Road disaster response support system and program, road management method

Abstract

It has traditionally been difficult to predict road impediments during disasters such as earthquakes and heavy rain, or to identify signs of road infrastructure risks such as cavities below the road surface, and there is a need for a support system that can balance maintenance during peacetime with rapid response in the event of a disaster. [Solution] The present invention is configured such that an information acquisition unit acquires at least two or more of survey information from optical fiber laid on roads, satellite survey information, and vehicle travel information, and an analysis unit analyzes the information. Based on the analysis results, in normal times, the cavity risk and infrastructure soundness of multiple road sections are evaluated to determine the need for maintenance, and in times of disaster, the need for road disaster response is determined based on the analysis results including the evaluation results. Furthermore, a prediction unit predicts future risks, and an improvement unit can continuously improve the accuracy of the model. This enables disaster response and infrastructure maintenance to be carried out with high precision and efficiency.

Description

[Technical Field]

[0001] The present invention relates to road infrastructure management and disaster response technologies, and in particular to a road disaster response support system, related programs, and road management methods that use multiple sensing means such as satellite sensing, optical fiber sensing, and vehicle travel sensing to predict and evaluate the risk of subsurface cavities in normal times, determine the need for road infrastructure maintenance, and execute disaster response support processing including determining emergency vehicle routes or road clearance routes based on various information in the event of a disaster. The present invention also aims to achieve both wide-area and continuous understanding of infrastructure health, which was difficult with conventional localized and intermittent inspection methods, and improved immediacy and reliability of information directly related to disaster response. [Background technology]

[0002] In the past, road infrastructure management was dominated by a system of emergency response after accidents such as road surface collapses occurred, and wide-area and continuous management of the risk of hollowing out during peacetime was not adequately implemented. Furthermore, in the event of a disaster, it is necessary to quickly identify road clearance routes to ensure the passage of emergency vehicles, but an integrated support system for this purpose had not been established. Until now, there has been a reliance on spotty and intermittent methods such as underground radar surveys and visual inspections, and although individual technologies such as satellite remote sensing, optical fiber sensing, and vehicle traffic sensing exist, there has not yet been a practical system that combines these to continuously evaluate and predict the risk of subsurface cavities in peacetime, and that dynamically determines and supports road clearance routes, etc., by utilizing the acquired information in the event of a disaster. Furthermore, existing technologies have issues with real-time performance, predictive detection, and disaster response responsiveness, which limits their ability to prevent disasters and speed up initial responses.In the event of a large-scale disaster, road managers typically first carry out emergency restoration (road clearance), followed by temporary and full restoration.However, emergency vehicles include fire engines, ambulances, Self-Defense Force vehicles, supply transport vehicles, and special disaster response vehicles, and appropriate route decision support based on on-site information is essential to ensure the rapid passage of these vehicles. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2024-014948 [Patent Document 2] Japanese Patent Publication No. 2024-071243 [Patent Document 3] Japanese Patent Publication No. 2023-162007 [Patent Document 4] JP 2024-136347 A Summary of the Invention [Problem to be solved by the invention]

