Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device, an information processing method and an information processing program that further precisely predict the number of persons deployed in construction work on the basis of construction information.SOLUTION: In an information processing system that includes: an information processing server (an information processing device) and one or more user terminals connected to the information processing server via a network, a CPU 200C of the information processing server for predicting the number of persons deployed at a construction site includes: a construction information obtaining unit that obtains construction information of the construction site; and a deployed-person-number predicting unit that predicts the number of persons deployed in construction work on the basis of the construction information obtained by the construction information obtaining unit.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Construction sites, especially those for mid- to high-rise buildings, are visited by many people, which creates a great business opportunity for nearby restaurants, retailers, and other businesses. However, the number of workers at a construction site fluctuates depending on the size of the site, the construction method, and other construction-specific factors, making it difficult for outsiders to know when and how many people are coming and going to the site. If the number of people entering and leaving the facility can be known at any time, nearby restaurants and retail stores can estimate the amount of demand and when, creating opportunity benefits. In addition, in recent years, efforts have been made in society to reduce food waste, and it is desirable to be able to predict demand from this perspective as well. Furthermore, since it is possible to flexibly respond to temporary demand by providing meals using food trucks, etc., this leads to the effective use and optimal allocation of land and human resources.

[0003] Conventionally, a scheduling method has been provided that considers the effects of weather in construction work plans. Specifically, in a construction work scheduling system that calculates the schedule for the entire work and manages the progress of the work based on work data related to the work of the construction work and material data related to the materials of the construction work, it is proposed to use weather data related to the weather at the work site, and to increase or decrease the number of workers engaged in the work or the amount of work completed in accordance with the efficiency change for the work affected by the weather data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2001-297131 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, conventional inventions simply increase or decrease the number of workers on the job or the amount of work done depending on the weather, and do not take into account information specific to construction, such as construction plans, making it difficult to accurately predict when and how many people will enter and leave the construction site.

[0006] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an information processing device, an information processing method, and an information processing program that can more accurately predict the number of employees based on building information. [Means for solving the problem]

[0007] In order to solve the above problems, the information processing device of the present invention is an information processing device for predicting the number of workers at a construction site, and is equipped with a construction information acquisition unit that acquires construction information of the construction site, and a worker number prediction unit that predicts the number of workers based on the construction information acquired by the construction information acquisition unit. Effect of the Invention

[0008] According to the present invention, it is possible to provide an information processing device, an information processing method, and an information processing program that can more accurately predict the number of employees based on building information. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of a schematic configuration of an information processing system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a configuration of an information processing server according to the embodiment. [Diagram 3] FIG. 4 is a diagram illustrating an example of a database of an information processing server according to the embodiment. [Figure 4] FIG. 13 is a diagram illustrating an example of a prediction model. [Diagram 5]FIG. 2 is a diagram illustrating an example of a functional block of the information processing server according to the embodiment. [Figure 6] FIG. 2 is a diagram showing an example of a configuration diagram and functional blocks of a user terminal according to the embodiment. [Figure 7] 4 is a flowchart illustrating an example of processing of the information processing system according to the embodiment. [Figure 8] FIG. 11 is a diagram illustrating an example of a screen presented on a user terminal according to the embodiment. [Figure 9] 4 is a flowchart illustrating an example of processing of the information processing system according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. [Embodiment] First, the configuration of an information processing system 1 will be described with reference to Fig. 1. As shown in Fig. 1, the information processing system 1 includes an information processing server 2 (information processing device) and one or more user terminals 3 connected to the information processing server 2 via a network 5. A server 4 storing building information may be connected to the information processing system 1 via the network 5. The server 4 stores, as building information, information that compiles "notices of building plans" that are made public by local government ordinances, for example, and the information processing server 2 acquires the building information from the server 4.

[0011] Here, the building information includes, for example, information on at least one of the building's total floor area, purpose, construction period, number of floors, structure, site area, building area, ratio of site area to building area, construction method, and location. In the example shown in Fig. 1, the information processing system 1 is configured to include one information processing server 2 and one user terminal 3, but the number of information processing servers 2 and user terminals 3 included in the information processing system 1 is arbitrary. The user terminal 3 may be a desktop PC (Personal Computer), a tablet PC, a smartphone, etc.

