Road disaster response support system and program, road management method

Abstract

In the past, road patrols would visually inspect roads and check the actual road conditions to determine whether to respond to road disasters in the event of a disaster, etc. However, making decisions based solely on visual patrols was time-consuming and labor-intensive, making it difficult to respond quickly. [Solution] A road disaster response support system, program, and road management method comprising: a road clearance unit that registers road clearance plans that have been prepared in advance; and a robotics unit that uses artificial intelligence analysis and robotics technology to have disaster response robots carry out road clearance work, wherein the road clearance plans, including road clearance routes that have been prepared in advance, are registered in the road clearance unit, and the disaster response robots carry out road clearance work using artificial intelligence analysis and robotics technology based on the road clearance plans registered in the road clearance unit, via the robotics unit.

Description

[Technical Field]

[0001] The present invention relates to a road disaster response support system, program, and road management method for restoring and managing roads in the event of a disaster such as an earthquake. [Background technology]

[0002] In the event of a large-scale disaster such as an earthquake, road administrators carry out emergency restoration (opening roads) to allow the passage of emergency vehicles and Self-Defense Force vehicles engaged in disaster response, and then respond to road disasters by carrying out emergency and full restoration work. Emergency vehicles are vehicles defined by the Basic Act on Disaster Management and the Road Traffic Act, etc., such as fire engines, ambulances, disaster recovery vehicles, doctor's cars, road patrol cars, tow trucks, government agency vehicles, vehicles from electric power companies, gas companies, and telephone companies, disaster response vehicles (drainage pump vehicles, lighting vehicles, disaster response headquarters vehicles, standby support vehicles, satellite communication vehicles, information gathering vehicles, disaster response vehicles with drones, etc.), supply transport vehicles, evacuation buses, etc. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2017-117323 Summary of the Invention [Problem to be solved by the invention]

[0004] In the past, roads were visually inspected by patrol or helicopter to see the actual road conditions and to determine whether to carry out road disaster response measures such as emergency restoration and temporary repairs in the event of a disaster such as an earthquake. However, making decisions based solely on visual patrols was time-consuming and labor-intensive, making it difficult to respond quickly. Road disaster response mainly involves grasping the overall situation, issuing a system, formulating basic disaster response guidelines, issuing commands for recovery (emergency recovery, emergency recovery, full recovery), and publicizing and publicizing the situation. Specifically, this includes collecting information immediately after a disaster occurs, ensuring contact and communication immediately after a disaster occurs, establishing an operation system, responding to government headquarters and related ministries and agencies, emergency inspections of roads and facilities immediately after a disaster occurs, securing disaster prevention equipment and materials, securing restoration equipment and materials, implementing emergency restoration work, implementing emergency restoration work, implementing main restoration work, restricting road traffic, ensuring road traffic, taking measures to prevent secondary disasters, emergency restoration of lifeline facilities, providing support to local governments, responding to disaster victims, and responding to voluntary support from volunteers and others. [Means for solving the problem]

[0005] The above problems can be solved by the invention having the following configuration. [1] A road disaster response support system comprising: an information acquisition unit that acquires at least two pieces of information from among information on patrol status, information on optical fiber investigation status, information on satellite investigation status, information on weather conditions, and information on vehicle driving status; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that determines the necessity of road disaster response from the results of the analysis by the analysis unit; and a road clearance unit that registers road clearance bases that function as dispatch bases for carrying out road disaster response work and road clearance routes from the road clearance bases, and formulates a road clearance implementation plan, wherein when the decision unit determines the necessity of road disaster response, the road clearance unit formulates the road clearance implementation plan that includes the optimal road clearance route from the road clearance plan registered in the road clearance unit and the results of the analysis by the analysis unit. [2] A road disaster response support system as described in [1], characterized in that the system continuously monitors the two or more pieces of information acquired by the information acquisition unit or the results analyzed by the analysis unit, and updates the road clearance implementation plan in real time to reflect one or more of the disaster situation, damage situation, or recovery situation in the road disaster response. [3] [1] A road disaster response support system as described in [1], characterized in that the road disaster response support system has a function of optimizing the road reopening route using an artificial intelligence model based on the results of the analysis by the analysis unit. [4] A road disaster response support system as described in any one of [1] to [3], characterized in that the road disaster response support system uses a large-scale language model to collect data on the Internet and learn about disaster prevention measures regardless of the type of language, thereby continuously improving the road clearance plan, the road clearance implementation plan, or the road clearance work. [5] A road disaster response support system as described in any one of [1] to [4], comprising a robotics unit that causes a disaster response robot to carry out the road clearance work using analysis by artificial intelligence and robotics technology, and characterized in that the disaster response robot carries out the road clearance work using the analysis by artificial intelligence and the robotics technology based on the road clearance implementation plan, via the robotics unit. [6] A program for causing a computer to function as a road disaster response support system described in any one of [1] to [3], wherein the computer carries out road clearance work using a disaster response robot controlled using artificial intelligence analysis and robotics technology based on a road clearance implementation plan, and collects data on the Internet using a large-scale language model and learns about disaster prevention measures regardless of the type of language, thereby executing a process to continuously improve the road clearance plan, the road clearance implementation plan, or the road clearance work. [7] A road management method using a computer, wherein the computer acquires, via a network, at least two or more pieces of information from among information on patrol status, information on optical fiber survey status, information on satellite survey status, information on weather conditions, and information on vehicle driving status, analyzes the acquired two or more pieces of information, determines and decides on the need for road disaster response from the analysis results, registers a road clearance plan that includes a road clearance base that functions as a dispatch base for carrying out road disaster response work and a road clearance route from the road clearance base, and, if the need for road disaster response is determined, executes a process to formulate a road clearance implementation plan that includes the optimal road clearance route from the registered road clearance plan and the analysis results. [8] [7] A road management method as described in [7], characterized in that the computer continuously monitors the two or more pieces of acquired information or the analyzed results via a network, and executes a process of updating the road clearance implementation plan in real time to reflect one or more of the disaster situation, damage situation, or recovery situation in the road disaster response. [9] [7] A road management method as described in [7], characterized in that the computer executes a process of optimizing the road clearance route using an artificial intelligence model based on the analyzed results.

