

In addition, in the behavioral characteristic quantification device, the calculation unit calculates the third index value using either the distance between a specific point in the first area and the near end of the second area, or the distance between a specific point in the first area and the far end of the second area.

In addition, in the behavioral characteristic quantification device, the calculation unit calculates the probability that the user will move to the second area among the multiple areas using a first calculated value indicating the tendency of the user to attempt the movement quantified based on the area of the second area and a second calculated value indicating the tendency of the user to attempt the movement, and calculates the second calculated value using a first index value based on the area of the second area and the area of the visible range, a second index value based on the area of the second area, the area of the movable range, and the area of the first area, and the third index value.

In the behavioral characteristic quantification device, the calculation unit corrects the first probability that the user will move to the second area among the multiple areas to a second probability using a first calculated value indicating the tendency of the user to attempt the movement quantified based on the area of the second area and a second calculated value indicating the tendency of the user to attempt the movement, and calculates the second calculated value using a first index value based on the area of the second area and the area of the visible range, a second index value based on the area of the second area, the area of the movable range, and the area of the first area, and the third index value.

In the behavioral characteristic quantification device, the calculation unit calculates a second probability that the user will move to the second area among the multiple areas based on a first calculated value indicating the tendency of the user to attempt the movement quantified based on the area of the second area, a second calculated value indicating the tendency of the user to attempt the movement, and a first probability that the user will move to the second area among the multiple areas, and calculates the second calculated value using a first index value based on the area of the second area and the area of the visible range, a second index value based on the area of the second area, the area of the movable range, and the area of the first area, and the third index value.

In another aspect of the present invention, the simulator device is a simulator device in which a plurality of areas each having a predetermined area are defined within a range in which a user can move in real space, the plurality of areas including a first area and a second area, the simulator device is equipped with a calculation unit that quantifies the tendency of a user in the first area to attempt to move to the second area using an analysis result of the connection between the first area and the second area, and a movement control unit that moves an avatar of the user in a virtual space corresponding to the real space based on the quantified tendency to attempt movement.

In another aspect of the design support device of the present invention, a plurality of regions each having a predetermined area are defined within a range in which a user can move in real space, the plurality of regions including a first region and a second region, and the design support device is provided with a calculation unit that quantifies the tendency of a user in the first region to attempt to move to the second region using an analysis result of the connection between the first region and the second region, and an evaluation value calculation unit that calculates an evaluation value of the position of either the first region or the second region located in the real space based on the calculation result by the calculation unit.

In another aspect of the present invention, the behavioral characteristic quantification device is a behavioral characteristic quantification device in which a plurality of areas each having a predetermined area are defined within a range in which an agent can move in a virtual space, the plurality of areas including a first area and a second area, and the behavioral characteristic quantification device is equipped with a calculation unit that quantifies the tendency of an agent in the first area to attempt to move to the second area using an analysis result of how the first area and the second area are connected.

In another aspect of the present invention, the behavioral characteristic quantification device is a behavioral characteristic quantification device in which a plurality of regions each having a predetermined area are defined within a range in which a user can move in real space, the plurality of regions including a first region and a second region, and the behavioral characteristic quantification device is equipped with a calculation unit that quantifies the degree of connection between the first region and the second region using an analysis result of the manner in which the first region and the second region are connected.

In another aspect of the present invention, the method for quantifying behavioral characteristics includes a step in which a computer defines a plurality of areas, each having a predetermined area, within a range in which a user can move in real space, the plurality of areas including a first area and a second area, and quantifies the tendency of a user in the first area to attempt to move to the second area using the analysis results of how the first area and the second area are connected.

In another aspect of the present invention, the program causes a computer to execute a step of defining a plurality of areas, each having a predetermined area, within a range in which a user can move in real space, the plurality of areas including a first area and a second area, and quantifying the tendency of a user in the first area to attempt to move to the second area using the results of an analysis of how the first area and the second area are connected.

The present invention provides a behavioral characteristic quantification device, a simulator device, a design support device, a behavioral characteristic quantification method, and a program that can estimate user movement trends with greater accuracy.

1 is a configuration diagram of an office planning and design support system according to an embodiment of the present invention. FIG. 2 is an explanatory diagram showing a data flow of the office planning and design support system according to the embodiment. FIG. 1 is a diagram illustrating an example of an office to be considered in an embodiment. FIG. 2 is a diagram for explaining the layout of a scene according to an embodiment. FIG. 2 is a diagram for explaining a "scene" relating to a user's action in the embodiment. 11 is a diagram for explaining correction of movement probability according to an embodiment. FIG. FIG. 11 is a diagram for explaining a first example of a visibility index. FIG. 11 is a diagram for explaining a first example of a visibility index. FIG. 13 is a diagram for explaining a second embodiment of a visibility index. FIG. 2 is a diagram for explaining the distance relationship between two scenes. FIG. 13 is a diagram for explaining a method for displaying the results of an analysis of appearance.

The following describes an embodiment of the present invention with reference to the attached drawings.

In this embodiment, a specific area within the analysis range is "visible" means that, in the results of an analysis based on layout data (plan data), the area is not blocked by obstacles such as structures (pillars, walls) depicted on the layout data. In this case, if there is something that exists in real space but is not depicted on the layout data (plan), that thing will prevent the area from being "visible," but this area may be treated as "visible."

Figure 1A is a configuration diagram of an office planning and design support system according to an embodiment of the present invention. Figure 1B is an explanatory diagram showing the data flow of the office planning and design support system according to an embodiment.