[0012] (Information Processing Server 2) Next, the hardware configuration of the information processing server 2 will be described with reference to Fig. 2. As shown in Fig. 2, the information processing server 2 has a configuration in which a communication IF 200A, a storage device 200B, and a CPU 200C are connected via a bus 200D. Although not shown in Fig. 2, the information processing server 2 may also include an input device (e.g., a mouse, a keyboard, a touch panel, etc.) and a display device (CRT (Cathode Ray Tube), a liquid crystal display, an organic EL display, etc.).

[0013] The communication IF 200A is an interface for communicating with other devices (for example, the user terminal 3 and the server 4).

[0014] The storage device 200B is, for example, a hard disk drive (HDD) or a semiconductor storage device (solid state drive (SSD)). Various types of information and information processing programs are stored in the storage device 200B.

[0015] Next, various information stored in the storage device 200B of the information processing server 2 will be described with reference to Fig. 3. The storage device 200B of the information processing server 2 stores a user DB1, a performance information DB2, a prediction model DB3, and the like.

[0016] (User DB1) The user DB1 stores information on users of the information processing system 1 according to the embodiment, for example, users who use the information on the number of workers predicted by the information processing server 2. The user information is, for example, company name, trade name, name of person in charge, contact information, etc. The users are all types of businesses that can expect demand from construction personnel, such as restaurants (restaurants, set meal restaurants, bento shops, mobile food stalls (kitchen cars), etc.), retail stores (convenience stores, supermarkets, home improvement centers, stores specializing in supplies for construction sites, etc.), parking lots, as well as operators of portal sites for restaurants and retail stores (for example, platform providers). The user DB1 also stores conditions set by the user of the information processing system 1. The conditions here refer to conditions under which the information processing server 2 transmits (outputs) information such as the number of workers predicted by the information processing server 2 (the change in the predicted number of workers) and the location of the building for which the number of workers is predicted. For example, the user can set the range of the location for which the predicted information is output as a condition. Here, the range of the location may be set by prefecture or by city, town, or village, or may be set by drawing a circle or polygon on a map. When the location information of the building included in the predicted information is within the set range, the information processing server 2 transmits (outputs) the predicted information to the user terminal 3 of the user who set the information. Also, for example, a user can set a condition that the number of employees is equal to or greater than a predetermined value. When the predicted number of employees is equal to or greater than the set value, the transmitting unit 202 of the information processing server 2 transmits (outputs) the predicted information to the user terminal 3 of the user who set the condition. By setting conditions in this manner, for example, it is possible to set the system so that forecast information is sent (output) to user terminal 3 when the predicted number of employees is 100 or more per day, and so that forecast information can be sent (output) to user terminal 3 when a certain level of demand is expected, thereby improving convenience. It is also possible to set a combination of the location range and the number of workers. In this case, the conditions for transmitting (outputting) the forecast information can be set in more detail, improving convenience.

[0017] (Actual information DB2) The performance information DB2 stores performance information of past construction sites. The information stored in the performance information DB2 is used as teacher data for the prediction model stored in the prediction model DB3. Here, the performance information is information in which at least one of the following information on the total floor area, use, construction period, number of floors, structure, site area, building area, ratio of the site area to the building area, construction method, and location of the building is added to the information on the number of workers at past construction sites by tagging or the like. By adding at least one of the following information on the total floor area, use, construction period, number of floors, structure, site area, building area, ratio of the site area to the building area, construction method, and location of the building to the information on the number of workers at past construction sites, the transition of the number of workers can be predicted in the prediction model described later according to at least one of the following information on the total floor area, use, construction period, number of floors, structure, site area, building area, ratio of the site area to the building area, construction method, and location of the building. Here, the total floor area is information about the total floor area of ​​a building that is written on the architectural plan, and is the sum of the floor areas of each floor of the building (including the basement and above ground floors). For example, if a building is two stories high, the total floor area of ​​the building is the sum of the floor areas of the first and second floors. The floor area of ​​each floor is measured based on the center line of the columns or walls. Also, generally, atriums, lofts, etc. are not included in the total floor area. The use is information about the use of the building described in the architectural plan, for example, the use described in the list of the main uses of the building (for example, various schools, apartment buildings, hospitals, etc.). The construction period is information about the period from the scheduled construction start date to the scheduled completion date (scheduled completion date) described in the architectural plan. The number of floors is information regarding the number of floors of a building described in a construction plan, and includes information regarding the number of above-ground floors and the number of underground floors. The structure is information about the structure of the building described in the architectural plan, such as reinforced concrete construction (RC), wall reinforced concrete construction (WRC), steel frame construction (S), steel reinforced concrete construction (SRC), and wood construction. The site area is information on the site area of ​​the building described in the architectural plan, and is the area of ​​the entire site (land) on which the building stands. The building area is information on the building area of ​​a building described in a building plan, and is the area of ​​the part on which the building stands. The ratio of the site area to the building area is the ratio of the building area to the site area. A small ratio value means that the building area is small relative to the site area, and a large ratio value means that the building area is large relative to the site area. In this embodiment, the ratio of the site area to the building area is the ratio of the building area to the site area, but it may be the ratio of the site area to the building area. The construction method is information on a foundation construction method described in a building plan, for example, on-site construction piles. Note that the construction method may include construction methods other than the foundation construction method (for example, a construction method for a building, etc.). The location is information on the place name and lot number described in the construction plan. Note that information other than the place name and lot number (for example, latitude and longitude) may be used as the location information. The information on the total floor area, purpose, construction period, number of floors, structure, site area, construction area, construction method, and location of the building may be information stipulated by the Building Standards Act Enforcement Regulations. The items to be added to the performance information are arbitrary and are not limited to the items mentioned above. For example, other items such as height, building owner, and contractor may be added to the performance information.

