Information processing program and information processing method

Abstract

To provide support information using highly reproducible simulation data in preventive maintenance of a road structure.SOLUTION: An information processing device according to an embodiment is a structure related to a traffic constructed on a road existing in a real world, or a facility related to the traffic, wherein the information processing device performs a simulation using a digital twin in the structure or facility, and executes a process to predict a deterioration of the structure or facility based on a performed simulation.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to an information processing program and an information processing method.

Background Art

[0002] In recent efforts towards the SDGs, it has been targeted to achieve planned preventive maintenance of road structures such as highways and minimize life cycle costs, and various systems leading to the achievement of the goals are expected to be proposed.

[0003] For example, maintenance planners and workers working on highways have the mission of driving on the highway and maintaining the good condition of road structures, and they carry out their work while performing dangerous operations. Regular inspections stipulated by laws and regulations and inspections for preventive maintenance are regularly carried out for maintenance work, and based on the results of crack condition discrimination and impact sound inspection, a maintenance plan for road repair is formulated. And since this maintenance plan is time-consuming and costly, a more effective and efficient plan considering various factors is required.

[0004] As a proposed method for achieving the goal, a technique is known in which simulations are performed for repair operations of various facilities, and a repair plan is formulated based on the results of the simulations. In this simulation, the time of equipment failure is predicted using data such as the failure rate of equipment, or the completion time of repair work is predicted using data such as past work performance.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, in the prior art, it is impossible to provide support information using highly reproducible simulation data in the preventive maintenance of road structures.

[0007] For example, in order to perform a simulation using a preset calculation formula or the like, the prior art can only obtain simulation results under specific conditions, and it is difficult to say that it can provide support information using highly reproducible simulation data.

[0008] In one aspect, an object is to provide an information processing program and an information processing method capable of providing support information using highly reproducible simulation data in the preventive maintenance of road structures.

Means for Solving the Problems

[0009] In the first aspect, the information processing program causes a computer to execute a process of performing a digital twin simulation on a structure or facility related to traffic constructed on a road existing in the real world, and predicting the deterioration of the structure or facility based on the simulation using the executed digital twin.

Effects of the Invention

[0010] According to one embodiment, in the preventive maintenance of road structures, it is possible to provide support information using highly reproducible simulation data.

Brief Description of the Drawings

[0011]

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MODE FOR CARRYING OUT THE INVENTION

[0012] Hereinafter, embodiments of the information processing program and the information processing method disclosed in the present application will be described in detail with reference to the drawings. Note that the present invention is not limited by this embodiment. Also, each embodiment can be appropriately combined within a non - conflicting range.

[0013] 〔First〕 First, problems in road maintenance work in the prior art will be described. As described above, at present, with the goal of realizing planned preventive maintenance of road structures such as highways and minimizing the life - cycle cost, techniques for simulating repair work of various facilities and formulating repair plans are known as the prior art.

[0014] However, in the prior art, simulations using calculation formulas with real-time traffic conditions and environments as parameters are common. In this case, since it takes time to set many parameters, the real-time performance deteriorates. Also, due to human errors in parameter setting, the reliability of the simulation decreases. For these reasons, it is difficult to say that the prior art can provide support information using highly reproducible simulation data, which is cited as a problem.

[0015] In view of the above problems, in this embodiment, by performing a simulation using a digital twin, support information using highly reproducible simulation data is provided. Here, the simulation technology using the digital twin according to this embodiment will be described. A digital twin is a technology that reproduces an actually operating facility or a real environment on a virtual space by utilizing a huge amount of measurement data collected in the real environment.

[0016] FIG. 1 and FIG. 2 are diagrams for explaining the simulation using the digital twin according to the embodiment. FIG. 1 shows an example of performing a simulation by reproducing a highway in the real space on a virtual space using a digital twin. In the example of FIG. 1, for various structures such as ETC (Electronic Toll Collection System), traffic signs, and tunnels on the highway in the real environment, a digital twin that reproduces the real environment is constructed using point cloud data of the structures on the highway, data on the service life of the structures, traffic data such as passing vehicles, and various measurement data such as weather data. Also, by acquiring various data from the real environment in real time and applying it to the digital twin, the real environment is reproduced in real time.

[0017] Incidentally, in FIG. 1, an example in which a digital twin is constructed for a certain structure on a highway is shown, but the present invention is not limited thereto, and a digital twin of the entire highway or a part of the highway can also be constructed, and simulations regarding each structure on the highway can be executed. FIG. 2 shows an example in which a digital twin reproduces an entire highway within a certain range in a virtual space and performs a simulation. In the example of FIG. 2, a digital twin that reproduces an entire highway within a certain range in real time is constructed, and a detailed digital twin image (see FIG. 1) of the selected location is displayed by selection from the outside, and simulation results regarding the structures and facilities in the image are also displayed.

[0018] In the present embodiment, by utilizing various measurement data on the road, a digital twin of a highway that reproduces the real environment in real time is constructed, and by executing a simulation in that environment, a real-time and highly accurate simulation is realized. Specifically, by performing a simulation regarding the maintenance work of road structures or facilities, support information using highly reproducible simulation data can be provided. Note that, in this example, a highway is used as an example for explanation, but the present invention is not limited thereto, and the same processing can be applied to structures such as general roads and traffic signals on general roads.

[0019] [Example 1] (Overall Configuration) Subsequently, a deterioration prediction device according to Example 1 will be described. FIG. 3 is a diagram for explaining an overall configuration example of the system according to Example 1. As shown in FIG. 3, the system according to Example 1 includes a deterioration prediction device 10 that predicts the deterioration time of structures and facilities on the road, and a work terminal 20 operated by a person in charge of making a repair plan.

[0020] The deterioration prediction device 10 is a computer that constructs a digital twin based on measurement data and performs a simulation for predicting deterioration on a structure or facility (hereinafter also referred to as an object) on the road that is the target of deterioration prediction.

[0021] The work terminal 20 is a terminal operated by a person in charge of creating a repair plan, and is a computer that instructs the deterioration prediction device 10 to execute a simulation, receives information on the simulation result, and displays it on the screen.

[0022] (Problems of the prior art) Conventionally, the inspection of road structures has been basically carried out by road administrators through close visual inspection once every five years. If damage is found, an application is made to the maintenance department planning section, and the person in charge of the repair plan formulates a plan for early repair. In recent years, in order to achieve the SDGs, it has become necessary to shift from post-maintenance, which involves large-scale repairs after damage has become serious, to "preventive maintenance", which involves repairs while the damage is minor, for road repairs as well.

[0023] In "preventive maintenance", based on the individual road environment, road administrators regularly conduct inspections and diagnoses, and make maintenance management and repair plans to ensure safety and security at the minimum life cycle cost. However, in "preventive maintenance", it is difficult to determine the time when repairs should be made due to the complexity of managing inspection results and variations such as the proficiency of road administrators.

[0024] Therefore, the deterioration prediction device 10 according to Example 1 utilizes log information (ETC monitoring log information) that can be obtained from an existing highway system and digital twin technology to predict deterioration considering not only the aging deterioration of the object itself but also the influence of external environments such as traffic and weather, and presents to the person in charge of the repair plan the time when the road structure should be repaired.

[0025] (Processing performed by the deterioration prediction device 10 according to Example 1) Here, the processing performed by the deterioration prediction device 10 will be described. FIG. 4 is a diagram for explaining the outline of the processing of the deterioration prediction device according to Example 1. The deterioration prediction device 10 is a structure related to traffic or a facility related to traffic constructed on a road existing in the real world. In the structure or facility, a simulation using a digital twin is performed, and based on the performed simulation, the deterioration of the structure or facility is predicted.

[0026] For example, the deterioration prediction device 10 captures data related to the service life of the object, data related to traffic volume, and data related to weather, and constructs digital twin data that reproduces the actual environment. Then, in the digital twin environment, the deterioration prediction device 10 simulates the deterioration status of the object and visualizes the deterioration status by showing the deterioration prediction result in a graph, table, map, etc. For example, the deterioration prediction device 10 highlights the predicted deterioration time by simulation at the location where the object in the digital twin image exists.