[10] A road management method described in any one of [7] to [9], characterized in that the computer collects data on the Internet via a network using a large-scale language model and learns about disaster prevention measures regardless of the type of language, thereby performing a process to continuously improve the road clearance plan, the road clearance implementation plan, or the road clearance work.

[11] A road management method described in any one of [7] to

[10] , characterized in that the computer executes, via a network, a process of carrying out the road clearance work using a disaster response robot controlled using artificial intelligence analysis and robotics technology based on the road clearance implementation plan. [Effects of the Invention]

[0006] The present invention enables rapid response to road disasters such as earthquakes. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a system configuration diagram of a road disaster response support system according to the present invention. [Figure 2] FIG. 10 is a flowchart illustrating an example of a patrol flow. [Figure 3] FIG. 10 is a flowchart illustrating an example of a flow of a fixed camera. [Figure 4] FIG. 10 is a diagram illustrating an example of a patrol situation. [Figure 5] FIG. 10 is a diagram illustrating an example of an optical fiber inspection situation. [Figure 6] FIG. 10 is a diagram illustrating an example of a satellite survey situation. [Figure 7] FIG. 10 is a diagram illustrating an example of weather conditions. [Figure 8] FIG. 2 is a diagram illustrating an example of a vehicle driving situation. [Figure 9] 1 is a flowchart illustrating an example of the flow of a road disaster response support system according to the present invention. [Figure 10] FIG. 10 is a diagram showing an example of a determination criterion in a determination unit of the present invention. [Figure 11] 1 is a diagram illustrating an example of a hardware configuration of a road disaster response support system according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, an embodiment of a road disaster response support system of the present invention will be described with reference to the drawings. Note that the embodiment described below does not unduly limit the contents of the present disclosure described in the claims. Furthermore, not all of the configurations described in this embodiment are necessarily essential components of the present disclosure. In addition, each individual configuration constituting a feature group can also be an invention.

[0009] 1 is a system configuration diagram of a road disaster response support system 600 of the present invention. The road disaster response support system 600 includes an information acquisition unit 610 that acquires information on patrol situations, information on optical fiber investigation situations, information on satellite investigation situations, information on weather conditions, and information on vehicle travel situations, an analysis unit 620 that analyzes the patrol situations, optical fiber investigation situations, satellite investigation situations, weather conditions, and vehicle travel situations obtained from the information acquired by the information acquisition unit 610, and a decision unit 630 that comprehensively decides the need for road disaster response based on the results of the analysis by the analysis unit 620. The road disaster response support system 600 of the embodiment is communicatively connected to a patrol status providing server 100, an optical fiber investigation status providing server 200, a satellite investigation status providing server 300, a weather condition providing server 400, and a vehicle driving status providing server 500 via a network NW. Although only one vehicle Vh and one terminal device TM are shown in FIG. 1 in order to grasp the patrol situation, a plurality of vehicles Vh and terminal devices TM may be connected to the network NW. Although only one fixed camera CAM is shown in FIG. 1 to grasp the disaster situation, multiple fixed cameras CAM may be connected to the network NW.

[0010] The terminal device TM, fixed camera CAM, patrol status providing server 100, optical fiber investigation status providing server 200, satellite investigation status providing server 300, weather status providing server 400, vehicle driving status providing server 500, and road disaster response support system 600 communicate via a network NW. The network NW includes, for example, some or all of a WAN (Wide Area Network), LAN (Local Area Network), the Internet, a provider device, a wireless base station, a dedicated line, a satellite line, etc. The communication method is not limited to the network NW, but data can also be sent and received via a memory card. Data can also be downloaded and uploaded via the network NW.

[0011] The terminal device TM is used by a user who gets into the vehicle Vh. The terminal device TM is a mobile phone such as a smartphone, a tablet terminal, or the like. The terminal device TM may be a communication-type drive recorder mounted on the vehicle Vh or a stationary in-vehicle device, and may be equipped with an image analysis function using AI (artificial intelligence). The terminal device TM has a road patrol application installed therein that cooperates with the patrol status providing server 100 . The terminal device TM has a positioning device such as a GPS (Global Positioning System) receiver, a communication device for connecting to the network NW, an input / output device such as a G sensor (acceleration sensor), a camera, and a touch panel, and a processor such as a CPU (Central Processing Unit).

[0012] 2 is a flowchart showing an example of the flow of patrol. When the terminal device TM presses a patrol start button on the road patrol app (S1), it starts collecting location information, acceleration information, video, etc. (S2). After the patrol is completed, the user presses the patrol end button in the road patrol application (S3), and the position information, acceleration information, video, etc. of the terminal device TM are transmitted to the patrol status providing server 100 (S4). The patrol status providing server 100 determines whether the road surface is uneven or not based on the measurement information transmitted from the terminal device TM, and identifies the location of the road surface that is determined to be uneven. Also, based on the transmitted video and images, it determines the state of damage to the road surface, and identifies the location of the road surface that is determined to be a risk location.