[0004] In road infrastructure, there is a high risk of road collapses and traffic disruptions during disasters due to cavitation and ground deformation under the road surface. Conventionally, these problems have mainly been addressed by taking emergency measures after an accident or disaster has occurred, and there has been insufficient management of the risk of cavitation over a wide area and continuously during peacetime. Furthermore, although individual technologies exist, such as optical fiber sensing, satellite remote sensing, and the use of vehicle driving data, a system that can comprehensively integrate and analyze these technologies and handle both the daily preventive maintenance of road infrastructure and emergency response support in the event of a disaster has not yet been established. For example, the following prior art is known. Patent Document 1 discloses technology for detecting road abnormalities using optical fiber sensors, but does not disclose a configuration for integrated analysis of satellite data and vehicle driving data, or for continuously improving AI (artificial intelligence) models by utilizing ground survey results. Patent Document 2 discloses a technology that uses satellite images to detect obstacles on roads and determine whether they are passable, but does not disclose configurations such as integrated analysis with optical fiber and vehicle driving data, output of risk scores using an AI (artificial intelligence) model, or continuous improvement of the model. Patent Document 3 discloses a technology for inputting image data acquired from a vehicle into an AI (artificial intelligence) model to determine the disaster situation on roads, but does not disclose integrated analysis with optical fiber data and satellite data, continuous re-learning configuration for the AI ​​(artificial intelligence) model, or integrated utilization configuration for disaster response decisions. Patent document 4 describes a technology for visualizing images and location information from radar-equipped vehicles on a map, but makes no mention of integrating heterogeneous sensing information, assessing cavity risk using AI (artificial intelligence), or improving models by utilizing the results of ground surveys. As such, conventional technologies have been limited to detecting local anomalies based on single sensing information and performing limited data analysis, and have not yet realized a comprehensive system that can assess the wide-area, continuous risk of hollowing out during peacetime or support rapid and rational responses in the event of a disaster. [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 optical fiber survey status obtained by optical fiber laid on roads, information on satellite survey status, and information on vehicle driving status; and an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit, and based on the results of analysis by the analysis unit, in normal times, evaluates the risk of subsurface cavities or the soundness of road infrastructure for multiple road sections including emergency vehicle routes or road clearance routes, and determines the need for road infrastructure maintenance, and in the event of a disaster, determines the need for road disaster response based on the analysis results including the evaluated results. [2] A road disaster response support system as described in [1], characterized in that the road disaster response support system is equipped with an artificial intelligence model including a neural network and is configured to perform data selection or learning processing based on active learning on the artificial intelligence model. [3] [1] A road disaster response support system as described in [1], characterized in that the road disaster response support system includes a prediction unit, and the prediction unit uses an artificial intelligence model that has learned meteorological conditions, topography, past disaster records, and road structure, and executes a process using the artificial intelligence model to predict the risk of future road disasters occurring, the risk of obstruction to emergency vehicle passage, or areas where it is difficult to secure the road obstruction route. [4] [1] A road disaster response support system as described in [1], characterized in that the road disaster response support system is equipped with an improvement unit, and the improvement unit or the analysis unit inputs at least one of cavity presence / absence information, cavity location information, cavity depth information, or cavity shape information obtained by on-site ground surveys as learning data or update data for an artificial intelligence model, and performs processing to continuously improve the prediction accuracy or judgment accuracy of the artificial intelligence model. [5] [1] A road disaster response support system as described in [1], characterized in that the road disaster response support system is equipped with an XAI (Explainable AI) configuration that presents the basis for the risk score and / or abnormality assessment corresponding to the output results of an artificial intelligence model, and the information presented by the XAI presents the validity of the output results and the basis for the judgment to the administrator or user in an understandable form. [6] [1] A road disaster response support system as described in [1], wherein the information acquisition unit acquires at least one of information regarding the optical fiber investigation status, information regarding the satellite investigation status, and information regarding the vehicle driving status, as well as information regarding patrol status, information regarding weather conditions, and information regarding road service status, and the analysis unit performs an integrated analysis of at least two or more pieces of information acquired by the information acquisition unit, and performs processing to grasp the occurrence status of road disasters, signs of abnormalities in road infrastructure, or risks of road obstructions based on the results of the integrated analysis. [7] [1] A road disaster response support system as described in [1], comprising a road clearance unit, which, in the event of a disaster, extracts multiple candidate emergency vehicle routes or road clearance routes based on the results of analysis by the analysis unit (the results include integrated analysis results), analyzes information regarding the risk of road disaster occurrence, passability, risk of traffic obstruction, or the soundness of the road infrastructure for each of the routes, and executes a process to select the optimal route from among the multiple candidate routes based on the analysis results. [8] A program for causing a computer to function as the road disaster response support system described in any one of [1] to [7]. [9] 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 optical fiber survey status obtained from optical fiber laid on the road, information on satellite survey status, and information on vehicle driving status, analyzes the acquired two or more pieces of information, and based on the results of the analysis, in normal times, evaluates the risk of subsurface cavities or the soundness of road infrastructure for multiple road sections including emergency vehicle routes or road clearance routes, and determines the need for road infrastructure maintenance, and in the event of a disaster, executes a process to determine the need for road disaster response based on the results of the analysis including the evaluated results.

[10] [9] A road management method as described in [9], characterized in that the computer uses an artificial intelligence model including a neural network and performs data selection or learning processing based on active learning on the artificial intelligence model.

[11] [9] A road management method as described in [9], characterized in that the computer uses an artificial intelligence model that has learned weather conditions, topography, past disaster records, and road structure, and executes a process using the artificial intelligence model to predict the risk of future road disasters occurring, the risk of obstruction to emergency vehicle passage, or areas where it is difficult to secure the road clearance route.

[12] [9] A road management method as described in [9], characterized in that the computer inputs at least one of cavity presence information, cavity location information, cavity depth information, or cavity shape information obtained by on-site ground surveys as learning data or update data for an artificial intelligence model, and performs a process to continuously improve the prediction accuracy or judgment accuracy of the artificial intelligence model.

[13] [9] A road management method as described in [9], characterized in that the computer uses an XAI (Explainable AI) configuration that presents the basis for the risk score and / or anomaly assessment corresponding to the output result of an artificial intelligence model, and performs a process to present the information presented by the XAI to an administrator or user in a form that allows them to understand the validity of the output result and the basis for the judgment.

[14] [9] A road management method as described in

[14] [9], characterized in that the computer acquires at least one of information regarding the optical fiber survey status, information regarding the satellite survey status, and information regarding the vehicle driving status, as well as information regarding patrol status, information regarding weather conditions, and information regarding road service status, performs an integrated analysis of at least two of the acquired information, and, based on the results of the integrated analysis, executes processing to grasp the occurrence status of road disasters, signs of abnormalities in road infrastructure, or risks of road obstructions.