[0027] As a result, the deterioration prediction device 10 can easily provide a highly accurate deterioration prediction time by performing simulation on structures and facilities on the road in the digital twin environment.

[0028] (Functional Configuration of Deterioration Prediction Device 10) Next, the functional configuration of the deterioration prediction device 10 will be described. FIG. 5 is a functional block diagram showing the functional configuration of the deterioration prediction device according to the first embodiment. As shown in FIG. 5, the deterioration prediction device 10 includes a communication unit 11, a control unit 12, and a storage unit 13. The communication unit 11 is a processing unit that controls communication with other devices and is realized by, for example, a communication interface or the like. For example, the communication unit 11 receives an instruction to execute a simulation from the work terminal 20 or mediates the process of displaying the deterioration prediction time by the display control unit 12c described later on the work terminal 20.

[0029] The storage unit 13 is a processing unit that stores various data and programs executed by the control unit 12 and is realized by, for example, a memory or a hard disk. The storage unit 13 stores, for example, a service life DB 13a, a traffic-related DB 13b, and a weather-related DB 13c.

[0030] The service life DB13a stores data related to the service life of facilities set for each structure and equipment on the road. FIG. 6 is a diagram for explaining the service life DB. As shown in FIG. 6, the service life DB13a stores items of "facility name", "type", and "facility service life".

[0031] Here, the "facility name" stores the names of structures and equipment on the road that can be objects. The "type" stores the types of data used for predicting the deterioration of objects such as the number of units and weight. The "facility service life" stores specific numerical values of the facility service life of the object. In the example of FIG. 6, for the "facility name: Transmission control device A", items such as "number of vehicles" and "vehicle weight" are set as items for setting the service life. And the service life of the "number of vehicles" is set to "(1000 vehicles / day) × 5 years", and the service life of the "number of vehicle weights" is set to "(1000 t / day) × 5 years". Specifically, taking an example, for the transmission control device A, it is defined that when assuming 1000 vehicles pass through per day, the service life will be reached in 5 years.

[0032] The traffic-related DB13b stores data monitored by an ETC (Electronic Toll Collection System) monitoring system, such as traffic volume and vehicle information, on the road where the facilities are installed, for each structure and equipment on the road. FIG. 7 is a diagram for explaining the traffic-related DB. As shown in FIG. 7, the traffic-related DB13b stores items of "facility name", "type", and "performance".

[0033] Here, the "performance" stores specific numerical values of traffic volume such as the number of units and weight in any past period such as per day or per week. In addition to the above information, the traffic-related DB13b can also store accident information and information on repairs performed after an accident. In the example of FIG. 7, for the transmission control device A, it is shown that "1500 vehicles" passed through on "2022 / 12 / 1" and "1100 vehicles" passed through on "2022 / 12 / 2".

[0034] The weather-related DB 13c stores data related to the weather in the area where each structure or facility on the road is located, for each structure or facility. FIG. 8 is a diagram for explaining the weather-related DB. As shown in FIG. 8, the weather-related DB 13c stores, for each item related to the weather such as "weather", "temperature", "sunshine", and "wind speed", a load factor which is a numerical value set according to the weather, for each day.

[0035] For example, the weather-related DB 13c stores the load factor such that for "weather", it is "1.0" in case of clear sky or cloudy, and "1.1" in case of rain. Note that in addition to the load factor, the weather-related DB 13c can also store arbitrary values set according to the characteristics of the weather, or actual meteorological data. In the example of FIG. 8, it shows that the load factor "1.0" is set for each of the weather, temperature, sunshine, and wind speed on "2022 / 12 / 1".

[0036] Returning to the description of FIG. 5. The control unit 12 is a processing unit that controls the entire deterioration prediction device 10 and is realized by, for example, a processor or the like. This control unit 12 includes an acquisition unit 12a, a prediction unit 12b, and a display control unit 12c. Note that the acquisition unit 12a, the prediction unit 12b, and the display control unit 12c are realized by an electronic circuit of the processor, a process executed by the processor, or the like.

[0037] The acquisition unit 12a acquires information used when performing a simulation of deterioration prediction using a digital twin for a structure or facility constructed on a road existing in the real world. For example, the acquisition unit 12a acquires, for each object, information on the service life (see FIG. 6), the vehicle passing record of the road where the structure or facility is installed (see FIG. 7), and the weather information of the area where the structure or facility is located (see FIG. 8), and stores them in each DB of the storage unit 13.

[0038] The prediction unit 12b refers to the information stored in each DB of the storage unit 13, performs a simulation using the digital twin, and predicts the deterioration of the structure or facility based on the executed simulation. Further, the prediction unit 12b can execute a plurality of simulations by changing the information and conditions used in the simulation.

[0039] For example, as a first simulation, the prediction unit 12b performs a simulation using the information on the equipment service life (aging deterioration) stored in the service life DB 13a, and predicts the deterioration time of the object. That is, the prediction unit 12b can perform a simulation using the information on the aging deterioration of the structure or equipment.

[0040] For example, as a second simulation, the prediction unit 12b performs a simulation using the ETC monitoring information including the usage history such as the number of vehicle passages on the road where the structure or equipment is installed, stored in the traffic-related DB 13b, and predicts the deterioration time of the object. That is, the prediction unit 12b can perform a simulation using the digital twin for predicting the deterioration of the structure or equipment using the monitoring information including the usage history of the structure or equipment.

[0041] For example, as a third simulation, the prediction unit 12b performs a simulation using the load coefficient set according to the weather in the area where the structure or equipment is located, stored in the weather-related DB 13c, and predicts the deterioration time of the object. That is, the prediction unit 12b can perform a simulation using the digital twin for predicting the deterioration of the structure or equipment using the weather information in the area where the structure or equipment is located.

[0042] Here, the first, second, and third simulations by the prediction unit 12b will be described. FIG. 9 is a diagram showing a specific example of simulation processing by the deterioration prediction device according to the first embodiment. FIG. 9 shows the deterioration prediction when the respective simulation results are output to a graph with the vertical axis representing the degree of deterioration and the horizontal axis representing the year and month. In FIG. 9, the portion indicated by the dotted line after "2022 / 12" is the predicted value of the ratio of the degree of deterioration. Also, in FIG. 9, the line indicating "deterioration (repair time)" is the allowable value of the degree of deterioration, and the time when the predicted value of the degree of deterioration reaches this line is determined as the repair time.

[0043] For example, in the first simulation, a linear graph is created based on the equipment service life. In contrast, in the second and third simulations, a line graph is created because the progress rate of the degree of deterioration changes according to the actual data for each month with reference to past traffic data and weather data.

[0044] From the graph shown in FIG. 9, it can be understood that the predicted deterioration time of the first simulation is "2024 / 10", the predicted deterioration time of the second simulation is "2024 / 1", and the predicted deterioration time of the third simulation is "2024 / 5". By performing the three simulations of the first, second, and third, the prediction unit 12b can compare the deterioration times of the respective simulations and determine the factor that accelerated the deterioration or the factor that slowed down the deterioration. For example, in the example of FIG. 9, since the difference between the deterioration time of the first simulation and the deterioration time of the second simulation is 9 months, the prediction unit 12b predicts that the deterioration time has advanced because the traffic volume of the road where the structure or equipment is installed is relatively large.

[0045] In the example of FIG. 9, deterioration prediction is performed by comparing the first simulation and the second simulation, but it is not limited to this. The prediction unit 12b can perform deterioration prediction by comparing a reference first simulation with another simulation in which deterioration factors are considered. For example, the prediction unit 12b can perform deterioration prediction by comparing a reference first simulation with a third simulation that takes into account weather factors. Also, in the above example, each simulation was performed using the information stored in one corresponding DB, but it is not limited to this. For example, simulations can be performed based on a combination of information stored in each DB and a plurality of pieces of information. For example, the prediction unit 12b can perform a fourth simulation based on a combination of the information stored in the service life DB 13a, the information stored in the traffic-related DB 13b, and the information stored in the weather-related DB 13c, based on a plurality of pieces of information.

[0046] Also, the data used for prediction by the prediction unit 12b is not limited to the past measurement data stored in each DB. For example, prediction values of measurement data at the time of deterioration prediction can be used based on past measurement data. For example, when the prediction unit 12b predicts the degree of deterioration in "December 2023", prediction data based on the measurement data in "December 2022" one year ago can be generated and used for the simulation.