[0018] In addition, the number of workers included in the performance information is provided with information on the date and process. By providing the number of workers included in the performance information with information on the date and process, it is possible to predict the number of workers for a specified period (e.g., one day, three days, one week, one month, etc.) or for a specified process (e.g., foundation work, structural work, interior work, exterior work, other work, etc.) in a prediction model described below. Here, the foundation work is, for example, the work of building a base (foundation) that connects the ground to a building. Moreover, structural construction is, for example, construction work to create the structural components (structure) of a building. Interior construction work includes the interior of a building, such as laying water supply and distribution pipes, constructing walls and ceilings, laying boards, wallpapering, and flooring. Exterior construction includes the exterior of a building, such as tiling, sealing tile joints, wiping and painting, and installing handrails. Other work includes work other than those mentioned above, such as facility work to install electrical equipment, water supply and drainage facilities for kitchens and toilets, exterior work such as leveling, paving, planting trees and installing landscaping stones, and removal of equipment used in construction. Note that the predetermined period may be any number of days, weeks, or months, and may be set by, for example, an administrator or a user of the information processing system 1. Furthermore, how the predetermined steps are divided and set is arbitrary, and for example, the administrator of the information processing system 1 may be able to set them.

[0019] (Prediction model DB3) The prediction model DB3 stores a prediction model generated based on the performance information stored in the performance information DB2. The prediction model can be generated using known statistical methods and machine learning algorithms, such as (1) cluster analysis, (2) random forest, (3) a model using a logistic curve, and (4) multiple regression analysis. Note that these statistical methods and machine learning algorithms are merely examples, and other statistical methods and machine learning algorithms may be used as long as they can generate a prediction model for the number of workers in a building. Note that a prediction model may be generated by combining multiple statistical methods and machine learning algorithms.

[0020] Here, cluster analysis is an unsupervised data classification method that automatically classifies given data without external criteria. Random forest is a machine learning algorithm that determines the final predicted value by taking a majority vote from the results of multiple decision trees. A logistic curve is a curve based on the logistic equation, which is a mathematical model. Multiple regression analysis is a type of multivariate analysis that refers to regression analysis with two or more independent variables (two or more dimensions).

[0021] Figure 4 is an example of the transition in the number of workers predicted by the prediction model. As shown in Figure 4, the number of workers in a building progresses from foundation work, which is mainly done with heavy machinery, to interior work, exterior work, and equipment removal, which are mainly done by hand, so as shown in Figure 4, the number of workers generally increases as time or the process progresses and then decreases at the end, but the transition in the increase or decrease in the number of workers is thought to differ depending on the building's total floor area, purpose, construction period, number of floors, structure, site area, building area, ratio of site area to building area, construction method, location, etc. As described above, in the information processing system 1, the number of workers at past construction sites is supplemented with information on the building information such as the building's total floor area, use, construction period, number of floors, structure, site area, building area, ratio of site area to building area (the ratio is calculated from the site area and building area), construction method, location, date, process, etc., making it possible to generate a prediction model that reflects the differences in the trends in the increase and decrease in the number of workers depending on the building's total floor area, use, construction period, number of floors, structure, site area, building area, ratio of site area to building area, construction method, location, etc., enabling more detailed predictions to be made.

