Road disaster response support system and program, road management method

Abstract

Traditionally, road infrastructure maintenance has focused on emergency response after accidents, making it difficult to manage the risk of hollowing out during normal times. Furthermore, when clearing roads during disasters, there have been challenges in dynamically utilizing advance plans and providing real-time decision support. Furthermore, with the increase in foreign visitors to Japan, there is a growing need for multilingual road disaster response information, creating a need for flexible support technology that can comprehensively realize preventive maintenance, disaster response, and multilingual support. [Solution] This invention is a technology related to road maintenance and disaster response, and provides a support system that uses sensing means such as satellites, optical fibers, and vehicle travel to predict the risk of infrastructure hollowing out during normal times, and in the event of a disaster, formulates a road clearance implementation plan based on a road clearance plan registered in advance. Furthermore, it is equipped with a function to provide information in multiple languages, making it possible to provide effective support to foreign visitors to Japan.

Description

[Technical Field]

[0001] The present invention relates to technology related to road maintenance and management and road clearance during disasters, and in particular to a road disaster response support system, related programs, and road management methods that, in normal times, evaluate the risk of hollowing out of road infrastructure and determine its soundness based on information acquired using multiple sensing means (satellite sensing, optical fiber sensing, vehicle travel sensing, etc.), and, in the event of a disaster, utilizes road clearance plans formulated in advance to quickly formulate road clearance implementation plans to ensure the passage of emergency vehicles, etc. Furthermore, the aim is to provide a flexible and advanced support system that functions effectively in both peacetime and disaster situations, by achieving both wide-area and continuous infrastructure monitoring, which has been difficult in the past, and rapid decision-making and planning support in the initial response to a disaster. This system will contribute to the creation of a road management system with high disaster resilience by seamlessly supporting the preventive maintenance phase and the disaster response phase. [Background technology]

[0002] In conventional road infrastructure management, emergency response measures are generally taken after accidents such as road surface collapses or subsidence have occurred, but there has been no adequate system established to monitor the risk of road hollowing or signs of deterioration over a wide area and continuously during peacetime.In addition, in the event of a disaster, rapid road clearance is required to ensure the passage of emergency vehicles such as ambulances, fire departments, and the Self-Defense Forces, but the information acquisition and judgment processing required to formulate an implementation plan are often personal, and there has been a lack of efficient and scientific support technology. In recent years, advances in advanced sensing technologies such as satellites, optical fiber, and connected cars have made wide-area road monitoring technically possible, but there have been limited examples of their integrated use in both infrastructure health assessments and disaster road clearance plans. Furthermore, ground-penetrating radar and other detection methods are point-specific and intermittent, and are not capable of continuous, area-wide assessments. Therefore, there are limitations to their use in detecting signs of disaster risk and in determining routes immediately after a disaster occurs. On the other hand, in the event of a large-scale disaster, prior to normal restoration procedures, an important administrative task is to carry out "emergency restoration (road clearance)" and ensure access to medical institutions, evacuation centers, and disaster bases. Road clearance here refers to the process of removing obstacles and making simple repairs immediately after a disaster to quickly enable the passage of emergency vehicles. To do this, a system is needed to accurately compare a road clearance plan formulated in advance with actual information at the time of the disaster and to quickly incorporate it into a road clearance implementation plan. However, conventional technology lacks the configuration to dynamically utilize road clearance plans, and does not adequately implement processes such as updating plans to reflect damage conditions and traffic closure information after a disaster, or optimizing implementation timing. Furthermore, the configuration to flexibly process information interpretation, weighting, route optimization, etc. using AI (artificial intelligence) technology is also insufficient, which leads to a burden on on-site decision-making and delays in initial response. Furthermore, with the recent increase in inbound tourism, the risk of foreign tourists visiting Japan facing natural disasters such as earthquakes, typhoons, heavy rains, and volcanic eruptions is also increasing. However, when a disaster occurs, foreign tourists have limited means of quickly and accurately obtaining information on whether roads are passable and evacuation routes, and language barriers and differences in information acquisition routes have led to delayed responses and confusion. Therefore, there is a need to develop a system that provides easy-to-understand, timely disaster-related traffic information to a diverse user base, including inbound tourists. [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 collapses and traffic disruptions during disasters due to subsurface cavities, ground deformation, etc. In the past, emergency responses were typically taken after these events became apparent, and there was insufficient wide-area and continuous understanding and management of cavity risks during peacetime. Furthermore, although individual sensing technologies exist, such as optical fiber sensing, satellite remote sensing, and vehicle driving data, no system had been established that could analyze these in an integrated manner to support both preventive infrastructure maintenance and emergency response in the event of a disaster. 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 utilizing the results of ground surveys to improve AI (artificial intelligence) models. Patent Document 2 describes a technology that uses satellite images to detect obstacles on roads and determine whether they are passable, but does not disclose integrated analysis with optical fiber and vehicle driving data, or configurations for risk score output and learning improvement using an AI (artificial intelligence) model. Patent Document 3 discloses a technology that analyzes images acquired from an in-vehicle camera using an AI (artificial intelligence) model to determine the disaster situation, but does not explicitly state how to combine data from different sensors or how to continuously improve the system by retraining the learning model. Patent document 4 discloses a configuration for visualizing data from radar-equipped vehicles on a map, but does not mention configurations such as the multi-dimensional integration of sensing information, cavity risk assessment using AI (artificial intelligence), or the utilization of ground survey results. As such, most conventional technologies are limited to detecting local anomalies based on single sensor data, and a comprehensive system that includes a comprehensive and continuous assessment of the risk of hollowing out during peacetime, as well as rational and immediate response support processing in the event of a disaster, has not yet been realized. [Means for solving the problem]