[0013] Fixed camera CAMs are installed on buildings and roadside posts and poles around areas prone to flooding, such as roads (highways, major trunk roads, roads with heavy traffic, major bus routes, roads connecting to schools, public facilities, and emergency hospitals, roads along mountains and in mountainous areas, etc.), underpasses (roads that are dug down at intersections), roads along rivers, and roads along the sea. Fixed camera CAMs include live cameras, web cameras, and network cameras that are capable of communication. The fixed camera CAM may be a communication-type drive recorder or a small unmanned aerial vehicle such as a drone, or it may be equipped with image analysis functions using AI (artificial intelligence). The fixed camera CAM has a built-in camera application that communicates with the patrol status providing server 100 . The fixed camera CAM includes a lens, an image sensor, a positioning device such as a GPS (Global Positioning System) receiver, a communication device for connecting to the network NW, and a processor such as a CPU (Central Processing Unit).

[0014] 3 is a flowchart showing an example of the flow of the fixed camera CAM. The fixed camera CAM periodically or intermittently collects road conditions (S5), and periodically or intermittently automatically transmits images, location information, date and time information, etc. to the patrol status providing server 100 (S6). The patrol status providing server 100 judges the state of damage to the road surface based on the video and images transmitted from the fixed camera CAM, and identifies the locations judged to be risky locations.

[0015] The patrol status providing server 100 provides the patrol status via the network NW to the road disaster response support system 600. The provided patrol status is information for each road, and includes some or all of the disaster status and obstacles to vehicle traffic, such as road damage, road subsidence, roadbed washout, roadway collapse, pavement damage, side ditch damage, road surface unevenness, liquefaction, snow accumulation, snow quality, and ice on the road surface, collapsed buildings, vehicle traffic history, power outages, earthquake damage, tsunami damage, typhoon damage, eruption damage, volcanic activity damage, tornado damage, landslides, landslides, soil runoff, slope collapses, tunnel collapses, collapsed shoulders, fallen trees, falling rocks, road scouring, total bridge damage, river bank collapses, river flooding, flooding, dense fog, avalanches, blizzards, and the presence or absence of accidental or stranded vehicles. FIG. 4 is a diagram showing an example of a patrol situation, in which damaged road areas 110 are displayed in black on the map.

[0016] The optical fiber survey status providing server 200 utilizes optical fiber sensing technology that uses optical fiber as a sensor, receives backscattered light from communication optical fiber included in a cable laid on a road, etc., detects vibration patterns according to the vehicle's driving status on the road, etc. based on the backscattered light, and acquires the vehicle's driving status on the road, etc. and the surrounding road conditions, etc. from the detected vibration patterns and a learning model (information from a camera connected to the optical fiber, etc., may also be used). The optical fiber inspection status providing server 200 provides the optical fiber inspection status to the road disaster response support system 600 via the network NW. The optical fiber inspection status provided is information for each road, and includes some or all of disaster conditions (including images of the road) and obstacles to vehicle traffic, such as vehicle traffic history, traffic volume, traffic congestion, sudden vehicle stops, traffic accidents, snow accumulation on the road surface, collapses in tunnels, accidents, power outages, water leaks and water outages, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, eruption damage, tornado damage, and optical fiber blockages and disconnections. FIG. 5 is a diagram showing an example of an optical fiber investigation situation, in which a collapsed area 210 in the tunnel is displayed in black on the map.

[0017] The satellite survey status providing server 300 utilizes satellite remote sensing technology, which uses observations by satellites equipped with SAR (Synthetic Aperture Radar), optical sensors, microwave sensors, etc., and obtains road conditions, vehicle conditions, etc. from the differences before and after a disaster using data (scattering intensity values, phase information, polarization information, etc.) and images (optical images, SAR images, etc.) observed by the satellites. In addition, it may be a device that uses AI (artificial intelligence) analysis to compare and detect road conditions, vehicle conditions, etc. before and after a disaster, or a device that uses AI (artificial intelligence) analysis to detect and detect road conditions, vehicle conditions, etc. during a disaster. The satellite survey status providing server 300 provides the satellite survey status via the network NW to the road disaster response support system 600. The provided satellite survey status is information for each road, and includes some or all of the disaster status and obstacles to vehicle traffic, such as collapsed buildings, landslides, landslides, mudslides, slope collapses, tunnel collapses, road damage, collapsed roadways, collapsed shoulders, fallen trees, falling rocks, total bridge damage, river breaches, river flooding, inundation, avalanches, vehicle traffic history, ground subsidence, power outages, water leaks and water outages, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, forest damage, crop damage, and the presence or absence of accidental or stranded vehicles. FIG. 6 is a diagram showing an example of a satellite survey situation, where a landslide location 310 is displayed in black on the map.

[0018] The weather condition providing server 400 provides weather conditions via the network NW to the road disaster response support system 600. The weather conditions provided are information for each region, and include some or all of the following: time, weather (clear, rainy, snowy, etc.), temperature, rainfall, snowfall, snow depth, wind speed, emergency warnings (heavy rain, strong winds, high tides, waves, heavy snow, blizzards), warnings (heavy rain, strong winds, floods, heavy snow, blizzards, etc.), information on record-breaking short-term heavy rain, landslide warning information, earthquake forecast information, earthquake information, tsunami information, eruption information, information on volcanic activity, typhoon information, tornado information, and disaster conditions (including images of roads). FIG. 7 shows an example of weather conditions, where a warning 410 and seismic intensity 420 are displayed in letters and numbers.

