JP7382434B2 - Decision support system and server - Google Patents

Description

Article 30, Paragraph 2 of the Patent Act applies March 2, 2021 Publication on the website “https://www.kensettsunews.com/web-kan/535125”

The present invention relates to a decision support system and server that support decision making by workers at a production site.

Production at production sites such as construction sites is managed using quality, cost, delivery time, and safety as management indicators, and the construction flow (process) is planned to set the delivery date of these indicators. However, there may be a discrepancy between the construction schedule plan and implementation due to the work environment, regulatory conditions, social conditions, etc., and in order to resolve this discrepancy, workers' skills, intuition, tips, and other experiential knowledge are required. Often.

JP2020-057425A

In recent years, with the decline in the working-age population, it has become difficult to pass on the experiential knowledge of workers. If this trend continues, it will become difficult to make decisions on the production floor that utilize the experiential knowledge of workers. This problem can occur not only in dealing with changes during construction, but also in all processes at the production site, such as planning, implementation, and inspection.

The present invention was made to solve such problems, and one of the objects is to provide a decision support system and a server that can support decision making by workers at a production site.

A decision-making support system as one aspect of the present invention is a decision-making support system for workers related to a production site, and is a database storing construction record data associated with a first worker. The record data is associated with work attribute evaluation data related to attributes of the work performed by the first worker and worker characteristic evaluation data indicating characteristics of the first worker who performed the work. a database, construction attribute evaluation data related to the attributes of construction in which a second worker who is a user is involved, worker characteristic evaluation data indicating characteristics of the second worker, and the first worker. a server that determines the construction record data to be presented to the second worker based on the construction attribute evaluation data of the first worker and the worker characteristic evaluation data of the first worker, the server a construction base score calculation unit that calculates a construction base score related to the degree of similarity between the construction attribute evaluation data of the first worker and the construction attribute evaluation data of the construction involving the second worker; , a worker base score calculation unit that calculates a worker base score related to the degree of similarity between the worker characteristic evaluation data of the first worker and the worker characteristic evaluation data of the second worker; The present invention is characterized by comprising a data determining unit that determines the construction record data to be presented to the second worker based on the construction base score and the worker base score.

A server as one aspect of the present invention is a server that supports decision-making by workers related to a production site, and includes attributes of construction performed by a first worker associated with construction record data stored in a database. a construction base score calculation unit that calculates a construction base score related to the degree of similarity between construction attribute evaluation data related to the second worker and construction attribute evaluation data related to the attribute of the construction work in which the second worker is involved; a worker base score calculation unit that calculates a worker base score related to the degree of similarity between worker characteristic evaluation data indicating characteristics of the worker and worker characteristic evaluation data indicating characteristics of the second worker; The present invention is characterized by comprising a data determining unit that determines the construction record data to be presented to the second worker based on the construction base score and the worker base score.

According to the present invention, it is possible to support decision-making by workers at a production site.

1 is an overall configuration diagram of a decision support system according to an embodiment of the present invention. FIG. 2 is a diagram showing the hardware configuration of a server. It is a diagram showing evaluation data associated with construction record data stored in a database. This is a table showing each item of construction classification, work classification, and type of work classification. It is a table showing items of worker characteristic evaluation data. It is a graph showing biorhythm. It is an example of a functional block diagram of a server. The curve on the left side shows a delayed S-curve, and the curve on the right side shows the biorhythm of FIG. 6. This is an example of a graph of skill scores. It is a figure which shows an example of a construction phase selection screen. It is a figure which shows an example of the image selection screen for a preference test. It is a figure which shows an example of the presentation screen of construction record data. 1 is an example of an operation flowchart of a decision support system according to an embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a decision support system according to the present invention will be described in detail with reference to the drawings. In this specification, for convenience of explanation, more detailed explanation than necessary may be omitted. For example, detailed explanations of already well-known matters or redundant explanations of substantially the same configurations may be omitted.

[1. composition]
[1-1. overview]
The decision support system of this embodiment is a system that supports decision making by workers at a production site. The decision-making support system uses construction record data by skilled workers for construction work similar to the work the workers are involved in at the production site, and that is performed by skilled workers with similar characteristics to the workers concerned. Provide to workers.

FIG. 1 is an example of a configuration diagram of a decision support system according to this embodiment. As shown in FIG. 1, the decision support system 1 includes a database 2, a server 3, and a terminal device 4.

The database 2 is a storage device that is connected to the server 3 directly or via a network N, and in which various information and data are stored in advance and can be additionally stored. The database 2 stores construction record data of construction work performed by a skilled worker (hereinafter also referred to as "first worker"). Construction record data can include data on all processes from the start of construction to completion, including construction planning, implementation, and inspection. The construction record data is data that includes experiential knowledge such as the first worker's skills, intuition, and tips as explicit knowledge. The construction record data can include, for example, document data, spreadsheet data, drawing data, image data, audio data, and video data. Each piece of data is associated with a construction project ID and an ID of the first worker who performed the construction.

The server 3 is a processing device that specifies construction record data to be presented to a second worker who is a user of the decision-making support system 1 from among the construction record data stored in the database 2. The server 3 identifies construction record data of a work performed by a skilled worker on a work similar to the work in which the second worker is involved, and that is performed by a skilled worker with similar characteristics to that of the second worker. . Server 3 is connected to network N and communicates with terminal device 4 . The server 3 transmits the specified construction record data to the terminal device 4.

The terminal device 4 is a terminal used by a second worker, and is connected to the server 3 via the network N. The terminal device 4 is presented with the construction record data specified by the server 3 and/or information regarding the data.

The first worker can include a skilled worker, for example, a worker who is 40 years old or older and has 15 years or more of experience. The second worker is a worker at the production site and is a user of the decision support system 1. The second worker may be, for example, younger than the first worker or may have fewer years of experience at the production site than the first worker.

FIG. 2 is a diagram showing the hardware configuration of the server. The server 3 includes a processor 10a, a storage device 10b, an input device 10c, an output device 10d, and a communication device 10e. Each configuration 10a-10e is connected by a bus 10f. Note that an interface may be interposed between the bus 10f and each of the components 10a to 10e, if necessary. The server 3 can include a computer such as a desktop computer, a tablet computer, or a notebook computer, and does not need to be composed of one physical device, but may be composed of multiple physical devices. good.

The processor 10a controls the overall operation of the server 3. The processor 10a is, for example, an electronic circuit such as a CPU or an MPU. The processor 10a executes various processes by reading and executing programs and data stored in the storage device 10b. The processor 10a may be composed of a plurality of processors.

