Road disaster response support system and program, road management support method

Abstract

This system provides road disaster response support that enables the advancement of rescue operations and overall road disaster response, as well as the acceleration of initial response. [Solution] The road disaster response support system includes an information acquisition unit 610 that acquires at least one of the following: information received from rescue requests, information acquired from rescue vehicles, etc., and member notification information, including information received from rescue requests; and an analysis unit 620 that analyzes these using AI or the like to analyze the possibility of road disasters occurring and the possibility of abnormalities in road infrastructure. Based on the analysis results, the road service unit determines whether rescue vehicles can pass and the optimal route, and outputs information to support rescue activities, such as the feasibility of rescue activities, prioritization, and the deployment of equipment and personnel, thereby supporting rapid and rational rescue activities.

Description

Technical Field

[0001] The present invention relates to a technology for supporting rescue activities in the event of disasters on roads. In particular, it integrates and analyzes multiple types of information such as rescue request reception information managed by road service operators, various information obtained at the rescue activity site, and reporting information from members, and supports the optimal deployment of rescue vehicles, route selection, determination of the feasibility of rescue activities, evaluation of the degree of disaster impact, etc. It relates to a road disaster response support system, related programs, and a road management support method. The purpose of the present invention is to develop the road service business, which has conventionally remained at the rescue activities at the disaster site, to the collection, analysis, and utilization of disaster information, enabling the improvement of the overall rescue activities and road disaster response and the quickening of the initial response.

Background Art

[0002] So far, road service operators have mainly engaged in emergency response at vehicle failure and accident sites. However, even during disasters, there are many cases where on-site rescue becomes difficult due to road closures and traffic obstacles. Conventionally, it is common for rescue vehicles to start responding after arriving at the site and only then grasping the disaster situation. There has been no established mechanism to scientifically grasp the situation at the time of receiving a rescue request, select an appropriate route, and cooperate and support with other organizations. In addition, on-site information that can be obtained by a drive recorder video mounted on a rescue vehicle, a small unmanned aerial vehicle camera (drone), etc., and reporting information provided by members are only used within a limited range after the rescue activity. There has been no mechanism to comprehensively utilize and analyze this information before or during the rescue activity to evaluate the disaster situation and road conditions in real time. Furthermore, in recent large-scale natural disasters, rescue requests tend to occur concentratedly in a short period of time. There is an increasing need to quickly and reasonably make judgments such as which request should be prioritized, whether a rescue vehicle can pass, and which route is optimal. In the prior art, these judgments largely depend on the experience and intuition of rescue team members, and it is difficult to optimize according to the situation.

Prior Art Documents

[0003] [Non-Patent Document 1] Japan Automobile Federation, "Disaster Relief and Training by Special Support Teams," JAF is also active in areas affected by earthquakes, typhoons, etc., [online], Publication date unknown, Japan, [Retrieved October 19, 2025], Internet<URL:https: / / jaf.or.jp / common / about-road-service / training> [Overview of the project] [Problems that the invention aims to solve]

