Road disaster response support system and program, road management method

Abstract

In road infrastructure, despite the high risk of subsurface cavities and traffic disruptions due to disasters, conventional methods have relied on localized inspections and subjective judgments, making it difficult to implement wide-area, continuous preventative maintenance or rational responses immediately after a disaster. In particular, there is a need for a system that can quickly process rescue requests and road conditions, and ensure the passage of emergency vehicles. [Solution] This invention acquires rescue request information managed by road service operators and uses an analysis unit to calculate risk scores, assign reliability, and determine priorities. These processes are enhanced by applying AI models in addition to conventional analysis methods. Furthermore, it integrates and analyzes information such as patrols, fiber optics, satellites, vehicle movement, and weather to support the assessment of the health of road infrastructure and the rationality of road clearing routes in the event of a disaster. This enables both preventive maintenance during normal times and rapid response during disasters, realizing road management with high disaster resilience.

Description

Technical Field

[0001] The present invention relates to technologies related to road maintenance management and road opening during disasters. In particular, based on information obtained using multiple sensing means (such as satellite sensing, fiber optic sensing, vehicle running sensing, etc.), during normal times, it evaluates the risk of cavity formation in road infrastructure and judges its soundness, and when a disaster occurs, it utilizes a pre-established road opening plan to quickly formulate an implementation plan for road opening to ensure the passage of emergency vehicles and the like. The present invention relates to a road disaster response support system, a related program, and a road management method. In addition, it aims to achieve both wide-area and continuous infrastructure monitoring, which was difficult in the past, and quick judgment and plan support at the initial stage of a disaster, and to provide a flexible and advanced support system that functions effectively in both normal and disaster phases. This system contributes to the construction of a road management system with high disaster resilience by seamlessly supporting the preventive maintenance phase and the disaster response phase.

Background Art

[0002] In conventional road infrastructure management, it is common to have an emergency response system after accidents such as road surface subsidence or settlement occur, and a system for comprehensively and continuously grasping the risk of cavity formation and signs of aging of roads during normal times has not been fully established. Also, during a disaster, in order to ensure the passage of emergency vehicles such as ambulances, fire trucks, and the Self-Defense Forces, quick road opening is required, but the information acquisition and judgment processing necessary for formulating the implementation plan are often personal, and there is a lack of efficient and scientific support technology. In recent years, with the development of advanced sensing technologies such as satellites, fiber optics, and connected cars, wide-area road monitoring has become technically possible. However, there have been limited implementation examples for integrating these technologies in both infrastructure soundness evaluation and road opening plans during disasters. Also, exploration means such as ground-penetrating radar are point-like and intermittent, and cannot provide continuous and comprehensive evaluation. Therefore, there are limitations in detecting signs of disaster risk and making immediate route judgments immediately after the onset of a disaster. On the other hand, in the event of a large-scale disaster, it is considered a crucial administrative task to carry out "emergency restoration (road clearing)" prior to normal restoration procedures to ensure access to medical facilities, evacuation centers, and disaster relief bases. Road clearing, in this context, refers to the process of removing obstacles and making simple repairs immediately after a disaster to enable the passage of emergency vehicles as quickly as possible. To achieve this, a system is needed to accurately compare pre-formulated road clearing plans with actual information at the time of the disaster and quickly translate this into a road clearing implementation plan. However, conventional technologies lack the functionality to dynamically utilize road clearing plans. For example, they do not adequately implement processes such as updating plans to reflect damage conditions and traffic disruption information after a disaster, or optimizing implementation timing. Furthermore, the use of AI (artificial intelligence) technology to flexibly process information interpretation, weighting, and route optimization is insufficient, leading to increased decision-making burdens on the ground 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 has also increased. However, when a disaster strikes, foreign travelers have limited means to quickly and accurately obtain information on passability and evacuation routes. Language barriers and differences in information acquisition channels have led to delays and confusion in responses. Therefore, there is a need to develop a system that provides easy-to-understand and timely disaster-related transportation information to a diverse range of users, including inbound tourists. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent 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] Japanese Patent Publication No. 2024-136347 [Overview of the project] [Problems that the invention aims to solve]

[0004] In road infrastructure, subsurface voids and ground deformation pose a high risk of sinkholes and traffic disruptions during disasters. Traditionally, emergency measures have been taken only after these issues have become apparent, and widespread and continuous monitoring and management of void risks during normal times have been insufficient. Furthermore, while individual sensing technologies such as fiber optic sensing, satellite remote sensing, and vehicle driving data exist, a system that integrates and analyzes these technologies to support both preventive maintenance of infrastructure and emergency response during disasters has not yet been established. For example, the following prior art is known: Patent Document 1 discloses a technology for detecting road anomalies using optical fiber sensors, but it does not disclose a configuration that integrates and analyzes this data with satellite data and vehicle driving data, or utilizes the ground survey results to improve an AI (artificial intelligence) model. Patent Document 2 describes a technology for detecting road obstacles using satellite imagery and determining whether a road is passable, but it does not disclose the integration of optical fiber data with vehicle driving data, or the configuration for outputting risk scores and improving learning 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 it does not explicitly describe the integration of heterogeneous sensor data or the configuration for continuous improvement through retraining of the learning model. Patent Document 4 discloses a configuration for visualizing data from radar-equipped vehicles on a map, but it does not mention configurations such as the multi-faceted integration of sensing information, cavity risk assessment using AI (artificial intelligence), or the utilization of ground survey results. Thus, most conventional technologies are limited to localized anomaly detection based on single sensor data, and a comprehensive system that includes area-wide and continuous assessment of hollowing-out risks during normal times, as well as rational and immediate response support processing during disasters, has yet to be realized. [Means for solving the problem]

[0005] To solve the above problems, the road disaster response support system according to the present invention has the following configuration. The Road Clearing Department registers pre-formulated road clearing plans and, in the event of a disaster, formulates a road clearing implementation plan based on those road clearing plans. An analysis unit that evaluates and predicts the health of road infrastructure using multiple sensing means (e.g., artificial satellites, optical fibers, vehicle movement, etc.) during normal times, The system is configured to include an improvement unit for using the results of the analysis unit to make decisions regarding infrastructure maintenance and management or disaster response support. Furthermore, for areas with a high risk of cavities predicted by the analysis department, ground surveys are conducted, and the results are re-inputted into the analysis department's AI (artificial intelligence) model to continuously improve the model's prediction accuracy. In addition, after a disaster occurs, the AI ​​model and accumulated geospatial data can be used to support the rapid and rational determination of road clearing routes. Furthermore, it can be configured to identify impassable sections immediately after a disaster using highly real-time information from sources such as fiber optics and satellites, and to determine priority routes for emergency vehicles. It can also be equipped with features to visualize risk scores and reasoning behind decisions using XAI (Explainable AI) technology. Furthermore, the improvement unit can be configured to selectively retrain the learning model based on the confidence score of the judgment result, or to apply different judgment logics depending on the type of disaster. These configurations enable rapid and accurate road clearing decisions during disasters, contributing to the resolution of previous challenges. [1] A road disaster response support system characterized by comprising: an information acquisition unit that acquires information on the status of road services, including requests for assistance caused by natural disasters, which is managed by a road service provider; an analysis unit that analyzes the information on the status of road services acquired by the information acquisition unit; and a unit that derives state quantities or evaluation information related to road disasters based on the analysis results analyzed by the analysis unit. A road disaster response support system as described in [2][1], wherein the analysis unit is configured to calculate a risk score related to road disasters based on information regarding the road service status and to output the risk score. A road disaster response support system according to any one of paragraphs [3](1) to [2], wherein the analysis unit is configured to assign a reliability score to the information regarding the road service status and to calculate the risk score or weight the analysis results based on the reliability score. A road disaster response support system according to any one of paragraphs [4], [1] to [3], wherein the analysis unit is configured to output the analysis results derived based on the information regarding the road service status, with supporting information and confidence level added. A road disaster response support system according to any one of paragraphs [5][1] to [4], wherein the analysis unit is equipped with a triage processing function that assigns priorities based on urgency and importance and determines the processing order in response to a concentration of rescue requests based on the information regarding the road service status. A road disaster response support system according to any one of paragraphs [6][1] to [5], wherein the road disaster response support system is configured to provide to external organizations, including administrative agencies or disaster prevention-related organizations, information on the road service status acquired by the information acquisition unit, or the analysis results analyzed by the analysis unit. A road disaster response support system as described in [7][1], wherein the information acquisition unit acquires at least one of the following information in addition to the information regarding the road service status: information regarding patrol status, information regarding fiber optic survey status, information regarding satellite survey status, information regarding vehicle driving status, or information regarding weather conditions, and the analysis unit is configured to integrate and analyze the information regarding the road service status and the other information. A road disaster response support system as described in [8][7], wherein the analysis unit is configured to evaluate the soundness of road infrastructure or the risk of subsurface cavities based on the results of the integrated analysis, to grasp the evaluation results in a comprehensive and continuous manner, and to enable advanced preventive maintenance. A road disaster response support system as described in [9][7], wherein the analysis unit is configured to output information for evaluating the rationality of road clearing routes in the event of a disaster, based on the results of the integrated analysis.

[10] A program for causing a computer to function as the road disaster response support system described in [1], wherein the program includes a sequence of instructions for causing the computer to perform (i) at least one of the functions described in [2] or [7], or (ii) at least one of the functions described in (1) to (10) below, or both: (1) a function for assigning and weighting reliability scores as described in [3], (2) a function for assigning and outputting supporting information and confidence levels as described in [4], (3) a function for performing triage processing as described in [5], (4) a function for providing information to external organizations as described in [6], and (5) a function for performing road infrastructure soundness assessment or subsurface cavity risk assessment as described in [8]. (6) A function to output information for evaluating the rationality of road clearing routes as described in [9]; (7) A function to analyze information regarding road service status and calculate a risk score using an artificial intelligence model with a learning algorithm; (8) A function to input sensing data, including on-site survey data, as learning data into the artificial intelligence model and continuously improve the accuracy of calculating the risk score; (9) A function to analyze image data of roads included in the information regarding road service status using image processing or the artificial intelligence model to detect disaster conditions or damage conditions; (10) A function to analyze the information regarding road service status chronologically or spatially, or analyze it using the artificial intelligence model to detect precursors to disasters or risks of damage expansion.