The storage device 10b includes a RAM 10b-1 which is a volatile memory and a ROM 10b-2 which is a non-volatile memory. The storage device 10b may include an external memory 10b-3. The RAM 10b-10 functions as a main memory and/or a work area for the processor 10a. The processor 10a loads programs and the like required for execution of processing from the ROM 10b-2 and the external memory 10b-3 into the RAM 10b-1, and executes the loaded programs to realize various operations. The ROM 10b-2 and the external memory 10b-3 store the BIOS and OS, which are control programs for the processor 10a, and various programs, data, tables, etc. necessary for realizing the functions executed by the computer. The external memory 10b-3 can include, for example, a flash memory, a hard disk, a DVD-RAM, a USB memory, an SSD, and the like.

The input device 10c receives operation instructions and inputs from a user or the like. The input device 10c is, for example, a user interface such as an input button, keyboard, mouse, touch panel, touch pad, wireless remote control, microphone, or camera. Note that the touch panel functions both as the input device 10c and the output device 10d.

The output device 10d outputs data processed by the processor 10a and data stored and/or stored in the storage device 10b. The output device 10d may include, for example, a display device such as a CRT display, a liquid crystal display, an organic EL display, or a plasma display, an audio device such as a speaker that emits sound, and a printing device such as a printer.

The communication device 10e is an interface that connects and communicates with external devices via a network or directly. The communication device 10e can be, for example, an interface such as a serial interface or a LAN interface.

Each part of the server 3 is realized by various programs stored in the ROM 10b-2 and the external memory 10b-3 using the components 10a to 10f as resources.

The terminal device 4 can have the same hardware configuration as the server 3, that is, a processor 10a, a storage device 10b, an input device 10c, an output device 10d, and a communication device 10e. The terminal device 4 can be a computer such as a notebook computer, a tablet computer, or a mobile terminal such as a smartphone.

[1-2. Detailed configuration]
FIG. 3 is a diagram showing evaluation data associated with construction record data stored in the database. The construction record data stored in the database 2 is associated with a construction record ID, construction event name, first worker ID, work date and time, work location, and evaluation data regarding the work performed by the first worker. . The work date and time can include year, month, day, hour, and minute. The work location can include, for example, the latitude, longitude, and address of the work site.

The evaluation data is metadata and supplementary data of the construction record data, and may also be referred to as an index, meta information, and supplementary information. This evaluation data includes construction attribute evaluation data and worker characteristic evaluation data.

The construction attribute evaluation data is evaluation data regarding the attributes of the construction work performed by the first worker. The construction attribute evaluation data includes evaluation data regarding categories and keywords related to construction performed by the first worker.

The evaluation data of categories related to construction includes construction classification, work classification, and construction type classification. FIG. 4 is a table showing each item of construction classification, work classification, and work type classification. As shown in FIG. 4, a plurality of items are set in advance for each category. For example, the construction classification can include items such as rivers, roads, erosion control/landslides, water supply/industrial water, and sewerage. Business classifications can include design/cost estimation, planning/construction, preservation/maintenance, environment/disaster, education/safety, and other items. The work type classification can include items such as tunnels, bridges, structures, earthworks, pavements, and temporary construction. The evaluation data of the category related to construction is character information indicating the item related to construction selected from each category by the first worker. In one example, when a first worker performs maintenance work on a bridge over a river, the first worker selects "River" as the work classification and "Maintenance/Maintenance" as the work classification. is selected and "bridge" is selected as the work type classification, the evaluation data of the category related to construction is text information including "river", "preservation/maintenance", and "bridge".

The keyword evaluation data includes keywords related to the construction work performed by the first worker. This keyword is a keyword selected by the first worker from a plurality of preset keywords. Keywords can be, for example, media names, manual names, and technical terms. The keyword evaluation data includes character information indicating the keyword. Items and keywords for construction classification, work classification, and type of work classification can be added as appropriate.

The worker characteristic evaluation data is evaluation data indicating the characteristics of the first worker who performed the construction work. The worker characteristic evaluation data includes evaluation data regarding biorhythm, skill phase, construction phase, and preference test.

FIG. 5 is a table showing items of worker characteristic evaluation data. As shown in Figure 5, multiple items are set in advance for the biorhythm, skill phase, construction phase, and preference test, and the evaluation data is, for example, data in which applicable items are 1 and non-applicable items are 0. be.

The three aspects of biorhythm, ``body,'' ``emotion,'' and ``intelligence,'' are each represented by preset sin curves (trigonometric functions), and each aspect is represented by ``active period,'' ``conversion period,'' and ``intelligence.'' Each period is further divided into three periods: a "resting period", and each period is further divided into "state +" when the curve is on an upward trend, and "state -" when the curve is on a downward trend. FIG. 6 shows a graph showing the biorhythm, and is a diagram in which three aspects of the biorhythm are reflected in one sin curve. In the graph of FIG. 6, the horizontal axis represents time, and the vertical axis represents the worker's condition in terms of "body," "emotion," and "intelligence."

In other words, as shown in Figure 5, biorhythms include three aspects: "body," "emotion," and "intelligence," three periods: "active period," "conversion period," and "rest period," and "state." A total of 18 items, 6 items for each side, are preset from two states: "+" and "state-." Since the biorhythm has three sinus curves set in advance, once a certain point in time is determined, the period and state of each aspect can be determined. In the example shown in FIG. 6, the aspects of "body" are determined to be "active period" and "state +". The aspect of ``emotion'' is determined by ``active period'' and ``state.'' The aspect of ``intelligence'' is determined by ``turning point'' and ``state.'' Therefore, once a certain point in time is determined, it can be determined which of the 18 biorhythm items applies.

The biorhythm evaluation data is the worker's biorhythm evaluation data, and here, it is the biorhythm evaluation data of the first worker at the time of construction. This evaluation data is a data string of 18 0s or 1s, where applicable items of the biorhythm are set as 1 and non-applicable items are set as 0. In one example, the first worker's biorhythm at the time of construction is such that the "body" aspect is in the "active phase" and "state +", and the "emotional" aspect is in the "active phase" and "state -". , and assuming that the aspects of "intelligence" are "turning stage" and "state-", the evaluation data is 1, 0, 0, 0, 0, 0, 0, 1 in the order of items shown in Figure 5. , 0, 0, 0, 0, 0, 0, 0, 1, 0, 0.

The skill phase indicates the skill level of the worker. As shown in FIG. 5, four items are preset in the skill phase: "unskilled", "skilled", "proficient", and "skilled". For example, applicable items can be set to 1, and non-applicable items can be set to 0. The skill phase evaluation data is a data string of four 0s or 1s. The skill phase evaluation data is the skill phase evaluation data of the first worker, and here is the biorhythm evaluation data of the first worker at the time of construction. In one example, if the first worker is a skilled worker, his skill level is "skilled", so the data string is 0, 0, 0, 1.