[0004] During large-scale disasters, road closures, flooding, and bridge collapses can make it difficult for rescue vehicles to pass, and a surge in rescue requests can disrupt rescue operations. Furthermore, while on-site information obtainable during rescue operations (such as dashcam footage and drone footage) and information reported by members themselves who are requesting rescue are crucial for streamlining and optimizing rescue efforts, there has traditionally been no system in place to analyze this information before rescue operations begin and utilize it for support. Furthermore, processes such as prioritizing rescue operations, determining the passability of rescue vehicles, and selecting optimal routes still rely on individual judgment, making it difficult to achieve efficient and rational disaster response. In addition, there was a lack of information-sharing infrastructure to quickly and accurately provide information on rescue operations, passability assessments, and passable routes to members and government agencies. The present invention aims to solve these problems and provide a road disaster response support system, related programs, and road management support methods that support rescue activities by integrating and analyzing rescue request reception information, rescue activity acquisition information, and member notification information, thereby enabling appropriate operation of rescue vehicles and rapid disaster response. [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. An information acquisition unit that acquires at least one of the following: information on receiving rescue requests from those requesting rescue (members, etc.), information on rescue activities acquired on-site using rescue vehicles or dashcams, small unmanned aerial vehicles, portable information devices, etc., and member reports, including information on receiving rescue requests. The system is configured to include an analysis unit that evaluates the likelihood of a road disaster occurring, the scale of damage, the impact on traffic, and the priority of rescue activities based on the information acquired by the aforementioned information acquisition unit. Furthermore, the analysis unit is configured to calculate the density of rescue requests using geographical information, disaster details, and time of occurrence, which are included in either the rescue request reception information, rescue activity acquisition information, or member report information, and to derive a road disaster score based on this. Furthermore, the Road Service Department generates support information (information on optimizing vehicle and personnel deployment, road passability determination results, passable route information, response proposal information, and display metadata used for visualization and notification) based on road disaster scores, information on equipment and materials held by each branch or base, and information on available personnel, to support the deployment of optimal rescue vehicles and personnel. This information is then output to support rescue operations. Furthermore, the system includes a configuration that analyzes information acquired at the rescue operation site using AI (artificial intelligence) models, determines whether rescue vehicles can pass, and supports the feasibility of rescue operations, prioritization, and selection of passable routes based on the results. Furthermore, the system can be configured to provide scores, feasibility assessments, and support information related to rescue operations as a visualized dashboard to the rescue command center and administrators, or to transmit it directly to field personnel and member terminals. In addition, it is possible to adopt a configuration that includes an improvement unit that retrains or updates the AI ​​model using collected information and rescue operation results to improve the accuracy of the analysis. This configuration enables a consistent process from receiving rescue requests and assessing the situation on-site to AI (artificial intelligence) evaluation, optimal route and vehicle selection, and result notification, thereby accelerating and streamlining rescue operations and contributing to the resolution of conventional challenges. An example of the configuration of the present invention is shown below in accordance with the claims. [1] A road disaster response support system comprising: an information acquisition unit that acquires information including the said rescue request acceptance information, which is managed by a road service provider and concerns the response to a rescue request caused by a natural disaster; and rescue activity acquisition information concerning road disaster conditions or road infrastructure conditions, which is acquired by at least one of the following: the road service provider's rescue vehicle, a drive recorder installed in the rescue vehicle, the road service provider's small unmanned aerial vehicle camera, or the road service provider's portable information device; an analysis unit that analyzes the possibility of a road disaster occurring or the possibility of an abnormality in road infrastructure based on the said information acquisition unit; and a road service unit that provides support for the road service provider's rescue activities, wherein the road service unit outputs support information for the road service provider to carry out the said rescue activities 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 evaluates the probability of a road disaster occurring or the scope of impact of a road disaster based on one or more pieces of information acquired by the information acquisition unit, and based on at least one of the following: content related to a road disaster, geographical concentration, and time of occurrence, which are included in either the rescue request acceptance information or the rescue activity acquisition information. A road disaster response support system as described in [3][1], wherein the analysis unit calculates the density of the rescue request or rescue activity based on one or more pieces of information acquired by the information acquisition unit, based on the content, geographical information and number of occurrences related to the road disaster included in either the rescue request acceptance information or the rescue activity acquisition information, and derives a road disaster score based on the density. A road disaster response support system as described in [4][3], wherein the road service unit outputs support information for optimizing the allocation of personnel or equipment based on the road disaster score derived by the analysis unit based on the density, as well as information on equipment and materials held at each branch or base and information on available personnel. A road disaster response support system as described in [5][1], wherein the analysis unit analyzes the contents relating to the road disaster, which are included in either the rescue request reception information or the rescue activity acquisition information, based on one or more pieces of information acquired by the information acquisition unit, using artificial intelligence; the road service unit determines, based on the analysis results, whether the rescue vehicle of the road service provider is passable; and based on the determination results, outputs at least one of the following: whether or not to carry out the rescue activity, the priority of the rescue activity, or the selection of the rescue vehicle to be used for the rescue activity. A road disaster response support system as described in [6][5], wherein the information acquisition unit acquires information on traffic restrictions managed by road administrators or administrative agencies (for example, traffic restriction information, road closure information, traffic restriction information, road clearing information, detour information), the analysis unit evaluates passability based on rescue location information, support base information and the traffic restriction information, and further evaluates passability in accordance with the results of analyzing the rescue activity acquisition information, and the road service unit selects a passable route for the rescue vehicle based on the evaluation results by the analysis unit. A road disaster response support system as described in [7][1], wherein the information acquisition unit has a function to send an SMS to a member of the road service provider and to acquire location information from a mobile terminal device carried by the member. A road disaster response support system as described in [8][1], wherein the road disaster response support system includes a dashboard that visualizes at least one of the following: a road disaster score derived by the analysis unit based on one or more pieces of information acquired by the information acquisition unit, and support information, decision results regarding rescue activities, or passable route information output by the road service unit. A road disaster response support system as described in [9][4], wherein the road service unit selects a response means suitable for the rescue activity from a plurality of response means based on the road disaster score derived by the analysis unit and the support information output by the road service unit, and outputs response proposal information regarding the execution of the response means based on the selected response means. A road disaster response support system as described in

[10] [1], wherein the improvement unit is configured to improve the optimization accuracy of the output relating to at least one of the following: the accuracy of determining road disasters, the accuracy of determining whether a road is passable, and the accuracy of the support information output by the road service unit, based on at least one of the following: the one or more pieces of information acquired by the information acquisition unit, the analysis results by the analysis unit, and the output results or operational results of the support information output by the road service unit, using at least one of the rescue request acceptance information and the rescue activity acquisition information as learning data. A road disaster response support system as described in