[0005] In order to solve the above problems, a road disaster response support system according to the present invention has the following configuration. a road clearance department that registers road clearance plans that have been formulated in advance and formulates road clearance implementation plans based on the road clearance plans when a disaster occurs; An analysis unit that evaluates and predicts the health of road infrastructure using multiple sensing methods (e.g., satellites, optical fibers, vehicle traffic, etc.) during normal times; The system is configured to include an improvement unit for using the results of the analysis unit to make decisions about infrastructure maintenance and management or disaster response support decisions. Additionally, areas predicted by the analysis unit to have a high risk of cavities are surveyed on the ground, and the results are fed back into the analysis unit's AI (artificial intelligence) model, thereby continuously improving the model's prediction accuracy.Furthermore, after a disaster occurs, the AI ​​(artificial intelligence) model and accumulated geospatial data can be used to help determine the quick and rational route for clearing roads. Furthermore, it can be equipped with a configuration that uses highly real-time information from optical fiber and satellites to identify impassable sections immediately after a disaster and determine priority routes for emergency vehicles, as well as a function that uses XAI (explainable AI) technology to visualize risk scores and the reasons for decisions. The improvement unit can also be configured to selectively retrain the learning model based on the reliability score of the judgment result, and can be configured to apply different judgment logic depending on the type of disaster. These configurations will enable quick and accurate decisions to open roads during disasters, helping to solve existing problems. [1] A road disaster response support system comprising: an information acquisition unit that acquires information on road disaster conditions when a disaster occurs; an analysis unit that analyzes the information acquired by the information acquisition unit and grasps the road disaster conditions; a registration means that registers road clearance plans that have been formulated in advance; and a road clearance unit that optimizes the road clearance plans based on the analysis results by the analysis unit and dynamically formulates and updates road clearance implementation plans. [2] A road disaster response support system as described in [1], characterized in that the road clearance unit has the function of evaluating multiple candidate road clearance routes based on cost, required time, and safety, and formulating an optimal road clearance implementation plan. [3] A road disaster response support system as described in [1] or [2], characterized in that the analysis unit is configured to perform integrated analysis based on multiple types of sensing data acquired by the information acquisition unit. [4] A road disaster response support system as described in any one of [1] to [3], characterized in that the information acquisition unit is configured to acquire at least two or more pieces of information from patrol information, optical fiber investigation information, satellite investigation information, road service information, weather information, and vehicle driving information. [5] A road disaster response support system as described in any one of [1] to [4], characterized in that the analysis results of the road disaster situation by the analysis unit include information identifying sections where traffic is obstructed or sections where road clearance is required. [6] A road disaster response support system as described in any one of [1] to [5], characterized in that it includes an improvement unit and is configured to improve the processing results by the analysis unit and the road clearance unit through feedback learning. [7] A road disaster response support system as described in any one of [1] to [6], characterized in that the improvement unit is configured to selectively re-learn a learning model based on the reliability score of the judgment result. [8] A road disaster response support system as described in any one of [1] to [6], characterized in that the improvement unit has a configuration that can apply different judgment logic depending on the type of disaster. [9] A road disaster response support system as described in any one of [1] to [6], characterized in that the road disaster response support system is configured to supplement or modify the road clearance implementation plan by the road clearance unit based on information acquired by the robotics unit.

[10] A road disaster response support system as described in any one of [1] to [6], characterized in that the road clearance department formulates the road clearance implementation plan based on route analysis using a machine-learned model by artificial intelligence.

[11] A road disaster response support system as described in any one of [1] to [6], characterized in that the analysis unit is configured to comprehensively analyze past disaster history information and current sensing information to evaluate disaster response priority.

[12] A road disaster response support system as described in any one of [1] to [6], characterized in that it includes an infrastructure maintenance unit and is configured to evaluate information regarding infrastructure health acquired by the analysis unit or the information acquisition unit.

[13] A road disaster response support system as described in any one of [1] to [6], wherein the road disaster response support system has a security configuration based on a zero trust architecture and is characterized by a configuration that performs authentication processing, encrypted communication, and access control.

[14] A road disaster response support system as described in any one of [1] to [6], characterized in that it has a switching control configuration for switching between normal operation mode and disaster operation mode.

[15] A road disaster response support system as described in any one of [1] to [6], characterized in that it has a foreign language support configuration and outputs the analysis results or the road clearance implementation plan in multiple languages.

[16] A program for causing a computer to function as the road disaster response support system described in any one of [1] to

[15] .

[17] A road management method using a computer, characterized in that the computer acquires information regarding road disaster conditions via a network when a disaster occurs, analyzes the acquired information to grasp the road disaster conditions, registers a road clearance plan that has been formulated in advance, and dynamically formulates and updates a road clearance implementation plan by optimizing the road clearance plan based on the analysis results.

[18]

[17] A road management method as described in

[17] , characterized in that the computer executes a process of selecting from a plurality of candidate routes the road clearance implementation plan in accordance with the road disaster situation in the disaster-stricken area, and optimizing the plan based on the evaluation results of the candidate route.

[19] A road management method according to

[17] or

[18] , characterized in that the computer determines the road clearance route included in the road clearance implementation plan based on the priorities of rescue operations, material transportation, and access to medical institutions. A road management method according to any one of

[20] ,

[17] to

[31] , characterized in that the computer uses, in addition to information on the road disaster situation, ground images, vibration information, slope information or topographical data acquired by robotics equipment or a robot when determining the road clearance implementation plan.

[21] A road management method according to any one of

[17] to

[31] , characterized in that the computer formulates the road clearance implementation plan by applying different analytical models depending on the type of disaster.

[22] A road management method according to any one of

[17] to

[31] , characterized in that the computer executes the judgment or analysis processing included in the road clearance implementation plan using a model clustered according to the characteristics of the affected municipality or region.

[23]

[17] to

[31] , a road management method described in any one of

[17] to

[31] , characterized in that the computer is configured to notify a plurality of relevant organizations of the road clearance implementation plan and to support rescue operations or traffic restrictions based on the notification. A road management method according to any one of

[24] ,

[17] to

[31] , characterized in that the computer executes a process to correct or improve the accuracy of the road clearance implementation plan using on-site image information, three-dimensional topographical information or obstacle information acquired by the robotics equipment or the robot. A road management method described in any one of

[25] ,

[17] to

[31] , characterized in that after formulating the road clearance implementation plan, the computer generates visualized data to ensure transparency of the analysis results and the basis for judgment, and provides the data to the road administrator. A road management method described in any one of

[26]

[17] to

[31] , characterized in that after the road clearance implementation plan is executed, the computer acquires the implementation results and performs a process of re-learning or re-evaluating the analytical model or judgment criteria based on the results.

[27] A road management method according to any one of

[17] to

[31] , characterized in that when acquiring information regarding the road disaster situation, the computer acquires at least one of satellite survey information, optical fiber survey information, vehicle driving information, patrol information, weather information, and road service information via a network.

[28] A road management method according to any one of

[17] to

[31] , characterized in that the computer forms regional clusters according to the characteristics of the affected municipalities or regions, and performs a process of optimizing the road clearance implementation plan by applying different judgment criteria or processing parameters to each cluster.

[29] A road management method according to any one of

[17] to

[31] , characterized in that, when creating the road clearance implementation plan, the computer receives feedback from an administrator as a human-in-the-loop process and executes a process of modifying the road clearance implementation plan by reflecting the feedback.