The construction phase indicates the progress stage of construction. As shown in FIG. 5, four items are preset in the construction phase: "preparation period", "growth period", "acceleration period", and "maturity period". For example, applicable items can be set to 1, and non-applicable items can be set to 0. The evaluation data for the construction phase is a data string of four 0s or 1s. The construction phase evaluation data is the result of the first worker evaluating the construction phase at the time of construction. In one example, when the first worker evaluates the phase of construction at the time of construction as "growth period", the evaluation data is a data string of 0, 1, 0, 0.

The preference test is a test for estimating the worker's preferences. As shown in Figure 5, the preference test includes six items: “Design/Estimation,” “Planning/Construction,” “Preservation/Maintenance,” “Environment/Disaster,” “Education/Safety,” and “Other.” It is set in advance. These items are the same as the business classifications in the above categories. The preference test allows the worker to select one image that matches the worker's preference from images showing the characteristics of each item of the work classification. The evaluation data of the preference test is a data string of six 0s or 1s, where the selected item is 1 and the unselected item is 0. The evaluation data of the preference test is a data string of the results selected by the first worker from the images of six items. In one example, when the first worker selects the "design/accumulation" image, the evaluation data is a data string of 1, 0, 0, 0, 0, 0.

FIG. 7 is an example of a functional block diagram of the server 3. The server 3 stores construction attribute evaluation data related to the attributes of the construction work in which the second worker, who is the user, is involved, worker characteristic evaluation data indicating the characteristics of the second worker, and construction attribute evaluation data of the first worker. The processing device determines construction record data to be presented to a second worker based on the data and worker characteristic evaluation data of the first worker. Specifically, the server 3 includes a construction base score calculation section 31, a worker base score calculation section 32, a data determination section 33, a display control section 34, a construction attribute evaluation data generation section 35, and a worker characteristic evaluation data generation section 36. Equipped with

The construction base score calculation unit 31 calculates a construction base score related to the degree of similarity between the construction attribute evaluation data of the first worker and the construction attribute evaluation data of the construction work in which the second worker is involved. Details of the construction attribute evaluation data for the construction work involving the second worker will be described later, but the evaluation data may be generated by the construction attribute evaluation data generation unit 35 described later, or may be obtained from outside the system 1. It may be. This evaluation data may be different from the first worker's construction attribute evaluation data in terms of the worker, the object of construction, the time of construction, etc., and is common in that they are construction attribute evaluation data.

Specifically, the construction base score calculation unit 31 includes vector generation units 311 and 312. The vector generation unit 311 generates a first construction attribute vector based on the construction attribute evaluation data of the first worker. In one example, the first construction attribute vector is obtained by vector-converting the construction attribute evaluation data of the first worker. Specifically, the construction attribute evaluation data is four character information of categories (construction classification, work classification, construction type classification) and keywords, and the vector generation unit 311 generates information included in the construction attribute evaluation data of the first worker. Convert the construction classification, work classification, construction type classification, and keyword words into vectors. This conversion can use a known embedded representation (distributed representation) technique. Then, the vector generation unit 311 calculates the average of the four obtained vectors, and sets this average vector as the first construction attribute vector. The vector generation unit 311 generates first construction attribute vectors for all construction projects stored in the database 2. That is, since construction attribute evaluation data is attached to each construction project ID, first construction attribute vectors are generated for the number of construction project IDs.

The vector generation unit 312 generates a second construction attribute vector based on the construction attribute evaluation data of the second worker. In one example, the second construction attribute vector is obtained by vector-converting the construction attribute evaluation data of the second worker. Specifically, the construction attribute evaluation data is four character information: category (construction classification, work classification, construction type classification), and keyword, and the vector generation unit 312 generates the information included in the construction attribute evaluation data of the second worker. Convert the construction classification, work classification, construction type classification, and keyword words into vectors. This conversion can use a known embedded representation (distributed representation) technique. Then, the vector generation unit 312 calculates the average of the four obtained vectors, and sets this average vector as the second construction attribute vector.

The construction base score calculation unit 31 calculates the inner product of the first construction attribute vector and the second construction attribute vector. The construction base score calculation unit 31 can use this inner product as the degree of similarity between the construction attribute evaluation data of the first worker and the second worker, and can also calculate the degree of similarity based on the inner product. You can also do it. The construction base score calculation unit 31 calculates a construction base score based on the obtained similarity. In one example, similarity may be equal to construction base score, but is not limited thereto. Calculation of the inner product is performed for each construction project ID in the database 2. That is, the construction base score calculation unit 31 calculates the degree of similarity with the construction attribute evaluation data of the second worker for each construction item ID in the database 2. Note that it can be determined that the larger the value of the inner product, the higher the degree of similarity.

The worker base score calculation unit 32 calculates a worker base score related to the degree of similarity between the worker characteristic evaluation data of the first worker and the worker characteristic evaluation data of the second worker. Details of the worker characteristic evaluation data of the second worker will be described later, but the evaluation data may be generated by the worker characteristic evaluation data generation unit 36 described later, or may be obtained from outside the system 1. It's okay. This evaluation data is different from the worker characteristic evaluation data of the first worker in that the object of the worker is different, and they are worker characteristic evaluation data.

Specifically, the worker base score calculation unit 32 includes vector generation units 321 and 322. The vector generation unit 321 generates a first characteristic vector based on the worker characteristic evaluation data of the first worker. In one example, the first characteristic vector is obtained by vector-converting the worker characteristic evaluation data of the first worker. Specifically, the worker characteristic evaluation data for the biorhythm, skill phase, construction phase, and preference test are each a data string of 0 or 1, and the vector generation unit 321 combines them into a first characteristic. Generate a vector. More specifically, since the worker characteristic evaluation data for the biorhythm, skill phase, construction phase, and preference test are data strings of 18, 4, 6, and 6 0s or 1s, the vector generation unit 321 The first characteristic vector generated by is a 34-dimensional (=18+4+6+6) vector having 34 components of 0 or 1. The vector generation unit 321 may add weighting to the data strings of the biorhythm, skill phase, construction phase, and preference test when generating the first characteristic vector. This weighting can be set as biorhythm: skill phase: construction phase: preference test = 3.0: 1.0: 2.0: 3.0, but is not limited to this, and may be determined as appropriate depending on the usage history of the system 1. Configurable.

The vector generation unit 321 generates first characteristic vectors for all construction projects stored in the database 2. That is, since worker characteristic evaluation data is attached to each piece of construction record data specified by a construction project ID, first characteristic vectors are generated as many times as there are construction project IDs.