[0047] Note that the above-described prediction data can have its numerical value corrected by an external input. For example, when it is scheduled that a specific event will be held during the deterioration prediction period and the traffic volume is expected to double, the prediction unit 12b can change the coefficient of the data on the number of vehicle passages and perform the simulation with data closer to reality.

[0048] Return to the description of FIG. 5. The display control unit 12c compares the degree of deterioration in the time series in the first simulation with the degree of deterioration in the time series in the second simulation, and when the result of the comparison satisfies a predetermined condition, an image indicating the occurrence of deterioration of the structure or facility is displayed on the screen of the terminal possessed by the operator. For example, the display control unit 12c uses the graph of FIG. 9 showing the degree of deterioration of the facility to compare the deterioration prediction timing between the first simulation and the second or third simulation. Then, the display control unit 52c displays, on the work terminal 20, an object whose difference in deterioration prediction timing is determined to be equal to or more than a predetermined period (for example, four months or more) as an object whose deterioration timing has changed significantly. Further, the display control unit 12c highlights a part where deterioration is predicted among the images on the digital twin displayed on the work terminal 20.

[0049] Here, the screen displayed on the work terminal 20 by the display control unit 12c will be described. FIG. 10 is a diagram showing an output example of the screen by the deterioration prediction device according to the first embodiment. In FIG. 10, a digital twin image reproducing a highway tollgate is shown, and the "start control device A", "vehicle detector A", and "vehicle detector B", which are facilities in the image, are highlighted as facilities for which deterioration is predicted. Specifically, the display control unit 12c highlights by coloring the location of the facility in the image where deterioration is predicted. It is assumed that the hatched portions in FIG. 10 are each colored.

[0050] In addition, the display control unit 12c can display the predicted deterioration time predicted by the prediction unit 12b together with the equipment name for the equipment whose deterioration is predicted. In the example of FIG. 10, the display control unit 12c displays the predicted deterioration time of the first simulation as "(1) 2024 / 10", the predicted deterioration time of the second simulation as "(2) 2024 / 1", and the predicted deterioration time of the third simulation as "(3) 2024 / 5" for the "Starting control device A". Also, the display control unit 12c can emphasize and display the deterioration prediction results where the difference in the predicted deterioration time is equal to or longer than a predetermined period by making the numbers thicker. For example, in FIG. 10, the display control unit 12c emphasizes and displays the second simulation result "(2) 2024 / 1" and the third simulation result "(3) 2024 / 5" for the "Transmission control device A" in boldface compared to the first simulation result "(1) 2024 / 10".

[0051] (Flow of processing of the deterioration prediction device 10) FIG. 11 is a flowchart showing an example of the flow of processing executed by the deterioration prediction device 10 according to the first embodiment. Note that each step in the flowchart shown in FIG. 11 can be executed in a different order, and there may be processing that is omitted.

[0052] As shown in FIG. 11, the deterioration prediction device 10 takes in data stored in each DB, such as the service life DB 13a, the traffic-related DB 13b, and the weather-related DB 13c (S101). Then, the prediction unit 12b constructs a digital twin that reproduces the actual environment based on the taken-in data (S102).

[0053] Subsequently, the prediction unit 12b refers to the service life DB 13a and performs a first simulation (S103). Next, the prediction unit 12b refers to the traffic-related DB 13b and performs a second simulation (S104). Then, the prediction unit 12b refers to the weather-related DB 13c and performs a third simulation (S105).

[0054] After that, the prediction unit 12b compares the deterioration prediction times of the respective simulations and determines whether or not the difference between them is equal to or longer than a predetermined period (S106). Subsequently, the display control unit 12c highlights, on the digital twin, the object for which the difference is equal to or longer than the predetermined period (S107), and the deterioration prediction device 10 ends the process.

[0055] (Effect according to Example 1) As described above, the deterioration prediction device 10 constructs a digital twin that reproduces the real world for structures and facilities on the road, performs simulations on the digital twin, and predicts the deterioration time of the object. As a result, the deterioration prediction device 10 can easily provide a highly accurate deterioration prediction time by performing simulations on the digital twin.

[0056] In addition, the deterioration prediction device 10 performs simulations that take into account not only the simulations of the aging deterioration of the object but also the effects of external environments such as traffic and weather, compares the deterioration prediction times output by the respective simulations, and highlights the objects whose deterioration prediction times have changed due to the external environment. As a result, the deterioration prediction device 10 can easily allow the user to grasp the objects whose deterioration prediction times have changed due to the external environment. Further, the deterioration prediction device 10 can allow the user to grasp the factors that have caused the change in the deterioration prediction time from the content of the comparison source simulation.

[0057] [Example 2] (Overall configuration) Next, a repair plan creation device 30 according to Example 2 will be described. FIG. 12 is a diagram for explaining an example of the overall configuration of the system according to Example 2. As shown in FIG. 12, the system according to Example 2 includes a repair plan creation device 30 that creates a repair plan for structures and facilities on the road, and a work terminal 40 operated by a person in charge of creating the repair plan.

[0058] The repair plan creation device 30 is a computer that captures measurement data, constructs a digital twin, performs simulations on the object, and creates a repair plan.

[0059] The work terminal 40 is a terminal operated by a person in charge of creating a repair plan. It is a computer that instructs the repair plan creation device 30 to execute a simulation, receives information on the simulation results, and displays it on the screen.

[0060] (Problems of the prior art) Conventionally, the maintenance and repair of road structures have been carried out regularly, such as regular inspections stipulated by laws and regulations and inspections for preventive maintenance. Also, in view of the state of the road structures, a more effective and efficient plan is required for the maintenance plan.

[0061] However, since maintenance and repair are tasks that require a great deal of labor, cost, and personnel, an improvement in productivity is also required. To achieve the above mission, it is conceivable to improve the efficiency of maintenance and repair operations by pinpointing deterioration information, predicting it, and creating an efficient repair plan without waste.

[0062] Therefore, in the present invention, from the result of simulating the ETC device state with reference to ETC logs, weather data, equipment installation time, etc., it is possible to create an efficient repair plan proposal by generating a pinpoint prediction value of a defect and a list of its causes. Also, by reflecting conditions managed in business operations, such as repair plan budgets and equipment investment available times, it is possible to create a practical repair plan proposal.

[0063] (Processing performed by the repair plan creation device 30 according to Embodiment 2) Here, the processing performed by the repair plan creation device 30 will be described. FIG. 13 is a diagram for explaining the outline of the processing of the repair plan creation device according to Embodiment 2. The repair plan creation device 30 is a structure related to traffic constructed on a road existing in the real world or equipment related to traffic. In the structure or equipment, a simulation using a digital twin is carried out, and based on the result of the carried-out simulation, a plan regarding the maintenance or repair of the structure or equipment is created.

[0064] For example, as shown in FIG. 13, the repair plan creation device 30 reads an information management DB that stores data related to the installation time and deterioration prediction of the object, a traffic-related DB that stores data related to traffic volume, and a weather-related DB that stores data related to weather, and constructs digital twin data that reproduces the actual environment. Then, in the digital twin environment, the repair plan creation device 30 performs a first simulation to simulate the defect prediction and cause of the object, and generates a defect prediction and cause list "facility, time, cause, repair budget, repair period", thereby visualizing the deterioration status of the object.

[0065] After that, the repair plan creation device 30 uses the defect prediction and cause list "facility, time, cause, repair budget, repair period" stored in the simulation result DB and the business-related DB that stores information such as the estimate and content of the repair work input by the operator to perform a second simulation to simulate the repair plan for the object and create a repair plan. Then, the repair plan creation device 30 stores the repair results when the adopted repair plan is executed in the DB and uses it for subsequent simulations.

[0066] As a result, the repair plan creation device 30 can easily provide a highly accurate repair plan by performing simulations on structures and facilities on the road in a digital twin environment. If adjustment is required among the persons in charge, the repair plan can be appropriately modified.