The office planning and design support system 1 (behavioral characteristic quantification device, design support device, simulator device) shown in FIG. 1A includes a subjective survey system 100, an objective survey system 200, a simulator 300 (calculation unit), a planning requirement extraction system 400 (behavioral characteristic quantification device), a memory unit 500, a basic data input system 800, and a data output system 700.

The subjective survey system 100 collects opinions of users who use an existing office facility 2 (an already used facility), visualizes what each user feels when using facility 2 based on the collected opinions, and outputs the data.

The objective survey system 200 analyzes office activities in a facility 2 configured so that multiple types of work are each performed by each user. Such an objective survey system 200 collects information indicating the usage status of the facility 2 without the intervention of the users. The objective survey system 200 analyzes the usage status of the facility 2 based on the collected data, and outputs the analyzed data.

The basic data input system 800 acquires basic data used for processing in the simulator 300 described below, and stores it in the memory unit 500. For example, the basic data input system 800 acquires data generated by the subjective survey system 100 and the objective survey system 200 as the above-mentioned basic data. Note that if the subjective survey system 100 and the objective survey system 200 directly store each data in the memory unit 500, this can be replaced with data from the basic data input system 800.

The simulator 300 simulates the situation in which work is performed in an actual facility 2 or an office (facility) in the design stage, assuming that the work is performed in a virtual facility corresponding to the facility 2 or the office (facility) in the design stage. Through this simulation, the simulator 300 generates data that estimates the office environment in the facility corresponding to the virtual facility. The simulator 300 may also verify the office plan designed by the designer. The simulator 300 may also generate data to be used for processing by the planning requirement extraction system 400, which will be described later.

The planning requirements extraction system 400 helps to ensure that the created office plan functions effectively and that an optimal office plan for the facility is created. For example, the planning requirements extraction system 400 supports the creation of an office plan for a facility in which multiple types of work are performed by each user and which is used by the users. The planning requirements extraction system 400 generates planning requirements for designing an office plan based on various survey data stored in the memory unit 500. For example, such a planning requirements extraction system 400 performs various processes by linking the subjective survey system 100, the objective survey system 200, the simulator 300, etc.

The data output system 700 outputs data generated by the subjective survey system 100, the objective survey system 200, the simulator 300, and the planning requirements extraction system 400. For example, the data output system 700 outputs data related to the planning requirements of the basic plan generated by the planning requirements extraction system 400, survey data including subjective survey data and objective survey data visualized by the planning requirements generation unit 411, and the verification results verified by the simulator 300.

The storage unit 500 stores various data generated and output in each of the subjective survey system 100, the objective survey system 200, the simulator 300, and the planning requirement extraction system 400, as well as various data used in the processing of each of these systems. For example, the storage unit 500 stores either or both of the subjective survey data including the results of a user's subjective survey or an analysis result based on the subjective survey result, and the objective survey data including the results of an objective survey on the user's activities or an analysis result based on the objective survey result, in the survey data. When the objective survey data includes information obtained from the results of an objective survey on the user's activities in an existing facility used by the user, the objective survey data includes information indicating the usage status of each function in the existing facility. Note that the information indicating the usage status of each function in the existing facility may include information such as user (worker) behavior information, schedule information, and communication information obtained from a portable or installed sensor. In addition, both the users who are the subjects of the subjective survey and the users who are the subjects of the objective survey will be users of existing facilities.

The subjective survey system 100, objective survey system 200, planning requirements extraction system 400, and simulator 300 may be configured in accordance with, for example, Japanese Patent Application Laid-Open No. 2016-115005.

Figure 2 shows an example of an office to be considered in an embodiment.

The facility 2 under consideration is, for example, located on one floor of a building with multiple floors. Within this floor, multiple areas with different usage purposes are arranged. It is assumed that users can move freely to various locations within this floor. This user can use the multiple areas with different usage purposes. The areas of the multiple areas with different usage purposes are, for example, set to a predetermined value. The floor and arrangement shown in FIG. 2 are one example and are not limiting.

Users move around the floor according to their purpose and perform desired actions that correspond to that purpose. Simulator 300 mimics such user behavior and causes an agent in the virtual space to perform similar actions. Simulator 300 performs a simulation that, for example, quantifies the tendency of a user in a first area to attempt to move to a second area using the analysis results of how the first and second areas are connected. Simulator 300 provides information for determining the layout of the floor by outputting the results of the simulation.

There is a known method of showing the characteristics of an individual node or an entire network using the "centrality" of graph theory. For example, by assigning spaces to nodes and networking the connections between the spaces, this can be used to show, for example, the relationship between a first area and a second area within a building.

For example, the relationship between the first area and the second area may include "ease of access" or "visibility" when the user moves around. By using this information to analyze how the first area and the second area are connected, it is possible to obtain information about the degree of connection of the second area from within the first area.

The user's actions are divided into categories, and the divided actions are called "scenes." Each "scene" is assigned an identifier to identify the category. For example, as shown in Patent No. 4500846, the user's actions may be divided into one of a number of "scenes (work scenes)." "Scene A" to "Scene H" are examples of such categories. For example, the above-mentioned scenes are set to the following seven scenes. The seven scenes are "Think," "Station," "Community," "Review," "Reception," "Academy," and "Break." For a detailed explanation of each of the above-mentioned scenes, please refer to Patent No. 4500846.

Figure 3 is a diagram for explaining the layout of scenes in an embodiment.

The placement of scenes will be explained using the example of the task of placing scene B in the optimal position on the same floor when the placement of scenes A-1 and A-2 on the same floor has already been decided.