The vector generation unit 322 generates a second characteristic vector based on the worker characteristic evaluation data of the second worker. In one example, the second characteristic vector is obtained by vector-converting the worker characteristic evaluation data of the second worker. Specifically, the worker characteristic evaluation data for the biorhythm, skill phase, construction phase, and preference test are each a data string of 0 or 1, and the vector generation unit 322 combines them into a second characteristic. Generate a vector. More specifically, since the worker characteristic evaluation data for the biorhythm, skill phase, construction phase, and preference test are data strings of 18, 4, 6, and 6 0s or 1s, the vector generation unit 322 The second characteristic vector generated by is a 34-dimensional (=18+4+6+6) vector having 34 components of 0 or 1. The vector generation unit 322 may weight the data strings of the biorhythm, skill phase, construction phase, and preference test when generating the first characteristic vector. This weighting can be set as biorhythm: skill phase: construction phase: preference test = 3.0: 1.0: 2.0: 3.0, but is not limited to this, and may be determined as appropriate depending on the usage history of the system 1. Configurable.

The worker base score calculation unit 32 calculates the inner product of the first characteristic vector and the second characteristic vector. The worker base score calculation unit 32 can use this inner product as the degree of similarity between the worker characteristic evaluation data of the first worker and the second worker, and also calculates the degree of similarity using the inner product. You may also do this. The worker base score calculation unit 32 calculates a worker base score based on the obtained degree of similarity. In one example, but not limited to, similarity may be equal to worker base score. Calculation of the inner product is performed for each construction project ID in the database 2. That is, the worker base score calculation unit 32 calculates the degree of similarity with the worker characteristic evaluation data of the second worker for each construction item ID in the database 2. Note that it can be determined that the larger the value of the inner product, the higher the degree of similarity.

The data determining unit 33 determines construction record data to be presented to the second worker based on the construction base score and the worker base score. Specifically, the data determination unit 33 calculates a score based on the construction base score and the worker base score for each construction project ID in the database 2, and assigns a predetermined number of construction record data with the high score to the second This is determined as the construction record data to be presented to the workers. As for how to calculate the score, in one example, score = construction base score + worker base score, and in another example, score = α x construction base score + (1-α) with α as a weighting factor. ) x worker base score. α is a real number satisfying 0<α<1, and may be stored in advance in the storage device 10b of the server 3 and read out when calculating the score. The number of construction record data to be presented is a predetermined number, but it can be, for example, 3, or any number less than the number of construction project IDs in the database 2, such as 5 or 10. can.

The display control unit 34 causes the terminal device 4 to display a presentation screen G1 of construction record data. FIG. 12 is a diagram illustrating an example of a construction record data presentation screen. The display control unit 34 acquires the construction record data determined by the data determination unit 33 from the database 2, and displays the construction record at a position corresponding to the work location of the construction record data on the map M in the presentation screen G1. icon I is displayed. When the second worker selects this icon I, the construction record data can be viewed. Further, as shown in FIG. 12, the display control unit 34 may display a link of the construction record data to be presented in the presentation area R1. By selecting this link, construction record data can be viewed. These display modes of construction record data are merely examples, and the design can be changed as appropriate as long as the construction record data can be viewed. The display control unit 34 may display links of construction record data that have been viewed by the second worker in the highest number of times in the presentation area R2.

The construction attribute evaluation data generation unit 35 generates construction attribute evaluation data for the construction work in which the second worker is involved, based on the input from the second worker. This construction attribute evaluation data is evaluation data regarding the attributes of the construction work in which the second worker is involved, and includes evaluation data regarding categories and keywords regarding the construction work in which the second worker is involved. The categories and keywords are the same as the work attribute evaluation data of the work performed by the first worker, and are identified from the items preset under the work classification, work classification, and work type classification (see Figure 4). This is text information and one keyword (text information) selected by the second worker from a plurality of preset keywords.

The storage device 10b of the server 3 has a first table in which a second worker ID (user ID), the organization to which the second worker belongs, and words of items under each classification within the category are associated. It is stored in advance. The construction attribute evaluation data generation unit 35 receives the input of the second worker ID, refers to the first table, specifies the word (character information) of the corresponding category item, and converts the word into the evaluation data of the category. shall be. In one example, if the organization to which the second worker belongs is an organization that preserves and maintains bridges over rivers, the construction attribute evaluation data generation unit 35 generates information such as "river", "preservation/maintenance", and "bridge". ” is identified, and these character information are used as evaluation data.

A plurality of preset keywords are stored in the storage device 10b of the server 3, and the plurality of keywords are presented to the second worker via the terminal device 4 and the second worker is involved. The user is prompted to select one or more keywords related to construction. The construction attribute evaluation data generation unit 35 receives the input of the selected keyword and uses the keyword as keyword evaluation data.

The construction attribute evaluation data obtained by the construction attribute evaluation data generation section 35 can be used in the construction base score calculation section 31.

The worker characteristic evaluation data generation unit 36 generates worker characteristic evaluation data of the second worker based on the input of the second worker. The worker characteristic evaluation data of the second worker includes evaluation data regarding the biorhythm, skill phase, construction phase, and preference test, similar to the worker characteristic evaluation data of the first worker.

The worker characteristic evaluation data generation unit 36 specifies three aspects, period, and condition of the second worker on the day of system use, and calculates the biorhythm of the second worker based on the specified aspect, period, and condition. Generate evaluation data. As explained above using FIG. 5 and the like, the biorhythm evaluation data is a data string of 18 0's or 1's, for example, when applicable biorhythm items are set to 1 and non-applicable items are set to 0. The example of this evaluation data is the same as above, so it will be omitted.

A method for generating biorhythm evaluation data will be explained. The storage device 10b of the server 3 stores in advance a second table in which the second worker ID is associated with the second worker's date of birth, age at the time of registration, and years of experience at the time of registration, Sin curves (trigonometric functions) (FIG. 6) regarding three aspects of "body," "emotion," and "intelligence" are stored in advance for each date of birth.

The worker characteristic evaluation data generation unit 36 uses the second worker ID acquired when using the system 1 (for example, when logging into the system 1), the date of use of the system 1, and the second table to generate the second table. The date of birth of the worker No. 2 is specified, and the period and state of each side are also specified from the sine curve for each side. Since the sin curve is a periodic function, its horizontal axis t corresponds to the date. By specifying the date of birth, a sin curve associated with the date of birth is specified, and the period and state of each aspect are specified based on the date of use of the system 1.

In this embodiment, the following sections of each sin curve (i) 0≦t<0.115, (ii) 0.115≦t<0.39, (iii) 0.39≦t<0.615, ( iv) 0.615≦t<0.89, (v) 0.89≦t<1 are (i) “conversion period”, (ii) “active period”, (iii) “conversion period”, ( iv) "resting period"; (v) "conversion period". The reason for dividing the biorhythm section in this way is because it is within the delay S-shaped curve G (formula (1), a, b>0 in formula (1)) that represents the progress of the construction shown on the left side of Figure 8. This is because the inventor's research has discovered that there is a specific point at which problems such as delays in construction occur, and this point was used as a point to separate the periods of the biorhythm.