[11] A road management method using a computer, wherein the computer obtains information on the status of road services, including requests for assistance caused by natural disasters, managed by road service providers via a network, analyzes the obtained information on the status of road services, and performs a process to derive state quantities or evaluation information related to road disasters based on the results of the analysis. A road management method as described in

[12]

[11] , characterized in that the computer calculates a risk score for road disasters based on information regarding the road service status and performs a process to output the risk score. A road management method according to any one of paragraphs

[13] ,

[11] , or

[12] , characterized in that the computer assigns a reliability score to the information regarding the road service status and performs a process of calculating the risk score or weighting the analysis results based on the reliability score. A road management method according to any one of paragraphs

[14] ,

[11] , or

[13] , characterized in that the computer performs a process of outputting the analysis result derived based on the information regarding the road service status, with supporting information and a degree of confidence added to it. A road management method according to any one of paragraphs

[15] ,

[11] , or

[14] , characterized in that the computer performs triage processing to determine the order of processing based on urgency and importance in response to a concentration of rescue requests based on information regarding the road service status. A road management method according to any one of paragraphs

[16] ,

[11] , or

[15] , characterized in that the computer performs a process of providing the acquired information regarding the road service status or the analyzed results to an external organization, including an administrative agency or a disaster prevention-related organization. The road management method described in

[17]

[11] , wherein the computer acquires at least one or more pieces of information including information on patrol status, optical fiber inspection status, satellite inspection status, vehicle running status, or weather status in addition to the information on the road service status, and executes a process of integrally analyzing the information on the road service status and the other information.

Advantages of the Invention

[0006] According to the present invention, it becomes possible to comprehensively and continuously grasp the risk of hollowing of road infrastructure in normal times, and the advancement of preventive maintenance, which was difficult in conventional inspection-type infrastructure management, is realized. At the time of a disaster, it is possible to quickly formulate a road opening implementation plan based on a pre-formulated road opening plan, and by selecting a reasonable route by reflecting the results of analyzing the infrastructure status and the disaster impact by AI (artificial intelligence), it becomes possible to ensure the passage of emergency vehicles and optimize the initial response. In addition, by integratively analyzing heterogeneous sensor information such as optical fiber, satellite, and vehicle running data, accurately grasping the cavity risk and obstacle status, and continuously training these information in an AI (artificial intelligence) model, the prediction accuracy and responsiveness can be improved in the long term. Furthermore, by providing a visualization function and a feedback mechanism based on XAI (explainable AI) technology, it supports the judgment of on-site users and local government staff, enabling more practical and reliable disaster response. In addition, by providing a function for acquiring correction information by robotics, a multilingual support function, and a notification function according to traveler attributes, it is possible to quickly provide information on passable roads and risk avoidance information during disasters to various users such as foreign tourists visiting Japan, contributing to the reduction of confusion and secondary disasters.

Brief Description of the Drawings

[0007] [Figure 1] It is a system configuration diagram of the road disaster response support system according to the present invention. [Figure 2] It is a flowchart showing the flow of an example of patrol. [Figure 3] It is a flowchart showing the flow of an example of a fixed-point camera. [Figure 4] It is a diagram showing an example of a patrol situation. [Figure 5] It is a diagram showing an example of an optical fiber inspection situation. [Figure 6] It is a diagram showing an example of a satellite inspection situation. [Figure 7] It is a diagram showing an example of a weather situation. [Figure 8] It is a diagram showing an example of a vehicle running situation. [Figure 9] It is a flowchart showing the flow of an example of the road disaster response support system of the present invention. [Figure 10] It is a diagram showing an example of the judgment criteria in the decision-making part of the present invention. [Figure 11] It is a diagram showing an example of the hardware configuration related to the road disaster response support system of the present invention. Note that the configuration diagrams and flowcharts shown in FIG. 1, FIG. 9, FIG. 10, FIG. 11, etc. show examples of the present invention and do not comprehensively illustrate all the components related to the present invention. Configurations and processes not explicitly described in the drawings are also included as implementable elements in the present invention.

Embodiments for Carrying Out the Invention

[0008] Hereinafter, referring to the drawings, an embodiment of the road disaster response support system of the present invention will be described. Note that the embodiment described below does not unduly limit the technical idea of the present invention described in the claims. Also, not all the configurations described in this embodiment are essential components of the present invention. In addition, each component constituting the feature group can also be an independent invention. The system according to the present invention can be applied not only to emergency response support during disasters, but also to assessing the soundness of road infrastructure and predicting the risk of subsurface decay during normal times. This can contribute to preventing accidents and road collapses, as well as optimizing long-term maintenance costs through infrastructure maintenance support. Furthermore, this system may be configured to provide information in multiple languages ​​to foreign users, including inbound tourists, through smartphones and travel apps. This would allow foreign travelers to immediately grasp their current location, the passability of areas near their destination, alternative routes to avoid danger, and road clearing status in the event of a disaster, thereby supporting them in making safe decisions. Furthermore, "information regarding road disaster conditions" is a comprehensive concept encompassing multiple types of information used to understand the status of road infrastructure during a disaster, including disaster information, road information, traffic information, and weather information.

[0009] Figure 1 is a system configuration diagram relating to the 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 status, optical fiber survey status, satellite survey status, weather conditions, and vehicle driving status; an analysis unit 620 that analyzes the patrol status, optical fiber survey status, satellite survey status, weather conditions, and vehicle driving status obtained from the information acquired by the information acquisition unit 610; and a decision unit 630 that comprehensively determines the necessity of road disaster response based on the results analyzed by the analysis unit 620. The road disaster response support system 600 of this embodiment is connected to a patrol status provision server 100, an optical fiber survey status provision server 200, a satellite survey status provision server 300, a weather status provision server 400, and a vehicle driving status provision server 500 via a network NW. To understand the patrol situation, Figure 1 shows only one vehicle Vh and terminal device TM, but multiple vehicles Vh and terminal devices TM may be connected to the network NW. Although Figure 1 shows only one fixed-point camera (CAM) to understand the disaster situation, multiple fixed-point cameras (CAM) may be connected to a network (NW). Furthermore, based on these analysis results, the decision unit 630 determines whether road infrastructure maintenance is necessary and whether disaster response is necessary, and instructs the appropriate response (for example, determining a repair plan or a road clearing route). Furthermore, in this invention, the road disaster response support system 600 is configured to include information regarding the road clearing unit, infrastructure maintenance unit, improvement unit, robotics unit, and road service status. On the other hand, the prediction unit and information provision unit may be added to the system as needed.

[0010] The terminal device TM, fixed-point camera CAM, patrol status provision server 100, fiber optic survey status provision server 200, satellite survey status provision server 300, weather status provision server 400, vehicle driving status provision server 500, and road disaster response support system 600 communicate via a network NW. The network NW includes some or all of the following: WAN (Wide Area Network), LAN (Local Area Network), the Internet, provider equipment, wireless base stations, dedicated lines, satellite lines, etc. Furthermore, data can be exchanged not only via the network (NW) but also via a memory card. Downloading and uploading data via the network (NW) is also acceptable. Furthermore, in this invention, the function of providing information on the status of road services is provided in the road disaster response support system 600 and is acquired via a network NW through a dedicated server or terminal device.

[0011] Terminal devices TM are used by passengers riding in the vehicle Vh. Terminal devices TM include mobile phones such as smartphones and tablet devices. The terminal device TM may be a communication-type drive recorder mounted on the vehicle Vh or a stationary in-vehicle device, or it may be equipped with an AI (artificial intelligence) image analysis function. The vehicle Vh may also be equipped with a subsurface cavity detection function (a technology that irradiates electromagnetic waves from the road surface toward the road surface and estimates the location of cavities and buried pipes from the reflected waves), and the vehicle Vh may be a subsurface cavity detection vehicle. The terminal device TM has a built-in road patrol application that works in conjunction with the patrol status provision server 100. The terminal device™ includes a positioning device such as a GPS (Global Positioning System) receiver, a communication device for connecting to a network NW, input / output devices such as a G-sensor (accelerometer), camera, and touch panel, and a processor such as a CPU (Central Processing Unit).

[0012] Figure 2 is a flowchart illustrating an example of a patrol. The terminal device TM starts collecting location information, acceleration information, video, etc. when the patrol start button of the road patrol app is pressed (S1) (S2). After the patrol is completed, pressing the "end patrol" button on the road patrol app (S3) transmits the terminal device TM's location information, acceleration information, video, etc. to the patrol status provision server 100 (S4). The patrol status server 100 determines the presence or absence of unevenness on the road surface based on measurement information transmitted from the terminal device TM, and identifies the location of the road surface determined to be uneven. It also determines the extent of damage to the road surface based on transmitted video and images, and identifies the location of the road surface determined to be a risk area.

[0013] Fixed-point cameras (CAMs) are installed on buildings, roadside supports, poles, etc., around areas prone to flooding, such as roads (highways, major arterial roads, roads with heavy traffic, major bus routes, roads connecting to schools, public facilities, and emergency hospitals, roads along mountains and mountainous areas, etc.), underpasses (roads below elevated intersections), roads along rivers, and roads along the coast. Fixed-point cameras (CAMs) include communication-enabled live cameras, web cameras, and network cameras. The fixed-point camera CAM may be a connected dashcam or a small unmanned aerial vehicle camera such as a drone, or it may be equipped with AI (artificial intelligence) image analysis capabilities. The fixed-point camera CAM has a built-in camera application that communicates with the patrol status provision server 100. A fixed-point 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 a network (NW), and a processor such as a CPU (Central Processing Unit).

[0014] Figure 3 is a flowchart showing an example of the workflow for a fixed-point camera CAM. The fixed-point camera CAM collects road conditions periodically or intermittently (S5), and periodically or intermittently transmits video, location information, date and time information, etc. to the patrol status provision server 100 (S6). The patrol status server 100 determines the extent of road surface damage, etc., based on video and images transmitted from the fixed-point camera CAM, and identifies the locations determined to be at risk.