[15] [9] A road management method according to the present invention, characterized in that the computer, in the event of a disaster, extracts multiple candidate routes for the emergency vehicle or the road clearance route based on the analysis results (including the integrated analysis results), analyzes information regarding the risk of road disasters occurring, passability, risk of traffic obstruction, or the soundness of the road infrastructure for each of the routes, and executes a process to select the optimal route from among the multiple candidate routes based on the analysis results. [Effects of the Invention]

[0006] According to this invention, by using different sensing technologies such as satellite sensing, optical fiber sensing, and vehicle movement sensing to acquire at least two pieces of information and then analyzing them individually or collectively, it becomes possible to monitor the risk of subsurface cavities over a wide area and continuously. This makes it possible to grasp premonitory abnormalities, which was difficult with conventional point-by-point and intermittent maintenance methods, and to make rational decisions about road infrastructure maintenance even during peacetime, contributing to the prevention of accidents and infrastructure collapse. Furthermore, by introducing an AI (artificial intelligence) model that inputs at least two of the above sensing data and outputs a subsurface cavity risk score, it becomes possible to quantify and automate the judgment. Furthermore, by utilizing actual measurement data obtained from on-site ground surveys, such as the presence, location, depth, and shape of cavities, and re-inputting this data into the AI ​​(artificial intelligence) model as learning data or update data, the accuracy of the judgment model can be continuously improved. As a result, the reliability of judgments is improved, enabling effective operation in long-term preventive maintenance. Furthermore, when a disaster occurs, in addition to the sensing information, additional information such as patrol information, weather information, and road service information can be acquired and analyzed, and combined with the risk assessment results accumulated during normal times, it can comprehensively determine the need for disaster response. Furthermore, by having a configuration that can determine road clearance routes as needed and execute disaster response support processing (securing emergency vehicle routes, supporting traffic restrictions, determining repair priorities, road clearance work, etc.), it is possible to realize a faster and more streamlined initial disaster response. [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, one embodiment of the 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 technical idea of ​​the present invention described in the claims. Furthermore, not all of the configurations described in this embodiment are necessarily essential components of the present invention. In addition, each individual component constituting a feature group may also be an independent invention. The system of the present invention can be applied not only to emergency response support in the event of a disaster, but also to the assessment of the soundness of road infrastructure and the predictive detection of the risk of subsurface cavities during normal times, thereby contributing to the prevention of accidents and road collapses, as well as the optimization of long-term maintenance costs through infrastructure maintenance support. In addition to supporting emergency response in the event of a disaster, this system can also be used in peacetime to manage the health of road infrastructure and detect signs of subsurface cavity risk, thereby contributing to accident prevention and long-term reduction in road maintenance costs through infrastructure maintenance support.

[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. Based on the results of these analyses, the decision unit 630 determines whether road infrastructure maintenance or disaster response is necessary, and issues instructions for response processing as needed (for example, determining a repair plan or a road clearance route).

[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 or a stationary in-vehicle device mounted on the vehicle Vh, or may have an image analysis function using AI (artificial intelligence).The vehicle Vh may also have an under-road cavity detection function (technology that irradiates electromagnetic waves from above the road toward below the road and estimates the locations of cavities and buried pipes from the reflected waves), or the vehicle Vh may be an under-road cavity detection vehicle. 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, roadside gutter damage, road surface unevenness, cavities under the road, 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 accident vehicles and 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 investigation status providing server 200 utilizes optical fiber sensing technology that uses optical fiber as a sensor, receives backscattered light from communication optical fibers included in cables laid on roads, etc., detects vibration patterns according to the driving conditions of vehicles on the roads, etc. based on the backscattered light, and acquires the driving conditions of vehicles on the roads, 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).In addition, by analyzing the intensity and frequency changes of minute vibrations propagating underground and continuous abnormal patterns of waveforms, it is possible to grasp the risk of underground cavities occurring and signs of ground deformation. 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 the following 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, cavities under 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 acquires road and 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 satellite. In particular, by using time-series interference analysis such as InSAR (Interferometric SAR), it is possible to detect minute displacements such as subsidence, uplift, and tilt of the ground with high accuracy, and to estimate signs of underground cavities and the risk of their formation from deformations that appear on the ground surface. 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, cavities under the road surface, 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 from vehicles such as connected cars, such as vehicle driving status and surrounding road conditions. In particular, it is possible to estimate abnormal behavior when a vehicle approaches a cavity based on information such as sudden braking, ABS activation, abnormal acceleration, tire spin, and bump response, making it an effective source of information for detecting risk areas caused by subsurface cavities. The vehicle travel status providing server 500 provides the vehicle travel status to the road disaster response support system 600 via the network NW. The vehicle driving conditions provided are information for each road obtained from private cars, taxis, buses, trucks, etc. (including electric vehicles), and may include some or all of the following: temperature, areas of sudden braking, skidding, tire spin, tire lock, ABS activated areas, flooded areas, and subsurface cavities from vehicle sensors, etc.; 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, etc.; identification of collapsed buildings, landslides, collapsed slopes, collapsed tunnels, total bridge damage, damaged roads, and impassable areas from 3D data, etc.; vehicle traffic history (including standard and large vehicles), traffic volume, traffic congestion, passing speed, average speed, acceleration, and whether or not there is rain or snow from wiper operation status from probe information (including ETC2.0), etc. 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. Based on the results of an integrated analysis of this information, the decision unit 630 can be configured to determine the need for preventive maintenance of road infrastructure and the need for response in the event of a disaster (e.g., securing emergency routes, determining repair priorities, etc.), and to execute the necessary response processing.