In Figure 3, the arrows indicate the distance from scene A-1 to candidate scenes B#1 and B#2 for the position of scene B. The distance from a point in scene A-1 to the representative point of candidate scene B#1 for the position of scene B and the distance to the representative point of candidate scene B#2 are approximately equal. In contrast, when comparing the distance from scene A-2 to the representative point of candidate scene B#1 with the distance from scene A-2 to the representative point of candidate scene B#2, it is clear that the distance from any point in scene A-2 to the representative point of candidate scene B#1 is longer. If the target scene is not essential to the user's business, the longer the distance to the target scene, the lower the probability that the user will use that scene.

By the way, there is a concept that the usage probability of each scene is determined according to the area of the area allocated to that scene (called the "area of the scene"). In a comparative example that follows this concept, the usage probability of a scene is determined by the area allocated to the scene, regardless of the distance traveled between scenes. Depending on the conditions, there may be a large deviation from actual user behavior. This is an example of a case where it is difficult to represent the tendency of users to move based on the area of the area located within a facility. Furthermore, when the usage probability of a scene is determined by the area allocated to the scene, the degree of connection between scenes does not affect the usage probability of the scene.

In the embodiment described below, for example, the degree of connection between scenes related to the arrangement of scenes is used as an evaluation index for the arrangement and analysis.

(Outline of Simulation Processing by Simulator 300)
4 to 9, an overview of the simulation process by the simulator 300 will be described. The simulation process is performed, for example, in the following procedure.

Step 1: The simulator 300 calculates the probability of moving between scenes from the results of behavior monitoring. FIG. 4 is a diagram for explaining "scenes" related to the user's behavior in this embodiment. The table shown in FIG. 4 is an example in which the user's current behavior is classified as "Scene A", and the behavior that the user can select next is any one of "Scene A" to "Scene H". The values in this table are the probability of transitioning to each scene. This probability is called the "movement probability". This movement probability is before the correction described below is made, and in the following explanation, it may be called the "old movement probability".

For example, if you are currently in scene A, the probability of moving to scene B next is "17%", and similarly the probability of moving to scene C is "29%", and so on. A probability value of "0%" indicates that the possibility of moving to that scene is extremely low.

Step 2: The simulator 300 corrects the movement probability before the layout change (old movement probability) to the movement probability after the layout change. For example, based on the premise that "if the office layout (arrangement) changes, the above movement probability also changes," the simulator 300 corrects the movement probability to the movement probability after the layout change. The movement probability after the layout change is called the "corrected movement probability." The processing of Step 2 is an example of a process (step) that quantifies, for example, the tendency of a user in scene A to attempt to move to scene B using the analysis results of how scenes A and B are connected.

Step 3: The simulator 300 moves the agent corresponding to the user within the virtual space, for example, based on the placement result of the new scene and the corrected movement probability. The simulator 300 verifies the utilization rate of each scene and the amount of passing with other agents. For the above process, refer to JP 2016-115005 A, etc.

Next, we will explain the process for further improving the accuracy of the "corrected movement probability" in the above process.

The required area for each scene is calculated before and after the layout change based on survey results, etc. The required area before the layout change is called the "current area," and the required area after the layout change is called the "planned area."

Here, as a comparative example, we present an example in which a corrected movement probability is generated based on the current area and planned area of each scene. In this comparative example, the layout relationship of each scene does not affect the movement probability, so it may not match the actual behavior of the user.

In such cases, it is advisable to add the inter-scene arrangement before and after the layout change to the correction conditions in order to get closer to actual user behavior. Note that the relationship of "inter-scene arrangement" can be shown using indicators such as how the scenes are connected, how easy it is to get to other scenes, and how clear the areas of other scenes are. Below, we will provide more specific examples and explain this in detail.

The correction of the movement probability will be described with reference to FIG. 5. FIG. 5 is a diagram for explaining the correction of the movement probability in the embodiment. As shown in FIG. 5, for example, scene A and scene B are arranged on a floor of a building. For example, the shape of the area RA of scene A is shown by a rectangle SA, and the shape of the area RB of scene B is shown by a rectangle SB. The area RA is surrounded by sides SAS1 and SAS4 where no obstacles such as walls are arranged, and sides SAS2 and SAS3 facing the exterior wall or window. The area RB is surrounded by sides SBS1 facing the exterior wall, sides SBS2 and SBS3 where no obstacles such as walls are arranged, and side SBS4 where a wall is provided. Note that a pillar SBP23 is provided between sides SBS2 and SBS3. For example, the wall of side SBS4 is configured so that light does not pass through, or is configured so that the image (situation) on the other side of the wall cannot be seen through the wall.

The movement probability is corrected based on the "Activity Encouragement Degree (AED)" before and after the layout change. The "movement probability" generated by the correction is sometimes referred to as the "new movement probability" in the following explanation. Below, we will explain the "activity encouragement degree" and the correction of the movement probability using the "activity encouragement degree" before and after the layout change in order.

(Adjustment based on the degree of inducement)
The "degree of behavior induction" is an index showing the degree of induction to move from a first scene to a second scene. For example, the "degree of behavior induction" when moving from scene A to scene B is defined using the following formula (1) and shown as "AED in scene A → scene B". For example, "AED in scene A → scene B" (second calculated value) is calculated using the visibility rate (first index value), the area ratio (second index value), the distance relationship (third index value), and the attraction power (fourth index value).