As shown in FIG. 8, the lag S-shaped curve is one of the so-called growth curves (also referred to as production functions), and is, for example, a curve in which the horizontal axis represents time and the vertical axis represents construction progress. The construction progress level can be expressed as the number of inspections since inspections are performed in every process. This curve has 23%, 50%, and 78% points in the section on the horizontal axis as specific points (hereinafter referred to as "management points"), and since there is a correlation between construction progress and biorhythm, management Points are used as indicators for delimiting sections for determining the period area of the sin curve. That is, as shown in FIG. 8, a half cycle of the biorhythm sin curve corresponds to one cycle of the delayed S-shaped curve. The section of this sin curve is synonymous with determining the sections of the active period, conversion period, and rest period, and therefore affects the evaluation data of the biorhythm and, by extension, the characteristic vector. That is, the characteristic vector depends on the section of the sin curve. Note that the delayed S-shaped curve is divided into four stages: a preparation period A, a growth period B, an acceleration period C, and a maturity period D, with the control point as the boundary. Preparation period A is a period in which the initial value increases gradually, and growth period B is a period in which the rate of increase is approximately constant. The acceleration period C is a period in which the value gradually increases again, and the maturity period D is a convergence period in which the value finally converges to a certain value.

The worker characteristic evaluation data generation unit 36 specifies the skill phase of the second worker on the day of system use based on the input of the second worker, and based on the specified skill phase, generate evaluation data for the skill phase of As explained above using FIG. 5 etc., the evaluation data of the skill phase is a data string of four 0s or 1s, for example, when applicable items of the skill phase are set as 1 and non-applicable items are set as 0. . In one example, if the skill phase of the second worker is identified as "unskilled," the evaluation data is a data string of 1, 0, 0, 0.

The method for generating evaluation data for the skill phase will be explained. First, the worker characteristic evaluation data generation unit 36 calculates the actual age of the second worker (i.e., the system The second worker's age on the first usage date) and the number of years of experience of the second worker on the usage date are specified. More specifically, the worker characteristic evaluation data generation unit 36 identifies the age and years of experience of the second worker at the time of system registration from the second worker ID, and calculates the difference between the date of use and the time of registration. This is added to the age and years of experience at the time of registration to determine the actual age and years of experience of the second worker on the date of use. Note that if the second worker has a period of separation from employment, the number of years of experience can be determined by subtracting the period of separation from employment from the above difference.

Next, the worker characteristic evaluation data generation unit 36 calculates the actual age skill value S1 based on the specified actual age and the following actual age skill value calculation formula (Equation (2)).

[Number 2]
S1=a ₁ ×x ³ +b ₁ ×x ² +c ₁ ×x+d ₁ …(2)
a ₁ = -4.00 x 10 ^-9 , b ₁ = 4.00 x 10 ^-5 , c ₁ = 1.73 x 10 ^-2 , d ₁ = -0.2599, x: age

Further, the worker characteristic evaluation data generation unit 36 calculates the years of experience skill value S2 based on the specified years of experience and the following years of experience skill value calculation formula (formula (3)).

[Number 3]
S2= _a2 × ^x3 + _b2 × ^x2 + _c2 ×x+ _d2 …(3)
a ₂ = -5.00 x 10 ^-6 , b ₂ = -3.00 x 10 ^-4 , c ₂ = 2.85 x 10 ^-2 , d ₂ = -6 x 10 ^-3 , x: years of experience

Furthermore, the worker characteristic evaluation data generation unit 36 substitutes the actual age skill value S1 and the years of experience skill value S2 into the following skill score calculation formula S=S(x) (formula (4)), and calculates each Skill scores S _A and S _Y are calculated (S _A =S(S1), S _Y =S(S2)).

[Number 4]
S=a ₃ ×x ⁵ +b ₃ ×x ⁴ +c ₃ ×x ³ +d ₃ ×x ² +e ₃ ×x…(4)
a ₃ = -4.04 x 10, b ₃ = 9.74 x 10, c ₃ = -7.34 x 10, d ₃ = 1.4979654 x 10, e ₃ = 1.3577875, x: skill value

FIG. 9 is an example of a graph of skill scores, and shows a graph of skill score calculation formula S. In this graph, the horizontal axis is the skill value and the vertical axis is the skill score, which represents the rate of skill proficiency. The worker characteristic evaluation data generation unit 36 calculates the median value C between point A (S1, S _A ) based on actual age and point Y (S2, S _Y ) based on years of experience. In the example shown in FIG. 9, the specified real age is 45 years old and the number of years of experience is 8 years, and in this case, the real age skill value S1 is 0.5992355, and the years of experience skill value S2 is 0.20536. . The skill score S _A is -0.15718097, and the skill score S _Y is 0.43342364.

The interval (0≦x≦1) on the horizontal axis of this skill score is divided into four categories: “unskilled,” “proficient,” “skilled,” and “proficient.” specifies to which category the calculated median value C belongs, and generates evaluation data regarding the skill phase of the second worker. In the example shown in FIG. 9, since the median value C belongs to the "proficiency" section, the worker characteristic evaluation data generation unit 36 identifies the skill phase of the second worker as "proficiency", A data string of 0.1, 0, 0 is generated as skill evaluation data. Note that, similarly to the biorhythm, the boundaries of the skill score sections are set at management points of 23%, 50%, and 78% of the section.

The method for deriving equations (2) to (4) will be explained. The method for deriving equations (2) and (3) is (i) using equation (1) of the delayed S-curve as a model, and (ii) substituting specific arbitrary values for x, parameters a, and b. y' is determined, and parameters a and b that have the minimum error from the actual value of y are determined. This procedure is the same as the procedure for finding a regression equation using the least squares method, and the parameters a and b that minimize the sum of errors (y'-y) are found. At this time, regarding equation (2), the range of actual age from 15 to 63 years corresponds to the range from 0 to 1. Specifically, parameters are determined by making real ages 15, 25, 43, 50, and 63 correspond to 0, 0.23, 0.5, 0.78, and 1, which include management points, respectively. In this case, a constraint condition is imposed that passes through these points. This converges to one cubic function, and the obtained equation is equation (2). Regarding formula (3), the years of experience 0 years, 10 years, 19 years, 35 years, and 43 years correspond to 0, 0.23, 0.5, 0.78, and 1, which include management points, respectively, When determining the parameters, a constraint condition is imposed on passing through these points. The equation obtained by converging to one cubic function is equation (3).