[30] A road management method described in any one of

[17] to

[29] , characterized in that the computer executes a process of continuously improving the road clearance implementation plan or analysis model using performance information obtained in conjunction with the execution of the road clearance implementation plan.

[31] A road management method according to any one of

[17] to

[30] , characterized in that the computer is configured to store the road clearance plan in a local terminal in advance in preparation for a communication failure during a disaster, and to have the terminal execute alternative processing. [Effects of the Invention]

[0006] According to the present invention, it is possible to grasp the risk of hollowing out of road infrastructure comprehensively and continuously during peacetime, thereby realizing advanced preventive maintenance that was difficult to achieve with conventional inspection-based infrastructure management. In the event of a disaster, a road clearance implementation plan can be quickly formulated based on a road clearance plan that was prepared in advance, and by using AI (artificial intelligence) to select rational routes based on the results of an analysis of the infrastructure condition and the impact of the disaster, it is possible to ensure the passage of emergency vehicles and optimize initial responses. In addition, by integrating and analyzing information from different sensors such as optical fiber, satellites, and vehicle driving data, it is possible to grasp cavity risks and fault conditions with high accuracy, and by continuously training this information into an AI (artificial intelligence) model, it is possible to improve prediction accuracy and responsiveness in the long term. Furthermore, by incorporating visualization functions and feedback mechanisms based on XAI (Explainable AI) technology, the system will support the decisions of on-site users and local government officials, enabling more practical and reliable disaster response. In addition, by equipping the system with functions for obtaining correction information using robotics, multilingual support, and notification functions based on traveler attributes, it will be possible to quickly provide information on passability and danger avoidance in the event of a disaster to a diverse range of users, including foreign tourists visiting Japan, thereby contributing to reducing confusion and secondary damage. [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 showing an example of a hardware configuration of a road disaster response support system according to the present invention. Note that the configuration diagrams and flowcharts shown in Figures 1, 9, 10, 11, etc. show examples of the present invention and do not comprehensively illustrate all of the components according to the present invention. Configurations and processes not explicitly shown in the drawings are also included as elements that can be implemented in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, an 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. In addition to supporting emergency response in the event of a disaster, the system of the present invention can also be applied to assessing the soundness of road infrastructure and predicting the risk of subsurface cavities during normal times, which can contribute to preventing accidents and road collapses, as well as optimizing long-term maintenance costs through infrastructure maintenance support. The system may also be configured to provide information in multiple languages ​​to foreign users, including inbound tourists, via smartphones, travel apps, etc. This will enable foreign tourists to instantly understand their current location and passability near their destination, risk avoidance routes, road conditions, etc. in the event of a disaster, and will support them in making decisions about safe actions. It should be noted that "information regarding road disaster conditions" is a concept that encompasses multiple types of information used to grasp the status of road infrastructure during a disaster, such as disaster information, road information, traffic information, and weather information.

[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). Furthermore, in the present invention, the road disaster response support system 600 is configured to store information on the road clearance department, infrastructure maintenance department, improvement department, robotics department, and road service status. On the other hand, the prediction unit and information providing unit may be additionally provided in the system as needed.

[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. In addition, in the present invention, the function of providing information on the road service situation is configured to be provided in the road disaster response support system 600, and is acquired via the network NW through a dedicated server, terminal device, etc.

[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 in the form of a map display, a list display, a time-series graph 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 measurement data (date and time, location, seismic intensity, weather data, vibration, vehicle behavior, probe information, three-dimensional topographical data, etc.) contained in the various information (patrol information, optical fiber survey information, satellite survey information, weather information, vehicle driving information, road service information) acquired by the information acquisition unit 610 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 future disaster prediction and improvement of judgment accuracy. 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] Furthermore, the analysis unit 620 may use a deep learning model that automatically detects abnormal signs from multiple disaster-related information sources. For example, by using LSTM (Long Short-Term Memory), abnormal patterns can be detected from time-series changes in weather information, earthquake waveforms, vehicle behavior, etc. This makes it possible to predict disaster precursors and the risk of secondary damage with high accuracy. Furthermore, a graph neural network (GNN) may be applied to analyze the road network structure and the spatial relationships between affected nodes, thereby extracting priority road clearance route candidates and routes with a high risk of damage spreading.

[0023] The analysis unit 620 may also include a process for assigning a reliability score to each data source. The reliability score is dynamically calculated based on the source (public institution / general user), observation conditions, past accuracy history, etc. The analysis unit 620 excludes or weights data below a predetermined threshold to prevent misjudgments due to inaccurate information. Furthermore, the analysis results 690 may be used to evaluate response priorities, and may be weighted and scored based on factors such as the likelihood of human casualties, the status of functional outages at evacuation centers and hospitals, and the impact of damage to lifelines, and visualized as a heat map on a map.

[0024] Furthermore, the analysis unit 620 may have a reliability score generation function that evaluates the reliability of various information used in the analysis. The reliability score generation function may be configured to score each information source acquired by the information acquisition unit 610 based on the frequency of information acquisition, past accuracy, acquisition method (automatic measurement / manual report), etc., and to supply highly reliable information to the analysis process with priority. For example, for disaster reports and resident notification information collected from social media, etc., a reliability score can be calculated taking into account the consistency of location information, the degree of agreement with the damage situation based on image analysis, past notification history, etc., and information with a score below a threshold can be excluded from analysis or corrected. The reliability score may be referred to in each process in the analysis unit 620, prediction unit, and improvement unit, and may be configured to contribute to improving information weighting and anomaly detection accuracy. Furthermore, the transparency of decision support may be increased by providing an interface (such as a reliability label display) that allows the user to explicitly check the information rating.

[0025] Furthermore, the analysis unit 620 may be provided with a citizen participation learning function that utilizes reports, posts, and feedback information from residents and users to improve the accuracy of AI (artificial intelligence) models and optimize judgment criteria. For example, the report contents (text, photos, videos, etc.) regarding damage, depressions, traffic obstructions, etc. on the road are collected together with location information and time information, and whether or not the information is consistent with the analysis result 690 or the judgment result 695 is evaluated, and accurate reports are used as training data for retraining an AI (artificial intelligence) model. In addition, the system may be configured to encourage residents to improve the quality of their posts through a feedback function (report evaluation and report correction) for false and inappropriate reports, thereby contributing to improving the overall performance of the model. This type of citizen-participation learning structure will link the flow of information from society as a whole with the AI ​​(artificial intelligence) learning platform, and is expected to lead to increased participation in the maintenance and management of public infrastructure and strengthening of local disaster prevention capabilities.