[0015] The patrol status provision server 100 provides patrol status to the road disaster response support system 600 via the network NW. The provided patrol status is road-specific information and includes some or all of the following: road damage, road collapse, roadbed washout, roadway collapse, pavement damage, gutter damage, uneven road surface, voids under the road surface, liquefaction, snow cover / snow quality / freezing on the road surface, collapsed buildings, vehicle traffic history, power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, volcanic activity damage, tornado damage, landslides, mudslides, mudslides, slope failures, tunnel collapses, shoulder collapses, fallen trees, rockfalls, road erosion, total bridge loss, river breaches, river flooding, inundation, dense fog, avalanches, blizzards, presence of accident vehicles / stuck vehicles, etc., and other disaster conditions or things that hinder vehicle traffic. Figure 4 shows an example of a patrol situation, where road damage location 110 is shown in black on the map.

[0016] The Optical Fiber Survey Status Provision Server 200 utilizes optical fiber sensing technology, which uses optical fibers as sensors. It receives backscattered light from communication optical fibers contained in cables laid on roads, etc., detects vibration patterns corresponding to the vehicle driving conditions on the roads, etc., based on the backscattered light, and obtains information on the vehicle driving conditions on the roads, etc., and the surrounding road conditions from the detected vibration patterns and a learned model (information from cameras connected to optical fibers may also be used). Furthermore, by analyzing the intensity, frequency changes, and continuous abnormal patterns of waveforms of minute vibrations propagating underground, it is also possible to grasp the risk of underground cavities and signs of ground deformation. The fiber optic survey status provision server 200 provides fiber optic survey status to the road disaster response support system 600 via the network NW. The provided fiber optic survey status is road-specific information and includes some or all of the following: vehicle traffic history, traffic volume, traffic congestion, sudden vehicle stops, traffic accidents, snow accumulation on the road surface, cavities beneath the road surface, tunnel collapses, accidents, power outages, water leaks / outages, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, fiber optic cable breaks / disconnections, and other disaster conditions (including images of the road) and obstacles to vehicle traffic. Figure 5 shows an example of the fiber optic survey situation, with the collapsed section 210 inside the tunnel indicated in black on the map.

[0017] The satellite survey status provision server 300 utilizes satellite remote sensing technology, which involves observations by artificial satellites equipped with SAR (Synthetic Aperture Radar), optical sensors, microwave sensors, etc. It uses data (scattering intensity values, phase information, polarization information, etc.) and images (optical images, SAR images, etc.) observed by the satellites to acquire road conditions, vehicle conditions, etc., from the difference before and after a disaster. In particular, by using time-series interferometric analysis such as InSAR (Interferometric SAR), it is possible to detect minute displacements such as ground settlement, uplift, and tilt with high precision, and estimate signs of underground cavities and the risk of their formation from the deformations that appear on the ground surface. Furthermore, AI (artificial intelligence) analysis may be used to compare and detect road conditions, vehicle conditions, etc., before and after a disaster, or to detect road conditions, vehicle conditions, etc., during a disaster. The satellite survey status provision server 300 provides satellite survey status to the road disaster response support system 600 via the network NW. The provided satellite survey status is road-specific information and includes some or all of the following: collapsed buildings, landslides, mudslides, mudslides, slope failures, tunnel collapses, road damage, roadway collapses, shoulder collapses, cavities under the road surface, fallen trees, falling rocks, complete bridge loss, river breaches, river flooding, inundation, avalanches, vehicle traffic records, ground subsidence, power outages, water leaks / outages, earthquake prediction information, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, forest damage, crop damage, presence of accident vehicles / stuck vehicles, etc., as well as any other information that may obstruct vehicle traffic or cause other disasters. Figure 6 shows an example of the satellite survey situation, where the landslide area 310 is shown in black on the map.

[0018] The weather information server 400 provides weather information to the road disaster response support system 600 via the network NW. The weather information provided is regional information and includes some or all of the following: time, weather (sunny, rainy, snowy, etc.), temperature, rainfall, snowfall, snow depth, wind speed, special warnings (heavy rain, strong winds, storm surge, high waves, heavy snow, blizzard), warnings (heavy rain, strong winds, flood, heavy snow, blizzard, etc.), record-breaking short-term heavy rainfall information, landslide warning information, earthquake prediction information, earthquake information, tsunami information, eruption information, information on volcanic activity, typhoon information, tornado information, and disaster situation (including images of roads). Figure 7 shows an example of weather conditions, where warning 410 and seismic intensity 420 are indicated by letters and numbers.

[0019] The vehicle driving status server 500 utilizes automotive sensing technology to acquire various data from vehicles such as connected cars, including vehicle driving conditions and surrounding road conditions. In particular, based on information such as sudden braking, ABS activation, acceleration abnormalities, tire slippage, and bump reactions, it is possible to estimate abnormal behavior when a vehicle approaches a cavity, making it an effective source of information for detecting risk areas caused by underground cavities. The vehicle driving status server 500 provides vehicle driving status to the road disaster response support system 600 via the network NW. The vehicle driving conditions provided include road-specific information obtained from vehicles (including electric vehicles) such as private cars, taxis, buses, and trucks, and include some or all of the following: temperature, sudden braking locations, skidding locations, tire spin locations, tire lock locations, ABS activation locations, flooded locations, underground cavities, etc. from vehicle sensors, etc.; power outages, earthquake damage, tsunami damage, typhoon damage, volcanic eruption damage, tornado damage, river flooding, liquefaction, road obstacles, etc. from camera images and other sources; collapsed buildings, landslides, slope failures, tunnel collapses, total bridge losses, road damage, and identification of impassable areas from 3D data, etc.; vehicle traffic history (including separate data for passenger cars and large vehicles), traffic volume, traffic congestion, passing speed, average speed, and acceleration from probe information (including ETC2.0), etc.; and presence or absence of rainfall and snowfall from wiper operation status, etc., including some or all of the following that may hinder vehicle traffic or cause disasters. Furthermore, data may be acquired using autonomous driving technology (sensing technology for autonomous vehicles), or it may be detected and identified through AI (artificial intelligence) analysis, such as disaster conditions or vehicle obstruction information. Figure 8 shows an example of vehicle traffic conditions, and areas 510 where there is no record of vehicle traffic are shown in black on the map.

[0020] The information acquisition unit 610, which operates in the road disaster response support system 600, acquires patrol status from the patrol status provision server 100, fiber optic survey status from the fiber optic survey status provision server 200, satellite survey status from the satellite survey status provision server 300, weather conditions from the weather conditions provision server 400, and vehicle driving status from the vehicle driving status provision server 500 via the network NW. Patrol status provided by patrol status server 100 is stored as patrol information 640. Fiber optic survey status provided by fiber optic survey status server 200 is stored as fiber optic survey information 650. Satellite survey status provided by satellite survey status server 300 is stored as satellite survey information 660. Weather conditions provided by weather status server 400 are stored as weather information 670. Vehicle driving status provided by vehicle driving status server 500 is stored as vehicle driving information 680. Furthermore, the information acquisition unit 610 may also be equipped with information regarding the status of road services. This information is road-specific and mainly consists of information 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: requests for assistance (date, time, location, details of assistance, etc.), requests for assistance due to abnormal weather (date, time, location, details of assistance, etc.), requests for assistance due to disasters (date, time, location, details of assistance, etc.), dead batteries, locked-out keys, running out of gas, flat tires, wheels coming off / falling off, flooding / submersion, recovery from snow / mud, accidents, falls, disaster / damage (including images of the road), vehicle towing / transportation, removal / towing / transportation of abandoned vehicles, removal / towing / transportation of damaged vehicles, removal / towing / transportation of accident vehicles, road conditions (including images of the road), traffic conditions, EV charging availability, vehicle inspection results, etc. The road service status is stored as road service information. Furthermore, the information acquired by the information acquisition unit 610 may be limited to only some of the following: patrol status, fiber optic survey status, satellite survey status, weather conditions, 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 preventative maintenance of road infrastructure and the necessity of responding to disasters (e.g., securing emergency routes, determining repair priorities, etc.), and to execute the necessary response processes.

[0021] The analysis unit 620, which operates within the road disaster response support system 600, analyzes patrol information 640, fiber optic survey information 650, satellite survey information 660, weather information 670, vehicle travel information 680, and road service information obtained from the information acquisition unit 610, and stores the analysis results 690. The stored analysis results 690 may be configured to be viewable in formats such as map display, list display, or time-series graph display. Furthermore, the analysis unit 620 may be configured not only to process individual pieces of information individually, but also to perform matching and comparison between these different types of data and to make a comprehensive evaluation based on integrated correlations. For example, by comprehensively matching and comparing at least two or more data from satellite data, fiber optic data, and vehicle driving data, it is possible to detect the overlap and consistency of anomalies at the same location from a multifaceted perspective such as ground displacement, vibration intensity, and driving anomalies, and to evaluate the level of the risk of subsurface cavities. Furthermore, the analysis unit 620 may be equipped with an AI (artificial intelligence) model. The AI ​​model may be configured to use a learning algorithm such as a neural network to integrate multiple sensing data as input and output a risk score (for example, a continuous value from 0.0 to 1.0 or a risk classification category) for each location. The output score can be used as auxiliary information for decision-making in road infrastructure maintenance and disaster response. Furthermore, the system may also include configurations in which information obtained from on-site ground surveys, such as the presence or absence of cavities, cavity location information, cavity depth information, and cavity shape information, is input into an AI (artificial intelligence) model as training data or update data, and processes (retraining, model updating) are carried out to continuously improve prediction accuracy and judgment results. For example, measurement data (date and time, location, seismic intensity, weather data, vibration, vehicle behavior, probe information, three-dimensional terrain data, etc.) included in various information (patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, road service information) acquired by the information acquisition unit 610 can be used as training data to analyze and visualize road disaster conditions and the degree of infrastructure damage risk. Furthermore, the analysis results 690 may be utilized in conjunction with the prediction and improvement units as needed, contributing to future improvements in disaster prediction and decision-making accuracy. Furthermore, the information analyzed by the analysis unit 620 may be configured to analyze only a portion of the information from among the patrol information 640, fiber optic survey information 650, satellite survey 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, seismic waveforms, vehicle behavior, etc. This makes it possible to predict precursors to disasters and the risk of secondary damage with high accuracy. Alternatively, a GNN (Graph Neural Network) may be applied to analyze the road network structure and the spatial relationships of damaged nodes, thereby extracting preferred road clearing route candidates and routes with a high risk of damage expansion.