[0067] (Functional Configuration of Repair Plan Creation Device 30) Next, the functional configuration of the repair plan creation device 30 will be described. FIG. 14 is a functional block diagram showing the functional configuration of the repair plan creation device according to the second embodiment. As shown in FIG. 14, the repair plan creation device 30 includes a communication unit 31, a control unit 32, and a storage unit 33. The communication unit 31 is a processing unit that controls communication with other devices and is realized, for example, by a communication interface or the like. For example, the communication unit 31 receives an instruction to execute simulation from the work terminal 40 or mediates the process of displaying the repair plan proposal created by the creation unit 32c described later on the work terminal 40.

[0068] The storage unit 33 is a processing unit that stores various data and programs executed by the control unit 32 and is realized, for example, by a memory or a hard disk. The storage unit 33 stores, for example, an information management DB 33a, a traffic-related DB 33b, a weather-related DB 33c, a simulation result DB 33d, and a business-related DB 33e. Note that the information stored in each of the traffic-related DB 33b and the weather-related DB 33c is the same as the information stored in each of the traffic-related DB 13b and the weather-related DB 13c described above, so the description thereof is omitted.

[0069] The information management DB 33a stores management information of each structure and facility on the road for each of the structures and facilities themselves. FIG. 15 is a diagram for explaining the information management DB. As shown in FIG. 15, the information management DB 33a stores items such as "equipment name", "equipment installation time", "equipment service life", "failure time", and "repair record".

[0070] Here, the "equipment name" stores the names of structures and facilities on the road that can be objects. The "equipment installation time" stores the time when the object was installed. The "equipment service life" stores specific numerical values of the equipment service life of the object. The "failure time" stores the past failure times of the object. The "repair record" stores the past repair times performed on the object.

[0071] In the example of FIG. 15, for "Equipment Name: Transmission Control Device A", it was installed on "2013 / 4 / 1", and "(1000 units / day) × 5 years" is set as the "Equipment Service Life". It is shown that this "Equipment Name: Transmission Control Device A" failed on "2018 / 9 / 15" and was repaired on "2018 / 10 / 1".

[0072] The simulation result DB 33d stores the data of the results of the first simulation and the data of the results of the second simulation implemented by the prediction unit 32b described later. FIG. 16 is a diagram for explaining the simulation result DB. For example, the simulation result DB 33d stores, as the result of the first simulation, a defect prediction and cause list (corresponding to FIG. 16(A)), and stores, as the result of the second simulation, a repair plan proposal (corresponding to FIG. 16(B)).

[0073] As shown in FIG. 16(A), the simulation result DB 33d stores, as a defect prediction and cause list, items such as "Equipment Name", "Defect Occurrence Time", "Defect Cause", "Repair Budget", and "Repair Period". Here, the "Defect Occurrence Time" stores the time when a defect is predicted to occur for the object. The "Defect Cause" stores the cause of the defect. The "Repair Budget" stores the budget required for repairing the object. The "Repair Period" stores the period required for repairing the object.

[0074] In the example of FIG. 16(A), for "Equipment Name: Vehicle Detector A", it is set that a defect occurred in "2024 / 3" and the cause of the defect is "Component deterioration due to service life". And it is shown that a budget of "￥2000000" is required for repairing "Equipment Name: Vehicle Detector A" and it takes a period of "20 days".

[0075] As shown in FIG. 16(B), the simulation result DB 33d stores, as a repair plan proposal, items such as "repair area", "defect prediction and cause list", "business-related DB conditions", "repair details", "construction period", and "cost". Here, the "repair area" stores the target area of the repair plan. The "defect prediction and cause list" stores the model number of the defect prediction and cause list used in creating the repair plan. The "business-related DB cases" store the business conditions used in creating the repair plan. The "repair details" store the equipment names of each repair target existing in the repair area, the names of the contractors performing the repair, the work summary, etc. The "construction period" stores the construction period required for the equipment of each repair target. The "cost" stores the construction cost required for the equipment of each repair target.

[0076] In the example of FIG. 16(B), for "repair area: No. 1 loop line", it shows that a repair plan proposal was created under the conditions where the model number "XXX-X-0000" defect prediction and cause list was used and the business requirements were set as "traffic control; (Four Bridges) lane control, repair plan budget: ¥20,000,000,...". And as an example of the content of the repair plan proposal, it is shown that the construction period for the repair of the "vehicle detector" is "20 days" and the repair cost is "¥2,000,000".

[0077] The business-related DB 33e stores data related to the conditioning of business contents when creating a repair plan. FIG. 17 is a diagram for explaining the business-related DB. As shown in FIG. 17, the business-related DB 33e stores items such as "major classification", "medium classification", "data", "related keywords", and "priority conditions". Here, the "major classification" stores the general classification contents of business requirements such as work estimates and work contents. The "medium classification" stores the classification contents that are more detailed than the major classification such as cost and period. The "data" stores information such as specific numerical values corresponding to the contents of the medium classification. The "related keywords" store the keywords related to the contents of the medium classification. The "priority conditions" store the data weighted in creating the repair plan.

[0078] In the example of FIG. 17, the business requirements referred to for creating the repair plan proposal are set so that they can be listed in a list format, and for each pattern, a selection of whether to prioritize each business requirement is shown. For example, in the priority conditions of "Pattern 1", it is shown that assuming a repair budget of "1 million yen" for the "cost" of the "work estimate" is set as a priority condition.

[0079] Return to the description of FIG. 14. The control unit 32 is a processing unit that controls the entire repair plan creation device 30 and is realized by, for example, a processor or the like. This control unit 32 includes an acquisition unit 32a, a prediction unit 32b, and a creation unit 32c. Note that the acquisition unit 32a, the prediction unit 32b, and the creation unit 32c are realized by an electronic circuit included in the processor, a process executed by the processor, or the like.

[0080] The acquisition unit 32a acquires information used when performing defect prediction and cause simulation using a digital twin for structures or facilities constructed on roads existing in the real world. For example, the acquisition unit 32a acquires, for each object, management information of the object itself (see FIG. 15), vehicle passing records of the road where the structure or facility is installed (see FIG. 7), and weather information of the area where the structure or facility is located (see FIG. 8), and stores them in each DB of the storage unit 33.

[0081] In addition, the acquisition unit 32a can acquire, from the input of the worker, the work performance of the repair plan proposal created by the repair plan creation device 30 and actually adopted by the worker, and store it in the information management DB 33a.

[0082] That is, the acquisition unit 32a acquires information such as the time when a failure actually occurred, the time when repair was performed, the work content of the repair, and supplementary information for the object on which repair was performed, from the input of the worker who performed the repair work. Thereby, the repair plan creation device 30 can feedback the content of the actually performed repair result and improve the simulation accuracy in subsequent cycles.

[0083] The prediction unit 32b refers to the information stored in each DB of the storage unit 33, reads the data onto the digital twin, executes the first simulation, generates information regarding the defects and / or causes of the structure or facility based on the results of the first simulation, and stores the generated information regarding the defects and / or causes in the first storage unit.

[0084] For example, the prediction unit 32b refers to any one or more of the data stored in the aforementioned information management DB 33a, the traffic-related DB 33b, and the weather-related DB 33c, and performs a simulation (the first simulation) on the digital twin to predict the defects and causes of the object. Then, the prediction unit 32b generates the results of the first simulation as a defect prediction and cause list (see FIG. 16(A)) and stores them in the simulation result DB 33d.

[0085] The creation unit 32c creates a plan for the maintenance or repair of the structure or facility based on the results of the executed first simulation. Specifically, the creation unit 32c uses the information regarding the defects and / or causes stored in the first storage unit as input, and executes a second simulation using the digital twin according to the conditions registered in the second storage unit to create a plan for the maintenance or repair of the structure or facility.

[0086] For example, the creation unit 32c takes the defect prediction and cause list stored in the simulation result DB 33d as input, and under the conditions stored in the business-related DB 33e, performs a simulation (second simulation) to create a repair plan on the digital twin. Then, the creation unit 32c creates a repair plan under the conditions where items to be prioritized and the like are set based on the results of the second simulation. Note that the creation unit 32c stores the created repair plan in the simulation result DB 33d. Also, an operator or the like can use the created repair plan as a draft to modify the content of the repair plan and can actually execute the modified repair plan. In this case, the repair plan creation device 30 can store the modified repair plan separately as an actually executed repair plan.