[11] [1], wherein the road disaster response support system includes a communication function that transmits at least one of the following, output by the road service unit, regarding the decision result concerning the rescue activity, passable route information, or response proposal information, based on one or more pieces of information acquired by the information acquisition unit, to a portable information device carried by a field team member of the road service provider, and further includes the ability to retrieve a Street View image corresponding to the passable route and automatically overlay it on the display screen of the portable information device. A road disaster response support system as described in

[12] [1], characterized in that the road disaster response support system has a function to transmit to a mobile terminal device carried by a member of the road service provider at least one of the following, output by the analysis unit or the road service unit, based on one or more pieces of information acquired by the information acquisition unit: information on traffic restrictions, the status of a road disaster, the estimated arrival time of the rescue vehicle, or information on passable routes. A road disaster response support system as described in

[13] [1], wherein the road disaster response support system has a function to transmit at least one of the following information, output by the analysis unit or the road service unit, based on one or more pieces of information acquired by the information acquisition unit, to an external system that can be used by road administrators, administrative agencies or disaster response agencies: information regarding the occurrence of a road disaster, impassable locations, the status of rescue activities, or the estimated arrival time of rescue vehicles. A road disaster response support system as described in

[14] [1], wherein the road disaster response support system optimizes the coordinated deployment of personnel or equipment across multiple branches or multiple municipalities, and the securing of accommodation or support bases for such personnel, based on one or more pieces of information acquired by the information acquisition unit, and based on the road disaster score, the judgment results regarding the relief activities, and information on equipment and materials held and personnel available at each branch or base, derived by the analysis unit or the road service unit, and provides a control linkage function to a traffic permit management system or entry management system for the affected area to provide traffic permit information or traffic restriction instructions. A road disaster response support system as described in

[15] [1], wherein the road disaster response support system is equipped with a control function that, when the road disaster score derived by the analysis unit based on one or more pieces of information acquired by the information acquisition unit exceeds a predetermined threshold, automatically switches the operating mode of the road disaster response support system from normal mode to disaster mode and changes the priority of output target information or the notification format. A road disaster response support system as described in

[16] [1], wherein the road disaster response support system collects at least one of the following: the actual occurrence of a road disaster, the results of the rescue activities, the traffic records of the rescue vehicles, the output results or operational results of the support information output by the road service unit, and information on the analysis results obtained by the analysis unit, and stores it as training data for an artificial intelligence model used in the analysis unit or the road service unit, and has a function to control a model update process for retraining or updating the artificial intelligence model. A road disaster response support system as described in

[17] [1], characterized in that the road disaster response support system has a configuration that ensures the continuity of operation of the entire system by making the failure of some functions due to communication failures, power failures or equipment failures in the event of a disaster redundant by using other network routes (including satellite communications), alternative servers or alternative processing mechanisms.

[18] [1] A road disaster response support system as described above, wherein the road service unit diagnoses the malfunction state or abnormal trend of the vehicle to be rescued based on vehicle diagnostic information obtained from a vehicle malfunction detection device connected to the vehicle to be rescued or a vehicle malfunction detection device that operates in cooperation with a portable information device, selects either on-site repair or towing as a means of rescue based on the diagnostic result, transmits the selection result or the diagnostic result to a portable information device carried by a field officer of the road service provider, and further transmits the diagnostic result or statistical information of the diagnostic result to the towing repair shop or automobile manufacturer via the road service provider's server, thereby contributing to fault response, analysis of fault causes, or consideration of measures to prevent recurrence. A road disaster response support system according to any one of paragraphs

[19] [1] to

[18] , wherein the road disaster response support system comprises a multilingual processing layer that utilizes a large-scale language model, automatically translates the rescue request reception information including foreign languages, extracts attribute information relating to language type, speaker classification, urgency, or location description, inputs the translation results into the analysis unit, uses them to derive the probability of a road disaster occurring, the scope of impact of a road disaster, or a road disaster score, and automatically translates the support information or passable route information output by the road service unit for foreign language speakers and outputs it.

[20] A program for causing a computer to function as the road disaster response support system described in [1], the program comprising a sequence of instructions for causing the computer to (i) perform at least one of the functions described in [2], [3], [5], [7], [8], and

[10] to

[18] , or (ii) perform at least one of the functions described in (1) to (3) below, or both: (1) a function to output support information for optimizing the deployment of personnel or equipment based on the road disaster score and information on equipment and materials held at each branch or base and information on available personnel; (2) a function to acquire information on traffic restrictions managed by road administrators or administrative agencies, evaluate passability based on rescue location information, support base information and traffic restriction information, and further evaluate passability in accordance with the analysis results of rescue activity acquisition information, and select a passable route for rescue vehicles based on the evaluation results; (3) a function to select a response measure suitable for rescue activities from a plurality of response measures based on the road disaster score and support information, and output response proposal information regarding the execution of the response measure.