(AED from Scene A to Scene B)
= α × (visibility rate from A to B) + β × (distance relationship between A and B) + γ × (area ratio of B) + δ × (attractive power of B) ... (1)

In the above formula (1), α, β, γ, and δ are weighting coefficients. Note that when scene B is not a magnet space, the value of δ should be set to 0 or a relatively small value close to 0. In the following explanation, we will first give an example where δ is set to 0. Details regarding magnet spaces will be provided later.

"A → B visibility rate" is an example of an index of how "Scene B" looks when viewed from "Scene A".

The "A-B distance relationship" is an example of an index that indicates the distance between two scenes, "Scene A" and "Scene B." The "A-B distance relationship" is calculated based on the placement results of "Scene A" and "Scene B."

"Area ratio of B" is the ratio of the area of area RB to the area of the entire area of the floor or the area within the floor where the user can move, excluding area RA of scene A.
"Attractiveness of B" is an example of an index (called an attractiveness index value) showing the attractiveness of "scene B." For example, even if a multifunction printer is placed far away, users are likely to go to the multifunction printer to accomplish their work goals, regardless of the distance. Such a multifunction printer is an example of a highly attractive component. "Attractiveness of B" is used, for example, in the case of a magnet space, which will be described later.
Each of the variables of the above "A → B visibility rate,""A-B distance relationship,""Area ratio of B," and "Attractiveness of B" is normalized to take a value between 0 and 1. An example of "Area ratio of B" is shown in Equation (2).

(Area ratio of B) = (total area of B) / (total area other than A) ... (2)

The above "total area of B" may be the total area of each divided area if the target area is divided into multiple areas. The total area of area RBPA2Aa and area RBPA2Ab in FIG. 6B described later is one example of the above.

Note that the above formula (2) is an example of an arithmetic formula for calculating the area ratio (second index value) based on the area of scene B (second area), the area of the range in which the user can move, and the area of scene A (first area). The "total area of scenes other than A" can be obtained by subtracting the "area of scene A (first area)" from the "area of the range in which the user can move" to obtain a "reference area" obtained by subtracting the "area of scene A" from the "area of the range in which the user can move". The simulator 300 may calculate the above area ratio using formula (2). Note that the range in which the user can move may be limited to part or all of the same floor.

Below, we will show and explain a more specific example of the above-mentioned "visibility index" and "distance relationship between A and B".

(First Example of Visibility Index)
Next, a first embodiment of the visibility index will be described.
6A and 6B are diagrams for explaining a first embodiment of the visibility index. The first embodiment of the visibility index calculates the "A→B visibility rate" by using the visible area of a target scene. For example, the "A→B visibility rate" may be defined as shown in formula (3).

(Visibility rate from A to B) = Average of ((total visible area from A to B) / (total area of B))
...(3)

"Total visible area from A to B" refers to the total area of the area in which scene B can be seen without being blocked by obstacles such as furniture, walls, or pillars when looking at scene B from a specific point in scene A. Note that "visible" above refers to a case where it can be determined that there are no obstacles such as furniture, walls, or pillars between the specific point and the area of scene B, for example, within the range that can be read from a floor plan. There is no requirement to verify in real space whether or not it can actually be seen.

The above formula (3) is an example of a formula for calculating the visibility rate (first index value) based on the area of scene B (second area) and the "sum of the visible area areas of A→B" (area of the visible range). The simulator 300 may calculate the above visibility rate using formula (3).

Observation points PA1 and PA2 in scene A are examples of observation points in scene A. First, we will explain what happens when scene B is viewed from each observation point.

For example, when scene B is viewed from observation point PA1 in scene A as shown in Figure 6A, within area RB of scene B, area RBPA1A is the visible area, and area RBPA1N, blocked by a wall or pillar, is the invisible area. The area of the area of scene B visible from observation point PA1 is the area of area RBPA1A. This allows the ratio RATE1 of the area (total) of the area where scene B is visible from observation point PA1 to the area (total) of scene B to be calculated.

Similarly, when scene B is viewed from observation point PA2 in scene A as shown in FIG. 6B, within region RB of scene B, the ranges of regions RBPA2Aa and RBPA2Ab are visible, and the range of region RBPA2N blocked by the pillar is invisible. The area of the region in which scene B is visible from observation point PA2 is the sum of the areas of regions RBPA2Aa and RBPA2Ab. This allows the ratio RATE2 of the total area of the region in which scene B is visible from observation point PA2 to the total area of scene B is calculated. As shown in FIG. 6, the presence of pillar SBP23 divides the region in which scene B is visible from observation point PA2 into two. If multiple obstacles are present, the region in which scene B is visible may be divided into three or more. In such a case, it is also advisable to calculate the total area of the region in which scene B is visible as described above.

As shown in Figures 6A and 6B, multiple observation points are provided within scene A. Using the above formula (3) for the observation points, the average value of the ratio (RATE) calculated for each observation point can be calculated and used as the representative value of the "visibility rate from A to B."

(Second Example of Visibility Index)
Next, a second embodiment of the visibility index will be described. This second embodiment corresponds to the first embodiment, in which the observation points are regularly arranged.

Figure 7 is a diagram for explaining a second embodiment of the visibility index. A grid with a predetermined interval is defined within area RA of scene A, and each observation point is placed at the intersection of the grid (in a grid pattern). The interval of the grid can be set arbitrarily, for example, to 1.5 m (meters). By selecting an interval of this order, it is possible to verify the visibility of scene B when looking at scene B from the position of each desk placed in scene A. Here, in order to evaluate the arrangement on a scene-by-scene basis, the visibility rate of scene B (target scene) calculated for each observation point is used to calculate the (visibility rate from A to B). This relationship is shown in formula (4).