[0023] Furthermore, the analysis unit 620 may include a process for assigning a reliability score to each data source. The reliability score is dynamically calculated based on the source of acquisition (public institution / general user), observation conditions, past accuracy history, etc. The analysis unit 620 excludes or weights data below a predetermined threshold to prevent judgment errors due to inaccurate information. In addition, the analysis results 690 may be used to evaluate the priority of response, and may be configured to score the results by weighting factors such as the probability of human casualties, the status of non-functioning shelters and hospitals, and the impact of damage to lifelines, and visualize them as a heat map on a map.

[0024] Furthermore, the analysis unit 620 may be equipped with a reliability score generation function that evaluates the reliability of various types of 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 factors such as the frequency of information acquisition, past accuracy, and acquisition method (automatic measurement / manual reporting), and to prioritize the supply of highly reliable information to the analysis process. For example, for disaster reports and resident reports collected from social media, a reliability score is calculated by taking into account the consistency of location information, the degree of agreement with the damage situation as determined by image analysis, and past reporting history. Information with a score below a threshold is then excluded from the analysis or corrected. Furthermore, the reliability score may be referenced in each process of the analysis unit 620, prediction unit, and improvement unit, and may be configured to contribute to improving information weighting and anomaly detection accuracy. In addition, the system may be configured to enhance the transparency of decision support 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 equipped with a resident-participatory learning function that utilizes reports, posts, and feedback information from residents and users to improve the accuracy of the AI ​​(artificial intelligence) model and optimize the decision criteria. For example, one configuration involves collecting reports (text, photos, videos, etc.) regarding road damage, sinkholes, and traffic obstructions, along with location and time information. This information is then evaluated to determine if it aligns with analysis result 690 or judgment result 695, and accurate reports are used as training data to retrain an AI (artificial intelligence) model. Furthermore, the system may be configured to encourage improvements in the quality of user submissions through feedback functions (report evaluation and report correction) for false reports and inappropriate reports, thereby contributing to an overall improvement in the model's performance. This type of community-participatory learning structure is expected to link information flow from society as a whole with an AI (artificial intelligence) learning platform, leading to increased awareness of participation in the maintenance and management of public infrastructure and strengthening regional disaster prevention capabilities.

[0026] Furthermore, the analysis unit 620 may also be equipped with a reliability visualization function that outputs the data that forms the basis of the judgment and the degree of confidence (reliability) of the judgment as supplementary information for various judgment results made by AI (artificial intelligence). In this configuration, the reason for the decision and the score are displayed together, for example, "Road closure recommended for this section: Confidence level 87% (basis: satellite imagery + vibration anomaly + SNS report)," allowing users or administrators to verify the transparency of the AI's (artificial intelligence) decisions. The confidence score can be calculated using the probability output (such as the softmax output) from within the AI ​​(artificial intelligence) model or the consistency of the evidence (the degree of agreement between multiple sources). Furthermore, if the judgment score falls below a threshold, notes such as "caution required" or "further investigation recommended" may be added to help prevent overconfidence in the judgment. This type of decision-making visualization configuration enhances social acceptance of AI (artificial intelligence) implementation and contributes to improving a sense of security and satisfaction in actual operation at disaster response sites.

[0027] Furthermore, the analysis unit 620 may be equipped with a reliability evaluation mechanism based on multiple information sources. During a disaster, in addition to official sensing data (satellite images, vibration sensors, vehicle behavior, etc.), near real-time information such as resident reports, social media posts, and patrol reports may be collected, but there is variability in the reliability of this information. Therefore, in this system, a predefined confidence score (e.g., Japan Meteorological Agency = high, SNS = medium, unregistered reports = low) may be assigned to each information source, and a processing configuration may be used to weight these scores when inputting them into the AI ​​(artificial intelligence) model. Furthermore, the system may include a process to evaluate the degree of consistency (consistency) and spatiotemporal consistency of content across multiple information sources, automatically determining the reliability rank (high, medium, low), and adding labels such as "needs verification" or "pending" to low-reliability information. Such an information reliability assessment framework is effective in preventing misjudgments at disaster sites and improving the social transparency of AI (artificial intelligence) decision-making.

[0028] Furthermore, the road disaster response support system 600 may be configured to cooperate with external mobility services and in-vehicle equipment. For example, it may be configured to mutually link information with mobility systems such as car navigation systems, smartphones, MaaS (Mobility as a Service) apps, autonomous vehicles, and electric vehicles (EVs) to provide real-time information on road clearing, passable routes, and hazard avoidance instructions during disasters. In this configuration, disaster conditions and road clearing route determination results are automatically notified to in-vehicle terminals and smartphones, and adaptive navigation instructions are possible based on the user's current location, direction of travel, and driving intentions. Furthermore, for autonomous vehicles, the system may be configured to directly reflect "avoidance orders" and "stop orders" in response to road disaster conditions into the control system. Furthermore, API integration with MaaS service providers enables processes such as optimizing emergency transportation methods, restructuring routes, and adjusting traffic demand during disasters, thus contributing to improving the overall disaster response capabilities of society.

[0029] Furthermore, the road disaster response support system 600 may also include a function for setting conditions regarding the timing and triggers for 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 are met: (1) When a new disaster occurs and actual damage information is obtained from the site (e.g., results of cavity detection, structural damage data, image diagnostic results, etc.) (2) If the system's judgment accuracy falls below a predetermined threshold (e.g., a continuous decline in the confidence score, an increase in false positive feedback, etc.) (3) When a model update order is issued by the government or a specialized agency (e.g., revision of disaster response specifications, change of regulations, etc.) By pre-setting and managing these update conditions, it is possible to prevent the decision-making model from becoming obsolete, thereby ensuring continuous system maintenance and optimizing the accuracy of disaster response.

[0030] Furthermore, the road disaster response support system 600 may be configured to dynamically change judgment rules or evaluation criteria depending on the type, scale, and extent of damage from the disaster. For example, the system can be configured to change the types of data to be targeted, the evaluation items to be prioritized (such as vibration intensity, flood depth, and traffic disruption rate), and the judgment thresholds depending on the type of disaster, such as earthquakes, heavy rains, and landslides. Furthermore, even within the same disaster, the judgment criteria may be optimized for each region by considering the characteristics of the affected area (such as urban / mountainous areas, aging rate, and damage to lifelines). Furthermore, by dynamically switching priority items and implementation content from a "life-saving-focused phase" to a "material support phase" and then to a "life recovery phase" depending on the stage of disaster progression, it becomes possible to enhance the flexibility of on-site response and real-time adaptability.

[0031] Furthermore, the analysis unit 620 may be configured to dynamically adjust the priority of disaster response according to regional characteristics. For example, in areas where facilities requiring special consideration, such as elderly care facilities, welfare facilities, hospitals, evacuation centers, and elementary and junior high schools, are concentrated, the system may be configured to prioritize road clearing for roads surrounding these facilities. It is also possible to perform a multidimensional weighting evaluation based on population density, the proportion of vulnerable people, the degree of concentration of lifelines, and the distribution of medical resources, and to automatically derive the optimal response order for each region. This enables flexible and rational decision-making that is not only based 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 and network disruptions during disasters. For example, various information acquisition units and terminals may be configured to ensure communication without infrastructure dependency by using communication methods such as local networks, mesh networks, or LPWA (Low Power Wide Area Network). In addition, a failover configuration can be used to enable field terminals to autonomously continue decision-making, road clearing plan formulation, etc., even if communication with the central server is impossible, thereby enhancing the system's continued operational capability (robustness) during disasters.

[0033] Furthermore, the road disaster response support system 600 may be equipped with a switching control mechanism between automatic decision-making and manual intervention. For example, while the system normally proceeds with responses based on automatic decisions made by AI (artificial intelligence), it is possible to configure the system so that in cases of high urgency or high uncertainty, a specialist or manager can manually intervene and make decisions. This creates a safety net against the risk of misjudgments by AI (artificial intelligence), improving reliability and flexibility in disaster response. In addition, records of manual interventions can be accumulated in the system, contributing 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 weighting in analysis processing, decision processing, and road clearing route selection, taking into account the different damage characteristics, progression speed, and affected area for each type of disaster, such as earthquakes, heavy rains, volcanic eruptions, and tsunamis. This enables optimal decision-making for each disaster and improves the accuracy of the response. Specifically, it may be implemented to select and switch AI (artificial intelligence) models that reflect the characteristics of the disaster, such as emphasizing shaking and ground deformation during earthquakes and emphasizing inundation depth and drainage capacity during heavy rains.

[0035] Furthermore, the Road Disaster Response Support System 600 may be configured to switch decision criteria and processing methods in stages according to the time-series phases of a disaster (pre-disaster, immediately after disaster, emergency response phase, and full-scale recovery phase). For example, it may prioritize rule-based processing that emphasizes speed immediately after a disaster, and then switch to AI (artificial intelligence) analysis that emphasizes accuracy once the situation has calmed down, thereby realizing optimal decision-making according to the time series. The configuration may also include RNN models (Risk Need Responsibility models) using time-series data and event trigger judgments based on the disaster progression flow.

[0036] Furthermore, the Road Disaster Response Support System 600 may also incorporate a hybrid AI (artificial intelligence) configuration. For example, by combining disaster response decisions made by an AI model with explicit rule-based (IF-THEN) decisions, the explainability of the AI ​​model's output and the basis for its decisions can be clarified. In particular, during large-scale disasters, it is important for residents and commanders to understand "why that route was chosen," and the presentation of decision results with explanations may be implemented. A configuration that utilizes a Large-Scale Language Model (LLM) to automatically generate explanations of route selection reasons in natural language may also be included.

[0037] Furthermore, the Road Disaster Response Support System 600 may be configured to utilize live video footage from the site acquired by patrol vehicles, drones, fixed-point cameras, surveillance cameras, robots, etc., and to perform AI (artificial intelligence) image recognition and anomaly detection processing. For example, the AI ​​can automatically detect visual anomalies such as rubble accumulation, flooding, and ground fissures in the video footage and execute control processing to increase the priority of response at those locations. This enables real-time assessment of disaster conditions and decision-making support without relying on visual inspection, improving the accuracy of road clearing and guidance of support vehicles.