[0022] The number of employees may be predicted using only one of the above-mentioned prediction models, or multiple prediction models may be combined to predict the number of employees. Also, the administrator or user of the information processing system 1 may be able to set which prediction model to use to predict the number of employees.

[0023] Note that while the performance information DB2 of the storage device 200B stores performance information that serves as training data for the prediction model, the performance information DB2 in which training data is stored does not necessarily need to be stored in the storage device 200B because the number of employed persons is predicted based on the prediction model. Also, some or all of the various information stored in the storage device 200B may be stored in an external storage device such as a Universal Serial Bus (USB) memory or an external HDD, or in a storage device of another information processing device connected via the network 5. In this case, the information processing server 2 refers to or acquires the various information stored in the external storage device or the storage device of the other information processing device.

[0024] The CPU 200C controls the information processing server 2 according to this embodiment, and includes a ROM and a RAM (not shown).

[0025] (Functions of Information Processing Server 2) Next, functions of the information processing server 2 will be described with reference to Fig. 5. As shown in Fig. 5, the information processing server 2 has functions such as a receiving unit 201, a transmitting unit 202 (output unit), a storage device control unit 203, a building information acquisition unit 204, a worker number prediction unit 205, a determining unit 206, an actual measurement value acquisition unit 207, and a correcting unit 208. The functions shown in Fig. 5 are realized by the CPU 200C executing an information processing program stored in the storage device 200B.

[0026] The receiving unit 201 receives information from the user terminal 3 and the server 4 via the network 5 .

[0027] The transmitting unit 202 (output unit) transmits (outputs) information to the user terminal 3 via the network 5.

[0028] The storage device control unit 203 controls the storage device 200B. For example, the storage device control unit 203 writes and reads information to and from the storage device 200B.

[0029] The construction information acquisition unit 204 acquires construction information of a construction site. The construction information acquisition unit 204 may acquire the construction information from the server 4, for example, or may acquire the construction information input by an administrator of the information processing system 1, etc.

[0030] The number of workers prediction unit 205 predicts the number of workers based on the building information acquired by the building information acquisition unit 204. Specifically, the number of workers prediction unit 205 refers to the prediction model stored in the storage device 200B, and predicts the number of workers based on the building information acquired by the building information acquisition unit 204 and the referenced prediction model.

[0031] The determination unit 206 refers to the user DB1 and determines whether or not information such as the number of workers and the location of buildings predicted by the number-of-workers prediction unit 205 satisfies the conditions set by the user.

[0032] The actual value acquisition unit 207 acquires information on the actual value of the construction site where the number of workers has been predicted by the number of workers prediction unit 205. Here, the information on the actual value is information on the actual number of workers at the construction site where the number of workers has been predicted by the number of workers prediction unit 205. For example, the actual value acquisition unit 207 may be configured to acquire information on the actual value input by an administrator of the information processing system 1, or may be configured to acquire information on the actual value from another server connected via the network 5. Note that it is preferable that the information on the actual value associates the number of workers with time information such as a date.

[0033] The correction unit 208 corrects the prediction model by comparing the number of workers predicted by the number of workers prediction unit 205 with the information on the actual value acquired by the actual value acquisition unit 207. For example, the correction unit 208 compares the number of workers predicted by the number of workers prediction unit 205 with the information on the actual value of the actual number of workers acquired by the actual value acquisition unit 207, and determines whether the deviation (difference) between the number of workers predicted by the number of workers prediction unit 205 and the actual value of the number of workers acquired by the actual value acquisition unit 207 exceeds a predetermined value. Then, if the deviation (difference) between the number of workers predicted by the number of workers prediction unit 205 and the actual value of the number of workers acquired by the actual value acquisition unit 207 exceeds a predetermined value, the correction unit 208 corrects the prediction model. At this time, the correction unit 208 corrects the prediction model so that the deviation (difference) between the predicted number of workers and the actual value of the number of workers is equal to or less than a predetermined value. In addition, the correction unit 208 may, for example, compare the number of workers predicted by the number of workers prediction unit 205 specified by an administrator or user of the information processing system 1 with the actual measurement information acquired by the actual measurement acquisition unit 207, or may compare the number of workers predicted by the number of workers prediction unit 205 that matches the information added by tagging or the like (for example, the building's total floor area, use, construction period, number of floors, structure, site area, building area, ratio of site area to building area, construction method, location, etc.) with the actual measurement information acquired by the actual measurement acquisition unit 207.