Note that the actual ages of 15 to 63 are the constituent ages of the organization, and when imposing constraints, actual ages of 25, 43, and 50, and years of experience of 10, 19, and 35 are used as management points. The reason for making them correspond to each other is that these ages and years can be turning points in a career, such as obtaining a qualification or being promoted to a position.Actual age and skill values and years of experience are delayed and can be expressed as an S-shaped curve. It is based on the idea that it is possible.

Equation (4) is derived in the same manner as Equations (2) and (3) to obtain an S-shaped curve. The constraint conditions differ from equations (2) and (3) in that the range of ages 20 to 65 corresponds to the range of 0 to 1. Specifically, 20 years old, 25 years old, 48 years old, 50 years old, 60 years old, and 65 years old are respectively included in management points of 0, 0.23, 0.53, 0.78, 0.95, and 1. When determining parameters, a constraint condition that the points pass through these points is imposed. This results in an S-shaped curve. The horizontal axis is age and the vertical axis is a value from 0 to 1 (20, 0), (25, 0.23), (48, 0.53), (50, 0.78), (60, 0. 95), (65, 1) is referred to as a plot value, equation (4) is obtained by calculating the difference between the plot value and the S-shaped curve, and approximating the difference with a quintic curve.

The worker characteristic evaluation data generation unit 36 identifies the construction phase based on the input from the second worker, and generates evaluation data for the construction phase. Specifically, the server 3 displays a construction phase selection screen that allows the terminal device 4 to select whether the construction phase corresponds to "preparation period," "growth period," "acceleration period," or "maturity period." (FIG. 10) is displayed, the worker characteristic evaluation data generation unit 36 acquires the selection information by the second worker, and specifies the construction phase. For example, the worker characteristic evaluation data generation unit 36 generates a data string of 0 or 1, with applicable items as 1 and non-applicable items as 0. The data string is the evaluation data for the construction phase. In one example, when the second worker evaluates the construction phase at the time of system use as "growth period", the evaluation data is a data string of 0, 1, 0, 0.

The worker characteristic evaluation data generation unit 36 generates preference test evaluation data based on the input of the second worker. Since the preference test is the same as above, the description will be omitted as appropriate. In the storage device 10b of the server 3, a plurality of images (for example, 20) showing the characteristics of each item of the work classification are stored in advance for each item, and the worker characteristic evaluation data generation unit 36 generates images for each item. Pick randomly. The server 3 causes the terminal device 4 to display an image selection screen (FIG. 11) for the preference test including the extracted images, and the worker characteristic evaluation data generation unit 36 obtains the selection information by the second worker. , identify preference test results. The worker characteristic evaluation data generation unit 36 generates six data strings of 0 or 1, for example, when selected items are set as 1 and unselected items are set as 0 among the preference test results. The data string is the evaluation data of the preference test. In one example, if the second worker selects the "design/accumulation" image as a result of a preference test when the second worker uses the system, the evaluation data will be 1, 0, 0, 0, It is a data string of 0, 0.

[1-3. motion]
FIG. 13 is an example of an operation flowchart of the decision support system according to this embodiment. First, the server 3 receives input regarding the second worker's evaluation data via the terminal device 4 and the network N (S01: Acceptance of input regarding the second worker's evaluation data). This input includes information about the second worker ID, keywords, construction phase, and preference test selection. Specifically, when the second worker enters the second worker ID when logging into the decision support system 1, the server 3 displays a keyword selection screen (not shown) on which a plurality of keywords are displayed, The construction phase selection screen (Fig. 10) and the image selection screen for preference test (Fig. 11) are displayed on the terminal device 4, respectively, and information on the keyword, construction phase, and image selected by the second worker is obtained. do.

Based on this input, the construction attribute evaluation data generation unit 35 generates construction attribute evaluation data for the construction work in which the second worker is involved (S02: Generation of construction attribute evaluation data). Specifically, the construction attribute evaluation data generation unit 35 identifies the word (character information) of the category item corresponding to the second worker ID, and uses the word as the evaluation data of the category. Further, the construction attribute evaluation data generation unit 35 generates the keyword selected by the second worker as keyword evaluation data.

Next, the vector generation units 311 and 312 generate a first construction attribute vector and a second construction attribute vector based on the construction attribute evaluation data of the first worker and the second worker (S03: construction attribute vector generation). Specifically, the vector generation unit 311 refers to the database 2 and generates first construction attribute vectors for all construction projects stored in the database 2 based on each factory attribute evaluation data. The vector generation unit 312 generates a second construction attribute vector from the construction attribute evaluation data generated in S02.

Then, the construction base score calculation unit 31 calculates the inner product of the first construction attribute vector and the second construction attribute vector, and calculates the construction base score (S04: Calculation of construction base score).

Further, the worker characteristic evaluation data generation unit 36 generates worker characteristic evaluation data of the second worker based on the input of the second worker (S05: Generation of worker characteristic evaluation data). Specifically, the worker characteristic evaluation data generation unit 36 specifies three aspects, a period, and a state of the second worker on the day of system use from the second worker ID and its input date and time, and identifies the identified aspects. , period, and condition, the second worker's biorhythm evaluation data is generated. In addition, the worker characteristic evaluation data generation unit 36 uses the input date and time of the second worker ID and formulas (2) to (4) to identify the skill phase of the second worker on the day of system use, Evaluation data regarding the skill phase of the second worker is generated based on the identified skill phase. Further, the worker characteristic evaluation data generation unit 36 identifies the construction phase from the construction phase input by the second worker, and generates evaluation data for the construction phase. Further, the worker characteristic evaluation data generation unit 36 generates preference test evaluation data based on the result input of the preference test by the second worker.

Next, the vector generation units 321 and 322 generate a first characteristic vector and a second characteristic vector based on the worker characteristic evaluation data of the first worker and the second worker (S06: Generation of characteristic vectors). ). Specifically, the vector generation unit 321 refers to the database 2 and generates first characteristic vectors for all construction projects stored in the database 2 based on each worker characteristic evaluation data. The vector generation unit 322 generates a second characteristic vector from the worker characteristic evaluation data generated in S05.

Then, the worker base score calculation unit 32 calculates the inner product of the first characteristic vector and the second characteristic vector to calculate the worker base score (S07: Calculation of the worker base score).

The data determining unit 33 calculates a score based on the construction base score and the worker base score (S08: score calculation). Then, the data determining unit 33 determines a predetermined number of construction record data having the high scores as construction record data to be presented to the second worker (S09: Determination of construction record data to be presented).

The display control unit 34 acquires the construction record data in S09 from the database 2, and displays the presentation screen G1 on the terminal device 4 as shown in FIG. 12 (S10: Presentation of the determined construction record data).