[0038] Furthermore, the road disaster response support system 600 may also be configured to support disaster response training and simulations during peacetime. For example, a processing configuration could be used in which past disaster data and hypothetical disaster scenarios are input, a prediction unit and an analysis unit 620 perform virtual judgments, and based on the results, simulated road clearing plans, support route generation, and confirmation of material transport plans are performed. This enables practical training before a disaster occurs, thereby improving the ability to respond quickly and accurately during an actual disaster.

[0039] Furthermore, the Road Disaster Response Support System 600 may also be configured to perform real-time processing of disaster information not only through cloud processing but also through edge AI (artificial intelligence) implemented on local terminals. For example, a configuration could be used in which patrol vehicles, drones, robots, etc., deployed at the disaster site perform on-site processing even in environments where cloud communication is difficult, and execute localized decision-making, notification, and control. This would enable a certain level of decision-making support, road clearing decisions, and danger avoidance even when communication is interrupted, resulting in a highly resilient disaster response.

[0040] Furthermore, the analysis unit 620 may be equipped with a triage processing function that prioritizes a large amount of disaster-related information according to its urgency and importance, anticipating situations where a large amount of disaster-related information is concentrated at once. For example, when a large number of disaster reports and sensor data are input simultaneously, the system can be configured to prioritize disaster locations directly related to life and essential infrastructure, and to make decisions regarding the appropriate allocation of resources and road clearing, thereby avoiding delays in decision-making and insufficient or excessive responses. The AI ​​(artificial intelligence) model can calculate a priority score considering 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 utilizes both cloud and local processing. For example, in situations where network bandwidth is limited, such as immediately after a disaster, important decision-making processes can be performed on local terminals or base servers, and then synchronized and integrated with analysis on the cloud once the network is restored. This ensures both continuity of processing during a disaster and overall optimization. Additionally, an architectural configuration may be adopted that records and shares the basis for decisions and processing results, contributing to subsequent analysis and improvement processes.

[0042] The decision unit 630, which operates in the road disaster response support system 600, determines the necessity of road disaster response and the necessity of 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, fiber optic 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. This judgment result 695 may include criteria that reflect risk score thresholds, correlation patterns between analysis data, consistency with ground surveys, etc. The judgment result 695 may be visualized on a map, list, time-series graph, or dashboard. Furthermore, the decision unit 630 may be equipped with an AI (artificial intelligence) model for decision support, and may be configured to determine maintenance priorities, disaster response urgency, etc., based on statistical threshold judgment, rule-based judgment, or a machine learning prediction model. In this case, the decision execution method can be configured as fully automated processing, semi-automatic processing with confirmation by an operator, or a suggestion-presenting support mode. Furthermore, the decision unit 630 may be configured to function as an execution trigger for disaster response support processing, including proposing repair plans, determining road clearing routes, recommending emergency vehicle routes, and deciding on traffic restrictions, as needed. This enables automatic or semi-automatic disaster response support linked to the analysis results 690. In addition, the decision-making unit 630 may be configured to cooperate with the road clearing unit or robotics unit to formulate a road clearing implementation plan or to instruct robots to carry out road clearing work based on the decision result 695. This links decision-making and execution, enabling a rapid and efficient disaster response. Furthermore, the judgment result 695 and analysis result 690 may be used in the improvement unit to retrain the AI ​​(artificial intelligence) model and update the judgment criteria, and may also be combined with past historical data and future scenario data to be reflected in the prediction unit's prediction of future disaster risks and the formulation of advance plans. Furthermore, the road disaster response support system 600 may include an information provision unit that provides some or all of the judgment results 695 and analysis results 690 to external parties. This information provision unit has the effect of quickly disseminating and sharing road disaster response policies and countermeasures information with administrative agencies, related businesses, and the general public. Furthermore, the decision unit 630 may be configured to automatically generate an explanatory text in natural language using an LLM (Large-Scale Language Model) for the judgment made by the AI ​​(Artificial Intelligence), thereby increasing the transparency and explainability of the basis for the judgment. Furthermore, the judgment result 695 may also be configured to be output in a foreign language (e.g., English, Chinese, etc.) via a multilingual translation configuration, and may be used for notifications and explanations to foreign users.

[0043] Figure 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 information on patrol status from the patrol status provision server 100 (for example, every few minutes) (S10). The information acquisition unit 610 periodically acquires information on optical fiber survey status from the optical fiber survey status provision server 200 (for example, every few minutes) (S11). The information acquisition unit 610 periodically acquires information on satellite survey status from the satellite survey status provision server 300 (for example, every few minutes) (S12). The information acquisition unit 610 periodically acquires information on weather conditions from the weather conditions provision server 400 (for example, every few minutes) (S13). The information acquisition unit 610 periodically acquires information on vehicle driving conditions from the vehicle driving status provision server 500 (for example, every few minutes) (S14). Then, the analysis unit 620 extracts road damage locations, etc. from patrol information 640 (S15). The analysis unit 620 extracts traffic records, etc. from optical fiber survey information 650 (S16). The analysis unit 620 extracts landslide locations, etc. from satellite survey information 660 (S17). The analysis unit 620 extracts earthquake and tsunami information, etc. from weather information 670 (S18). The analysis unit 620 extracts areas where travel is impossible, etc. from vehicle travel information 680 (S19). Next, based on the analysis results described above, the decision unit 630 makes a comprehensive judgment regarding the necessity of responding to the road disaster and, if necessary, decides to implement response measures (for example, determining a repair plan or a road clearing route) (S20). Furthermore, the Road Disaster Response Support System 600 may also be equipped with information from road users and residents using SNS (Social Networking Service) (disaster information, damage information, rescue information, recovery information, etc.), as well as information from government agencies (police, fire department, Self-Defense Forces, etc.), infrastructure operators (telecommunications, electricity, gas, water, sewage, etc.), transportation operators (railways, buses, ferries, airplanes, etc.), construction and civil engineering companies (including construction industry associations), delivery companies, tourism businesses (inns, hotels, tourist facilities, roadside stations, etc.), and designated public institutions (Disaster Countermeasures Basic Act) (disaster information, damage information, rescue information, recovery information, etc.), and information from the government's Emergency Disaster Countermeasures Headquarters. Note that Figure 9 shows one example of a typical processing flow, and is not limited to this example.

[0044] Figure 10 shows an example of the criteria used by the decision unit 630. The decision unit 630 can determine the necessity of road disaster response based on triggers such as: when a tsunami occurs (1 element) 631; when a slope collapses and optical fibers are severed (2 elements) 632; when there is road damage and collapsed buildings and no history of vehicle traffic (3 elements) 633; ​​when a heavy rain warning is issued and mudslides cause tires to spin, preventing vehicles from moving forward and resulting in severe traffic congestion (4 elements) 634; or when a heavy snow warning is issued, there is snow on the road surface, ABS is activated, and there is severe traffic congestion with many stranded vehicles (5 elements) 635. Furthermore, some or all of the judgment results 695 and analysis results 690 determined by the decision unit 630, and various information (patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, road service information) acquired by the information acquisition unit can be provided through the information provision unit, etc., to road administrators via email notification, data provision to road information board systems, data provision to road restoration visualization maps (which unify and display road restoration status, major damaged locations and damage status, road traffic restrictions, intercity travel time, vehicle speed data, vehicle traffic history, population mesh data, etc., on a web map, etc.), data provision to car navigation systems, data provision to autonomous driving systems, data provision to MaaS (Mobility as a Service), information provision to government agencies (police, fire department, Self-Defense Forces, etc.) and the mass media, and information disclosure functions for road users and residents (homepage, smartphone app, etc.), and it is not necessarily required to go through the information provision unit. An example of information disclosure functions for road users and residents is as follows, but is not limited to this. A road disaster response support system characterized by transmitting one or more pieces of information acquired by the Information Acquisition Unit, or the results of analysis by the Analysis Unit, or the necessity of road disaster response determined by the Decision Unit, or a road clearing implementation plan formulated by the Road Clearing Unit, or the recovery status by the Robotics Unit, or the necessity of road disaster response predicted by the Prediction Unit, to a mobile terminal device (mobile phone, smartphone, tablet, laptop, game console, etc.) or a fixed terminal device (desktop computer, smart TV, set-top box, digital signage, kiosk terminal, car navigation system, car display audio, etc.). Figure 10 shows an example of typical judgment criteria, and is not limited to this example.

[0045] The road disaster response support system 600 may include an improvement unit that continuously improves some or all of 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 the judgment criteria. The improvement unit aims to improve the accuracy of analysis processing and judgment processing and suppress misjudgments by utilizing disaster-related data (images, numerical data, simulation results, etc.) acquired from external sources. The improvement unit can also cooperate with the infrastructure maintenance unit and the road clearing unit, and can optimize the improvement targets in each process (cavity risk assessment, repair judgment, road clearing route determination, etc.). One example of an improvement process is a processing configuration in which hypothesis data, verification data, and future prediction data related to disasters are input, and after referring to the history of past judgment results 695 and analysis results 690, the improvement unit performs re-evaluation and retraining using methods such as statistical analysis and machine learning, and the results are fed back into the existing model to improve the accuracy of the judgment. The improvement unit may be equipped with AI (artificial intelligence) analysis functions and may perform model retraining processing for image recognition and risk score calculation. For example, it may take image data and various associated sensing data (location information, vibration, temperature, speed, terrain information, etc.) generated from patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, and road service information acquired by the information acquisition unit 610 as input, predict and output disaster situations, and tune the AI ​​(artificial intelligence) model and judgment criteria using the corrected and supplemented results. Furthermore, the improvement department can use information obtained from on-site ground surveys regarding the presence or absence of cavities, cavities, cavities, depths, or cavities as "ground truth data (training data)" and continuously improve the prediction accuracy of cavity risk assessments and the results of infrastructure maintenance decisions through retraining of the AI ​​(artificial intelligence) model. The above improvement process may be carried out by the improvement unit alone, or it may be configured to operate in conjunction with the infrastructure maintenance unit or the road clearing unit. Furthermore, the improvement unit may be configured to work with the prediction unit as needed to improve the model and reset the judgment criteria based on the disaster prediction results.