[0034] In the above description, the correction unit 208 corrects the prediction model by comparing the number of workers predicted by the number of workers prediction unit 205 with the information of the actual measurement value acquired by the actual measurement value acquisition unit 207, but the correction unit 208 may correct the number of workers predicted by the number of workers prediction unit 205 by comparing the number of workers predicted by the number of workers prediction unit 205 with the information of the actual measurement value acquired by the actual measurement value acquisition unit 207. In this case, the correction unit 208 may be configured to correct the number of workers predicted by the number of workers prediction unit 205 by comparing the number of workers predicted by the number of workers prediction unit 205 with the information of the actual measurement value acquired by the actual measurement value acquisition unit 207, or may be configured to correct both the prediction model and the number of workers predicted by the number of workers prediction unit 205.

[0035] (User terminal 3) Fig. 6 is a configuration diagram of the user terminal 3. Fig. 6(a) shows the main hardware configuration of the user terminal 3, in which the user terminal 3 has a configuration in which a communication IF 300A, a storage device 300B, an input device 300C, a display device 300D, and a CPU 300E are connected via a bus 300F.

[0036] The communication IF 300A is an interface for communicating with other devices (the information processing server 2 in this embodiment).

[0037] The storage device 300B is, for example, a hard disk drive (HDD) or a semiconductor storage device (solid state drive (SSD)). The storage device 300B stores a terminal identifier, an information processing program, etc. The terminal identifier is an identifier for identifying the user terminal 3. By assigning the terminal identifier to information transmitted from the user terminal 3, the information processing server 2 can determine which user terminal 3 transmitted the received information. Note that the terminal identifier may be an Internet Protocol (IP) address, a Media Access Control (MAC) address, etc., or may be assigned to the user terminal 3 by the information processing server 2.

[0038] The input device 300C is, for example, an input device such as a keyboard, a mouse, or a touch panel, but may be any other device or equipment that allows input. Also, it may be a voice input device.

[0039] The display device 300D is, for example, a liquid crystal display, a plasma display, an organic EL display, or the like, but may be any other device or equipment (for example, a CRT: Cathode Ray Tube) as long as it is capable of displaying.

[0040] The CPU 300E controls the user terminal 3 according to this embodiment, and includes a ROM and a RAM (not shown).

[0041] Fig. 6(b) is a functional block diagram of the user terminal 3. As shown in Fig. 6(b), the user terminal 3 has functions such as a receiving unit 301, a transmitting unit 302, a storage device control unit 303, an input accepting unit 304, and a display device control unit 305. Note that the functions shown in Fig. 6(b) are realized by the CPU 300E executing an information processing program stored in the storage device 300B.

[0042] The receiving unit 301 receives, for example, information transmitted from the information processing server 2.

[0043] The transmitting unit 302 transmits, for example, information corresponding to the input operation accepted by the input accepting unit 304 to the information processing server 2.

[0044] The storage device control unit 303 controls, for example, the storage device 300B to write and read information.

[0045] The input receiving unit 304 receives an input operation from, for example, the input device 300C.

[0046] The display device control unit 305 controls, for example, the display device 300D, and displays the information received by the receiving unit 301 on the display device 300D. An example of a screen displayed on the display device 300D will be described later (see FIG. 8).

[0047] (Information Processing) Fig. 7 and Fig. 9 are flowcharts showing an example of information processing of the information processing system 1. Fig. 8 is an example of a screen displayed on the display device 300D of the user terminal 3. Hereinafter, the information processing of the information processing system 1 will be described with reference to Figs. 7 to 9. In the following description, the same components as those described with reference to Figs. 1 to 6 are denoted by the same reference numerals, and duplicated description will be omitted.

[0048] (Employment forecast processing) 7 is a flowchart showing an example of the processing of the information processing system 1. Hereinafter, an example of the number of workers prediction processing of the information processing system 1 will be described with reference to FIG.

[0049] (Step S101) The construction information acquisition unit 204 of the information processing server 2 acquires construction information of a construction site. As described above, the construction information acquisition unit 204 may acquire the construction information from the server 4, for example, or may acquire the construction information input by an administrator of the information processing system 1, etc.

[0050] (Step S102) The employee number prediction unit 205 of the information processing server 2 refers to the prediction model stored in the prediction model DB3 of the storage device 200B, and predicts the number of employees based on the building information acquired by the building information acquisition unit 204 and the referenced prediction model.

[0051] (Step S103) The determination unit 206 of the information processing server 2 refers to the conditions set by the user stored in the user DB1 of the storage device 200B.