[0021] An 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, such as patrol information 640, optical fiber inspection information 650, satellite inspection information 660, weather information 670, vehicle travel information 680, and road service information, and stores the results as analysis results 690. The stored analysis results 690 may be configured to be viewable on a map display, a list display, or the like. Furthermore, the analysis unit 620 may be configured not only to process each piece of information individually, but also to collate and compare these different types of data and make a comprehensive evaluation based on the integrated correlation. For example, it may be possible to comprehensively collate and compare at least two or more types of data from satellite data, optical fiber data, and vehicle driving data, and detect the redundancy and consistency of abnormalities at the same location from multiple perspectives such as ground surface displacement, vibration intensity, and driving abnormalities, and evaluate the risk of subsurface cavities. Furthermore, the analysis unit 620 may include an AI (artificial intelligence) model. The AI ​​(artificial intelligence) model may be configured to use a learning algorithm such as a neural network to integrate multiple pieces of sensing data as input and output a risk score (e.g., a continuous value between 0.0 and 1.0 or a risk classification category) for each location. The output score is used as auxiliary information for making decisions in road infrastructure maintenance and disaster response. It may also include a configuration in which cavity presence / absence information, cavity location information, cavity depth information, cavity shape information, etc. obtained from on-site ground surveys are input into an AI (artificial intelligence) model as learning data or update data, and processing (relearning, model update) is performed to continuously improve prediction accuracy and judgment results. For example, it is possible to use the measurement data (date and time, location, seismic intensity, weather data, vibration, vehicle behavior, probe information, three-dimensional topographical data, etc.) contained in 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) as learning data to analyze and visualize the road disaster situation and the degree of risk of infrastructure damage. Furthermore, the analysis results 690 may be configured to be utilized in cooperation with the prediction unit or improvement unit as necessary, contributing to improving the accuracy of future disaster predictions and judgments. In addition, the information analyzed by the analysis unit 620 may be configured to analyze only a portion of the patrol information 640, optical fiber inspection information 650, satellite inspection 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 determines the need for road disaster response and road infrastructure maintenance in an integrated and comprehensive manner based on some or all of the analysis results 690 of each piece of information (including patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, and road service information) analyzed by the analysis unit 620, and stores the determination result 695. The determination result 695 may include criteria that reflect risk score thresholds, correlation patterns between analytical data, consistency with ground surveys, etc. The determination result 695 may be visualized on a map display, a list display, or a dashboard. In addition, the decision unit 630 may be equipped with a decision support function using an AI (artificial intelligence) model, and may be configured to determine maintenance priorities, the urgency of disaster response, etc. based on statistical threshold decisions, rule-based decisions, or predictive models using machine learning. Furthermore, the determination unit 630 may be configured to function as a trigger for executing disaster response support processing, including proposing repair plans, determining road clearance routes, recommending emergency vehicle routes, determining traffic restrictions, etc., as necessary, thereby enabling automatic or semi-automatic disaster response support linked to analysis results. Additionally, the decision unit 630 may be configured to cooperate with the road clearance unit or the robotics unit to formulate an implementation plan for road clearance or to give instructions for road clearance work by robots based on the determination result 695. This links determination and execution, realizing a fast and efficient disaster response. Furthermore, the judgment results 695 and the analysis results 690 may be used in the improvement department to retrain the AI ​​(artificial intelligence) model and update the judgment criteria, and may be combined with past history data and future scenario data to be reflected in the prediction department's prediction of future disaster risks and the development of advance plans. Furthermore, the road disaster response support system 600 may include an information providing unit that provides to an external party a part or all of the judgment result 695 and the analysis result 690. This information providing unit has the effect of enabling road disaster response policies and countermeasure information to be quickly disseminated and shared with administrative agencies, related businesses, general residents, etc.

[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 makes a comprehensive judgment on the necessity of road disaster response, and makes a decision (S20) to execute response processing (for example, determining repair plans and road clearance routes, etc.) as necessary. 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 determination criteria in the determination unit 630. The determination unit 630 can determine the necessity of road disaster response when triggered by events such as the occurrence of a tsunami (element 1) 631, a slope collapse causing an optical fiber disconnection (element 2) 632, road damage and collapsed buildings resulting in no vehicle traffic (element 3) 633, a heavy rain warning being issued and landslides causing tires to spin, preventing vehicles from moving forward and causing severe traffic congestion (element 4) 634, or a heavy snow warning being issued and snow having accumulated on the road surface causing the ABS to activate, causing severe traffic congestion with many stranded vehicles (element 5) 635. Furthermore, with regard to some or all of the judgment result 695 decided by the decision unit 630, the analysis result 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 the information provision unit (described in paragraph 0022), etc., it is possible to provide a function to notify the road administrator 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., in a unified manner), 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.), and it is not necessarily necessary to go through the information provision unit. An example of an information disclosure function for road users and residents is, but is not limited to, the following: A road disaster response support system characterized by transmitting one or more pieces of information acquired by the information acquisition unit, the results of analysis by the analysis unit, the necessity of road disaster response determined by the decision unit, the road clearance implementation plan formulated by the road clearance unit (described in paragraph 0029), the restoration status by the robotics unit (described in paragraph 0030), or the necessity of road disaster response predicted by the prediction unit (described in paragraph 0026), etc., 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 continuously improves the analysis results 690 (including the analysis method) obtained by the analysis unit 620, the judgment results 695 output by the decision unit 630, and some or all of the judgment criteria. The improvement unit aims to improve the accuracy of the analysis and judgment processes and reduce erroneous judgments by utilizing disaster-related data (images, numerical data, simulation results, etc.) obtained from external sources. The improvement unit can also cooperate with the infrastructure maintenance unit and the road clearance unit, and can optimize the improvement targets for each process (cavity risk assessment, repair judgment, road clearance route determination, etc.). An example of the improvement process is a processing configuration in which hypothesis data, verification data, future prediction data, etc. related to a disaster are input, and after referring to the history of past judgment results 695 and analysis results 690, the improvement unit performs re-evaluation and relearning using methods such as statistical analysis and machine learning, and the results are fed back to the existing model to improve the accuracy of judgment. The improvement unit may have an analysis function using AI (artificial intelligence) and perform model re-learning processing for image recognition and risk score calculation. For example, the improvement unit inputs image data generated from patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, and road service information acquired by the information acquisition unit 610, as well as various associated sensing data (position information, vibration, temperature, speed, topographical information, etc.), predicts and outputs disaster situations, and uses the corrected and supplemented results to tune the AI ​​(artificial intelligence) model and judgment criteria. In addition, the improvement department can use information on the presence or absence of cavities, cavity location information, cavity depth information, or cavity shape information obtained from on-site ground surveys as "correct data (teaching data)" and, through re-training of the AI ​​(artificial intelligence) model, improve the predictive accuracy of cavity risk assessments and continuously improve the results of infrastructure maintenance decisions. The improvement process may be performed by the improvement unit alone, or may be configured to operate in cooperation with an infrastructure maintenance unit or a road clearance unit. The improvement unit may also be configured to work with the prediction unit as needed to improve the model or reset the criteria based on the disaster prediction results.