[0019] The vehicle driving status providing server 500 uses automobile sensing technology to acquire various data such as vehicle driving status and surrounding road conditions from vehicles such as connected cars. The vehicle driving status providing server 500 provides vehicle driving status information to the road disaster response support system 600 via the network NW. The provided vehicle driving status information is information for each road obtained from vehicles (including electric vehicles) such as private cars, taxis, buses, and trucks, and includes some or all of the following disaster status and obstacles to vehicle traffic, such as temperature, sudden braking locations, skidding locations, tire spin locations, tire lock locations, ABS activation locations, and flooded locations from vehicle sensors, power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, river flooding, liquefaction, and obstacles on the road from camera footage and images, collapsed buildings, landslides, slope collapses, tunnel collapses, total bridge damage, damaged roads, and impassable areas identified from three-dimensional data, and vehicle traffic history (including passenger cars and large vehicles), traffic volume, traffic congestion, passing speed, average speed, acceleration, and whether or not there is rain or snow from windshield wiper operation status from probe information (including ETC2.0). In addition, data may be acquired using autonomous driving technology (sensing technology for autonomous vehicles), or may be detected and interpreted using AI (artificial intelligence) analysis to detect disaster situations and vehicle obstruction information. FIG. 8 is a diagram showing an example of vehicle travel conditions, and a location 510 where no vehicle has traveled is displayed in black on the map.

[0020] The information acquisition unit 610 operating in the road disaster response support system 600 acquires the patrol status from the patrol status providing server 100, the optical fiber investigation status from the optical fiber investigation status providing server 200, the satellite investigation status from the satellite investigation status providing server 300, the weather conditions from the weather condition providing server 400, and the vehicle driving status from the vehicle driving status providing server 500 via the network NW. The patrol status provided by the patrol status providing server 100 is stored as patrol information 640. The optical fiber inspection status provided by the optical fiber inspection status providing server 200 is stored as optical fiber inspection information 650. The satellite inspection status provided by the satellite inspection status providing server 300 is stored as satellite inspection information 660. The weather conditions provided by the weather condition providing server 400 are stored as meteorological information 670. The vehicle driving status provided by the vehicle driving status providing server 500 is stored as vehicle driving information 680. Furthermore, the information acquisition unit 610 may store information related to road service status. The information related to road service status is information for each road and is mainly managed by road service providers (such as the Japan Automobile Federation) (such as information from a road service management system), and includes some or all of the following: rescue requests (date, time, location, rescue details, etc.), rescue requests due to abnormal weather (date, time, location, rescue details, etc.), rescue requests due to disasters (date, time, location, rescue details, etc.), dead battery, locked keys in the car, running out of gas, flat tires, wheels coming off or falling off, flooding or submersion, recovery from snowy or muddy roads, accidents, slips and falls, disaster or damage conditions (including photographed images of the road), towing or transportation of vehicles, removal, towing or transportation of abandoned vehicles, removal, towing or transportation of damaged vehicles, removal, towing or transportation of accident vehicles, road conditions (including photographed images of the road), traffic conditions, EV charging capabilities, vehicle inspection results, etc. The road service status is stored as road service information. The information acquired by the information acquisition unit 610 may be only a part of the patrol status, optical fiber survey status, satellite survey status, weather status, vehicle driving status, and road service status.

[0021] The analysis unit 620 operating in the road disaster response support system 600 analyzes each piece of information obtained from the information acquired by the information acquisition unit 610: patrol information 640, optical fiber survey information 650, satellite survey information 660, weather information 670, and vehicle driving information 680, and stores the analysis results as 690. The stored analysis results 690 can be viewed in map view or list view. The analysis unit 620 may also analyze road service information. Furthermore, the analysis unit 620 may have an analysis function using AI (artificial intelligence). An example of AI (artificial intelligence) is as follows. A road disaster response support system characterized by inputting image data of the road to be analyzed, which is taken from each piece of information acquired by the information acquisition unit (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), along with image data of the road to be learned and disaster conditions on that road, into a learning model that has undergone machine learning using learning data, to output road disaster conditions, and analyze the severity of the road disaster conditions (earthquakes of seismic intensity 6 or higher, wide-area disasters such as wind and water damage, snow damage, volcanic activity, landslides, etc.) based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, landslide data, various sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.), and complementing and correcting the information acquired by the information acquisition unit based on the results of the analysis. A road management method characterized by inputting image data of the road to be analyzed, which is taken from each piece of acquired information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), along with image data of the road to be learned and the disaster situation on that road, into a learning model that has undergone machine learning using learning data, to output the road disaster situation, and analyzing the severity of the road disaster situation (earthquakes of seismic intensity 6 or higher, wide-area disasters such as wind and water damage, snow damage, volcanic activity, landslides, etc.) based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, landslide data, various sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.), and complementing and correcting the acquired information based on the results of the analysis. The information analyzed by the analysis unit 620 may be limited to only a portion of the patrol information 640, optical fiber survey information 650, satellite survey information 660, weather information 670, vehicle driving information 680, and road service information.

[0022] The decision unit 630 operating in the road disaster response support system 600 comprehensively determines the need for road disaster response from some or all of the analysis results 690 for each piece of information analyzed by the analysis unit 620 (which may include road service information), and stores the result as a judgment result 695. The stored judgment results 695 can be checked on a map display or a list display. Furthermore, the decision unit 630 may be equipped with an analysis function, a judgment function, and a decision function using AI (artificial intelligence). Furthermore, the road disaster response support system 600 may include an information providing unit that provides a part or all of the analysis result 690, the judgment result 695, etc. to the outside. By including the information providing unit, it is possible to obtain the effect of widely disseminating information about road disaster response, such as public relations.