(Visibility rate from A to B)
= Σ ((visibility rate of the target scene at each observation point) / (number of observation points)) ... (4)

Next, the distance relationship between the two scenes will be described with reference to FIG.
8 is a diagram for explaining the distance relationship between two scenes. The shortest distance from observation point PA1 to the area of scene B is indicated as DA1Bmin, and the longest possible distance within the floor or building is indicated as DA1Bmax. The distance relationship from observation point PA1 to the area of scene B (called the "distance relationship from PA1 to B") is shown in formula (5).

(Distance relationship from PA1 to B)
= (shortest distance DA1Bmin) / (longest distance DA1Bmax) ... (5)

The above method derives the distance relationship of scene B from each observation point in scene A. The above formula (5) is an example of calculating the distance relationship (third index value) using at least one of the distance between a specific point in scene A and the near end of scene B (shortest distance DA1Bmin) and the longest distance that can be walked within the floor or building (longest distance DA1Bmax). The above "distance relationship" can be used without being limited to formula (5). The "longest distance that can be walked within the floor or building (longest distance DA1Bmax)" refers to, for example, the maximum length of a path when a first point and a second point are set at any position within the floor or building when no furniture or fixtures are arranged within the floor or building, and the first point is followed by walking from the second point to the second point along the shortest route. The "longest distance that can be walked within the floor or building" does not depend on the arrangement of the scenes and furniture, but on the floor shape or the positions of structures such as the walls and interior walls of the building. More specifically, a route search is performed between any two points among routes that can be traveled by walking, and the maximum value of the searched routes is set as the longest distance DA1Bmax.

The simulator 300 may use the above formula (5) to quantify the tendency of the user to attempt movement by using a third index value (distance relationship) based on the positional relationship between each observation point (specific point) in scene A and scene B. Using the results of these analyses, the distance relationship from each observation point in scene A to scene B ("A → B distance relationship") may be derived using the relationship shown in formula (6).

(Distance relationship from A to B)
= (Σ((shortest distance)/(longest distance)))/(number of observation points) ... (6)

The "distance relationship from A to B" derived from the above equation (6) is the average value of the ratio between (shortest distance) and (longest distance) for each observation point.

(Correction based on magnet space characteristics)
Next, correction based on the characteristics of magnet spaces will be described. There are cases where a scene includes an area called a magnet space. A magnet space is a "space where people in an office naturally gather," just as magnets attract each other and draw closer to each other due to the magnetic force of magnets, and is a place where people visit when they have a specific errand or for a break. For example, it includes "in front of a multifunction printer," a "tea room," a "kitchen," a break room, etc. In contrast, a magnet space does not include a place where people gather at a set time, such as a conference room. More specifically, a magnet space includes, for example, the following components, which are arranged in the area. A sink, a tea dispenser (water server), a refrigerator, a refrigerator, a snack area, a stationery area, a multifunction printer, etc. are examples of the above components.

Such magnet spaces generate informal communication with other departments using the same floor, or across positions. For this reason, the layout and planning of magnet spaces within the office is considered important. Scenes that include magnet spaces include "Community," "Academy," and "Break."

Thus, magnet spaces have their own unique "attractive force." For magnet spaces that have this kind of attractive force, it is desirable to use that "attractive force" to correct the movement rate.

The attractive force of the magnet space is formulated using equation (7). Equation (7) is an example of an equation for calculating the attractive force of scene B.

(B's Incentive)
= (Incentive of component 1 x (0 or 1) + Incentive of component 2 x (0 or 1) + ...)
/ (attractive power of component 1 × 1 + attractive power of component 2 × 1 + ...) ... (7)

The "component" in the above formula (7) refers to something that is in the magnetic space, such as a tea dispenser or a refrigerator, and that acts to attract people due to its presence. For example, a tea dispenser and a refrigerator correspond to component 1 and 2, respectively. The number of components used in the calculation of the above formula (7) may be set to an appropriate predetermined value. When targeting a general building, for example, even if the number of components is 10 to 20, it is possible to cover the main components.
"Attractiveness of component n" (n is a natural number) refers to a feature quantity that a component has, which is determined based on the number of visits to each component and the time spent there. For example, the attractiveness of each component may be defined based on the above-mentioned "actual values of the number of visits and the time spent there." The "time spent" may be defined as, for example, the total value of the "stay time" during one visit and the travel distance required for one visit. More specifically, a table is provided for each component n that allows reference to the number of visits to each component and the value for the time spent there, and the value referenced from this table is defined as the "attractiveness of component n."
Not all of the components are necessarily present in a particular scene. For example, each component may or may not be present in scene B. Such conditions can be specified using weighting coefficients. Each component is weighted as "1" if it is present in scene B, and "0" if it is not. The above "(0 or 1)" is an example of a weighting coefficient that takes the value of 0 or 1.

If the attractiveness of a magnet space is high, users tend to use the components within the magnet space regardless of their placement location. Therefore, by setting δ in the above formula (1) to a value other than 0, the "attractiveness" can be included in the calculation of the AED of the magnet space. For example, assume that scene B includes a magnet space.

For example, simulator 300 models the behavioral characteristics of a user moving from scene A to scene B, which is a magnet space, within a floor. In doing so, simulator 300 uses the results of a calculation process that includes as variables the area of scene B, a layout relationship index value indicating the layout relationship between scene A and scene B, and an attractiveness index value indicating the attractiveness of scene B. Simulator 300 can mimic the behavior of a user by performing a simulation using the above model.