[0052] (Step S104) The judgment unit 206 of the information processing server 2 judges whether the information such as the number of workers and the location of buildings predicted by the number of workers prediction unit 205 satisfies the conditions set by the user. If the conditions are satisfied (YES), the information processing server 2 proceeds to the process of step S105. If the conditions are not satisfied (NO), the information processing server 2 proceeds to the process of step S107.

[0053] (Step S105) The transmission unit 202 (output unit) of the information processing server 2 transmits (outputs) information (hereinafter also referred to as prediction information) such as the number of employees predicted by the employee number prediction unit 205 (trend in the predicted number of employees) and the location of the building where the number of employees is predicted, to the user terminal 3 of the user who satisfies the set conditions.

[0054] (Step S106) The receiving unit 301 of the user terminal 3 receives the prediction information transmitted (output) from the information processing server 2. The display device control unit 305 of the user terminal 3 causes the display device 300D to display the screen shown in FIG.

[0055] (Step S107) If the determination unit 206 has determined all the conditions (YES), the information processing server 2 ends the number of employees prediction process. If the determination unit 206 has not determined all the conditions (NO), in other words, if there are conditions that have not been determined, the information processing server 2 returns to the process of step S104, and the determination unit 206 determines the conditions set for the remaining users.

[0056] In the information processing system 1, as shown in Fig. 8(a), circles X1 and X2 are displayed on a map based on the location information included in the prediction information. In the information processing system 1, the number of employed people is indicated by the size (area) of the circles X1 and X2. Note that the larger (wider) the circles X1 and X2 are, the greater the number of employed people are, but the number of employed people does not necessarily have to be expressed by the size of the circle, and the number of employed people may be expressed in other ways, for example, by changing the color or line thickness of the circles X1 and X2. In addition, the display device 300D displays a time scale TS (in FIG. 8, it is displayed at the bottom right of the screen, but it does not necessarily have to be at the bottom right of the screen). The time scale TS changes the size of the circles X1 and X2 according to the number of workers corresponding to the position of the pointer P3 (date or process) by sliding the pointer P3 over the period from any given date P1 to P2. In this way, by moving the pointer P3 between P1 and P2, the change in the number of workers (increase or decrease in the number of workers) can be confirmed, which is very convenient.

[0057] In the above explanation, the screen illustrated in FIG. 8 is configured to be displayed on the display device 300D of the user terminal 3, but if the user of the information processing system 1 of this embodiment is a portal site operator (e.g., a platform provider) of a restaurant or retail store, the screen illustrated in FIG. 8 is displayed on the display device of the terminal of the user of the portal site operator (e.g., the platform provider).

[0058] (Prediction model correction process) 9 is a flowchart showing an example of the process of the information processing system 1. Hereinafter, an example of the prediction model correction process of the information processing system 1 will be described with reference to FIG.

[0059] (Step S201) The actual value acquisition unit 207 of the information processing server 2 acquires information on the actual value of the number of employees. As described above, the information on the actual value of the number of employees may be configured to acquire information on the actual value input by an administrator of the information processing system 1 or the like, or may be configured to acquire information on the actual value from another server 4 connected via the network 5.

[0060] (Step S202) The correction unit 208 of the information processing server 2 compares the number of workers predicted by the number of workers prediction unit 205 with the information of the actual value acquired by the actual value acquisition unit 207. Specifically, the correction unit 208 compares the number of workers predicted by the number of workers prediction unit 205 with the information of the actual value acquired by the actual value acquisition unit 207 (the actual number of workers at the construction site where the number of workers predicted by the number of workers prediction unit 205)

[0061] (Step S203) The correction unit 208 of the information processing server 2 corrects the prediction model based on the comparison result in step S202. Note that the correction method by the correction unit 208 has already been described, so a duplicated description will be omitted.

[0062] As described above, the information processing server 2 according to this embodiment is an information processing device for predicting the number of workers at a construction site. The information processing server 2 includes a construction information acquisition unit 204 that acquires construction information on the construction site, and a worker number prediction unit 205 that predicts the number of workers based on the construction information acquired by the construction information acquisition unit 204. Therefore, the number of workers can be predicted based on information related to the construction information, so that the number of workers can be predicted more accurately.

[0063] The building information of this embodiment also includes information on the total floor area of ​​the building. The employee number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of employees based on the information on the total floor area. Typically, the number of workers required varies depending on the total floor area of ​​a building (for example, a larger total floor area will require a larger number of workers, and a smaller total floor area will require a smaller number of workers), but the information processing server 2 in this embodiment can more accurately predict the number of workers based on information related to the total floor area of ​​the building.