[0026] Furthermore, the analysis unit 620 may have a reliability visualization function that outputs, as accompanying information, data on which the judgment is based and the certainty (reliability) of the judgment for various judgment results by AI (artificial intelligence). In this configuration, the reason for the decision and the score are displayed side by side, for example, "This road section is recommended to be closed: reliability 87% (basis: satellite imagery + vibration anomaly + SNS report)," allowing users or administrators to confirm the transparency of the AI ​​(artificial intelligence) decision. The reliability score can be calculated using the probability output (such as softmax output) within the AI ​​(artificial intelligence) model or the consistency of the evidence (the agreement rate between multiple information sources). Furthermore, if the judgment score falls below a threshold, a note such as "Caution required" or "Further investigation recommended" can be added to help prevent overconfidence in judgment. This type of decision visualization configuration will increase social acceptance of the introduction of AI (artificial intelligence) and also contribute to improving the sense of security and satisfaction in actual operations at disaster response sites.

[0027] Furthermore, the analysis unit 620 may include a reliability evaluation mechanism based on multiple information sources. In the event of a disaster, in addition to official sensing data (satellite images, vibration sensors, vehicle behavior, etc.), near-real-time information such as reports from residents, social media posts, and patrol reports may be collected, but the reliability of this information varies. For this reason, this system may be configured to assign a predefined reliability score to each information source (e.g., Japan Meteorological Agency = high, SNS = medium, unregistered reports = low), and weight the information when inputting it into the AI ​​(artificial intelligence) model. In addition, it is also possible to evaluate the consistency (degree of consistency) and spatiotemporal consistency of content across multiple information sources, and automatically determine the reliability rank of the information (high, medium, low), or to add labels such as "needs confirmation" or "pending" to the judgment result for low-reliability information. This type of information reliability evaluation structure is also effective in preventing misjudgments at disaster sites and improving the social transparency of AI (artificial intelligence) decisions.

[0028] The road disaster response support system 600 may also be configured to cooperate with external mobility services and in-vehicle devices. For example, the system may be configured to mutually cooperate with information from mobile systems such as car navigation systems, smartphones, MaaS (Mobility as a Service) apps, self-driving vehicles, and electric vehicles (EVs) to provide road clearance information, passable routes, danger avoidance instructions, and the like in real time during a disaster. In this configuration, disaster conditions and road clearance route determination results are automatically notified to in-vehicle terminals or smartphones, and adaptive navigation instructions are possible based on the user's current location, direction of travel, driving intentions, etc. Furthermore, for autonomous vehicles, a configuration is possible in which "road avoidance commands" and "stop commands" according to road disaster conditions are directly reflected in the control system. Furthermore, API integration with MaaS service providers will enable processes such as optimizing emergency transportation methods, reconstructing routes, and adjusting traffic demand during disasters, contributing to improving disaster response capabilities across society as a whole.

[0029] The road disaster response support system 600 may also have a condition setting function for the timing and trigger of updating the AI ​​(artificial intelligence) model. The model update process may be configured to be executed automatically or semi-automatically when any of the following conditions is met: (1) When a new disaster occurs and actual damage information on the site is acquired (e.g., cavity detection results, structural damage data, image diagnosis results, etc.) (2) When the system's accuracy falls below a predetermined threshold (e.g., a continuous decline in confidence score, an increase in false positive feedback, etc.) (3) When a government or specialist agency issues a model update order (e.g., earthquake response specification revisions, regulation changes, etc.) By setting and managing such update conditions in advance, it is possible to prevent the judgment model from becoming obsolete, and to achieve continuous system maintenance and optimization of disaster response accuracy.

[0030] Furthermore, the road disaster response support system 600 may be configured to be able to dynamically change the decision rules or evaluation criteria depending on the type, scale, and damage situation of the disaster. For example, one possible configuration is to change the type of data to be targeted, the evaluation items to be prioritized (vibration intensity, flood depth, traffic blockage rate, etc.), judgment thresholds, etc. depending on the type of disaster, such as earthquake, heavy rain, landslide, etc. Furthermore, even within the same disaster, the judgment criteria may be optimized for each region, taking into account the characteristics of the affected area (urban / mountainous area, aging rate, damage to lifelines, etc.). Furthermore, by configuring the system to dynamically switch decision priorities and implementation details from the "lifesaving priority phase" to the "supply support phase" and "life restoration phase" depending on the stage of the disaster, it is possible to increase the flexibility and real-time adaptability of on-site responses.

[0031] Furthermore, the analysis unit 620 may be configured to dynamically adjust disaster response priorities according to regional characteristics. For example, in areas where facilities requiring special care, such as elderly care facilities, welfare facilities, hospitals, evacuation shelters, and elementary and junior high schools, are concentrated, the analysis unit 620 may be configured to prioritize road clearances around these facilities. It is also possible to perform a multidimensional weighting evaluation based on factors such as population density, the proportion of people vulnerable to disasters, the concentration of lifelines, and the distribution of medical resources, and automatically determine the optimal response order for each region. This enables flexible and rational decisions based not only on the physical damage situation but also on the social needs of the region.

[0032] Furthermore, the road disaster response support system 600 may be configured to handle damage to communication infrastructure or network interruptions during a disaster. For example, various information acquisition units and terminals may be configured to ensure communication without infrastructure dependency by using communication means such as a local network, mesh network, or LPWA (low-power wide area network). Furthermore, a failover configuration allows on-site terminals to autonomously continue making decisions and formulating road clearance plans even when communication with the central server is unavailable, thereby enhancing operational continuity (robustness) during a disaster.

[0033] The Road Disaster Response Support System 600 may also be equipped with a control mechanism for switching between automatic judgment and manual intervention. For example, while responses are usually handled automatically by AI (artificial intelligence), it is possible to configure the system so that expert staff or administrators can manually intervene in cases of high urgency or where there is a high degree of uncertainty. This creates a safety net against the risk of misjudgment by AI (artificial intelligence), improving reliability and flexibility in disaster response. Records of manual intervention are also stored in the system, allowing for a configuration that can contribute to future model improvements.

[0034] Furthermore, the road disaster response support system 600 may be configured to perform specialized processing according to the type of disaster. For example, it may be configured to change the evaluation criteria and weightings used in analysis processing, decision processing, and road clearance route selection, taking into account the different damage characteristics, progression speed, and impact range for each disaster type, such as earthquakes, heavy rain, volcanic eruptions, and tsunamis. This enables optimal decisions to be made for each disaster, improving response accuracy. Specifically, it may be possible to implement AI (artificial intelligence) model selection and switching processing that reflects disaster characteristics, such as prioritizing shaking and ground movement during earthquakes and flood depth and drainage capacity during heavy rain.