The simulator 300 may use, as the placement relationship index value, a ratio based on the area of the visible range of scene B when scene B is viewed from scene A. In this case, the simulator 300 may use, as the placement relationship index value, a ratio between the area of the visible range of scene B when scene B is viewed from scene A and a predetermined area. For example, the predetermined area is the area of scene B or the area of the user's range of activity.

(Movement probability correction)
Next, the necessity for correcting the "movement probability" will be described.
When the "movement probability" is corrected for each scene, the sum of the corrected movement probabilities may not be "1." In order to adjust for such results, the following formulas (8) and (9) are used to define a "new movement probability" that is normalized so that the sum becomes 1.

The third term in the above equation (8) is the standardization coefficient k. The standardization coefficient k is defined, for example, by equation (9). The denominator of equation (9) is the sum of the movement probabilities before standardization for each scene. As described above, by providing the standardization coefficient k to equation (8), the "movement probability" of each scene can be standardized to derive the "new movement probability," and the sum of these can be made 1.

For example, when correcting the "probability of movement" based on the "degree of behavioral induction," the "new AED" calculated using the "degree of behavioral induction" is used.

According to the above formula (8), for example, the "old AED (first calculated value indicating the tendency of the user to attempt to move)" quantified based on the area of scene B and the corrected "new AED (second calculated value indicating the tendency of the user to attempt to move)" can be used to calculate the "new movement probability" that the user will move to scene B among the multiple areas. The simulator 300 may calculate the "new movement probability" using the above formula (8). More specifically, the simulator 300 calculates the "new movement probability (second probability)" that the user will move to scene B among the multiple scenes (areas) based on the "old AED" quantified based on the area of scene B, the corrected "new AED," and the "old movement probability (first probability)" described later.

The above formula (8) is an example of a calculation formula for correcting the "old movement probability (first probability)" to the "new movement probability (second probability)" using, for example, the "old AED" quantified based on the area of scene B and the corrected "new AED." The above "old movement probability (first probability)" is an example of a "movement probability" when the probability that a user moves to scene B among multiple scenes (areas) is calculated based on the area of the scenes.

As shown in the above formula (1), the "new AED" may be calculated using the visibility rate (first index value), the area ratio (second index value), and the distance relationship (third index value).

A method for displaying the results of the analysis of the visibility of a target scene will be described with reference to FIG. 9. FIG. 9 is a diagram for explaining a method for displaying the results of the analysis of the visibility. The heat map shown in FIG. 9 indicates the degree of visibility when the target scene is viewed from a specific position.

For example, the above-mentioned specific positions are determined within the area RA corresponding to scene A, and the degree of visibility when scene B is viewed from each of the above-mentioned specific positions is indicated. In this case, the above-mentioned specific positions may be made to correspond to the positions of the grid shown in FIG. 7 above. In this case, the gradation display shown by the heat map on the scene A side indicates that, for example, the darker the display in each square, the more difficult it is to see the area RB of scene B from the position of that square.

Similarly, the gradation displayed in the heat map on scene B indicates that, for example, the darker the display in each square, the more difficult it is to see area RA of scene A from the position of that square.

The simulator 300 may calculate an evaluation value for the position of either scene A or scene B arranged in the real space based on the quantified tendency to attempt movement, and display this as a heat map. For example, the above-mentioned analysis results of the appearance may be associated with this evaluation value.

According to the above embodiment, multiple scenes (areas), each having a predetermined area, are defined within the range within which a user can move in the real space that is the subject of analysis by the office planning and design support system 1. When at least the multiple scenes (areas) include scene A and scene B, the simulator 300 quantifies the tendency of a user in scene A to attempt to move to scene B using the analysis results of how scene A and scene B are connected. The above analysis results of how scene A and scene B are connected show an example of the visibility of scene B from within scene A.

The simulator 300 may also use the area of the visible range in scene B when scene B is viewed from a specific point in scene A as the degree of visibility of scene B. The simulator 300 may, for example, calculate an evaluation value for the position of either scene A or scene B arranged in real space based on the quantified tendency of the user to attempt movement. In this way, the office planning and design support system 1 can more accurately estimate the tendency of the user to move.

(First Modification of the First Embodiment)
A first modified example of the first embodiment will be described. In the above embodiment, the analysis result of the connection between scene A and scene B indicates the visibility of scene B from scene A. In this modified example, instead of or in addition to this, the analysis result of the connection between scene A and scene B may be the visibility of scene A from scene B.

(Second Modification of the First Embodiment)
A second modified example of the first embodiment will be described. If scene B is assumed to be a magnet space, the behavioral characteristics of a user moving from scene A to scene B in a building are modeled using variables including the area of scene B, a layout relationship index value indicating the layout relationship between scene A and scene B, and an attraction index value indicating the attraction of scene B, so that the office planning and design support system 1 can estimate the tendency of user movement with higher accuracy.

Second Embodiment
A second embodiment will now be described. In the first embodiment, a case where a tendency of a user to attempt to move in a real space is analyzed is described. In this embodiment, instead, a case where a tendency of an agent to attempt to move in a virtual space is determined is described.

The fact that agents have different tendencies to attempt movement within a virtual space helps to characterize the autonomous movement of the agents that are moved within the virtual space.

Within the range in which the agent can move in the virtual space, multiple scenes (areas) each having a predetermined area are defined, and the simulator 300 moves the agent in the virtual space within the virtual space. At least the multiple scenes include scene A and scene B, and the simulator 300 quantifies the tendency of the agent in scene A to attempt to move to scene B using the analysis results of how scene A and scene B are connected. This allows the simulator 300 to increase the accuracy of the agent's feature data associated with the agent when moving the agent in the virtual space. Note that the method of analyzing how scene A and scene B are connected may be the same as that of the first embodiment.