[0064] The building information of this embodiment also includes information on the purpose of the building. The employee number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of employees based on the information on the purpose. Typically, the number of workers required changes depending on the purpose of the building as the construction work and tasks required change, but the information processing server 2 in this embodiment can more accurately predict the number of workers based on information related to the purpose of the building.

[0065] The construction information of this embodiment also includes information on the construction period of the construction site. The worker number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of workers based on the information on the construction period. Typically, the number of workers required changes depending on the construction period at a construction site (for example, for the same building, a shorter construction period may require more workers per unit period, and a longer construction period may require fewer workers), but the information processing server 2 in this embodiment can more accurately predict the number of workers based on information related to the construction period.

[0066] The building information of this embodiment also includes information about the number of floors of the building. The employee number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of employees based on the information about the number of floors. Typically, the number of workers required varies depending on the number of floors of a building (for example, a building with many floors will require more workers, and a building with few floors will require fewer workers), but the information processing server 2 in this embodiment can more accurately predict the number of workers based on information related to the number of floors of the building.

[0067] The building information of this embodiment includes information about the structure of the building. The worker number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of workers based on the information about the structure. Typically, the number of workers required changes depending on the structure of the building as the construction work and tasks required change, but the information processing server 2 in this embodiment can more accurately predict the number of workers based on information related to the structure of the building.

[0068] The building information of this embodiment includes information on at least one of the site area and building area of ​​the building. The employee number prediction unit 205 of the information processing server 2 of this embodiment predicts the number of employees based on information on the site area, building area, or the ratio of the site area to the building area. Normally, the number of workers required changes depending on the site area, building area, or the ratio between the site area and building area (for example, if the site area is large compared to the building area, the efficiency of loading and unloading improves and more people can work at one time, resulting in a larger number of workers per unit period, and if the site area is small compared to the building area, the efficiency of loading and unloading decreases and fewer people can work at one time, resulting in a smaller number of workers per unit period). However, the information processing server 2 in this embodiment can more accurately predict the number of workers based on information related to the site area, building area, or the ratio between the site area and building area.

[0069] The building information of this embodiment also includes information on the construction method of the building. The worker number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of workers based on the information on the construction method. Typically, the number of workers required changes depending on the construction method used to build a building, as the work content and tasks change depending on the construction method used. However, the information processing server 2 in this embodiment can more accurately predict the number of workers required based on information related to the construction method used to build a building.

[0070] The construction information of this embodiment also includes information about the location of the construction site. The worker number prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of workers based on the information about the location.

[0071] The worker number prediction unit 205 of the information processing server 2 according to this embodiment refers to a prediction model generated based on past performance information including construction information and the number of workers of a construction site, and predicts the number of workers based on the prediction model. Therefore, the number of workers can be accurately predicted based on the prediction model generated from past performance.

[0072] The information processing server 2 in this embodiment is equipped with an actual value acquisition unit 207 that acquires the actual value of the number of employees, and a correction unit 208 that corrects the prediction model based on a comparison between the predicted number of employees and the actual value acquired by the actual value acquisition unit 207. In this way, the information processing server 2 of this embodiment corrects the prediction model by comparing the predicted number of employees with the actual value acquired by the actual value acquisition unit 207, and is therefore expected to predict the number of employees more accurately. As described above, the correction unit 208 may be configured to correct the number of employees predicted by the number of employees prediction unit 205 based on a comparison between the number of employees predicted by the number of employees prediction unit 205 and the information on the actual measurement value acquired by the actual measurement value acquisition unit 207, or may be configured to correct both the prediction model and the number of employees predicted by the number of employees prediction unit 205.

[0073] The number of workers prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of workers for a predetermined period or for each predetermined process based on the building information acquired by the building information acquisition unit 204. In this way, the number of workers prediction unit 205 of the information processing server 2 according to this embodiment predicts the number of workers for a predetermined period or for each predetermined process, and is therefore highly convenient. [Explanation of symbols]

[0074] 1. Information Processing Systems 2. Information processing server (information processing device) 200A Communication IF 200B storage device 200C CPU 200D Bus 201 Receiving unit 202 Transmission unit (output unit) 203 Storage device control unit 204 Architectural Information Acquisition Department 205 Employment Forecast Department 206 Judgment section 207 Actual measurement acquisition section 208 Correction Section 3. User terminal 300A communication IF 300B storage device 300C Input Device 300D display device 300E CPU 300F Bus 301 Receiving unit 302 Transmitter 303 Storage device control unit 304 Input reception section 305 Display device control unit 4 Server 5. Network DB1 user database DB2 Performance Information Database DB3 Predictive Model Database