Thereby, the second worker can view construction record data similar to the construction work in which the second worker is involved, and by workers with similar characteristics to the second worker. The server 3 may count the number of times the construction record data has been viewed and associate the number of views with the corresponding construction record data in the database 2. The server 3 (display control unit 34) may display links of the construction record data with the highest number of views in the presentation area R2. Note that the number of views can be counted, for example, by transmitting information indicating which construction record data has been viewed among the presented construction record data from the terminal device 4 to the server 3.

[1-4. Action/Effect]
(1) The decision support system 1 of the present embodiment is a decision support system for workers at a production site, and is a database 2 that stores construction record data associated with a first worker, The construction record data is associated with construction attribute evaluation data related to the attributes of the construction work performed by the first worker and worker characteristic evaluation data indicating the characteristics of the first worker who performed the construction work. , a database 2, construction attribute evaluation data related to the attributes of the construction work in which the second worker, who is the user, is involved, worker characteristic evaluation data indicating the characteristics of the second worker, and construction attributes of the first worker. a server 3 that determines construction record data to be presented to the second worker based on the evaluation data and the worker characteristic evaluation data of the first worker; a construction base score calculation unit 31 that calculates a construction base score related to the degree of similarity between the construction attribute evaluation data of the construction work in which the second worker is involved and the construction attribute evaluation data of the construction work that the second worker is involved in, and the worker characteristic evaluation of the first worker. a worker base score calculation unit 32 that calculates a worker base score related to the degree of similarity between the data and the worker characteristic evaluation data of the second worker; 2, the data determining unit 33 determines the construction record data to be presented to the second worker.

As a result, the second worker can refer to the first worker's construction record data and gain insight into the first worker's skills, tricks, etc. from the construction record data. It is possible to support decision-making by workers at the production site. For example, production at a production site (for example, a construction site) is managed using quality, cost, delivery date, and safety as management indicators. Planned. However, due to the work environment, regulatory conditions, social conditions, etc., there may be a discrepancy between the construction schedule plan and the implementation, and in order to deal with this, the first worker's skills, intuition, tips, and other experiential knowledge are utilized. Experienced knowledge will be reflected in construction record data, which is explicit knowledge. By using this construction record data as a reference when the second worker is involved in the construction work he or she is involved in, he or she can become aware of how to respond to changes in the construction process and inherit the first worker's experiential knowledge. It is possible to support the decision making of the second worker. Furthermore, in this embodiment, the information is presented to the second worker based not only on the similarity between the work performed by the first worker and the work performed by the second worker, but also on the similarity between the characteristics of the workers. Now determines the construction record data to be used. As a result, it is possible to refer to the construction record data of the first worker who has similar characteristics, so it is easier to notice and more effective than referring to the construction record data of the first worker who has different characteristics. You can gain a lot of insight.

(2) The construction base score calculation unit 31 calculates a first construction attribute vector based on the construction attribute evaluation data of the first worker corresponding to the construction record data in the database 2 and the construction attribute evaluation data of the second worker. The worker base score calculation unit 32 calculates the inner product with the second construction attribute vector based on the construction attribute vector as the similarity of the construction attribute evaluation data of the first worker and the second worker, respectively, and The inner product of the first characteristic vector based on the worker characteristic evaluation data of the first worker corresponding to the recorded data and the second characteristic vector based on the worker characteristic evaluation data of the second worker is calculated as The data determination unit 33 calculates the degree of similarity between the worker characteristic evaluation data of the worker and the second worker, and the data determination unit 33 calculates the degree of similarity between the worker characteristic evaluation data of the worker and the second worker based on the construction base score and the worker base score among the construction record data stored in the database 2. A predetermined number of construction record data with high scores are determined as construction record data to be presented to the second worker.

Thereby, it is possible to quantitatively measure the degree of similarity between the works of the first worker and the second worker and the degree of similarity between the characteristics of the first worker and the second worker.

(3) The construction attribute evaluation data is character information including preset construction classification, work classification, and construction type classification, as well as keywords related to construction. The character information of the classification and keyword is converted into a vector, and the average of the vectors is calculated to generate a first construction attribute vector and a second construction attribute vector, and an inner product of the first construction attribute vector and the second construction attribute vector is generated. Calculated.

As a result, it is possible to reflect the overall characteristics of the construction classification, work classification, construction type classification, and construction-related keywords in the construction attribute vector, so it is possible to obtain the similarity of the overall characteristics of the construction attributes, and the second It is possible to present construction record data that is more suitable for the construction work performed by workers.

(4) The worker characteristic evaluation data includes evaluation data regarding the biorhythm, skill phase, construction phase, and preference test, and the worker base score calculation unit 32 calculates the biorhythm, skill phase, construction phase, and preference test of the second worker. The first characteristic vector and the second characteristic vector are generated based on the evaluation data regarding the preference test, and the inner product of the first characteristic vector and the second characteristic vector is calculated.

As a result, construction record data can be presented in accordance with the worker's condition, preferences, skills, and construction stage, so it is possible to improve the recommendation accuracy of construction record data.

(5) The decision support system 1 of this embodiment further includes a terminal device 4 connected to the server 3 via the network N and used by the second worker. An input device that receives input regarding construction attribute evaluation data of the related construction work and worker characteristic evaluation data of a second worker, a communication device that transmits the input to the server 3 via the network N, and a data determination method via the communication device. A display device for presenting the construction record data determined by the section 33 is provided.

Thereby, the presentation of the first worker's construction record data can be viewed via the terminal device 4 at the production site. Therefore, for example, even if a change occurs during construction, the second worker can make decisions on his own and respond quickly.

[2. Other embodiments]
In other embodiments of the present invention, a program that implements the functions of the embodiment of the present invention described above and the information processing shown in the flowchart, and a computer-readable storage medium storing the program may be used. In other embodiments, a method may be used to implement the functions of the embodiments of the present invention described above and the information processing shown in the flowcharts. In other embodiments, a server can be used that can supply a computer with a program that implements the functions of the embodiments of the present invention described above and the information processing shown in the flowcharts. In other embodiments, a virtual machine may be used that implements the functions of the embodiments of the present invention described above and the information processing shown in the flowcharts.

In the processing or operation described above, as long as there is no contradiction in the processing or operation, such as using data that should not be available at that step in a certain step, the processing or operation can be freely performed. Can be changed. Further, each of the embodiments described above is an illustration for explaining the present invention, and the present invention is not limited to these embodiments. The present invention can be implemented in various forms without departing from the gist thereof.