[0026] The road disaster response support system 600 may include a prediction unit that predicts future disaster occurrences and road damage risks in advance by utilizing the history of previously acquired analysis results 690 and judgment results 695. The prediction unit can input and analyze various time-series data related to disasters (actual data, future scenario data, weather forecast data, etc.) and output the need for future road disaster response quantitatively or probabilistically. This will enable road managers to plan and implement advance preparations, response plans, and training plans in preparation for the risk of disasters during peacetime, thereby speeding up and streamlining initial responses when a disaster actually occurs. One example of prediction processing is a configuration in which hypothesis data, verification data, training data, and future prediction data (such as expected patterns of earthquakes or heavy rain disasters that may occur once every few decades) are input into the prediction section, and this is compared with existing analysis history and judgment results to simulate in advance the scope of impact and necessary responses in the event of a disaster. The prediction unit may be equipped with a prediction function using AI (artificial intelligence), and may be configured to use a prediction model that has learned multiple input factors, such as weather conditions, topography, past disaster records, and road structure, to preliminarily identify areas where it is difficult to secure road clearance routes and risk areas that may hinder the passage of emergency vehicles. Furthermore, the prediction results may be utilized in cooperation with the analysis department and improvement department, and may be fed back to the infrastructure maintenance department and road clearance department in formulating advance plans as needed.

[0027] The Road Disaster Response Support System 600 functions effectively by using one or more pieces of information from among patrol status, optical fiber survey status, satellite survey status, weather conditions, vehicle driving status, and road service status, and in particular, by combining two or more pieces of information, mutually complementary analysis becomes possible, realizing more accurate situational understanding and decision support. For example, by understanding wide-area surface movements through satellite surveys, detecting local underground vibrations through optical fiber surveys, and acquiring driving records and abnormal behavior through vehicle driving conditions, it becomes possible to achieve both wide-area monitoring and local detection. This will create a multi-layered decision-making platform, from predictive monitoring in peacetime to emergency response decisions in the event of a disaster. In addition, by conducting an integrated analysis that checks and compares these multiple pieces of information in an integrated manner, it is possible to go beyond simply listing information and achieve the following advanced judgment processing: ●Improved accuracy through matching image data with non-image data: For example, if road damage is detected through image analysis, it can be compared with the corresponding optical fiber displacement and vehicle vibration history to reduce false positives and improve the reliability of disaster assessment. ●Improved situational awareness based on multi-perspective information: Even in situations where detection is difficult using a single sensor, such as at night or in bad weather, by using other information sources (satellite, driving, weather, etc.), it is possible to understand the damage situation in a timely and spatially complementary manner. ●Improved rationality and responsiveness of decisions: Through integrated matching processing based on diverse sensing information and structural analysis using AI (artificial intelligence), etc., risk assessment of road infrastructure, prioritization of disaster response, and determination of road clearance routes can be carried out quickly and rationally. In this way, this system will contribute to minimizing damage and the early recovery of disaster-stricken areas by achieving both a faster initial response when a disaster occurs and more advanced preventive maintenance during peacetime.

[0028] Another embodiment of the present invention may employ a simple configuration for acquiring and analyzing information in stages. In this embodiment, a preliminary determination is first made of the occurrence and severity of a road disaster based on information about weather conditions and information about patrol status. Then, only if a road disaster is determined to be severe, additional information on at least one of vehicle driving status, optical fiber survey status, satellite survey status, and road service status is acquired, and detailed analysis and disaster response decisions are made. This configuration allows for practical disaster response support while reducing system costs and load. In this configuration, the information acquisition unit first acquires information on weather conditions and patrol situations, and the analysis unit uses this information to assess the occurrence and severity of road disasters (for example, earthquakes of magnitude 6 or higher, widespread wind and flood damage, snow damage, landslides, etc.). If the assessment results exceed a predetermined threshold, the information acquisition unit will acquire additional sensing information, and the analysis unit will reanalyze this information, enabling more advanced disaster response decisions. This allows for efficient system operation through step-by-step information utilization. Furthermore, in the processing of this embodiment, similar to the normal configuration, the decision unit may determine whether a disaster response is necessary and the response policy, and may perform emergency response, notification processing, etc. as necessary. Furthermore, this tiered configuration can also be applied to specific operation modes and simple implementation forms as an auxiliary configuration for the multiple sensing integrated analysis processing, which is the main configuration.