Third Embodiment
A third embodiment will now be described. In the first embodiment, a case where a tendency of a user to attempt to move in a real space is analyzed has been described, and in the second embodiment, a case where a tendency of an agent to attempt to move in a virtual space is analyzed has been described. This embodiment is a combination of the above two embodiments.

For example, the simulator 300 may derive (quantify) the user's tendency to attempt movement in real space, and then move the user's avatar in a virtual space corresponding to the real space based on the quantified tendency of the user to attempt movement.

Fourth Embodiment
A fourth embodiment will now be described. In this embodiment, correction based on the characteristics of the magnet space will be described.

For example, if the area assigned to each scene is obscured by a wall or other object and cannot be seen through, the desired accuracy can be obtained by applying the second correction method (second correction case) of the two correction methods described above.

At least according to the above embodiment, the office planning and design support system 1 includes a simulator 300. A plurality of areas (scenes), each having a predetermined area, are defined within the range in which the user can move in the real space to be analyzed. The plurality of areas include a first area and a second area, and the simulator 300 can estimate the user's movement tendency with greater accuracy by quantifying the tendency of a user in the first area to attempt to move to the second area using the analysis results of how the first area and the second area are connected.

The above describes an embodiment of the present invention. The subjective survey system 100, objective survey system 200, simulator 300, and planning requirement extraction system 400 in the office planning and design support system 1 shown in FIG. 1 each have a computer system inside. The series of steps related to the above-mentioned processes are stored in the form of a program on a computer-readable storage medium, and the above-mentioned processes are performed by the computer reading and executing this program. Here, computer-readable storage medium refers to a magnetic disk, optical magnetic disk, CD-ROM, DVD-ROM, semiconductor memory, etc. Also, the computer program may be distributed to a computer via a communication line, and the computer that receives the program may execute the program. Also, the "computer system" here includes an OS, etc.

Although one embodiment of the present invention has been described above, the embodiment of the present invention is not limited to the above. For example, the embodiment of the present invention can be a modification of the above embodiment as follows.

For example, in the above embodiment, for convenience, the configuration related to the present invention has been described by dividing the office planning and design support system 1 into a subjective survey system 100, an objective survey system 200, a simulator 300, and a planning requirement extraction system 400. The division of the subjective survey system 100, the objective survey system 200, the simulator 300, and the planning requirement extraction system 400 may be changed from that exemplified above, and the systems may be integrated together. In addition, some of the configuration included in each system may be included in the configuration of the other systems.

In addition, the parts described above as "office" or "floor" may also be read as "workplace."

1 Office planning and design support system (behavioral characteristic quantification device, design support device, simulator device), 100 Subjective survey system, 200 Objective survey system, 300 Simulator (calculation unit), 400 Planning requirement extraction system (behavioral characteristic quantification device), 500 Storage unit

Claims (23)