[0046] Furthermore, the improvement unit may be configured to flexibly select and apply different decision logics depending on the type and circumstances of the disaster, in cooperation with the analysis unit 620 and the road clearing unit. For example, in the case of an earthquake, an analysis logic that emphasizes the vibration frequency of structures may be adopted, and in the case of a flood, a priority decision logic based on flood prediction and road flood height may be adopted, dynamically adapting according to the type of disaster. This enables flexible and accurate decision-making that is in line with the reality of the disaster.

[0047] Furthermore, the improvement unit may be configured to continuously collect and learn from the results of various disaster responses (road clearing results, passability, recovery speed, etc.) and improve the accuracy of future decisions and implementation plans through feedback learning. In this case, linking the information reliability evaluation results (score) will prevent bias in learning due to misinformation. In addition, the improvement unit may explicitly maintain model update conditions (accuracy deterioration, change in disaster type, occurrence of new data, etc.) and have a function to automatically update the model when the predetermined conditions are met.

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

[0049] The road disaster response support system 600 may include a prediction unit that utilizes historical data such as previously acquired analysis results 690 and judgment results 695 to predict future disaster occurrences and road damage risks in advance. The prediction unit can analyze various time-series data related to disasters (actual data, future scenario data, weather forecast data, etc.) as input and output the future need for road disaster response quantitatively or probabilistically. This will enable road administrators to plan and implement preparatory measures, response plans, and training plans in advance to prepare for the risk of disasters, thereby accelerating and streamlining initial responses in the event of an actual disaster. One example of a prediction process involves inputting hypothesis data, verification data, training data, and future prediction data (for example, assumed patterns of earthquakes or heavy rain disasters that may occur once every few decades) into the prediction unit, comparing them with existing analysis history and judgment results, and simulating the scope of impact and necessary responses in advance when a disaster occurs. The prediction unit may be equipped with AI (artificial intelligence) prediction capabilities. For example, it can 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 pre-extract areas where securing road clearing routes is difficult or risk areas that may hinder the passage of emergency vehicles. Furthermore, the prediction results may be used in cooperation with the analysis and improvement departments, and may be fed back into the infrastructure maintenance and road clearing departments' pre-planning efforts as needed.

[0050] Furthermore, the prediction unit may be capable of evaluating not only future disaster risks but also the risk of secondary damage resulting from aging infrastructure and insufficient maintenance. For example, one configuration could take into account past repair history and infrastructure deterioration indicators to predict potential areas of damage expansion and the risk of chain reactions, and reflect these predictions in pre-repair and road clearing plans.

[0051] Furthermore, the prediction unit may be equipped with a scenario simulation function that performs multi-condition analysis using multiple disaster occurrence conditions (rainfall intensity, epicenter location, time of occurrence, etc.) as variables, allowing for comparison and examination of the optimal initial response, traffic routes, and emergency supply transport routes for each case. In this case, it may be linked with a human-in-the-loop type decision support function to create a proposal mechanism that assumes the intervention of administrators' judgments.

[0052] The Road Disaster Response Support System 600 functions effectively by using one or more pieces of information from among patrol status, fiber optic survey status, satellite survey status, weather conditions, vehicle movement status, and road service status. In particular, by combining two or more pieces of information, complementary analysis becomes possible, enabling more accurate situation assessment and decision support. For example, by understanding wide-area ground deformation through satellite surveys, detecting localized underground vibrations through fiber optic surveys, and acquiring driving records and abnormal behavior through vehicle traffic data, it becomes possible to achieve both wide-area monitoring and localized detection. This creates a multi-layered decision-making platform, from predictive monitoring during normal times to emergency response decisions during disasters. Furthermore, by conducting integrated analysis that comprehensively compares and compares these multiple pieces of information, it becomes possible to perform advanced decision-making processes beyond mere enumeration of information, such as the following: (1) Improved accuracy through matching image data with non-image data: For example, if road damage is detected in image analysis, matching it with the corresponding optical fiber displacement and vehicle vibration history can reduce false detections and improve the reliability of disaster assessment. (2) Advanced situation assessment based on multi-viewpoint information: Even in situations where detection is difficult with a single sensor, such as at night or in severe weather, it becomes possible to understand the damage situation in a temporally and spatially complementary manner by using other information sources (satellite, driving, weather, etc.). (3) Improved rationality and responsiveness of decisions: Integrated matching processing based on diverse sensing information and structural analysis using AI (artificial intelligence) will enable rapid and rational assessment of road infrastructure risk, prioritization of disaster response, and determination of road clearing routes. In this way, this system aims to minimize damage and contribute to the early recovery of disaster-stricken areas by achieving both rapid initial response during disasters and advanced preventive maintenance during peacetime.

[0053] In another embodiment of the present invention, a simplified configuration that acquires and analyzes information in stages can be adopted. In this embodiment, first, a preliminary determination of the occurrence and severity of a road disaster is made based on information regarding weather conditions and patrol status. Subsequently, only if it is determined that the road disaster is severe, at least one of the following pieces of information is acquired: vehicle driving status, fiber optic survey status, satellite survey status, and road service status. This configuration allows for detailed analysis and disaster response decisions, thereby enabling practical disaster response support while keeping system costs and load low. In this configuration, the information acquisition unit 610 first acquires information on weather conditions and patrol status, and the analysis unit evaluates the occurrence and severity of road disasters (for example, earthquakes of magnitude 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 acquires additional sensing information, and the analysis unit 620 re-analyzes this information, enabling more sophisticated disaster response decisions. This allows for efficient system operation through the phased utilization of information. Furthermore, the processing in this embodiment may be similar to that of a normal configuration, with the decision unit 630 determining whether disaster response is necessary and what the response policy should be, and performing emergency response or notification processing as needed. Moreover, this stepwise configuration can be applied to specific operating modes and simplified implementation forms as an auxiliary configuration to the main configuration, which is a multi-sensing integrated analysis processing.

[0054] The road disaster response support system 600 may include a road clearing unit that registers a pre-formulated road clearing plan and determines a road clearing route based on the road clearing plan and acquired information when a disaster occurs. By equipping the road disaster response support system 600 with a road clearing unit, the effect of being able to quickly clear roads in the event of a disaster can be obtained. In typical disasters, the process involves emergency restoration followed by full-scale restoration. However, in large-scale disasters, emergency restoration (road clearing) is necessary before emergency restoration. Road clearing involves quickly removing minimal debris and repairing uneven surfaces to secure rescue routes, enabling emergency vehicles to pass for life-saving and rescue operations, emergency supply support, and restoration work. Road administrators formulate road clearing plans in advance, which include road clearing bases (disaster prevention bases such as bases for support units and collection points for supplies and equipment), road clearing routes (wide-area movement routes, access routes, and routes within the affected area), and specific action plans (timelines). A timeline is an action plan that organizes and shares in advance, in chronological order, when, who, and what actions will be taken by relevant organizations in cooperation during a disaster. One example of a road clearing unit in the road disaster response support system 600 is a configuration in which a road clearing plan formulated in advance before or after a disaster is registered (registration can be either before or after the disaster), and based on multiple disaster, damage, and traffic-related data acquired by the information acquisition unit 610, or the results of integrated analysis by the analysis unit 620, a road clearing route during the disaster is determined, and an optimized road clearing implementation plan is formulated based on that determination. The registration of this road clearing plan may be configured to register plan information that has been entered in advance by the administrator, or it may be configured to allow a computer to automatically register road clearing plans obtained from an external system. This makes it possible to achieve both the flexibility of human input and the speed of automated processing. Furthermore, the road clearing unit may be equipped with AI (artificial intelligence) analysis, interpretation, and route determination functions. For example, two or more pieces of information acquired by the information acquisition unit 610, such as patrol information, fiber optic survey information, satellite survey information, weather information, vehicle driving information, and road service information, or images, sensor data, and three-dimensional terrain data based on these, may be input into a machine learning model, and after outputting, supplementing, and correcting the road disaster situation, the road clearing route may be dynamically determined. Furthermore, the determination of the road clearing route may be configured to select the optimal route from multiple candidate routes that avoid high-risk points that should be avoided, based on the disaster situation. The road clearing route and road clearing implementation plan determined by the road clearing department may be notified to the road administrator via a management terminal or external server and automatically reflected in conjunction with restoration work and traffic restriction instructions. Furthermore, the decision-making process may include a configuration that references on-site information acquired by the robotics unit to supplement or modify the route determination process performed by the road clearing unit. In addition, the road clearing unit may, if necessary, cooperate with the analysis unit 620, improvement unit, prediction unit, or infrastructure maintenance unit to improve the accuracy of road clearing routes during disasters and to facilitate dynamic replanning.

[0055] Furthermore, the road clearing unit may be equipped with a function to automatically generate a road clearing order based on prioritizing relief activities, comprehensively evaluating past disaster history, current damage status, and the functional status of evacuation, medical, and logistics infrastructure. For example, it may prioritize areas with significant damage and a high probability of saving lives, while also considering accessibility to important logistics hubs and medical facilities, and determine the priority for each road clearing target segment. This score may be configured to be updated in real time based on dynamic conditions (weather, traffic disruptions, aftershock risk, etc.).

[0056] Furthermore, the road clearing unit may 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, it is also possible to adopt a human-in-the-loop configuration in which the AI ​​(artificial intelligence) proposed route plan is reviewed or feedback is received from humans to update the model. The determined road clearing implementation plan is notified via a management terminal or external server and used for information sharing and work instructions with relevant organizations. It may also be configured to execute dynamic replanning that reflects on-site information in cooperation with the robotics unit, analysis unit 620, and improvement unit.

[0057] Furthermore, the road disaster response support system 600 may be equipped with security measures to prepare for communication failures and cyberattacks that occur during a disaster. This configuration employs a "zero trust architecture" that implements user authentication, communication encryption, and access control in multiple layers, thereby enhancing the overall security resilience of the system. Furthermore, encrypted communication and mutual authentication are introduced between each subsystem to minimize the risk of unauthorized access and data tampering during disasters. Furthermore, to prepare for main server failures or network outages, a failover configuration (automatic switching to a redundant system) or a configuration with alternative processing capabilities on local terminals may be implemented. For example, even if instructions from the cloud server become unreceivable, the system can be configured so that the local terminal autonomously presents and executes a response plan using pre-downloaded road clearing plans and AI (artificial intelligence) models. This enhanced security and resilience configuration ensures high availability and safety even under large-scale disasters, contributing to improved reliability for full-scale implementation 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 individually optimized decision processing on a regional basis. For example, by using regional characteristic data such as population density, topography, traffic infrastructure density, and disaster history to cluster multiple similar municipalities and applying different models and priority evaluation criteria to each cluster, a flexible, non-uniform disaster response becomes possible.