[0087] Here, the process of creating a repair plan under the conditions where the above-described items to be prioritized are set will be described. For example, the creation unit 32c refers to the items of the priority conditions in the above-described business-related DB, determines the business requirements to be prioritized, and creates a repair plan based on the business requirements to be prioritized as a prerequisite.

[0088] For example, in FIG. 17, it is shown that marks are attached to "priority conditions (pattern 1)" for "cost; 1 million yen", "period; 10 days", "work type; bridge (slab replacement)", "traffic volume; 100 vehicles / hour", and "regulation; lane regulation". When the creation unit 32c is instructed to execute the simulation under "priority conditions (pattern 1)", for the objects in the defect prediction and cause list created by the first simulation, a repair plan (pattern 1) is created considering the conditions of "priority conditions (pattern 1)" with priority.

[0089] On the one hand, in Fig. 17, it shows that marks are attached to "Priority Conditions (Pattern 2)" for "Cost; 100,000 yen", "Period; 30 days", "Work type; Bridge (slab replacement)", "Traffic volume; 50 vehicles / hour", and "Regulation; Lane regulation". When the creation unit 32c is instructed to execute the simulation under "Priority Conditions (Pattern 2)", for the objects in the defect prediction and cause list created by the first simulation, it creates a repair plan proposal (Pattern 2) when considering the conditions of "Priority Conditions (Pattern 2)" with priority.

[0090] That is, the creation unit 32c can create a repair plan proposal corresponding to an arbitrarily set priority condition pattern for the objects in the defect prediction and cause list created by the first simulation.

[0091] Subsequently, the repair plan proposal created by the creation unit 32c will be specifically described. Fig. 18 is a diagram showing a specific example of the repair plan proposal created by the repair plan creation device according to the second embodiment. In Fig. 16(B) described above, an example of the repair plan proposal was shown, but the description will be made with reference to Fig. 18 which shows a more specific example.

[0092] Fig. 18 exemplifies the repair plan proposal for the "No. 1 Ring Road" in "2024" created by the creation unit 32c. As an example, the repair plan proposal includes "Input information (defect prediction and cause list, business-related DB requirements)", "Number", "Toll gate name", "Facility / Target", "Contractor", "Work summary", "Construction period", "Repair cost", etc.

[0093] Here, the "input information" refers to the defect prediction and cause list inputted in the creation of the repair plan proposal and the information on business-related DB requirements. The "number" describes the number assigned to identify the facility to be repaired, the "toll gate name" describes the name of the toll gate where the object is installed. The "contractor" describes the name of the contractor responsible for repairing the object, and the "work summary" describes the specific content of the repair work. The "construction period" describes the period for implementing the repair of the object, and the "repair cost" describes the cost required for repairing the object.

[0094] For example, the creation unit 32c indicates in the repair plan proposal, as input information, the model number "XXX-X-0000" of the defect prediction and cause list used in the simulation. Also, the creation unit 32c indicates, as the business requirements showing the conditional requirements of the business content when creating the repair plan, the priority conditions used in the simulation, such as "traffic control; (Four Bridges) lane control, repair plan budget: ¥20000000,...".

[0095] Furthermore, the creation unit 32c indicates in the repair plan proposal, for each facility to be repaired, the "number", "toll gate name", "facility / object", "contractor", "work summary", "construction period", and "repair cost". More specifically, for the object with "number: 1", the creation unit 32c indicates "toll gate name: Four Bridges", "facility / object: vehicle detector", "contractor: Company A", "work summary: replace deteriorated parts", "construction period: 2024 / 4 / 1 to 2024 / 4 / 20", and "repair cost: ¥2000000".

[0096] Also, the creation unit 32c indicates the above-mentioned information for each object, and shows the total repair cost and supplementary information about the created repair plan proposal. More specifically, the creation unit 32c indicates in the remarks column the information "2024 / 4 / 1 to 2024 / 4 / 20: Plan traffic control at the Four Bridges toll gate" and "within the range of the budget of ¥20000000, adoptable".

[0097] As described above, the creation unit 32c creates a repair plan proposal as shown in FIG. 18 and presents it to the person in charge of creating the repair plan, so that for the repair of the object described in the defect prediction and cause list, a highly accurate repair plan proposal showing detailed information such as the budget and period required for the repair can be grasped.

[0098] (Flow of processing of the repair plan creation device 30) FIG. 19 is a flowchart showing an example of the flow of processing executed by the repair plan creation device 30 according to the second embodiment. Note that each step in the flowchart shown in FIG. 19 can also be executed in a different order, and there may be processes that are omitted.

[0099] As shown in FIG. 19, the repair plan creation device 30 fetches the data stored in each DB, such as the information management DB 33a, the traffic-related DB 33b, and the weather-related DB 33c (S301). Then, the prediction unit 32b constructs a digital twin that reproduces the actual environment based on the fetched data (S302).

[0100] Subsequently, the prediction unit 32b refers to each DB fetched in S301 and performs a first simulation to create a defect prediction and cause list (S303). Next, the creation unit 32c fetches the data of the created defect prediction and cause list (S304). Then, the creation unit 32c performs a second simulation under the conditions registered in the business-related DB 33e to create a repair plan proposal (S305), and the repair plan creation device 30 ends the process.

[0101] (Effect according to the second embodiment) As described above, the repair plan creation device 30 is a traffic-related structure or facility constructed on a road existing in the real world. In the structure or facility, a simulation using a digital twin is performed, and based on the results of the performed simulation, a plan regarding the maintenance or repair of the structure or facility is created. As a result, the repair plan creation device 30 can easily provide a highly accurate repair plan proposal by performing a simulation on the digital twin.

[0102] Further, the repair plan creation device 30 performs a simulation by taking as inputs the defect prediction and cause output by a simulation taking into account the influence of external environments such as traffic and weather, and the priority conditions set in creating the repair plan, and creates a plan for the maintenance or repair of the structure or facility. As a result, the repair plan creation device 30 can create an efficient repair plan based on the equipment state in order to create a repair plan from a highly accurate prediction of the occurrence of defects by performing a simulation on the digital twin. Also, the repair plan creation device 30 can reflect in the repair plan the preconditions to be considered in performing the repair, such as the equipment investment available time and traffic regulations, not only the equipment state.

[0103] 〔Example 3〕 (Overall configuration) Next, the education support device 50 according to Example 3 will be described. FIG. 20 is a diagram for explaining an example of the overall configuration of the system according to Example 3. As shown in FIG. 20, the system according to Example 3 includes an education support device 50 that creates a repair plan for structures and facilities on a road, and a work terminal 60 operated by a repair planner or a repair worker.

[0104] The education support device 50 is a computer that, after capturing measurement data and constructing a digital twin, performs a simulation on the object and displays teacher information such as the content of the repair work to be performed and the items to be confirmed during the work.

[0105] The work terminal 60 is a terminal operated by a repair planner or a repair worker (hereinafter also simply referred to as a worker), and is a computer that instructs the educational support device 50 to execute a simulation, receives information on the simulation result, and displays it on the screen.

[0106] (Problems of the prior art) Conventionally, the maintenance and repair of road structures have been carried out regularly, such as regular inspections stipulated by laws and regulations and inspections for preventive maintenance. When conducting inspections, skilled workers rely on experience and intuition to carry out operations such as determining the state of cracks in roads and structures and impact sound inspections. Also, in view of the state of road structures, a more effective and efficient maintenance plan is required.

[0107] However, in recent years, the shortage of labor due to the progress of the declining birthrate and aging population has become apparent, and the number of personnel involved in maintenance and repair is insufficient. Also, since maintenance and repair are labor-intensive, costly, and require a large number of personnel, an improvement in productivity is also required. To achieve the above mission, it is conceivable to share the experience and intuition of skilled workers among workers and planners in an effective and efficient manner.

[0108] Therefore, in the present invention, it is possible to simulate the maintenance and repair work performed by workers on a digital twin that reproduces the actual road conditions. Also, on the digital twin, information on the maintenance and repair work of skilled workers (inspection viewpoints, implementation times, etc.) can be incorporated and referred to as teaching materials. Furthermore, the results and achievements of the simulation are accumulated to assist in formulating a more refined maintenance plan.