a calculation unit that, when simulating the movement of a user in a real space as the movement of an agent in a virtual space, defines a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtains an analysis result of a connection between the first area and the second area that indicates visibility of the second area from within the first area, and quantifies a tendency of the agent in the first area to attempt to move to the second area using the analysis result . a calculation unit that, when simulating the movement of a user in a real space as the movement of an agent in a virtual space, defines a plurality of areas each having a predetermined area within a range in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtains an analysis result of a connection between the first area and the second area, which indicates the visibility of the first area from within the second area, and quantifies a tendency of the agent in the first area to attempt to move to the second area using the analysis result;
A behavioral characteristic quantification device comprising : The calculation unit is
and quantifying the tendency of the user to attempt the movement by using an area of a range that is visible in the second area when the second area is viewed from a specific point in the first area as a degree of visibility.
The behavioral characteristic quantification device according to claim 1 . The calculation unit is
a first index value based on an area of the second region and an area of the visible range is used to quantify a tendency of the user to attempt the movement;
The behavioral characteristic quantification device according to claim 3 . The calculation unit is
a second index value based on an area of the second region, an area of the movable range, and an area of the first region is used to quantify a tendency of the user to attempt the movement;
The behavior characteristic quantification device according to any one of claims 3 and 4. The calculation unit is
a second index value based on a ratio between an area of the second region and a reference area obtained by subtracting an area of the first region from an area of the movable range, the second index value being used to quantify a tendency of the user to attempt the movement;
The behavioral characteristic quantification device according to claim 5 . The calculation unit is
a third index value based on a positional relationship between a specific point in the first area and the second area is used to quantify the tendency of the user to attempt the movement;
The behavior characteristic quantification device according to any one of claims 3 to 5. The calculation unit is
calculating the third index value using either a distance between a specific point in the first region and a near end of the second region or a distance between a specific point in the first region and a far end of the second region;
The behavioral characteristic quantification device according to claim 7. The calculation unit is
calculating a probability that the user will move to the second area among the plurality of areas using a first calculated value indicating the tendency of the user to attempt the movement, the first calculated value being quantified based on an area of the second area, and a second calculated value indicating the tendency of the user to attempt the movement;
The second calculated value,
a first index value based on an area of the second region and an area of the visible range;
a second index value based on an area of the second region, an area of the movable range, and an area of the first region;
Calculation is performed using the third index value.
The behavioral characteristic quantification device according to claim 7 or 8. The calculation unit is
correcting a first probability that the user will move to the second region among the plurality of regions to a second probability using a first calculated value indicating a tendency of the user to attempt the movement, the first calculated value being quantified based on an area of the second region, and a second calculated value indicating a tendency of the user to attempt the movement;
The second calculated value,
a first index value based on an area of the second region and an area of the visible range;
a second index value based on an area of the second region, an area of the movable range, and an area of the first region;
Calculation is performed using the third index value.
The behavioral characteristic quantification device according to claim 7 or 8. The calculation unit is
calculating a second probability that the user will move to the second area among the plurality of areas based on a first calculated value indicating a tendency of the user to attempt the movement quantified based on an area of the second area, a second calculated value indicating a tendency of the user to attempt the movement, and a first probability that the user will move to the second area among the plurality of areas;
The second calculated value,
a first index value based on an area of the second region and an area of the visible range;
a second index value based on an area of the second region, an area of the movable range, and an area of the first region;
Calculation is performed using the third index value.
The behavioral characteristic quantification device according to claim 7 or 8. When simulating a user's movement in a real space as an agent's movement in a virtual space, a plurality of areas each having a predetermined area are defined within a range in which the agent can move in the virtual space, the plurality of areas including a first area and a second area;
obtaining an analysis result of a connection between the first area and the second area, which indicates visibility of the second area from within the first area, and quantifying a tendency of the agent in the first area to attempt to move to the second area using the analysis result;
a calculation unit that moves an avatar of the user in a virtual space corresponding to the real space based on the quantified tendency to attempt movement. When simulating the movement of a user in a real space as the movement of an agent in a virtual space, a plurality of areas each having a predetermined area are defined within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, an analysis result of a connection between the first area and the second area, which indicates the visibility of the second area from within the first area, is obtained, and a tendency of the agent in the first area to attempt to move to the second area is quantified using the analysis result,
a calculation unit that calculates an evaluation value of a position of either the first area or the second area arranged in the real space based on the quantified tendency to attempt the movement. a calculation unit that, when simulating the movement of a user in a real space as the movement of an agent in a virtual space, defines a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtains an analysis result of a connection between the first area and the second area that indicates visibility of the second area from within the first area, and quantifies a tendency of the agent in the first area to attempt to move to the second area using the analysis result. When simulating a user's movement in a real space as an agent's movement in a virtual space, a plurality of areas each having a predetermined area are defined within a range in which the agent can move in the virtual space, and the plurality of areas include a first area and a second area;
a calculation unit that obtains an analysis result of a connection between the first area and the second area, which indicates visibility of the second area from within the first area, and quantifies a tendency of the agent in the first area to attempt to move to the second area using the analysis result. On the computer,
When simulating a user's movement in a real space as an agent's movement in a virtual space, a plurality of areas each having a predetermined area are defined within a range in which the agent can move in the virtual space, and the plurality of areas include a first area and a second area;
obtaining an analysis result of a connection between the first area and the second area, which indicates visibility of the second area from within the first area, and quantifying a tendency of the agent in the first area to attempt to move to the second area using the analysis result. On the computer,
A program that executes a step of simulating the movement of a user in a real space as the movement of an agent in a virtual space, defining a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtaining an analysis result of a connection between the first area and the second area that indicates the visibility of the second area from within the first area, and quantifying a tendency of the agent in the first area to attempt to move to the second area using the analysis result. When simulating a user's movement in a real space as an agent's movement in a virtual space, a plurality of areas each having a predetermined area are defined within a range in which the agent can move in the virtual space, the plurality of areas including a first area and a second area;
obtaining an analysis result of a connection between the first area and the second area, which indicates visibility of the first area from within the second area, and quantifying a tendency of the agent in the first area to attempt to move to the second area using the analysis result;
a calculation unit that moves an avatar of the user in a virtual space corresponding to the real space based on the quantified tendency to attempt movement. When simulating the movement of a user in a real space as the movement of an agent in a virtual space, a plurality of areas each having a predetermined area are defined within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, an analysis result of a connection between the first area and the second area, which indicates the visibility of the first area from within the second area, is obtained, and a tendency of the agent in the first area to attempt to move to the second area is quantified using the analysis result,
a calculation unit that calculates an evaluation value of a position of either the first area or the second area arranged in the real space based on the quantified tendency to attempt the movement. a calculation unit that, when simulating the movement of a user in a real space as the movement of an agent in a virtual space, defines a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtains an analysis result of a connection between the first area and the second area that indicates visibility of the first area from within the second area, and quantifies a tendency of the agent in the first area to attempt to move to the second area using the analysis result. a calculation unit that, when simulating the movement of a user in a real space as the movement of an agent in a virtual space, defines a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtains an analysis result of a connection between the first area and the second area that indicates visibility of the first area from within the second area, and quantifies a tendency of the agent in the first area to attempt to move to the second area using the analysis result. On the computer,
1. A method for quantifying a behavioral characteristic, comprising: simulating the movement of a user in a real space as the movement of an agent in a virtual space, defining a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtaining an analysis result of a connection between the first area and the second area indicating visibility of the first area from within the second area, and quantifying a tendency of the agent in the first area to attempt to move to the second area using the analysis result. On the computer,
A program that executes a step of : simulating the movement of a user in a real space as the movement of an agent in a virtual space, defining a plurality of areas, each having a predetermined area, within an area in which the agent can move in the virtual space, the plurality of areas including a first area and a second area, obtaining an analysis result of a connection between the first area and the second area that indicates the visibility of the first area from within the second area, and quantifying a tendency of the agent in the first area to attempt to move to the second area using the analysis result.

Images