[0023] FIG. 9 is a flowchart showing an example of the flow of the road disaster response support system 600. The information acquisition unit 610 periodically acquires (e.g., every few minutes) information on patrol status from the patrol status providing server 100 (S10). The information acquisition unit 610 periodically acquires (e.g., every few minutes) information on optical fiber inspection status from the optical fiber inspection status providing server 200 (S11). The information acquisition unit 610 periodically acquires (e.g., every few minutes) information on satellite inspection status from the satellite inspection status providing server 300 (S12). The information acquisition unit 610 periodically acquires (e.g., every few minutes) information on weather conditions from the weather condition providing server 400 (S13). The information acquisition unit 610 periodically acquires (e.g., every few minutes) information on vehicle driving status from the vehicle driving status providing server 500 (S14). The analysis unit 620 then extracts road damage locations and the like from the patrol information 640 (S15). The analysis unit 620 extracts travel history and the like from the optical fiber inspection information 650 (S16). The analysis unit 620 extracts landslide locations and the like from the satellite inspection information 660 (S17). The analysis unit 620 extracts earthquake and tsunami information and the like from the weather information 670 (S18). The analysis unit 620 extracts impassable areas and the like from the vehicle travel information 680 (S19). Next, based on the results of the above analysis, the decision unit 630 comprehensively decides whether road disaster response is necessary (S20). Furthermore, the road disaster response support system 600 may be equipped with information from road users and residents using SNS (Social Networking Service) (disaster information, victim information, relief information, recovery information, etc.), information from government (police, fire department, Self-Defense Forces, etc.), infrastructure operators (communications, electricity, gas, water, sewerage, etc.), transportation operators (railways, buses, ferries, airplanes, etc.), construction and civil engineering operators (including construction industry associations), delivery companies, tourism operators (inns, hotels, tourist facilities, roadside stations, etc.), designated public institutions (Disaster Countermeasures Basic Act) (disaster information, victim information, relief information, recovery information, etc.), and information from the government's Emergency Disaster Countermeasures Headquarters and Emergency Disaster Countermeasures Headquarters.

[0024] 10 is a diagram showing an example of the judgment criteria in the decision unit 630. The decision unit 630 makes a decision on road disaster response in the following cases: when a tsunami occurs (element 1) 631, when a slope collapses and optical fiber is disconnected (element 2) 632, when roads are damaged and buildings have collapsed and no vehicles have passed through (element 3) 633, when a heavy rain warning is issued and landslides cause tires to spin, preventing vehicles from moving forward and causing severe traffic congestion (element 4) 634, when a heavy snow warning is issued and there is snow on the road surface, the ABS is activated, and severe traffic congestion with many stranded vehicles occurs (element 5) 635, etc. Furthermore, with regard to some or all of the judgment results 695 decided by the decision unit 630, the analysis results 690, and the information acquired by the information acquisition unit (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), etc., through an information provision unit (described in paragraph 0022), etc., the system may be provided with a function to notify road managers by email, a function to provide data to a road information board system, a function to provide data to a road restoration visualization map (a map on the web or the like that displays the road restoration status, main affected areas and damage status, road traffic restrictions, inter-city travel time, vehicle speed data, vehicle traffic history, population mesh data, etc.), a function to provide data to a car navigation system, a function to provide data to an autonomous driving system, a function to provide data to MaaS (Mobility as a Service), a function to provide information to the government (police, fire department, Self-Defense Forces, etc.) and the media, and a function to disclose information to road users and residents (website, smartphone app, etc.) (this does not necessarily have to be provided through the information provision unit). An example of an information disclosure function for road users and residents is as follows: A road disaster response support system characterized by transmitting one or more pieces of information acquired by an information acquisition unit, or the results of analysis by an analysis unit, or the necessity of road disaster response determined by a decision unit, or the road clearance implementation plan formulated by a road clearance unit (described in paragraph 0028), or the restoration status by a robotics unit (described in paragraph 0029), or the necessity of road disaster response predicted by a prediction unit (described in paragraph 0026), to a mobile terminal device (such as a mobile phone, smartphone, tablet device, laptop computer, game console, etc.) or a fixed terminal device (such as a desktop computer, smart TV, set-top box, digital signage, kiosk terminal, car navigation system, car display audio, etc.).

[0025] The road disaster response support system 600 may include an improvement unit that improves some or all of the analysis results 690, the judgment results 695, and the judgment criteria used by the decision unit 630 (an example of the judgment criteria is shown in FIG. 10 ). Improvements can be made by inputting various disaster-related data (data, images), etc. into the road disaster response support system 600, and the inclusion of the improvement unit increases the accuracy of road disaster response. An example flow is as follows: Various disaster-related data (data, images), etc. are input into the improvement unit → history of the analysis results 690, judgment results 695, etc. is referenced → analysis by the improvement unit → improvement results by the improvement unit → feedback (review of accuracy improvements, etc.). Examples of various data include hypothesis data, verification data, sample data, training data, past data, current data, and future data (future data on large-scale disasters that may or may not occur once every few decades, data on large-scale disasters that may occur in the future, data on large-scale disasters that humanity has never experienced before, etc.). The improvement unit may also be equipped with analytical functions and improvement functions using AI (artificial intelligence). Examples of AI (artificial intelligence) are as follows: A road disaster response support system characterized by having an improvement unit that inputs image data of the road to be analyzed, which is taken from each piece of information acquired by the information acquisition unit (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), along with image data of the road to be learned and disaster conditions on the road into a learning model that has undergone machine learning using learning data, to output road disaster conditions, and complements and corrects the output road disaster conditions based on the measurement data included in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, landslide data, various sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.), and improves the results analyzed by the analysis unit, the judgment criteria in the decision unit, or the results decided by the decision unit. A road management method characterized by inputting image data of the road to be analyzed, which is taken from each piece of acquired information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), along with image data of the road to be learned and disaster conditions on that road, into a learning model that has undergone machine learning using learning data, thereby outputting road disaster conditions, and complementing and correcting the output road disaster conditions based on the measurement data contained in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, landslide data, various sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.), thereby improving the analyzed results, judgment criteria, or determined results.