[0035] The road disaster response support system 600 may also be configured to switch judgment criteria and processing methods in stages according to the chronological phases of a disaster (before the disaster, immediately after the disaster, emergency response period, and full-scale restoration period). For example, it may prioritize rule-based processing that emphasizes speed immediately after the disaster occurs, and switch to AI (artificial intelligence) analysis that emphasizes accuracy when the situation calms down, thereby achieving optimal judgment according to the time series. The configuration may also include an RNN model (Risk Need Responsibility model) using time series data and event trigger judgment based on the disaster progression flow.

[0036] The road disaster response support system 600 may also have a hybrid AI (artificial intelligence) configuration. For example, by combining disaster response decisions based on an AI (artificial intelligence) model with explicit rule-based (IF-THEN) decisions, the explainability of the output from the AI ​​(artificial intelligence) model and the basis for the decision can be clarified. In particular, during large-scale disasters, it is important for residents and commanders to understand "why a particular route was selected," so the system may be implemented to present the decision results with an explanation. It may also include a configuration that utilizes LLM (large-scale language model) to automatically generate explanations in natural language for the reasons for route selection.

[0037] The road disaster response support system 600 may also be configured to use live footage of the scene captured by patrol vehicles, drones, fixed cameras, surveillance cameras, robots, etc., and perform image recognition and anomaly detection processing using AI (artificial intelligence). For example, AI (artificial intelligence) can automatically detect visual abnormalities in the footage, such as debris piles, flooding, and cracks in the ground, and execute control processing to increase the priority response level of the relevant location. This enables real-time understanding of the disaster situation and support for judgment without relying on visual inspection, improving the accuracy of road clearance and support vehicle guidance.

[0038] Furthermore, the road disaster response support system 600 may be configured to be capable of disaster response training and simulations in peacetime. For example, it may be configured to input past disaster data and hypothetical disaster scenarios, perform hypothetical judgments using the prediction unit and analysis unit 620, and then, based on the results, execute simulated road clearance plans, support route generation, and confirmation of material transportation plans. This enables practical training before a disaster occurs, improving the ability to respond quickly and accurately in the event of an actual disaster.

[0039] Furthermore, the Road Disaster Response Support System 600 may be configured to process disaster information in real time using not only cloud processing but also edge AI (artificial intelligence) implemented on local terminals. For example, patrol cars, drones, robots, etc. deployed at disaster sites may perform on-site processing even in environments where cloud communication is difficult, and perform local judgment, notification, and control. This makes it possible to provide a certain level of judgment support, road clearance judgment, and danger avoidance even when communication is interrupted, achieving highly resilient disaster response.

[0040] The analysis unit 620 may also be equipped with a triage processing function that assumes a situation in which a large amount of disaster-related information is concentrated at once and prioritizes it according to urgency and importance. For example, when a large number of disaster reports and sensor data are input simultaneously, the system can be configured to prioritize disaster areas directly connected to life and daily infrastructure, and to appropriately allocate resources and determine whether to clear roads, thereby avoiding delays in decision-making and excessive or insufficient responses. The AI ​​(artificial intelligence) model can calculate a priority score based on factors such as the extent of damage, surrounding conditions, and the reliability of the communication source, and determine the processing order based on that score.

[0041] Furthermore, the road disaster response support system 600 may have a hybrid configuration that flexibly uses both cloud and local processing. For example, in situations where network bandwidth is limited, such as immediately after a disaster, important decision-making processes may be executed on local terminals or base servers, and then synchronized and integrated with analysis on the cloud once the network is restored. This achieves both continuity of processing during a disaster and overall optimization. An architecture configuration may also be adopted in which the basis for decisions and processing results are recorded and shared, contributing to subsequent analysis and improvement processes.

[0042] 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 various 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 determination criteria that reflect the risk score threshold, correlation patterns between analytical data, consistency with ground surveys, etc. The determination result 695 may be configured to be visualized on a map display, a list display, a time series graph display, or a dashboard. The decision unit 630 may also have a decision support function using an AI (artificial intelligence) model, and may be configured to determine the priority of maintenance, the urgency of disaster response, etc. based on statistical threshold decision, rule-based decision, or a predictive model using machine learning. In this case, the decision execution method may be configured as fully automatic processing, semi-automatic processing requiring confirmation by an operator, or a proposal-based support mode. 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. This enables automatic or semi-automatic disaster response support linked with the analysis results 690. Additionally, the decision unit 630 may be configured to cooperate with the road clearance unit or the robotics unit to formulate a road clearance implementation plan or 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. In addition, the decision unit 630 may be configured to automatically generate an explanation in natural language using a large-scale language model (LLM) for the judgment made by AI (artificial intelligence), thereby increasing the transparency and explainability of the basis for the judgment. Furthermore, the determination result 695 may be configured to be output in a foreign language (such as English or Chinese) through a multilingual translation configuration, and may be used to notify or explain to foreign users.

[0043] 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 above analysis results, the decision unit 630 comprehensively determines 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. It should be noted that FIG. 9 shows an example of a typical processing flow, and the present invention is not limited to this.

[0044] 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 determined by the decision unit 630, the analysis result 690, and the various 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, etc., the system may be provided with a function to notify road administrators 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 a web site that centrally displays road restoration status, major 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 automated driving system, a function to provide data to MaaS (Mobility as a Service), a function to provide information to government agencies (police, fire departments, Self-Defense Forces, etc.) and the media, and a function to disclose information to road users and residents (websites, smartphone apps, etc.), and does not necessarily have to go through the information provision unit. Examples of the information disclosure function to road users and residents are as follows, but are not limited to these. 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 need for road disaster response determined by a decision unit, or a road clearance implementation plan formulated by a road clearance unit, or the recovery status by a robotics unit, or the need for road disaster response predicted by a prediction unit, 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.). It should be noted that FIG. 10 shows an example of a typical determination criterion, and the present invention is not limited to this.

[0045] 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.

[0046] Furthermore, the improvement unit may be configured to flexibly select and apply different decision logic depending on the type and occurrence of the disaster in cooperation with the analysis unit 620 and the road clearance unit. For example, the improvement unit dynamically adapts to the type of disaster, such as adopting analysis logic that emphasizes the vibration frequency of structures in the case of earthquake disasters, and priority decision logic based on flood predictions and road flooding height in the case of flood disasters. This enables flexible and accurate decisions to be made in accordance with the actual situation of the disaster.