(1) In the above embodiment, the server 3 includes the construction attribute evaluation data generation section 35 and the worker characteristic evaluation data generation section 36, but these generation sections 35 and 36 may not necessarily be provided. The construction attribute evaluation data and worker characteristic evaluation data for the second worker may be acquired from outside the system. These evaluation data may be acquired, for example, from a database associated with the second worker, construction attribute evaluation data, and/or worker characteristic evaluation data. In one example, the second worker characteristic evaluation data (e.g., skill phase evaluation data) may be input directly by the user, or may be inputted directly by the user, or may be a work data for which the characteristics of the worker have been evaluated in advance by the human resources department of the company to which he or she belongs. It may also be obtained from a personnel characteristic evaluation database.

(2) In the above embodiment, the first construction attribute vector and the first characteristic vector of the first worker are generated by the vector generation units 311 and 321 of the server 3, but they are associated with the first construction project ID and stored in the database in advance. 2 may be stored.

(3) After the second worker has performed the construction work, the server 3 stores the second worker's construction record data, the second worker's construction attribute evaluation data, and worker characteristic evaluation data. They may be stored in the database 2 in association with each other. This makes it possible to add viewable construction record data.

(4) In the above embodiment, the database 2 and the server 3 are configured separately, but the server 3 may include the database 2.

1 Decision support system 2 Database 3 Server 4 Terminal device 10a Processor 10b Storage device 10b-1 RAM
10b-2 ROM
10b-3 External memory 10c Input device 10d Output device 10e Communication device 10f Bus 31 Construction base score calculation unit 311 Vector generation unit 312 Vector generation unit 32 Worker base score calculation unit 321 Vector generation unit 322 Vector generation unit 33 Data determination unit 34 Display control section 35 Construction attribute evaluation data generation section 36 Worker characteristic evaluation data generation section N Network

Claims (5)

A decision-making support system for workers at a production site,
A database storing construction record data associated with a first worker, the construction record data including construction attribute evaluation data related to attributes of the construction work performed by the first worker, and construction record data associated with the construction work performed by the first worker. a database associated with worker characteristic evaluation data indicating the characteristics of the first worker who performed the
Work attribute evaluation data related to the attributes of the work in which the second worker who is the user is involved, worker characteristic evaluation data indicating the characteristics of the second worker, and the work attribute evaluation data of the first worker. and a server that determines the construction record data to be presented to the second worker based on the worker characteristic evaluation data of the first worker;
Equipped with
The server is
a construction base score calculation unit that calculates a construction base score related to the degree of similarity between the construction attribute evaluation data of the first worker and the construction attribute evaluation data of the construction involving the second worker;
a worker base score calculation unit that calculates a worker base score related to the degree of similarity between the worker characteristic evaluation data of the first worker and the worker characteristic evaluation data of the second worker;
a data determining unit that determines the construction record data to be presented to the second worker based on the construction base score and the worker base score;
has
The construction base score calculation unit calculates a first construction attribute vector based on the construction attribute evaluation data of the first worker corresponding to the construction record data in the database and the construction attribute of the second worker. Calculating an inner product with a second construction attribute vector based on the evaluation data as the degree of similarity of the construction attribute evaluation data of the first worker and the second worker, respectively;
The worker base score calculation unit calculates a first characteristic vector based on the worker characteristic evaluation data of the first worker corresponding to the construction record data in the database and the work of the second worker. Calculating an inner product with a second characteristic vector based on the worker characteristic evaluation data as the degree of similarity of the worker characteristic evaluation data of the first worker and the second worker, respectively;
The data determining unit selects a predetermined number of the construction record data having high scores based on the construction base score and the worker base score from among the construction record data stored in the database, to the second worker. determined as the said construction record data to be presented to,
The worker characteristic evaluation data includes evaluation data regarding biorhythm, skill phase, construction phase, and preference test,
The worker base score calculation unit generates the first characteristic vector and the second characteristic vector based on evaluation data regarding the second worker's biorhythm, skill phase, construction phase, and preference test, and calculating an inner product of the first characteristic vector and the second characteristic vector;
Decision support system. The construction attribute evaluation data is character information including preset construction classification, work classification, construction type classification, and keywords related to construction,
The construction base score calculation unit converts the construction classification, the work classification, the construction type classification, and the character information of the keyword into vectors, and calculates the average of the vectors to calculate the first construction attribute vector and the first construction attribute vector. generating a second construction attribute vector and calculating an inner product of the first construction attribute vector and the second construction attribute vector;
The decision support system according to claim 1 . further comprising a terminal device connected to the server via a network and used by the second worker,
The terminal device is
an input device that receives input regarding the construction attribute evaluation data of the construction work in which the second worker is involved and the worker characteristic evaluation data of the second worker;
a communication device that transmits the input to the server via the network;
a display device that presents the construction record data determined by the data determination unit via the communication device;
Equipped with
The decision support system according to claim 1 or 2 . The first worker is a skilled worker,
The second worker is younger in actual age than the skilled worker, or is a worker who has fewer years of experience at the production site than the skilled worker.
The decision support system according to any one of claims 1 to 3 . A server that supports decision-making by workers related to production sites,
Work attribute evaluation data related to the attributes of the work performed by the first worker associated with the construction record data stored in the database, and work attribute evaluation data related to the attributes of the work the second worker is involved in. a construction base score calculation unit that calculates a construction base score related to the degree of similarity;
a worker base score for calculating a worker base score related to the degree of similarity between worker characteristic evaluation data indicating the characteristics of the first worker and worker characteristic evaluation data indicating the characteristics of the second worker; A calculation section,
a data determining unit that determines the construction record data to be presented to the second worker based on the construction base score and the worker base score;
has
The construction base score calculation unit calculates a first construction attribute vector based on the construction attribute evaluation data of the first worker corresponding to the construction record data in the database and the construction attribute of the second worker. Calculating an inner product with a second construction attribute vector based on the evaluation data as the degree of similarity of the construction attribute evaluation data of the first worker and the second worker, respectively;
The worker base score calculation unit calculates a first characteristic vector based on the worker characteristic evaluation data of the first worker corresponding to the construction record data in the database and the work of the second worker. Calculating an inner product with a second characteristic vector based on the worker characteristic evaluation data as the degree of similarity of the worker characteristic evaluation data of the first worker and the second worker, respectively;
The data determining unit selects a predetermined number of the construction record data having high scores based on the construction base score and the worker base score from among the construction record data stored in the database, to the second worker. determined as the said construction record data to be presented to,
The worker characteristic evaluation data includes evaluation data regarding biorhythm, skill phase, construction phase, and preference test,
The worker base score calculation unit generates the first characteristic vector and the second characteristic vector based on evaluation data regarding the second worker's biorhythm, skill phase, construction phase, and preference test, and calculating an inner product of the first characteristic vector and the second characteristic vector;
server.

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