[0029] The road disaster response support system 600 may be provided with a road clearance unit that registers a road clearance plan that has been formulated in advance and determines a road clearance route based on the road clearance plan and acquired information when a disaster occurs. By providing the road disaster response support system 600 with a road clearance unit, it is possible to obtain the effect of quickly clearing roads in the event of a disaster. In normal disasters, the process is emergency restoration followed by full restoration, but in large-scale disasters, emergency restoration (road clearance) must be carried out before emergency restoration. Road clearance involves quickly removing a minimum amount of rubble and repairing uneven sections to ensure a rescue route, allowing emergency vehicles to pass through for rescue and emergency relief activities, emergency supply support, and restoration. Road administrators formulate road clearance plans in advance, including road clearance bases (bases for support units, disaster prevention bases such as collection points for supplies and equipment), road clearance routes (wide-area travel routes, access routes, routes within the disaster area), and timelines (specific action plans). An example of the road clearance section in the road disaster response support system 600 is a configuration that registers road clearance plans that have been formulated in advance before or after a disaster, determines road clearance routes in the event of a disaster based on multiple disaster, damage, and traffic-related data acquired by the information acquisition section or the results of an integrated analysis by the analysis section, and formulates an implementation plan for road clearance based on that decision. The road clearance unit may also have an analysis function and a route determination function using AI (artificial intelligence).For example, two or more pieces of information acquired by the information acquisition unit, such as patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, and road service information, or image, sensor, and three-dimensional topographical data based on these, may be input into a machine learning model, and the road disaster situation may be output, supplemented, and corrected, and then a process may be performed to dynamically determine a road clearance route. Furthermore, the determination of the road clearance route may be configured to select the optimal route in accordance with the disaster situation from multiple candidate routes that avoid high-risk points that should be avoided. The road clearance route and implementation plan determined by the road clearance unit may be notified to the road administrator via a management terminal or an external server, and may be automatically reflected in coordination with restoration work and traffic regulation instructions. In addition, the determination process may be configured to supplement or correct the route determination process by the road clearance unit by referring to on-site information acquired by the robotics unit (described in paragraph 0030). Furthermore, the road clearance unit may be configured to cooperate with the analysis unit, improvement unit, prediction unit, or infrastructure maintenance unit as necessary, to improve the accuracy of clearance routes in the event of a disaster and to contribute to dynamic re-planning.

[0030] 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 use AI (artificial intelligence) to determine which roads should be prioritized for clearance, specify the road clearance route, and 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, and 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 embodiment of the robotics section in the road disaster response support system 600 is as follows, but is not limited to this (each robot is connected to a network NW). A road disaster response support system (including road management methods) characterized in that the Robotics Department uses artificial intelligence analysis and robotics technology to carry out road clearance work using disaster response robots (large heavy machinery, autonomous heavy machinery, small robots, humanoid robots, four-legged robots, snake-type robots, multi-legged robots, earthworm-type robots, transforming robots, articulated robots, crawler robots, autonomous excavation robots, small unmanned flying robots, underwater search robots, rescue robots, etc.) 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) include, but are not limited to, the following: Remotely or autonomously controlling autonomous heavy machinery (such as bulldozers and excavators) 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; and using AI (artificial intelligence) to analyze the progress of road clearance in real time and automatically adjust optimal work instructions for the robots. Additionally, integrated control of multiple different robots (such as autonomous heavy machinery, small robots, and drones) to optimally allocate tasks. Examples of AI (artificial intelligence) include, but are not limited to: Route optimization AI for calculating road clearance routes and determining priorities (Dijkstra algorithm, reinforcement learning, multi-agent, 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-agent, 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 include, but are not limited to, the following: Route optimization AI (artificial intelligence): 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 (artificial intelligence): 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 (artificial intelligence): 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 (artificial intelligence): Input data (work video data, robot work logs, weather information, etc.) → Output data (progress reports, anomaly detection alerts, work optimization instructions, etc.). By utilizing large-scale language models, road clearance plans, road clearance implementation plans, and road clearance operations can be continuously improved regardless of the type of language. This is particularly effective for understanding road clearance plans formulated in advance, learning disaster countermeasures 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 include, but are not limited to, the following: complementing and optimizing road clearance plans and road clearance implementation plans, learning from global disaster countermeasure data on the Internet, real-time support for robots, and real-time use of disaster data. The Robotics Department may work in cooperation with the Analysis Department, Improvement Department, Prediction Department, and Road Clearance Department, and may include a configuration that dynamically updates the target areas and priorities for road clearance work based on the disaster risk information and judgment results provided by each department. Furthermore, it may work in cooperation with the Infrastructure Maintenance Department as needed to coordinate with the actual restoration work carried out immediately after disaster response and to provide support for regular infrastructure repairs. The robot types listed above are merely examples and are not limited to these. Other types of robots may be used as appropriate depending on the purpose of disaster response and the environment in which they are used.