[0047] The improvement unit may also be configured to sequentially collect and learn the results of various disaster responses (road clearance record, passability, restoration speed, etc.), and use feedback learning to improve the accuracy of future decisions and implementation plans. In this case, linking the information reliability evaluation results (score) prevents bias in learning due to erroneous information. Furthermore, the improvement unit may have a function to explicitly store model update conditions (deterioration in accuracy, change in disaster type, occurrence of new data, etc.) and automatically update the model when specified conditions are met.

[0048] Furthermore, the improvement unit may be configured to sequentially acquire information such as obstacle removal work performed by the robotic equipment by the road clearance unit, passability information on the road clearance route, and on-site restoration progress logs, and use this information to retrain the AI ​​(artificial intelligence) model used by the analysis unit 620. This realizes optimization of robotics control and improvement of environmental adaptability, enabling continuous improvement of road clearance processing accuracy and initial response capabilities in the next disaster. In addition, the improvement unit may be configured to accumulate on-site environmental sensor information (vibration, collision, temperature, terrain change, etc.) acquired during robotics work as training data and use it for model performance evaluation and tuning.

[0049] 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.

[0050] Furthermore, the prediction unit may be configured to be able to evaluate not only future disaster risks but also the risk of secondary damage caused by infrastructure aging and insufficient maintenance. For example, the prediction unit may take into account past repair history and infrastructure deterioration indexes to predict areas where damage may expand and the risk of chain collapses that may occur in the future, and reflect this in advance repair and road clearance plans.

[0051] The prediction unit may also be equipped with a scenario simulation function, which performs multi-condition analysis using multiple disaster occurrence conditions (rainfall intensity, location of the epicenter, time of occurrence, etc.) as variables, and may be able to compare and consider the optimal initial response, traffic route, emergency supply transportation route, etc. for each case. In this case, it may be possible to link with a human-in-the-loop decision-making support function to configure a proposal mechanism that assumes the intervention of a manager's judgment.

[0052] 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 comprehensively compares and collates these multiple pieces of information, the system goes beyond simply listing information and enables the following advanced decision-making processes: (1) Improved accuracy through matching of 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. (2) Enhanced 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, it is possible to understand the damage situation in a timely and spatially complementary manner by using other information sources (satellite, driving, weather, etc.). (3) Improved rationality and responsiveness of decisions: Through integrated matching processing based on diverse sensing information and structural analysis using AI (artificial intelligence), 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.

[0053] 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 610 first acquires information on weather conditions and patrol situations, and the analysis unit evaluates the occurrence and severity of road disasters (for example, earthquakes of seismic intensity 6 or higher, widespread wind and flood damage, snow damage, landslides, etc.) based on this information. If the evaluation result exceeds a predetermined threshold, the information acquisition unit 610 additionally acquires other sensing information, and the analysis unit 620 reanalyzes 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, the decision unit 630 may determine whether or not a disaster response is necessary and the response policy, as in the normal configuration, 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.

[0054] 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 typical disasters, the process is emergency restoration followed by full restoration. However, 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 rescue operations, emergency supply support, and restoration. Road administrators develop road clearance plans in advance, including 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 specific action plans (timelines). A timeline is an action plan that coordinates relevant organizations in the event of a disaster, organizing and sharing in advance a chronological order of who will do what and when. An example of the road clearance unit in the road disaster response support system 600 is a configuration that registers a road clearance plan that was formulated in advance before or after a disaster (it can be registered before or after the disaster), determines a road clearance route in the event of a disaster based on multiple disaster / disaster / traffic related data acquired by the information acquisition unit 610 or the results of an integrated analysis by the analysis unit 620, and formulates an optimized road clearance implementation plan based on that decision. The road clearance plan can be registered by registering plan information entered in advance by an administrator, or by having the computer automatically register road clearance plans obtained from an external system. This allows for both the flexibility of manual input and the speed of automatic processing. 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 among 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, or image, sensor, and three-dimensional topographical data based on these pieces of information, may be input into a machine-learned 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 road clearance 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 complement or correct the route determination process by the road clearance unit by referring to on-site information acquired by the robotics unit. Furthermore, the road clearance unit may be configured to cooperate with the analysis unit 620, improvement unit, prediction unit, or infrastructure maintenance unit as necessary, to improve the accuracy of road clearance routes in the event of a disaster and to contribute to dynamic re-planning processing.

[0055] Furthermore, the road clearance unit may have a function to comprehensively evaluate past disaster history, current damage status, and the functional status of evacuation, medical, and logistics infrastructure, and automatically generate a road clearance order based on the priority of rescue activities. For example, it may give top priority to areas with the greatest degree of damage and the highest possibility of saving lives, while also performing scoring that takes into account accessibility to important logistics centers and medical institutions, and determine the priority of each segment to be cleared. The score may be configured to be updated in real time based on dynamic conditions (weather, traffic congestion, aftershock risk, etc.).

[0056] The road clearance department may also be equipped with a multi-criteria optimization function that automatically generates multiple route candidates, evaluates each candidate from the perspectives of cost, time, and safety, and determines the optimal route. In this case, a human-in-the-loop configuration may be adopted, in which the route proposals proposed by AI (artificial intelligence) are manually reviewed or feedback is received and the model is updated accordingly. The determined road clearance implementation plan is notified via a management terminal or external server and used to share information and provide work instructions with related organizations. The department may also be configured to work with the robotics department, analysis department 620, and improvement department to dynamically re-plan the route, reflecting information acquired on-site.

[0057] Furthermore, the road disaster response support system 600 may be provided with a security configuration to prepare for communication failures and cyber attacks when a disaster occurs. This configuration employs a "zero trust architecture" that implements multiple layers of user authentication, communication encryption, access control, etc., thereby enhancing the security resilience of the entire system. In addition, encrypted communication and mutual authentication are also implemented between each subsystem, minimizing the risk of unauthorized access and information tampering in the event of a disaster. Furthermore, in preparation for a main server failure or network disconnection, a failover configuration (automatic switching to a redundant system) or a configuration with an alternative processing function on a local terminal may be used. For example, even if instructions from the cloud server cannot be received, the local device can be configured to autonomously present and execute response policies using pre-downloaded road clearance plans and AI (artificial intelligence) models. With this enhanced security and resilience configuration, the Road Disaster Response Support System 600 ensures high availability and safety even in the event of a large-scale disaster, contributing to improved reliability for full-scale adoption by local governments and public institutions.