[0026] The road disaster response support system 600 may include a prediction unit that predicts the need for road disaster response by, for example, referring to the history of the analysis results 690 and the judgment results 695. Providing a prediction unit in the road disaster response support system 600 has the effect of enabling preparation, planning, and training for road disaster response in advance. An example flow is as follows: Various data (data, images), etc. related to the disaster are input to the prediction unit → the history of the analysis results 690 and the judgment results 695 are referred to → analysis, etc. by the prediction unit → prediction results by the prediction unit → action (planning, training, etc.). Examples of various data include hypothesis data, verification data, sample data, training data, past data, current data, and future data (future data on large-scale disasters that may or may not occur once every few decades, data on large-scale disasters that may occur in the future, and data on large-scale disasters that humanity has never experienced before). The prediction unit may also be equipped with analytical and prediction functions using AI (artificial intelligence).

[0027] The road disaster response support system 600 can be effective in one or more of the following conditions: patrol situation, optical fiber survey situation, satellite survey situation, weather situation, vehicle driving situation, and road service situation. Furthermore, combining two or more elements can produce synergistic effects. As a specific example, satellite survey conditions (satellite remote sensing technology) can be used to obtain wide-area information (changes in infrastructure facilities and transportation networks over a wide area), while optical fiber survey conditions (optical fiber sensing technology) can be used to obtain local information (local changes in road facilities, roads, etc.), making it possible to monitor both wide areas and local areas. Next, adding vehicle driving conditions (automotive sensing technology) makes it possible to grasp diverse and sophisticated road and surrounding conditions, maximizing the benefits of each and achieving synergistic effects. Furthermore, by being able to quickly respond to road disasters (understanding the overall situation, issuing system announcements, issuing recovery orders, publicizing information, etc.), it will be possible to minimize damage and carry out emergency, temporary, or full recovery according to the disaster situation, which will hopefully lead to a speedy recovery of the affected areas.

[0028] The road disaster response support system 600 may include a road clearance unit that registers road clearance plans that have been formulated in advance and formulates implementation plans for road clearance. By providing the road clearance unit in the road disaster response support system 600, it is possible to achieve the effect of quickly clearing roads in the event of a disaster. In typical disasters, the process is emergency restoration followed by full restoration. However, in large-scale disasters, emergency restoration (road clearance) must precede emergency restoration. Road clearance involves quickly clearing minimal debris and repairing simple uneven sections to open rescue routes for emergency vehicles conducting rescue and relief activities, providing emergency supplies, and restoring roads. Road administrators develop road clearance plans in advance, which include road clearance bases (bases for support units, disaster prevention centers such as storage areas for supplies and equipment), road clearance routes (wide-area travel routes, access routes, and routes within the affected area), and a timeline (specific action plan). Road clearance involves assessing the damage situation, assessing the risk of secondary disasters, implementing emergency measures (such as traffic restrictions) immediately after the disaster, formulating a road clearance implementation plan (including calculating the optimal route), and carrying out road clearance operations. An example of an embodiment of the road clearance section in the road disaster response support system 600 is as follows. A road disaster response support system (including a road management method) characterized in that a road clearance plan formulated in advance is registered in the road clearance department (this may be registered before or after a disaster), and the road clearance department makes a road clearance implementation plan based on the road clearance plan registered in the road clearance department and two or more pieces of information acquired by the information acquisition department or the results of analysis by the analysis department. In addition, the road clearance section may be equipped with analytical, planning, and analysis functions using AI (artificial intelligence). Examples of AI (artificial intelligence) are as follows: A road disaster response support system characterized in that the image data of the road to be analyzed, which is taken from each piece of information acquired by the information acquisition unit (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), is input into a learning model that has undergone machine learning using the learning data along with image data of the road to be learned and the disaster situation on that road, thereby outputting the road disaster situation, and the road disaster situation is supplemented and corrected based on the measurement data included in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, landslide data, various sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.), and when road disaster response is required, a road clearance plan formulated in advance is registered in the road clearance unit, and the road clearance unit makes a road clearance implementation plan based on the road clearance plan registered in the road clearance unit and two or more pieces of information acquired by the information acquisition unit, or the results of analysis by the analysis unit, or the supplemented and corrected road disaster situation. A road management method characterized by inputting image data of a road to be analyzed, which is taken from each piece of acquired information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information), along with image data of a road to be learned and disaster conditions on that road, into a learning model that has undergone machine learning using learning data, to output road disaster conditions, and supplementing and correcting the output road disaster conditions based on measurement data included in each piece of information (date and time, address, latitude and longitude, seismic intensity, weather data, landslide data, various sensing data, acceleration, vibration, vehicle data, probe data, three-dimensional data, etc.), and when road disaster response is required, registering a road clearance plan that has been formulated in advance, and making a road clearance implementation plan based on the registered road clearance plan and two or more pieces of information acquired by the information acquisition unit, or the analyzed results, or the supplemented and corrected road disaster conditions. In addition, the Road Clearance Department may formulate a road clearance implementation plan based on a road clearance plan that has been formulated in advance (a road clearance plan registered with the Road Clearance Department) and information obtained by the Robotics Department (described in paragraph 0029).