[0109] (Processing performed by the educational support device 50 according to Embodiment 3) Here, the processing performed by the education support device 50 will be described. FIG. 21 is a diagram for explaining the outline of the processing of the education support device according to the third embodiment. The education support device 50 is a traffic-related structure or facility constructed on a road existing in the real world. In the structure or facility, a simulation using a digital twin is carried out, and based on the result of the carried-out simulation, as information for assisting the work of the operator, the work content of a skilled person for the structure or facility is output.

[0110] For example, as shown in FIG. 21, the education support device 50 reads a structure-related DB that stores point cloud data regarding the installation position and structure of an object, a traffic-related DB that stores data regarding traffic volume, and a monitoring operation DB that stores data regarding the state (abnormal or normal) of the object, and constructs a simulation environment with a digital twin that reproduces the actual environment. Then, after receiving the registration of the work scheduled to be carried out from the operator, the education support device 50 performs a simulation of the repair work in the digital twin environment and displays the work information of a skilled person in the registered work.

[0111] As shown in FIG. 21, as the work information of a skilled person, when the repair work of the start control device is registered, the education support device 50 displays on the work terminal that the skilled person performs the work of confirming that the related device is stopped in the pre-work confirmation of the actual work.

[0112] Thereby, the education support device 50 can carry out a simulation in the digital twin environment for the structures and facilities on the road and easily provide a work example of a highly accurate repair work.

[0113] (Functional Configuration of Education Support Device 50) Next, the functional configuration of the education support device 50 will be described. FIG. 22 is a functional block diagram showing the functional configuration of the education support device according to the third embodiment. As shown in FIG. 22, the education support device 50 includes a communication unit 51, a control unit 52, and a storage unit 53. The communication unit 51 is a processing unit that controls communication with other devices, and is realized by, for example, a communication interface or the like. For example, the communication unit 51 receives an instruction to execute a simulation from the work terminal 60, or mediates the process of displaying the work content of the skilled worker displayed by the display control unit 52c described later on the work terminal 60.

[0114] The storage unit 53 is a processing unit that stores various data, programs executed by the control unit 52, and the like, and is realized by, for example, a memory or a hard disk. The storage unit 53 stores, for example, a structure-related DB 53a, a traffic-related DB 53b, a monitoring operation DB 53c, a skilled worker operation DB 53d, and a work record DB 53e. Note that since the information stored in the traffic-related DB 53b is the same as the information stored in the traffic-related DB 13b described above, the description thereof will be omitted.

[0115] The structure-related DB 53a stores point cloud data indicating information such as the structure of each structure and facility on the road and the location where the structure and facility are installed for each structure and facility on the road. The point cloud data of each structure and facility is data having three-dimensional coordinate values, color information, etc., acquired by using a measuring device such as a 3D laser scanner.

[0116] The monitoring operation DB 53c stores information acquired by the monitoring operation system for each structure and facility on the road. For example, the monitoring operation DB 53c determines whether the shape and operation of the structure and facility to be monitored are normal or abnormal, and stores the determined information.

[0117] The skilled worker operation DB 53d stores the content of the skilled worker's repair work, which is displayed by the display control unit 52c described later. FIG. 23 is a diagram for explaining the skilled worker operation DB. As shown in FIG. 23, the skilled worker operation DB 53d stores items such as "operation ID (major classification)", "operation ID (minor classification)", "implemented operation", "operation date and time", "points for attention", and "operation video".

[0118] Here, the "operation ID (major classification)" stores the ID for roughly classifying the content of the repair work, and the "operation ID (minor classification)" stores the ID for classifying each operation content within the "operation ID (major classification)". The "implemented operation" stores the specific operation content of the repair work, and the "operation date and time" stores the date and time when the repair work was implemented. The "points for attention" stores the content that should be noted during the repair work, and the "operation video" stores the data of the operation video while the repair work is being carried out.

[0119] In the example of FIG. 23, the content of the repair work by a skilled worker for "operation ID (major classification): W001 (repair work of ●● equipment)" is set. For example, for the operation with "operation ID (minor classification)" being "S002", it is shown that the content of the implemented operation is "AA confirmation", the operation date and time is "2023 / 4 / 1 12:10:00", the point for attention during the operation is to confirm that "●● equipment is stopped", and the operation video is recorded in the data of "XXXXX.mp4".

[0120] The work record DB 53e stores data such as the content of the repair work registered by the worker, the simulation results, and the work performance of the actually carried out repair work. FIG. 24 is a diagram for explaining the work record DB. As shown in FIG. 24, the work record DB 53e stores items such as "operation ID (major classification)", "operation ID (minor classification)", "implemented operation", "simulation record", "work performance", and "points for attention".

[0121] Here, the "Work ID (major category)", "Work ID (minor category)", "Operation to be performed", and "Precautions" store data similar to the data stored in the aforementioned skilled worker business DB53d, so the description thereof is omitted. The "Simulation Record" stores the time (time taken for the work) when each work is started when a simulation of the work registered on the digital twin is performed. The "Work Performance" stores the time (time taken for the work) when each work is started when the registered work content is actually performed.

[0122] In the example of FIG. 24, the simulation results and work performance content for the "Work ID (major category): W001 (●● equipment repair work)" registered by the worker are set. For example, for the work with the "Work ID (minor category)" of "S002", the content of the operation to be performed is "AA confirmation", and the simulation result shows that the work starts at "2023 / 4 / 1 12:05:00". At the same time, it is shown that as a precaution for the "AA confirmation" work, it is necessary to confirm that "●● equipment is stopped". Then, when the worker who has confirmed the simulation results and work content actually performs the work for "Work ID (major category): W001 (●● equipment repair work)", it is shown that the work of "S002" started at "2023 / 4 / 1 12:08:00".

[0123] Returning to the description of FIG. 22. The control unit 52 is a processing unit that controls the entire education support device 50 and is realized by, for example, a processor or the like. This control unit 52 includes an acquisition unit 52a, a prediction unit 52b, and a display control unit 52c. The acquisition unit 52a, the prediction unit 52b, and the display control unit 52c are realized by an electronic circuit included in the processor, a process executed by the processor, or the like.

[0124] The acquisition unit 52a acquires information used when performing a simulation of repair work using a digital twin for structures or facilities constructed on roads existing in the real world. For example, for each object, the acquisition unit 52a acquires point cloud data of the object, the vehicle passing history of the road where the structure or facility is installed (see Fig. 7), and the monitoring data collected by the monitoring operation system that determines whether the object is normal or abnormal, and stores them in each DB of the storage unit 53.

[0125] In addition, the acquisition unit 52a can acquire the work content of the repair work performed by a skilled worker by importing a work report, and store the information described in the work report in the skilled worker operation DB 53d. Similarly, the acquisition unit 52a can acquire the content of the repair work performed by a worker by importing a work report, and store the information described in the work report in the work record DB 53e.

[0126] That is, since the acquisition unit 52a can acquire the work achievements of skilled workers or workers from work reports and store them in each DB, the education support device 50 can feed back the work achievements to improve the simulation accuracy in subsequent cycles.

[0127] The prediction unit 52b refers to the information stored in each DB of the storage unit 53, constructs a digital twin that reproduces the structures or facilities arranged on the roads existing in the real world in the virtual space, and in the constructed digital twin, uses an agent corresponding to the worker to perform a simulation of work related to maintenance or repair on the structures or facilities in the virtual space that reproduces the real world.

[0128] For example, the prediction unit 52b constructs a digital twin by referring to any one or more of the data stored in the above-described structure-related DB 53a, traffic-related DB 53b, and monitoring operation DB 53c. Then, on the constructed digital twin, the prediction unit 52b uses an agent that reproduces the work proficiency of the operator who instructed the simulation to simulate the work time and work content of the maintenance or repair work of the object. Then, the prediction unit 52b stores the simulation results of the work time and work content in the work record DB 53e for each work ID.

[0129] Based on the results of the implemented simulation, the display control unit 52c outputs the work content of a skilled person for the structure or facility as information to support the work of the operator. Specifically, the display control unit 52c generates operator information associated with the simulation results for each work type based on the implemented simulation, refers to the skilled person information associated with the work content of the skilled person for the structure or facility for each work type, uses the first work type selected by the operator, specifies the work content of the skilled person associated with the first work type, and displays the specified work content of the skilled person on the screen of the terminal possessed by the operator.