[0031] The road disaster response support system 600 may include an infrastructure maintenance unit. The infrastructure maintenance unit is responsible for evaluating the soundness of road infrastructure and making maintenance management decisions in normal times, and is also configured to link the results of that determination with disaster response processing in the event of a disaster, thereby contributing to the compatibility of maintenance and initial response. The infrastructure maintenance unit may be configured to calculate a subsurface cavity risk score using an AI (artificial intelligence) model based on at least two of the satellite data, optical fiber data, and vehicle driving data acquired by the information acquisition unit, thereby enabling quantitative extraction of locations where subsurface cavity formation is a concern and supporting prioritization of infrastructure inspections and repairs. Additionally, for locations that are judged to have a high risk of cavities, on-site ground surveys (for example, underground radar surveys, vibration measurements, camera photography, etc.) are conducted, and the results (presence, location, depth, shape, etc. of cavities) are re-input as learning data or update data for the AI ​​(artificial intelligence) model, thereby enabling continuous improvement in the prediction accuracy of the AI ​​(artificial intelligence) model or infrastructure maintenance decisions. Furthermore, the system may be equipped with XAI (Explainable AI) technology that visualizes the output risk score and the basis for anomaly assessment, and may be configured to prioritize correct data acquisition through an active learning strategy or to use augmentation processing of learning data using GAN (Generative Inverse Network), etc. This will enable improvements in model accuracy and learning efficiency. This configuration will enable road managers to make accurate and rational repair decisions based on infrastructure assessment results and on-site survey information based on AI (artificial intelligence), contributing to the optimization of maintenance costs and the prevention of accidents. The infrastructure maintenance unit may operate in cooperation with the analysis unit or improvement unit, and may be configured to update and optimize cavity risk assessments based on analysis and judgment results. It may also be configured to cooperate with the prediction unit, road clearance unit, and robotics unit as necessary to support actual restoration work after disaster response and ongoing infrastructure maintenance.

[0032] The road disaster response support system 600 according to the present invention may include, in addition to the integrated analysis configuration using the AI ​​(artificial intelligence) model described above, analysis processing using non-AI methods such as statistical analysis, threshold comparison, rule-based inference, etc., without being limited to AI (artificial intelligence) models. This enables flexible configuration selection according to the operating environment and optimization from the perspectives of real-time performance and processing load. The system may also be configured to switch between integrated analysis using these non-AI methods and analytical processing using an AI (artificial intelligence) model depending on the application, system configuration, and operational conditions. Examples include, but are not limited to, the following: An information acquisition unit that acquires at least two of information on satellite survey status, information on optical fiber survey status, and information on vehicle driving status, an analysis unit that performs an integrated analysis of the information by statistical analysis, threshold comparison, or rule-based inference, and a decision unit that determines whether road infrastructure maintenance or disaster response is required based on the analysis results.

[0033] Furthermore, the road disaster response support system 600 according to the present invention may include a configuration for switching the operation policy between a normal operation mode and a disaster operation mode. In normal times, the main purpose is to identify predictive abnormalities in road infrastructure, assess cavity risks, and make decisions about regular maintenance. However, in the event of a disaster, the system will switch to an operational mode where the main purpose is to secure emergency routes, determine priority for road clearance, and support rescue efforts based on sensing information and integrated analysis results. Such switching is controlled by software based on the operational policy of the entire system, and it is not necessary to provide a dedicated "operation switching unit." For example, in normal mode, the analysis unit can apply processing parameters that focus on cavity risk assessment and abnormality detection, and in disaster mode, the determination unit can perform processing that focuses on extracting roadblocks with high disaster priority and selecting corresponding routes.

[0034] Furthermore, the normal operation mode and the disaster operation mode may include a configuration for switching the risk determination criteria (threshold setting) in the integrated analysis and the prioritization logic for the on-site investigation target points. For example, in normal times, to prioritize wide-area and comprehensive predictive detection, the threshold for anomaly detection is set relatively loose, making it easier to detect potential risks, while in the event of a disaster, the threshold for determining anomaly scores is set stricter, enabling operations to prioritize the extraction and notification of high-risk locations that require a rapid response. The road disaster response support system may be configured to dynamically change a risk score calculation, a judgment threshold for anomaly detection, or a prioritization logic for on-site investigation target points depending on whether the system is in a normal operation mode or a disaster operation mode, as shown in the following examples, but is not limited to these.

[0035] The road disaster response support system 600 of the present invention may be configured to continuously acquire and analyze sensing information on disasters, damage, and recovery status, etc., and dynamically reevaluate and reconfigure the analysis results and response plans based on the latest information as needed. This enables flexible responses that respond quickly to changes in the disaster situation.

[0036] <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.

[0037] 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]

[0038] 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 among information on optical fiber survey status obtained by optical fibers laid on roads, information on satellite survey status, and information on vehicle driving status; an analysis unit that analyzes the two or more pieces of information acquired by the information acquisition unit, Based on the results of the analysis by the analysis unit, In normal times, we evaluate the risk of subsurface cavities or the soundness of road infrastructure for multiple road sections, including emergency vehicle routes or road clearance routes, and determine the need for road infrastructure maintenance. A road disaster response support system characterized in that, in the event of a disaster, the necessity of road disaster response is determined based on the analyzed results including the evaluated results.

2. The road disaster response support system according to claim 1, The road disaster response support system is equipped with an artificial intelligence model including a neural network, A road disaster response support system characterized by having a configuration that performs data selection based on active learning or learning processing using GAN (generative inverse network) on the artificial intelligence model.