[0058] Furthermore, the road disaster response support system 600 may be configured to perform clustering processing according to the characteristics of the affected municipality or region, and to perform judgment processing that is individually optimized for each region. For example, by using regional characteristic data such as population density, topography, traffic infrastructure density, and disaster history to cluster multiple similar municipalities and apply different models and priority evaluation criteria to each cluster, it becomes possible to respond to disasters in a flexible manner that is not uniform.

[0059] Furthermore, the road clearance unit may be provided with a cooperation configuration for carrying out road clearance work at disaster sites using robotic equipment (autonomous heavy machinery, remotely operated removal devices, etc.). The road clearance unit cooperates with the analysis unit 620 or the improvement unit to determine the type, size, removal means, etc. of obstacles on the road clearance route, and sends work instruction data according to the determination results to the robotic equipment, thereby automating or semi-automating on-site work. Furthermore, work performance information (processing time, obstacle processing history, on-site image and sensor information, etc.) fed back from the robotics equipment can be sent to the analysis unit 620 or improvement unit and reflected in route decisions, work estimates, and work model selection for the next disaster response. This allows for a link between decisions made by AI (artificial intelligence) and robotics as an execution unit, significantly improving responsiveness and safety in disaster response.

[0060] 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, multi-joint 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 unit may operate in cooperation with the analysis unit 620, improvement unit, prediction unit, and road clearance unit, and may include a configuration that dynamically updates the target areas and priorities for road clearance work based on disaster risk information and judgment results provided by each unit. Furthermore, the robotics unit may cooperate with the infrastructure maintenance unit as needed to coordinate with 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.

[0061] The road disaster response support system 600 may include an infrastructure maintenance department. In this specification, "information related to infrastructure integrity" means information related to maintaining the functional or physical integrity of social infrastructure, such as structural abnormalities, cavity risk, subsidence tendency, cracks, abnormal vibrations, signs of deterioration, and other information. The Infrastructure Maintenance Department is responsible for evaluating the health of road infrastructure and making maintenance decisions during peacetime, and in the event of a disaster, it is equipped with a system that links the results of these decisions with disaster response processing, 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 620 or the improvement unit, and may be configured to update and optimize cavity risk assessment based on analysis results and judgment results. Furthermore, the infrastructure maintenance unit may 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.

[0062] Furthermore, the road disaster response support system 600 may have a function for controlling cooperation with multiple robotics devices (unmanned vehicles, unmanned heavy machinery, drones, etc.) deployed at the disaster site. In this configuration, it is possible to switch between remote control mode and autonomous operation mode for each robotic device. For example, at the initial stage of a disaster, the device can be deployed within a safe range by remote control, and once stable operation has been confirmed, it can be switched to autonomous operation mode depending on the situation on site. In addition, in cooperation with the analysis unit 620 or the road clearance unit, missions based on the disaster situation (e.g., removing obstacles, taking images, securing access routes) may be automatically assigned to robotic devices, allowing multiple machines to work in parallel and cooperatively. Furthermore, by integrating and analyzing sensing information (images, three-dimensional terrain, vibrations, obstacle detection, etc.) obtained from each device in real time and providing feedback to the behavior of other units, a cooperative control mechanism can be installed, enabling efficient and safe disaster response operations. This type of robotics collaborative control configuration contributes to reducing human risks, improving work efficiency, and expanding the area that can be covered at disaster sites.

[0063] Furthermore, the road disaster response support system 600 may also have an emergency supply transportation support function. For example, it may be configured to link roads to be cleared with the logistics network (medical supplies, food, water, fuel, etc.) and prioritize roads necessary for emergency vehicle traffic for restoration. It may also be possible to link with information on relief supply collection and distribution centers and perform road selection processing that maximizes logistics efficiency. It may also be implemented with processing that optimizes the logistics network during a disaster using AI (artificial intelligence) judgment that takes into account transport schedules, traffic history, road damage levels, etc.

[0064] The road disaster response support system 600 may also have a user interface configuration that visually presents output information such as the analysis results 690 and the judgment results 695 in a variety of output formats. Specifically, information such as response priorities, road reopening routes, and the extent of the disaster impact may be output in the form of a map display using a geographic information system (GIS), augmented reality (AR) navigation, or a list format. This allows for optimal information presentation according to the situation to a variety of users, such as field workers, local government officials, and command headquarters, improving the effectiveness of decision support.

[0065] 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.

[0066] 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.

[0067] 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 sign detection, the threshold for anomaly detection is set relatively lenient, 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 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.

[0068] 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.

[0069] To accommodate a variety of users, including foreign tourists, the road disaster response support system 600 may execute notification control processing in the analysis unit 620, the determination unit 630, or the information provision unit according to the user's attribute information and language used. For example, the system may be configured to utilize language setting information and GPS information of the terminal device to automatically notify the user of disaster information and travel route information in the user's language if a dangerous area is present within the foreign tourist's range of movement. Disaster response information may also be provided through a traveler application in cooperation with local governments and tourist facilities. Furthermore, the road disaster response support system 600 may be configured to support foreign languages, and may be configured to output and notify analysis results or road clearance implementation plans translated into multiple languages ​​such as English, Chinese, and Korean via information terminals for foreigners, smartphones, web portals, etc. This makes it possible to deliver accurate and immediate disaster response information to users whose native language is a foreign language. In this case, translation processing and multilingual support can employ an automatic translation configuration that utilizes a large-scale language model (LLM). For example, a pre-trained multilingual translation model can be used to convert specialized disaster terminology and road management terminology into accurate and natural expressions. Furthermore, the system can be configured to dynamically switch the optimal translation model or output format based on the user's device language settings, location information, past usage history, etc., enabling real-time, individually optimized multilingual support. This minimizes delays and misunderstandings in information transmission due to language barriers, enabling safe and effective disaster response support for all users, including foreigners.

[0070] <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. Furthermore, the road disaster response support system 600 may be configured to be able to communicate with each information providing server in order to acquire and process information relating to the road service situation and the like. Furthermore, the road disaster response support system 600 is configured in a computing environment (cloud or on-premise) that has the memory, processors and storage space required for processing each component such as an improvement unit, a prediction unit, an information provision unit, a road clearance unit, an infrastructure maintenance unit and a robotics unit. Furthermore, the configuration may include computational resources including a GPU (Graphics Processing Unit), a TPU (Tensor Processing Unit), or an AI accelerator to execute AI (Artificial Intelligence) models used in each component. Note that this diagram is an example of the hardware configuration shown in FIG. 11, and other configurations (edge ​​device configuration, IoT node configuration, distributed processing environment, etc.) may be used depending on the embodiment.