[0059] Furthermore, the road clearing unit may be equipped with a collaborative configuration for performing road clearing work at disaster sites using robotic equipment (such as autonomous heavy machinery and remotely operated removal devices). The type, size, and removal methods of obstacles to the road clearing route are determined in cooperation with the analysis unit 620 or the improvement unit, and work instruction data corresponding to the determination results is transmitted to the robotic equipment to automate or semi-automate on-site work. Furthermore, the system may be configured to transmit work performance information (processing time, obstacle handling history, on-site images, sensor information, etc.) fed back from robotic equipment to the analysis unit 620 or the improvement unit, and to reflect this information in route determination, work estimation, and equipment selection for the next disaster response. This enables the coordination of AI (artificial intelligence) decision-making with robotics as the execution force, significantly improving the responsiveness and safety of disaster response.

[0060] The Road Disaster Response Support System 600 may include a robotics unit that uses AI (artificial intelligence) and robotics technology to have robots (mainly disaster response robots) carry out road clearing work. The system uses AI (artificial intelligence) to determine which roads should be prioritized for clearing, clearly indicating the road clearing route, and then robots use robotics technology to carry out the road clearing work. In carrying out road clearing work, the system uses AI (artificial intelligence) to perform optimal route analysis based on pre-formulated road clearing plans registered in the road clearing unit or road clearing implementation plans formulated by the road clearing unit, and information acquired by the robotics unit (disaster / damage situation, weather conditions, road conditions, traffic conditions, impassable road status, rescue status, recovery status, obstacle information, topographic information, road clearing 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: It will be able to perform tasks quickly without relying on human labor by utilizing autonomous robots. Furthermore, autonomous work by robots will minimize the deployment of workers to hazardous areas. In addition, the coordination of heavy machinery, small robots, and drones will enable efficient obstacle removal and other tasks. The following is an example of an embodiment of the robotics unit in the Road Disaster Response Support System 600, 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, based on a pre-formulated road clearing plan (a road clearing plan registered with the road clearing department) or a road clearing implementation plan, the robotics department uses artificial intelligence analysis and robotics technology to have disaster response robots (large heavy machinery, autonomous heavy machinery, small robots, humanoid robots, quadruped robots, snake-like robots, multi-legged robots, worm-like robots, shapeshifting robots, articulated robots, crawler robots, autonomous excavation robots, small unmanned aerial robots, underwater exploration robots, rescue robots, etc.) perform road clearing work. Examples of robotics technology (including disaster response robots) are as follows, but are not limited to these: Remotely or autonomously controlling autonomous heavy machinery (bulldozers, excavators, etc.) to remove obstacles and repair uneven surfaces. Removing small-scale debris in cooperation with radio-controlled debris removal robots, etc. Utilizing quadruped robots or drones to support reconnaissance of disaster areas and removal of small obstacles. Furthermore, AI (artificial intelligence) analyzes the progress of road clearing in real time and automatically adjusts the optimal work instructions for robots. In addition, it integrates and controls multiple different robots (autonomous heavy machinery, small robots, drones, etc.) to ensure optimal work allocation. Examples of AI (Artificial Intelligence) are as follows, but are not limited to these: Route optimization AI for calculating road clearing routes and determining priorities (Dijkstra's algorithm, reinforcement learning, multi-agent, etc.). Image recognition AI for obstacle identification using drone and robot sensors and generation of 3D maps (convolutional neural networks, PointNet, etc.). Robotics control AI for controlling autonomous heavy machinery, collaborative work of 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 AI-based work monitoring (convolutional neural networks, Long Short-Term Memory, Transformer, etc.). Examples of input and output data in robotics technology and AI (artificial intelligence) analysis are as follows, but are not limited to these: Route Optimization AI: Input data (road network data, obstacle data, real-time traffic data, priority route information, weather / land number data, etc.) → Output data (optimal road clearing route, emergency route, work instruction list, etc.). Image Recognition AI: Input data (drone footage, LiDAR point cloud data, past disaster data, etc.) → Output data (obstacle map, obstacle type determination, work priority map, etc.). Robotics Control AI: Input data (work area map, obstacle information, robot status data, terrain data, etc.) → Output data (robot work plan, movement path instruction, obstacle removal operation, etc.). Work Monitoring AI: Input data (work video data, robot work log, weather information, etc.) → Output data (progress report, anomaly detection alert, work optimization instruction, etc.). By utilizing large-scale language models, road clearing plans, implementation plans, and road clearing operations can be continuously improved regardless of the language used. This is particularly effective for understanding pre-formulated road clearing plans, learning about disaster countermeasures using vast amounts of data on the internet, and making real-time decisions regarding disaster response. Examples of large-scale language model applications are, but are not limited to, the following: supplementing and optimizing road clearing plans and implementation plans, learning from global disaster countermeasure data on the internet, real-time support for robots, and real-time utilization of disaster data. Furthermore, the robotics unit may operate in conjunction with the analysis unit 620, the improvement unit, the prediction unit, and the road clearing unit, and may include a configuration that dynamically updates the target area and priority of road clearing operations based on disaster risk information and judgment results provided by each unit. In addition, it may cooperate with the infrastructure maintenance unit as needed to coordinate with the main recovery work carried out immediately after a disaster response, and to provide support for ongoing infrastructure maintenance. The robot types listed above are merely examples and are not the only ones that may be used. Other types of robots may be used as appropriate depending on the purpose of disaster response and the operating environment.

[0061] The road disaster response support system 600 may also include an infrastructure maintenance section. In this specification, "information relating to infrastructure integrity" means information related to maintaining the functional or physical integrity of social infrastructure, such as structural abnormalities, cavity risks, settlement trends, cracks, vibration abnormalities, signs of deterioration, and other such information. The Infrastructure Maintenance Department is responsible for assessing the health of road infrastructure and making maintenance decisions during normal times. In the event of a disaster, it is configured to link the results of these assessments with disaster response procedures, thereby contributing to both maintenance and initial response. This infrastructure maintenance unit may include a configuration that calculates a subsurface cavity risk score using an AI (artificial intelligence) model based on at least two of the following information sources acquired by the information acquisition unit: satellite data, fiber optic data, and vehicle driving data. This allows for the quantitative identification of locations where subsurface cavity formation is a concern, and supports the prioritization of infrastructure inspections and repairs. Furthermore, for areas deemed to have a high risk of cavities, on-site ground surveys (e.g., ground-penetrating radar surveys, vibration measurements, camera photography, etc.) are conducted, and the results (presence, location, depth, shape, etc. of cavities) are re-inputted as training data or update data for the AI ​​(artificial intelligence) model. This allows for continuous improvement of the AI ​​model's prediction accuracy or infrastructure maintenance decisions. Furthermore, the system may be equipped with XAI (Explainable AI) technology to visualize the basis for the outputted risk score and anomaly assessment. It may also be configured to preferentially acquire correct data through an active learning strategy, or to use data augmentation processing for training data using GANs (Generative Inverse Networks), etc. This makes it possible to improve model accuracy and learning efficiency. This configuration enables road administrators to make accurate and rational repair decisions based on AI-based infrastructure evaluation results and on-site survey information, contributing to the optimization of maintenance costs and the prevention of accidents. Furthermore, the Infrastructure Maintenance Unit may operate in conjunction with the Analysis Unit 620 or the Improvement Unit, and may be configured to update and optimize cavity risk assessments based on analysis results and judgment results. In addition, it may be configured to cooperate with the Prediction Unit, Road Clearing Unit, and Robotics Unit as needed to support full-scale recovery work after disaster response and ongoing infrastructure maintenance.

[0062] Furthermore, the road disaster response support system 600 may also be equipped with a function for coordinating and controlling multiple robotic devices (unmanned vehicles, unmanned heavy machinery, drones, etc.) deployed at the disaster site. In this configuration, each robotic device can be controlled to switch between remote control mode and autonomous operation mode. For example, in the initial stages of a disaster, the devices can be deployed remotely within a safe range, and once stable operation is confirmed, they can be switched to autonomous operation mode according to the situation on site. Alternatively, the system may be configured to automatically assign missions based on the disaster situation (e.g., obstacle removal, image capture, securing access routes) to robotic devices in cooperation with the analysis unit 620 or the road clearing unit, allowing multiple units to perform tasks in parallel and collaboratively. Furthermore, by incorporating a cooperative control mechanism that aggregates and analyzes sensing information obtained from each device (images, 3D terrain, vibration, obstacle detection, etc.) in real time and feeds it back into the behavior of other units, efficient and safe disaster response operations can be achieved. Such robotics-based integrated control configurations contribute to reducing human risk, improving work efficiency, and expanding the area that can be responded to in disaster situations.

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

[0064] Furthermore, the road disaster response support system 600 may be equipped with a user interface configuration that visually presents output information such as analysis results 690 and judgment results 695 in various output formats. Specifically, this configuration may include outputting information such as response priority, road clearing routes, and disaster impact areas using a geographic information system (GIS), augmented reality (AR) navigation, or a list format. This enables the presentation of optimal information according to the situation to various users such as field workers, local government officials, and command centers, thereby 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, and rule-based inference, not limited to the AI ​​(artificial intelligence) model. This enables flexible configuration selection according to the operating environment and optimization from the standpoint of real-time performance and processing load. Furthermore, the system may include a configuration that allows switching between integrated analysis using non-AI methods and analysis processing using AI (artificial intelligence) models, depending on the application, system configuration, and operating conditions. The following are examples, but are not limited to, a configuration that includes: an information acquisition unit that acquires at least two of the following: information on satellite survey status, information on fiber optic survey status, and information on vehicle driving status; an analysis unit that comprehensively analyzes the said information by statistical analysis, threshold comparison, or rule-based inference; and a decision unit that determines whether road infrastructure maintenance or disaster response is necessary based on the analysis results.