3. The road disaster response support system according to claim 1, The road disaster response support system includes a prediction unit, The prediction unit uses an artificial intelligence model that has learned at least one of weather conditions, topography, past disaster records, and road structure, A road disaster response support system characterized by using the artificial intelligence model to execute a process to predict at least one of the following: the risk of future road disasters, the risk of obstruction to emergency vehicle passage, or areas where it is difficult to secure the road reopening route.

4. The road disaster response support system according to claim 1, The road disaster response support system includes an improvement unit, The improvement unit or the analysis unit inputs at least one of cavity presence information, cavity position information, cavity depth information, and cavity shape information obtained by the on-site ground survey as learning data or update data for the artificial intelligence model, A road disaster response support system characterized by executing a process to continuously improve the prediction accuracy or judgment accuracy of the artificial intelligence model.

5. The road disaster response support system according to claim 1, The road disaster response support system, regarding the output results of the artificial intelligence model, An XAI (Explainable AI) configuration is provided that presents a risk score and / or a reason for an anomaly assessment corresponding to the output result, A road disaster response support system characterized in that the information presented by the XAI presents the validity of the output results and the basis for the judgment in an understandable form to the administrator or user.

6. The road disaster response support system according to claim 1, The information acquisition unit, in addition to the information about the optical fiber investigation status, the information about the satellite investigation status, and the information about the vehicle traveling status, Acquire at least one of information regarding patrol status, information regarding weather conditions, and information regarding road service status; the analysis unit performs an integrated analysis of at least two pieces of information acquired by the information acquisition unit, A road disaster response support system characterized by performing processing to grasp at least one of the following based on the results of the integrated analysis: the occurrence status of road disasters, signs of abnormalities in road infrastructure, or the risk of road obstruction.

7. The road disaster response support system according to claim 1, The road disaster response support system includes a road clearance unit, In the event of a disaster, the road clearance unit, based on the results of analysis by the analysis unit (including cases where the results include an integrated analysis result obtained by integratedly analyzing at least two or more pieces of information acquired by the information acquisition unit), extracting a plurality of candidates for the emergency vehicle route or the road clearance route; Analyzing at least one of information regarding the risk of road disasters, passability, risk of road obstruction, or the soundness of the road infrastructure for each of the routes; A road disaster response support system characterized by executing a process of selecting the optimal route from among the multiple candidate routes based on the analysis results.

8. A program for causing a computer to function as the road disaster response support system according to any one of claims 1 to 7.

9. A road management method using a computer, comprising: The computer communicates with the network via Acquire at least two pieces of information from among information on optical fiber survey status obtained by optical fiber laid on roads, information on satellite survey status, and information on vehicle driving status; Analyzing the two or more pieces of information acquired, and based on the analysis results, In normal times, we evaluate the risk of subsurface cavities or the soundness of road infrastructure for multiple road sections, including emergency vehicle routes or road clearance routes, and determine the need for road infrastructure maintenance. A road management method characterized in that, in the event of a disaster, a process is carried out to determine the need for road disaster response based on the analyzed results including the evaluated results.

10. 10. The road management method according to claim 9, the computer uses an artificial intelligence model including a neural network; A road management method characterized by performing data selection based on active learning or learning processing using GAN (generative inverse network) on the artificial intelligence model.

11. 10. The road management method according to claim 9, The computer uses an artificial intelligence model that has learned at least one of weather conditions, topography, past disaster records, and road structure, A road management method characterized by using the artificial intelligence model to perform a process of predicting at least one of the following: the risk of future road disasters, the risk of obstruction to emergency vehicle passage, or areas where it is difficult to secure the road opening route.

12. 10. The road management method according to claim 9, The computer inputs at least one of cavity presence information, cavity position information, cavity depth information, and cavity shape information obtained by the on-site ground survey as learning data or update data for the artificial intelligence model, A road management method characterized by performing a process to continuously improve the prediction accuracy or judgment accuracy of the artificial intelligence model.

13. 10. The road management method according to claim 9, The computer, regarding the output result of the artificial intelligence model, Using an explainable AI (XAI) configuration that presents the basis for the risk score and / or anomaly assessment corresponding to the output result, A road management method characterized by performing a process to present the information presented by the XAI to an administrator or user in a form that allows them to understand the validity of the output results and the basis for the judgment.

14. 10. The road management method according to claim 9, In addition to the information on the optical fiber survey status, the information on the satellite survey status, and the information on the vehicle traveling status, the computer Acquire at least one of information regarding patrol status, information regarding weather conditions, and information regarding road service status; Comprehensively analyzing at least two pieces of information among the acquired information; A road management method characterized by performing a process to grasp at least one of the following based on the results of the integrated analysis: the occurrence of road disasters, signs of abnormalities in road infrastructure, or the risk of road obstruction.

15. 10. The road management method according to claim 9, In the event of a disaster, the computer performs the following based on the analysis results (including cases where the results include an integrated analysis result obtained by integratedly analyzing at least two or more pieces of information obtained): extracting a plurality of candidates for the emergency vehicle route or the road clearance route; Analyzing at least one of information regarding the risk of road disasters, passability, risk of road obstruction, or the soundness of the road infrastructure for each of the routes; A road management method characterized by executing a process of selecting an optimal route from the plurality of candidate routes based on the analysis results.

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