[0071] Although the embodiments of the present invention have been described above with reference to the drawings, the present invention is not limited to these embodiments or the illustrated configurations. For example, the technical scope of the present invention also includes configurations not shown but described in this specification, such as processing functions involved in generating the analysis results 690 and the judgment results 695, the process of formulating a road clearance implementation plan based on a registered road clearance plan, the integrated analysis process of various sensing information (optical fiber survey information, satellite survey information, vehicle driving information, etc.), the learning and improvement process of an AI (artificial intelligence) model, and implementation configurations of visualization functions and feedback functions. Therefore, the present invention can be modified, altered, and substituted in various ways without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0072] 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 information about weather conditions and information about patrol conditions; an analysis unit that analyzes a road disaster situation based on the weather conditions and the patrol conditions acquired by the information acquisition unit and determines the severity of the road disaster situation; In the case where the analysis unit determines that the road disaster situation is severe due to an earthquake of seismic intensity 6 or more, widespread wind and flood damage, widespread snow damage, or widespread landslide disaster, the information acquisition unit acquires at least one of information regarding a vehicle driving status, information regarding an optical fiber investigation status, information regarding a satellite investigation status, and information regarding a road service status; the analysis unit analyzes the road disaster situation based on at least two or more pieces of information among the vehicle driving situation, the optical fiber investigation situation, the satellite investigation situation, the road service situation, and the patrol situation acquired by the information acquisition unit; a road clearance department that registers a road clearance plan that has been formulated in advance, including at least road clearance bases, road clearance routes, and a timeline for road clearance work, before a disaster occurs; The road clearance unit optimizes the road clearance plan based on the registered road clearance plan and the analysis results of the road disaster situation by the analysis unit, and dynamically formulates and updates a road clearance implementation plan that includes at least information regarding the road clearance work for each road clearance target segment.

2. The road disaster response support system according to claim 1, The road disaster response support system is characterized in that the road clearance unit has the function of evaluating multiple candidate road clearance routes based on cost, required time, and safety, and formulating an optimal road clearance implementation plan.

3. The road disaster response support system according to claim 1, The road disaster response support system is characterized in that the analysis unit is configured to perform integrated analysis based on multiple types of sensing data acquired by the information acquisition unit.

4. The road disaster response support system according to claim 1, The road disaster response support system is characterized by comprising an improvement unit, and the improvement unit is configured to improve the processing results by the analysis unit and the road clearance unit through feedback learning.

5. The road disaster response support system according to claim 1, The road disaster response support system is characterized in that it includes an improvement unit, and the improvement unit is configured to selectively re-learn a learning model based on the reliability score of the judgment result.

6. The road disaster response support system according to claim 1, The road disaster response support system is characterized in that it includes an improvement unit, and the improvement unit is configured to be able to apply different judgment logic depending on the type of disaster.

7. The road disaster response support system according to claim 1, The road disaster response support system is characterized by having a robotics unit and being configured to complement or modify the road clearance implementation plan by the road clearance unit based on information obtained by the robotics unit.

8. The road disaster response support system according to claim 1, A road disaster response support system characterized in that the road clearance department formulates the road clearance implementation plan based on route analysis using a machine-learned model using artificial intelligence.

9. The road disaster response support system according to claim 1, The analysis unit is a road disaster response support system characterized by a configuration in which it comprehensively analyzes past disaster history information and current sensing information to evaluate disaster response priorities.

10. The road disaster response support system according to claim 1, The road disaster response support system is characterized by comprising an infrastructure maintenance unit, and the infrastructure maintenance unit is configured to evaluate information regarding infrastructure health acquired by the analysis unit or the information acquisition unit.

11. A program for causing a computer to function as the road disaster response support system according to any one of claims 1 to 10.

12. A road management method using a computer, comprising: The computer communicates with the network via Obtain information about weather conditions and patrol situations, Analyzing a road disaster situation based on the acquired weather conditions and patrol conditions, and determining the severity of the road disaster situation; In the event of an earthquake with a seismic intensity of 6 or higher, widespread wind and flood damage, widespread snow damage, or widespread landslides, the road damage situation will be severe. Acquire at least one of information regarding vehicle driving conditions, information regarding optical fiber investigation conditions, information regarding satellite investigation conditions, and information regarding road service conditions; Analyzing the road disaster situation based on at least two or more pieces of information among the acquired vehicle driving situation, the optical fiber investigation situation, the satellite investigation situation, the road service situation, and the patrol situation; Register a road clearance plan that has been formulated in advance, including at least road clearance bases, road clearance routes, and a timeline for road clearance work, before a disaster occurs; A road management method characterized by optimizing the road clearance plan based on the registered road clearance plan and the analysis results of the road disaster situation, and dynamically formulating and updating a road clearance implementation plan that includes at least information regarding the road clearance work for each segment to be cleared.

13. The road management method according to claim 12, A road management method characterized in that the computer selects from a plurality of candidate routes according to the road disaster situation in the disaster-stricken area, and optimizes the road clearance implementation plan based on the evaluation results of the candidate routes.

14. The road management method according to claim 12, A road management method characterized in that the computer determines the road clearance route included in the road clearance implementation plan based on the priorities of rescue operations, material transportation, and access to medical facilities.

15. The road management method according to claim 12, A road management method characterized in that the computer uses, in addition to information regarding the road disaster situation, ground images, vibration information, slope information, or topographical data obtained by robotics equipment or a robot when determining the road clearance implementation plan.

16. The road management method according to claim 12, A road management method characterized in that the computer executes the judgment or analysis processing included in the road clearance implementation plan using a model clustered according to the characteristics of the affected municipality or region.

17. The road management method according to claim 12, The road management method is characterized in that the computer notifies a plurality of related organizations of the road clearance implementation plan and supports rescue operations or traffic restrictions based on the notification.

18. The road management method according to claim 12, A road management method characterized in that, after formulating the road clearance implementation plan, the computer generates visualized data to ensure transparency of the analysis results and decision-making basis, and provides the data to the road administrator.

19. The road management method according to claim 12, A road management method characterized in that the computer, after executing the road clearance implementation plan, obtains the implementation results and performs a process of re-learning or re-evaluating the analytical model or judgment criteria based on the results.

20. The road management method according to claim 12, A road management method characterized in that the computer forms regional clusters according to the characteristics of the affected municipalities or regions, and performs a process to optimize the road clearance implementation plan by applying different judgment criteria or processing parameters to each cluster.

21. The road management method according to claim 12, A road management method characterized in that the computer performs a process of continuously improving the road clearance implementation plan or analysis model using performance information obtained in conjunction with the execution of the road clearance implementation plan.

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