[0029] The road disaster response support system 600 may include a robotics unit that uses AI (artificial intelligence) and robotics technology to use robots (mainly disaster response robots) to perform road clearance work. The robots calculate which roads should be prioritized for clearance through optimal route analysis using AI (artificial intelligence), and then perform road clearance work using robotics technology. When performing road clearance work, the robots use AI (artificial intelligence) to perform optimal route analysis based on a pre-established road clearance plan registered in the road clearance unit or a road clearance implementation plan established by the road clearance unit, as well as information acquired by the robotics unit (disaster and damage situation, weather conditions, road conditions, traffic conditions, impassable conditions, rescue situation, recovery situation, obstacle information, topographical information, road clearance progress information, latest on-site information, etc.) (The robots do not necessarily have to be autonomous robots). The Robotics Department will achieve the following benefits: By utilizing autonomous robots, work can be carried out quickly without relying on human labor. Furthermore, by using robots to carry out autonomous work, the dispatch of workers to dangerous areas can be minimized. Furthermore, the coordination of large heavy machinery, small robots, drones, etc. will enable efficient obstacle removal, etc. An example of an embodiment of the robotics section in the road disaster response support system 600 is as follows (each robot is connected to a network NW). A road disaster response support system (including road management methods) characterized by the use of artificial intelligence (AI) analysis and robotics technology to perform road clearance work using a robot from the robotics department based on a pre-established road clearance plan (a road clearance plan registered with the road clearance department) or a road clearance implementation plan. Examples of robotics technology (including disaster response robots) are as follows: Remotely or autonomously controlling autonomous heavy machinery (bulldozers, excavators, etc.) to remove obstacles and repair uneven surfaces; Working in conjunction with radio-controlled debris removal robots to remove small amounts of debris; Utilizing four-legged robots or drones to assist in reconnaissance of disaster areas and light-duty removal of obstacles; Furthermore, AI (artificial intelligence) is used to analyze the progress of road clearance in real time, automatically adjusting optimal work instructions for the robots; and integrated control of multiple different robots (autonomous heavy machinery, small robots, drones, etc.) to optimally allocate tasks. Examples of AI (artificial intelligence) include: Route optimization AI for calculating road clearance routes and determining priorities (Dijkstra algorithm, reinforcement learning, multi-agents, etc.); Image recognition AI for identifying obstacles using drone and robot sensors and generating 3D maps (convolutional neural networks, PointNet, etc.); Robotics control AI for controlling autonomous heavy machinery, collaborative work by small robots, automatic obstacle avoidance and path planning (imitation learning, reinforcement learning, deep reinforcement learning, multi-agents, simultaneous localization and mapping, etc.); Dynamic route update AI (machine learning, long short-term memory, etc.), and work monitoring by AI (convolutional neural networks, long short-term memory, Transformer, etc.). Examples of input and output data in robotics technology and AI (artificial intelligence) analysis are as follows: Route optimization AI: Input data (road network data, obstacle data, real-time traffic data, priority route information, weather and parcel number data, etc.) → Output data (optimal road clearance routes, emergency routes, work instruction lists, etc.). Image recognition AI: Input data (drone footage, LiDAR point cloud data, past disaster data, etc.) → Output data (obstacle maps, obstacle type determination, work priority maps, etc.). Robotics control AI: Input data (work area maps, obstacle information, robot status data, terrain data, etc.) → Output data (robot work plans, movement route instructions, obstacle removal operations, etc.). Work monitoring AI: Input data (work video data, robot work logs, weather information, etc.) → Output data (progress reports, anomaly detection alerts, work optimization instructions, etc.). Utilizing large-scale language models can enhance the development of road clearance plans and road clearance implementation plans, as well as the optimization of operations. They are particularly effective in understanding road clearance plans that have been developed in advance, learning disaster prevention measures using vast amounts of data on the Internet, and making real-time decisions in response to disasters. Examples of the use of large-scale language models are as follows: Complementing and optimizing road clearance plans and road clearance implementation plans, learning from disaster prevention data from around the world on the Internet, real-time support for robots, and real-time utilization of disaster data.

[0030] The Road Disaster Response Support System 600 continuously monitors the disaster, damage, and recovery status, updates each piece of information in real time, and updates again at regular intervals using the latest data and information.

[0031] <Hardware configuration> FIG. 11 illustrates an example of the hardware configuration of a terminal device TM, a fixed camera CAM, a patrol status providing server 100, an optical fiber investigation status providing server 200, a satellite investigation status providing server 300, a weather status providing server 400, a vehicle driving status providing server 500, and a road disaster response support system 600. This diagram illustrates an example in which the terminal device TM is a mobile phone such as a smartphone. The terminal device TM includes, for example, a CPU 701, a RAM 702, a ROM 703, a secondary storage device 704 such as a flash memory, a touch panel 705, and a wireless communication module 706, all interconnected via an internal bus or a dedicated communication line. Application programs such as a road patrol app are downloaded via a network NW and stored in the secondary storage device 704. The fixed camera CAM includes, for example, a CPU 901, a RAM 902, a ROM 903, a secondary storage device 904 such as a flash memory, a lens / image sensor 905, and a communication device 906, all interconnected via an internal bus or a dedicated communication line. Application programs such as a camera application are downloaded via the network NW and stored in the secondary storage device 904 . Each server includes, for example, a NIC 801, a CPU 802, a RAM 803, a ROM 804, a secondary storage device 805 such as a flash memory or a hard disk drive (HDD), and a drive device 806, all interconnected via an internal bus or a dedicated communication line. A portable storage medium such as an optical disk is attached to the drive device 806. A program stored in the secondary storage device 805 or the portable storage medium attached to the drive device 806 is loaded into the RAM 803 by a DMA controller (not shown) or the like, and executed by the CPU 802, thereby realizing the functional units of each server. Patrol information 640, optical fiber inspection information 650, satellite inspection information 660, weather information 670, vehicle driving information 680, analysis results 690, and judgment results 695 are stored in the secondary storage device 805. Note that each server may be implemented using cloud computing.