[0130] For example, the display control unit 52c refers to the information stored in the work record DB 53e and associates the work content of the skilled person stored in the skilled person operation DB 53d for each work ID. Then, when the operator selects an arbitrary work ID (first work type), the display control unit 52c displays the work content of the skilled person for the selected work ID on the work terminal 60.

[0131] Here, the screen displayed on the work terminal 60 by the display control unit 52c will be described. FIG. 25 is a diagram showing a specific example of the screen displayed by the education support device according to the third embodiment. FIG. 25 shows an example in which a digital twin environment of a tollgate existing in the real world is constructed and the start control device is registered as the facility to be repaired by the operator.

[0132] For example, when the display control unit 52c receives a work ID (major category) corresponding to the work name "Starting control device repair work" from an operator, it displays the items "Work name" and "Work performed" on the image of the digital twin of the tollgate. Here, the "Work name" indicates the content of the repair work registered by the operator. The "Work performed" indicates the work content performed by a skilled worker in the registered repair work. As shown in FIG. 25, the display control unit 52c displays "Pre-check, AA check, BB check, ···", which are the work contents performed by a skilled worker for the "Starting control device repair work".

[0133] In addition, when the working time indicated by the simulation result in the first work type exceeds a preset time, the display control unit 52c causes the work content of the skilled worker associated with the first work type to be displayed on the screen of the terminal possessed by the operator.

[0134] For example, the display control unit 52c refers to the simulation records stored in the work record DB 53e, and when the working time for each of the registered work IDs (minor categories) is 1.5 times or more (preset time) the working time of the skilled worker, it causes the work content of the skilled worker to be displayed on the work terminal 60.

[0135] Here, a description will be given of the screen displayed on the work terminal 60 when the working time indicated by the simulation result exceeds the preset time. FIG. 26 is a diagram showing a specific example of the screen displayed by the education support device according to the third embodiment. Similar to FIG. 25, FIG. 26 shows an example in which a digital twin environment of a tollgate existing in the real world is constructed and the starting control device is registered as the facility to be repaired by the operator.

[0136] For example, the display control unit 52c refers to the "simulation record" of the "Starting control device repair work" stored in the work record DB 53e, and calculates the working time taken by the operator for each process of the "Starting control device repair work". Similarly, the display control unit 52c refers to the "work date and time" stored in the skilled worker business DB 53d, and calculates the time taken by the skilled worker for each process of the "Starting control device repair work".

[0137] Then, for each process, when the time taken by the operator for the work is 1.5 times or more the time taken by a skilled worker for the work, the display control unit 52c displays, on the digital twin screen, the work content of the skilled worker, the planned work time predicted to be taken by the worker to perform by simulation, the skilled worker work time taken when the skilled worker performed the work, and information on the points to note for the work content.

[0138] As shown in FIG. 26, in addition to the "work name" and "work performed" shown in FIG. 25, the display control unit 52c displays items of "planned work time" and "skilled worker work time", and "points to note during work". Here, the "planned work time" indicates the work time predicted to be taken when the worker performs the work corresponding by simulation. The "skilled worker work time" indicates the work time taken when the skilled worker performed the corresponding work. The "points to note during work" indicates the information on the "points to note" stored in the skilled worker operation DB 53d for the corresponding work.

[0139] In the example of FIG. 26, the predicted result that the time taken by the worker for the "AA confirmation" process of the "starting control device repair work" is "8 minutes" is shown as the "planned work time". Similarly, the fact that the time taken by the skilled worker for the "AA confirmation" process is "5 minutes" is shown as the "skilled worker work time". That is, for the registered "AA confirmation" process, since the work time of the worker is 1.5 times or more the work time of the skilled worker, the work content of the skilled worker and the comment "confirm that the related device (AA device) is stopped", which is the point to note for the "AA confirmation" process, are shown.

[0140] (Flow of processing of the education support device 50) FIG. 27 is a flowchart showing an example of the flow of processing executed by the education support device 50 according to the third embodiment. Note that each step in the flowchart shown in FIG. 27 can also be executed in a different order, and there may be processing that is omitted.

[0141] As shown in FIG. 27, the education support device 50 takes in data stored in each database, such as the structure-related database 53a, the traffic-related database 53b, and the monitoring operation database 53c (S501). Then, the prediction unit 52b constructs a digital twin that reproduces the actual environment based on the taken-in data (S502).

[0142] Subsequently, the education support device 50 receives registration of the work that the worker plans to perform (S503). Next, the prediction unit 52b uses an agent that reproduces the proficiency of the worker's repair work to perform a simulation of the registered work and calculates the scheduled execution time of the work (S504). Then, the display control unit 52c displays the calculated scheduled execution time and the work information of a skilled worker in the registered work on the digital twin (S505), and the education support device 50 ends the process.

[0143] (Effect according to Embodiment 3) As described above, the education support device 50 is a structure or facility related to traffic constructed on a road existing in the real world. In the structure or facility, a simulation using a digital twin is performed, and based on the result of the performed simulation, as information for supporting the work of the worker, the work content of a skilled worker for the structure or facility is output. As a result, by performing a simulation on the digital twin, the education support device 50 can easily provide data that reflects the experience and intuition of a skilled worker in the data that reproduces the actual working environment as teacher data for repair work.

[0144] In addition, in the digital twin, the education support device 50 uses an agent that reproduces the proficiency of the worker's repair work to perform a simulation of the repair work, and displays the simulation result of the repair work registered by the worker and the work content of a skilled worker on the terminal possessed by the worker. Thereby, since the education support device 50 performs a simulation of the actual repair work using an agent that reproduces the proficiency of the worker's repair work, it can accurately predict the scheduled work time, which is the time required for the worker to perform the repair work, and display the simulation result.

[0145] Further, when the work scheduled time calculated by the simulation exceeds a preset time, the education support device 50 causes the work content of a skilled worker corresponding to the repair work to be displayed on the terminal possessed by the worker. Thereby, when the work scheduled time exceeds the preset time, the education support device 50 determines that the proficiency of the worker for the registered repair work is insufficient, and in order to display the work content of the skilled worker, the worker can be made to understand the work content of the skilled worker for the work with insufficient proficiency.

[0146] [Example 4] Now, although the embodiments of the present invention have been described respectively so far, the present invention may be implemented in various different forms other than the above-described embodiments.

[0147] (Numerical values, etc.) The DB examples, numerical examples, number of processes, process names, names of requests and commands, etc. used in the above embodiments are merely examples and can be arbitrarily changed. Also, the flow of the processing described in each flowchart can be appropriately changed within a non-contradictory range. For example, in the case of Example 1, the execution targets of the plurality of simulations described in each example, such as executing the first simulation and the third simulation, can be arbitrarily combined.

[0148] (Control target) In the above embodiments, the deterioration prediction device 10, the repair plan creation device 30, and the education support device 50 have been described as examples, but the present invention is not limited thereto. For example, various devices (agent applications) that execute commands and processes can be targeted according to instructions from an external device (master).

[0149] (System) Regarding the processing procedures, control procedures, specific names, and information including various data and parameters shown in the above documents and drawings, they can be arbitrarily changed unless otherwise specified.

[0150] In addition, each component of each of the illustrated devices is functionally conceptual and does not necessarily have to be physically configured as shown in the figures. That is, the specific forms of distribution and integration of each device are not limited to those shown in the figures. In other words, all or part of it can be functionally or physically distributed and integrated in any unit according to various loads, usage situations, etc.

[0151] Furthermore, each processing function performed by each device can be realized in whole or in any part by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware by wired logic.

[0152] (Hardware) Figure 28 is a diagram for explaining a hardware configuration example. Since the deterioration prediction device 10, the repair plan creation device 30, and the education support device 50 each have the same configuration, they will be described here as the information processing device 100. As shown in Figure 28, the information processing device 100 includes a communication device 100a, an HDD (Hard Disk Drive) 100b, a memory 100c, and a processor 100d. Also, each part shown in Figure 28 is interconnected by a bus or the like.