Claims

1. An information processing device for predicting the number of workers at a construction site, comprising: A construction information acquisition unit that acquires construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit, The building information includes information regarding the total floor area of ​​the building, The employment number prediction unit, predicting the number of employees based on the information about the total floor area; 23. An information processing apparatus comprising:

2. An information processing device for predicting the number of workers at a construction site, comprising: A construction information acquisition unit that acquires construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit, The building information includes information regarding the use of the building, The employment number prediction unit, predicting the number of employees based on information about the use; 23. An information processing apparatus comprising:

3. An information processing device for predicting the number of workers at a construction site, comprising: A construction information acquisition unit that acquires construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit, The building information includes information regarding the number of floors of a building, The employment number prediction unit, predicting the number of employees based on information about the number of floors; 23. An information processing apparatus comprising:

4. An information processing device for predicting the number of workers at a construction site, comprising: A construction information acquisition unit that acquires construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit, The building information includes information regarding the structure of the building, The employment number prediction unit, predicting the number of employees based on information about the structure; 23. An information processing apparatus comprising:

5. An information processing device for predicting the number of workers at a construction site, comprising: A construction information acquisition unit that acquires construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit, The building information includes information regarding at least one of the site area and building area of ​​the building, The employment number prediction unit, predicting the number of employees based on information about the site area, the building area, or the ratio of the site area to the building area; 23. An information processing apparatus comprising:

6. An information processing device for predicting the number of workers at a construction site, comprising: A construction information acquisition unit that acquires construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit, The construction information includes information regarding the location of the construction site; The employment number prediction unit, predicting the number of employees based on information about the location; 23. An information processing apparatus comprising:

7. The employment number prediction unit, A prediction model is generated based on past construction information of the construction site and performance information including the number of workers, and the number of workers is predicted based on the prediction model.

7. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

8. An actual measurement value acquisition unit that acquires an actual measurement value of the number of employees; A correction unit that corrects the number of employees predicted by the number of employees prediction unit based on a comparison between the predicted number of employees and the actual value acquired by the actual value acquisition unit; 8. The information processing apparatus according to claim 1, further comprising:

9. An information processing method for predicting the number of workers at a construction site, comprising: A construction information acquisition unit acquires construction information of the construction site; A step of an employee number prediction unit predicting the employee number based on the building information acquired by the building information acquisition unit, The building information includes information regarding the total floor area of ​​the building, The employment number prediction unit, predicting the number of employees based on the information about the total floor area; 23. An information processing method comprising:

10. An information processing method for predicting the number of workers at a construction site, comprising: A construction information acquisition unit acquires construction information of the construction site; A step of an employee number prediction unit predicting the employee number based on the building information acquired by the building information acquisition unit, The building information includes information regarding the use of the building, The employment number prediction unit, predicting the number of employees based on information about the use; 23. An information processing method comprising:

11. An information processing method for predicting the number of workers at a construction site, comprising: A construction information acquisition unit acquires construction information of the construction site; A step of an employee number prediction unit predicting the employee number based on the building information acquired by the building information acquisition unit, The building information includes information regarding the structure of the building, The employment number prediction unit, predicting the number of employees based on information about the structure; 23. An information processing method comprising:

12. An information processing program for predicting the number of workers at a construction site, Computer, A construction information acquisition unit for acquiring construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit; The building information includes information regarding the total floor area of ​​the building, The employment number prediction unit, predicting the number of employees based on the information about the total floor area; 2. An information processing program comprising:

13. An information processing program for predicting the number of workers at a construction site, comprising: Computer, A construction information acquisition unit for acquiring construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit; The building information includes information regarding the use of the building, The employment number prediction unit, predicting the number of employees based on information about the use; 2. An information processing program comprising:

14. An information processing program for predicting the number of workers at a construction site, comprising: Computer, A construction information acquisition unit for acquiring construction information of the construction site; a worker number prediction unit that predicts the number of workers based on the building information acquired by the building information acquisition unit; The building information includes information regarding the structure of the building, The employment number prediction unit, predicting the number of employees based on information about the structure; 2. An information processing program comprising:

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