[0032] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0033] 100: Patrol status server 200: Optical fiber inspection status server 300: Satellite survey status server 400: Weather information server 500: Vehicle driving status server 600: Road Disaster Response Support System 610: Information acquisition department 620: Analysis Department 630: Decision Section 640: Patrol Information 650: Optical fiber survey information 660: Satellite Survey Information 670: Weather information 680: Vehicle driving information 690:Analysis results 695: Judgment result

Claims

1. An information acquisition unit that acquires at least two pieces of information from among information on patrol status, which is information for each road, information on optical fiber investigation status, which is information for each road, information on satellite investigation status, which is information for each road, information on weather conditions, and information on vehicle driving conditions; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit; a decision unit that determines the necessity of road disaster response based on the results of the analysis by the analysis unit; a road clearance unit that registers road clearance plans that have been formulated in advance, including road clearance points and road clearance routes from the road clearance points, and formulates a road clearance implementation plan; When the decision unit determines that a road disaster response is necessary, the road clearance unit formulates a road clearance implementation plan including an optimal road clearance route based on the road clearance plan registered in the road clearance unit and the results of the analysis by the analysis unit.

2. The road disaster response support system according to claim 1, continuously monitoring the two or more pieces of information acquired by the information acquisition unit or the results of the analysis by the analysis unit; Reflecting one or more of the disaster situation, damage situation, or restoration situation in the road disaster response, A road disaster response support system characterized by updating the road clearance implementation plan in real time.

3. The road disaster response support system according to claim 1, The road disaster response support system comprises: A road disaster response support system characterized by having a function of using the results of the analysis by the analysis unit as input and outputting, using an artificial intelligence model, road clearance routes included in the road clearance plan and / or road clearance routes included in the road clearance implementation plan.

4. The road disaster response support system according to any one of claims 1 to 3, A road disaster response support system characterized by continuously complementing or optimizing the road clearance plan, road clearance implementation plan, or road clearance work by using a large-scale language model to collect data on the Internet and learn about disaster prevention measures regardless of the type of language.

5. The road disaster response support system according to any one of claims 1 to 3, The facility has a robotics department where disaster response robots use artificial intelligence analysis and robotics technology to carry out road clearance work. A road disaster response support system characterized in that the disaster response robot carries out the road clearance work based on the road clearance implementation plan using the analysis by the artificial intelligence and the robotics technology, and is controlled by the robotics department.

6. A program including a sequence of instructions for causing a computer to execute at least one of the following (A) or (B): (A) a function of the road disaster response support system according to any one of claims 1 to 3; (B) At least one of the following functions (1) or (2): (1) A function of continuously complementing or optimizing road clearance plans, road clearance implementation plans, or road clearance work by collecting data on the Internet using a large-scale language model and learning about disaster prevention measures regardless of the type of language, as described in claim 4; (2) A function of carrying out the road clearance work using a disaster response robot controlled using artificial intelligence analysis and robotics technology based on the road clearance implementation plan described in claim 5.

7. A road management method using a computer, comprising: The computer communicates with the network via acquire at least two or more pieces of information from among information on patrol status, which is information for each road, information on optical fiber investigation status, which is information for each road, information on satellite investigation status, which is information for each road, information on weather conditions, and information on vehicle driving conditions; Analyzing the two or more pieces of information obtained; Based on the analysis results, we will determine the necessity of road disaster response, Registering a road clearance plan formulated in advance, including road clearance points and road clearance routes from the road clearance points; A road management method characterized by the fact that, when the need for road disaster response is determined, a process is executed to formulate a road clearance implementation plan including the optimal road clearance route based on the registered road clearance plan and the analyzed results.

8. 8. The road management method according to claim 7, The computer communicates with the network via continuously monitoring the two or more pieces of information obtained or the analyzed results; Reflecting one or more of the disaster situation, damage situation, or restoration situation in the road disaster response, A road management method characterized by executing a process for updating the road clearance implementation plan in real time.

9. 8. The road management method according to claim 7, The computer A road management method characterized by using the analyzed results as input and executing a process using an artificial intelligence model to output the road clearance route included in the road clearance plan and / or the road clearance route included in the road clearance implementation plan.

10. 10. The road management method according to claim 7, further comprising: The computer communicates with the network via A road management method characterized by using a large-scale language model to collect data on the Internet and learn about disaster prevention measures regardless of the type of language, thereby performing a process to continuously complement or optimize the road clearance plan, the road clearance implementation plan, or the road clearance work.

11. 10. The road management method according to claim 7, further comprising: The computer communicates with the network via A road management method characterized by carrying out a process of carrying out road clearance work using disaster response robots controlled using artificial intelligence analysis and robotics technology based on the road clearance implementation plan.

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