[0066] Furthermore, the road disaster response support system 600 according to the present invention may include a configuration that switches its operating policy between a normal operation mode and a disaster operation mode. In normal times, the system's primary objectives are to identify predictive abnormalities in road infrastructure, assess the risk of cavities, and make decisions regarding regular maintenance. However, in the event of a disaster, it shifts to an operational mode that prioritizes securing emergency routes, clearing roads, and supporting rescue operations, based on sensing information and integrated analysis results. Such switching is controlled by software based on the overall system operation policy and does not necessarily require a dedicated "operational switching unit." For example, in normal mode, the analysis unit can apply processing parameters that focus on cavity risk assessment and anomaly detection, while in disaster mode, the decision unit can perform processing that focuses on extracting high-priority road obstructions and selecting corresponding routes.

[0067] Furthermore, the normal operation mode and the disaster operation mode may include a configuration that switches the risk assessment criteria (threshold setting) in the integrated analysis and the prioritization logic for the sites to be surveyed on-site. For example, during normal times, the system prioritizes wide-area and comprehensive early detection of potential problems. To achieve this, the anomaly detection threshold is set relatively loosely, making it easier to identify potential risks. However, in the event of a disaster, the anomaly score determination threshold is set strictly, enabling the system to prioritize the extraction and notification of high-risk locations requiring rapid response. The following are examples, but are not limited to, of the road disaster response support system. The road disaster response support system may be configured to dynamically change the judgment threshold for calculating risk scores and detecting anomalies, or the prioritization logic for on-site survey target locations, depending on whether it is a normal operation mode or a disaster operation mode.

[0068] The road disaster response support system 600 of the present invention may include a configuration that continuously acquires and analyzes sensing information such as disaster, damage, and recovery status, and dynamically re-evaluates and reconstructs the analysis results and response plan based on the latest information as needed. This enables flexible responses that can immediately adapt to changes in the disaster situation.

[0069] The road disaster response support system 600 may, in order to accommodate diverse users such as foreign tourists, perform notification control processing in the analysis unit 620, decision unit 630, or information provision unit according to the user's attribute information and language used. For example, by utilizing the language setting information of the terminal device and GPS information, the system may be configured to automatically notify foreign tourists of disaster information and travel route information in their language if there are dangerous areas within their range of activity. Furthermore, disaster response information may be provided through traveler applications in cooperation with local governments and tourist facilities. Furthermore, the road disaster response support system 600 may also be equipped with a foreign language support configuration. For example, it may be configured to output and notify foreigners of analysis results or road clearing implementation plans translated into multiple languages ​​such as English, Chinese, and Korean via guidance terminals, smartphones, or web portals. This makes it possible to deliver accurate and immediate disaster response information to users whose native language is a foreign language. In this case, for translation processing and multilingual support, an automatic translation configuration utilizing LLM (Large-Scale Language Model) may be adopted. For example, a pre-trained multilingual translation model can be used to accurately and naturally convert specialized disaster terminology and road management terminology into natural expressions. Alternatively, the system may dynamically switch the optimal translation model or output format based on the user's terminal language settings, location information, past usage history, etc., enabling real-time and 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> Figure 11 shows an example of the hardware configuration of a terminal device TM, a fixed-point camera CAM, a patrol status provision server 100, an optical fiber survey status provision server 200, a satellite survey status provision server 300, a weather status provision server 400, a vehicle driving status provision server 500, and a road disaster response support system 600. This figure shows an example where the terminal device TM is a mobile phone such as a smartphone. The terminal device TM has a configuration in which, for example, a CPU 701, RAM 702, ROM 703, a secondary storage device 704 such as flash memory, a touch panel 705, and a wireless communication module 706 are interconnected by an internal bus or a dedicated communication line. Application programs such as road patrol apps are downloaded via the network NW and stored in the secondary storage device 704. The fixed-point camera CAM has a configuration in which, for example, a CPU 901, RAM 902, ROM 903, a secondary storage device 904 such as flash memory, a lens / image sensor 905, and a communication device 906 are interconnected by an internal bus or a dedicated communication line. Application programs such as camera apps are downloaded via the network and stored in the secondary storage device 904. Each server has a configuration in which components such as a NIC 801, CPU 802, RAM 803, ROM 804, secondary storage devices 805 such as flash memory or HDDs, and a drive device 806 are interconnected by an internal bus or dedicated communication line. A portable storage medium such as an optical disc is mounted on the drive device 806. Programs stored in the secondary storage device 805 or the portable storage medium mounted on the drive device 806 are loaded into the RAM 803 by a DMA controller (not shown), and executed by the CPU 802, thereby realizing the functional parts of each server. Patrol information 640, fiber optic survey information 650, satellite survey 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 also be cloud computing. Furthermore, the road disaster response support system 600 may be configured to communicate with each information provision server in order to acquire and process information such as the status of road services. Furthermore, the Road Disaster Response Support System 600 is comprised of a computing environment (cloud or on-premise) equipped with the memory, processor, and storage space necessary for processing each component, such as the Improvement Unit, Prediction Unit, Information Provision Unit, Road Clearing Unit, Infrastructure Maintenance Unit, and Robotics Unit. Furthermore, the system may be configured to include computing resources such as a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit), or AI accelerator for executing the AI ​​(Artificial Intelligence) models used in each component. Note that this figure is just one example of the hardware configuration shown in Figure 11, and other configurations (edge ​​device configuration, IoT node configuration, distributed processing environment, etc.) may be used depending on the embodiment.

[0071] Although embodiments of the present invention have been described above with reference to the drawings, the present invention is not limited to these embodiments or illustrated configurations. For example, embodiments described herein, even if not illustrated, include processing functions involved in generating analysis results 690 and judgment results 695, processing for formulating road clearing implementation plans based on registered road clearing plans, integrated analysis processing of various sensing information (optical fiber survey information, satellite survey information, vehicle driving information, etc.), AI (artificial intelligence) model learning improvement processing, and implementation configurations for visualization and feedback functions, which are also included in the technical scope of the present invention. Therefore, the present invention can be modified, altered, or substituted in various ways without departing from its essence. [Explanation of symbols]

[0072] 100: Patrol status server 200: Fiber Optic Survey Status Provision 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: Fiber Optic Survey Information 660: Satellite Survey Information 670: Weather information 680: Vehicle operation information 690:Analysis results 695: Judgment result

Claims

1. An information acquisition unit that acquires information on the status of road service operations caused by natural disasters, managed by road service providers, An analysis unit analyzes the information regarding the road service status obtained by the information acquisition unit, Equipped with, The analysis unit, based on the date, time, location, and details of the rescue request included in the information regarding the road service status, associates the rescue request with a road and analyzes the information regarding the road service status for each road. A road disaster response support system characterized by outputting information regarding the road disaster situation on the road in question, based on the analysis results.

2. A road disaster response support system according to Claim 1, The aforementioned analysis unit is characterized by analyzing the spatial relationships in the road network structure using the location of the rescue request.

3. A road disaster response support system according to Claim 1, The road disaster response support system is characterized in that the analysis unit analyzes the occurrence of the rescue request in a time series based on the date and time of the rescue request.

4. A road disaster response support system according to claim 1, The information acquisition unit acquires, in addition to the information regarding the road service status, at least one of the following: information regarding patrol status, information regarding fiber optic survey status, information regarding satellite survey status, information regarding vehicle driving status, or information regarding weather conditions. The road disaster response support system is characterized in that the analysis unit has a configuration that integrates and analyzes the information regarding the road service status and one or more pieces of information.

5. A road disaster response support system according to claim 1, The road disaster response support system is characterized in that the analysis unit has a triage processing function that prioritizes rescue requests according to their urgency and importance when a concentration of rescue requests occurs based on information regarding the status of road services.

6. A road disaster response support system according to Claim 1, The road disaster response support system is characterized in that the analysis unit displays the analysis results in at least one of the following formats: map display, list display, or time-series graph display.

7. A road disaster response support system according to claim 1, The road disaster response support system uses the information on the road service status acquired by the information acquisition unit, or the analysis results analyzed by the analysis unit, A road disaster response support system characterized by having a configuration that provides services to external organizations, including administrative agencies or disaster prevention-related organizations.

8. A road disaster response support system according to Claim 1, The road disaster response support system is characterized in that the analysis unit has a configuration that analyzes the information regarding the road service status using an artificial intelligence model.

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

10. A road management method using computers, The aforementioned computer, via the network, We obtain information regarding the status of road services due to natural disasters, which is managed by road service providers. Based on the date, time, location, and details of the rescue request included in the acquired road service status information, the rescue request is associated with a road, and the road service status information is analyzed for each road. A road management method characterized by performing a process to output information regarding the road disaster situation on the road in question, based on the analysis results.

11. A road management method according to claim 10, The road management method is characterized in that the computer performs a process to analyze the spatial relationships in the road network structure using the location of the rescue request.

12. A road management method according to claim 10, The road management method is characterized in that the computer performs a process to analyze the occurrence of the rescue request in chronological order based on the date and time of the rescue request.

13. A road management method according to claim 10, In addition to the information regarding the road service status, the computer acquires at least one of the following pieces of information: information regarding patrol status, information regarding fiber optic survey status, information regarding satellite survey status, information regarding vehicle driving status, or information regarding weather conditions. A road management method characterized by performing a process to integrate and analyze the information regarding the road service status and one or more pieces of information.

14. A road management method according to claim 10, A road management method characterized in that the computer performs triage processing to prioritize requests for assistance according to their urgency and importance when a concentration of such requests occurs based on information regarding the status of road services.

15. A road management method according to claim 13, The road management method is characterized in that the computer performs a process of displaying the results obtained from the integrated analysis process in at least one of the following formats: map display, list display, or time-series graph display.

16. A road management method according to claim 13, The road management method is characterized in that the computer performs a process of providing the acquired information regarding the road service status, the acquired one or more pieces of information, or the results obtained from the integrated analysis process to an external organization, including an administrative agency or a disaster prevention-related organization.

17. A road management method according to claim 10, The road management method is characterized in that the computer performs a process of analyzing the information regarding the road service status using an artificial intelligence model.

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