[0153] The communication device 100a is a network interface card or the like and communicates with other devices. The HDD 100b stores programs and databases that operate the functions respectively shown in Figures 5, 14, and 22.

[0154] The processor 100d reads a program that executes the same processing as each processing unit shown in FIGS. 5, 14, and 22 from the HDD 100b or the like and expands it in the memory 100c, thereby operating a process that executes each function described in FIGS. 5, 14, 22, etc. For example, this process executes the same functions as each processing unit included in the deterioration prediction device 10, the repair plan creation device 30, and the education support device 50. Specifically, taking the deterioration prediction device 10 as an example, the processor 100d reads a program having the same functions as the acquisition unit 12a, the prediction unit 12b, the display control unit 12c, etc. from the HDD 100b or the like. Then, the processor 100d executes a process that executes the same processing as the acquisition unit 12a, the prediction unit 12b, the display control unit 12c, etc.

[0155] In this way, the information processing apparatus 100 operates as an information processing apparatus that executes an information processing method by reading and executing a program. Further, the information processing apparatus 100 can also read the program from a recording medium by a medium reading device and realize the same functions as the above-described embodiments by executing the read program. Note that the program referred to in this other embodiment is not limited to being executed by the information processing apparatus 100. For example, the above embodiments may be similarly applied when another computer or server executes the program, or when these cooperate to execute the program.

[0156] This program may be distributed via a network such as the Internet. Further, this program may be recorded on a computer-readable recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, a MO (Magneto-Optical disk), a DVD (Digital Versatile Disc), etc., and may be executed by being read from the recording medium by a computer.

Explanation of Reference Numerals

[0157] 10 Deterioration prediction device 11 Communication unit 12 Control Unit 12a Acquisition Unit 12b Prediction Unit 12c Display Control Unit 13 Memory Unit 13a Useful Life Database 13b Transportation-Related Database 13c Weather-Related Database 20 Work Terminal 30 Repair Plan Creation Device 31 Communication Unit 32 Control Unit 32a Acquisition Unit 32b Prediction Unit 32c Creation Unit 33 Memory Unit 33a Information Management Database 33b Transportation-Related Database 33c Weather-Related Database 33d Simulation Result Database 33e Business-Related Database 40 Work Terminal 50 Education Support Device 51 Communication Unit 52 Control Unit 52a Acquisition Unit 52b Prediction Unit 52c Display Control Unit 53 Memory Unit 53a Structure-Related Database 53b Transportation-Related Database 53c Monitoring Business Database 53d Skilled Worker Business Database 53e Work Record Database 60 Work Terminal

Claims

1. Cause a computer to perform a simulation using a digital twin on a structure or facility related to traffic constructed on a road existing in the real world, and predict the deterioration of the structure or facility based on the simulation performed. An information processing program for executing the process.

2. The process of performing the simulation is to perform the simulation on the structure or facility using information on the aging deterioration of the structure or facility in the structure or facility. The information processing program according to claim 1, characterized in that.

3. The process of performing the simulation is to perform a first simulation using a digital twin to predict the deterioration of the structure or facility using information on the aging deterioration of the structure or facility, and a second simulation using a digital twin to predict the deterioration of the structure or facility using monitoring information including the usage history of the structure or facility, and The process of predicting is to predict the deterioration of the structure or facility based on the result of the first simulation and the result of the second simulation. The information processing program according to claim 1, characterized in that.

4. Cause the computer to compare the degree of deterioration over time in the first simulation with the degree of deterioration over time in the second simulation, and when the result of the comparison satisfies a predetermined condition, display an image indicating the occurrence of deterioration of the structure or facility on the screen of the terminal possessed by the operator. The information processing program according to claim 3, characterized in that the process is executed.

5. Cause the computer to construct a digital twin that reproduces the structure or facility in a virtual space, and in the constructed digital twin, perform a simulation to predict the deterioration of the structure or facility on a virtual space that reproduces the real world using information on the aging deterioration set for the structure or facility, monitoring information including the usage history of the structure or facility, and information on the weather in the area where the structure or facility is located, and display an image in which the part where the deterioration of the structure or facility is predicted is highlighted on the screen of the terminal possessed by the operator based on the result of the simulation performed. The information processing program according to claim 1, characterized by causing the processing to be executed.

6. A computer is a traffic-related structure or traffic-related facility constructed on a road existing in the real world, and in the structure or facility, a simulation using a digital twin is performed, Based on the performed simulation, predict the deterioration of the structure or facility An information processing method for executing the process.

7. On a computer is a traffic-related structure or traffic-related facility constructed on a road existing in the real world, and in the structure or facility, a simulation using a digital twin is performed, Based on the result of the performed simulation, create a plan for the maintenance or repair of the structure or facility An information processing program for causing the process to be executed.

8. The process of performing the simulation Performs a simulation using a digital twin that predicts the deterioration of the structure or facility, The process of creating Using the result of the simulation and external factors, identify the timing of maintenance for the structure or facility, and create a plan for the maintenance or repair of the structure or facility including the identified timing of maintenance The information processing program according to claim 7, characterized by the above.

9. On the computer By reading data on the digital twin, execute the first simulation, Based on the result of the first simulation, generate information on the defects and / or causes of the structure or facility, Store the generated information on defects and / or causes in the first storage unit, Using the information on defects and / or causes stored in the first storage unit as input, execute a second simulation using a digital twin according to the conditions registered in the second storage unit, and create a plan for the maintenance or repair of the structure or facility The information processing program according to claim 7, characterized by causing the process to be executed.

10. On the computer Construct a digital twin that reproduces the structure or facility in a virtual space In the constructed digital twin, using the monitoring information obtained by monitoring the usage status of the structure or facility and the information regarding the weather in the region where the structure or facility is located, a simulation for predicting the deterioration of the structure or facility is performed on a virtual space that reproduces the real world. Based on the results of the performed simulation, identify the malfunctions and / or causes that are predicted to occur in the structure or facility. Using the work content for the identified malfunctions and / or causes, create a plan for maintaining or repairing the structure or facility. The information processing program according to claim 7, characterized in that it causes the above-described processing to be executed.

11. A computer is a structure related to traffic or a facility related to traffic constructed on a road existing in the real world, and in the structure or facility, a simulation using a digital twin is performed. Based on the results of the performed simulation, create a plan regarding the maintenance or repair of the structure or facility. An information processing method for executing the processing.

12. On a computer is a structure related to traffic or a facility related to traffic constructed on a road existing in the real world, and in the structure or facility, a simulation using a digital twin is performed. Based on the results of the performed simulation, output, as information for assisting the work of an operator, the work content of a skilled person with respect to the structure or facility. An information processing program for causing the above-described processing to be executed.

13. On the computer construct a digital twin that reproduces a structure or facility arranged on a road existing in the real world on a virtual space. In the constructed digital twin, using an agent corresponding to the operator, perform a simulation of work related to maintenance or repair on the structure or facility on a virtual space that reproduces the real world. Based on the results of the performed simulation, identify the work content of the structure or facility that a skilled person focuses on, and display the identified work content on the screen of the terminal possessed by the operator. The information processing program according to claim 12, characterized in that it causes the above-described processing to be executed.

14. On the computer in the digital twin, using the agent, perform a simulation of work related to maintenance or repair on the structure or facility. Based on the executed simulation, for each type of work, generate worker information associated with the result of the simulation. For each type of work, refer to the skilled worker information associated with the work content of a skilled worker for the structure or facility, and use the first type of work selected by the worker to identify the work content of the skilled worker associated with the first type of work. Cause the work content of the identified skilled worker to be displayed on the screen of the terminal possessed by the worker. The information processing program according to claim 13, characterized by causing the execution of the process.

15. The process of causing the display is When the working time indicated by the result of the simulation in the first type of work exceeds a preset time, cause the work content of the skilled worker associated with the first type of work to be displayed on the screen of the terminal possessed by the worker. The information processing program according to claim 14, characterized by this.

16. A computer A structure related to traffic or a facility related to traffic constructed on a road existing in the real world. In the structure or facility, a simulation using a digital twin is performed, and based on the result of the executed simulation, as information for assisting the work of a worker, output the work content of a skilled worker for the structure or facility. An information processing method for executing the process.

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