[21] A road management support method using a computer, wherein the computer acquires, via a network, rescue request reception information related to responding to rescue requests caused by natural disasters managed by a road service provider, and rescue activity acquisition information related to road disaster conditions or road infrastructure conditions acquired by at least one of the road service provider's rescue vehicles, drive recorders mounted on the rescue vehicles, the road service provider's small unmanned aerial vehicle cameras, or the road service provider's portable information devices, the computer acquires, analyzes the possibility of road disasters occurring or road infrastructure abnormalities based on the acquired information, and outputs support information for the road service provider to carry out rescue activities based on the results of the analysis. A road management support method as described in

[22]

[21] , characterized in that the computer evaluates the probability of a road disaster occurring or the scope of impact of a road disaster based on one or more pieces of information obtained via a network, based on at least one of the following: content relating to a road disaster, geographical concentration, and time of occurrence, which are included in either the rescue request acceptance information or the rescue activity acquisition information. A road management support method as described in

[23]

[21] , wherein the computer calculates the density of the rescue request or rescue activity based on the content, geographical information and number of occurrences of road disasters included in either the rescue request reception information or the rescue activity acquisition information, based on the one or more pieces of information acquired via the network, and derives a road disaster score based on the density. A road management support method as described in

[24]

[23] , characterized in that the computer outputs support information via a network for optimizing the allocation of personnel or equipment based on the score of the road disaster derived based on the density, as well as information on the equipment and materials held at each branch or base and information on the personnel that can respond. A road management support method as described in

[25]

[21] , wherein the computer analyzes, via a network, the contents relating to a road disaster, which are included in either the rescue request acceptance information or the rescue activity acquisition information, based on the one or more pieces of information acquired, using artificial intelligence; determines, based on the results of the analysis, whether the rescue vehicle of the road service provider is passable; and outputs, based on the determination result, at least one of the following: whether or not the rescue activity should be carried out, the priority of the rescue activity, or the selection of the rescue vehicle to be used for the rescue activity. A road management support method as described in

[26]

[25] , wherein the computer acquires information on traffic regulations managed by a road administrator or administrative agency via a network (e.g., traffic regulation information, road closure information, traffic restriction information, road clearing information, detour information), evaluates passability based on rescue location information, support base information and the traffic regulation information, and further evaluates passability in accordance with the results of analyzing the rescue activity acquisition information, and selects a passable route for the rescue vehicle based on the evaluation result. A road management support method as described in

[27]

[21] , characterized in that the computer sends an SMS to a member of the road service provider via a network and obtains location information from a mobile terminal device carried by the member. A road management support method as described in

[28]

[21] , characterized in that the computer visualizes and displays as a dashboard at least one of the following, output based on the one or more pieces of information acquired, via a network: a road disaster score, the support information, the judgment result regarding the rescue activity, and passable route information. A road management support method as described in

[29]

[24] , characterized in that the computer selects a response means suitable for the relief activity from a plurality of response means based on the road disaster score and the support information via a network, and outputs response proposal information regarding the execution of the response means based on the selected response means. A road management support method as described in

[30]

[21] , characterized in that the computer, via a network, performs training or updating of an artificial intelligence model using at least one of the rescue request reception information and rescue activity acquisition information as training data, based on at least one of the acquired information, the analysis results, and the output results or operational results of the support information, thereby improving the optimization accuracy of the output related to at least one of the road disaster determination accuracy, the passability determination accuracy, and the support information accuracy. A road management support method as described in

[31]

[21] , characterized in that the computer transmits, via a network, at least one of the following, output based on the one or more pieces of information acquired, regarding the rescue activity, passable route information, or suggested response information, to the portable information device carried by the field personnel of the road service provider, and further retrieves a Street View image corresponding to the passable route and automatically overlays it on the display screen of the portable information device. A road management support method as described in

[32]

[21] , characterized in that the computer transmits, via a network, at least one of the following information output based on the one or more pieces of information acquired: information on traffic restrictions, the status of road disasters, the estimated arrival time of the rescue vehicles, or information on passable routes, to a portable terminal device carried by a member of the road service provider. A road management support method as described in

[33]

[21] , characterized in that the computer transmits, via a network, at least one of the following information output based on the one or more pieces of information acquired: the status of a road disaster, a section of the road that is impassable, the status of the rescue activities, or the estimated arrival time of the rescue vehicle, to an external system that can be used by a road administrator, an administrative agency, or a disaster response agency. A road management support method as described in

[34]

[21] , characterized in that the computer optimizes the coordinated deployment of personnel or equipment across multiple branches or multiple municipalities, and the securing of accommodation or support bases for such personnel, based on the road disaster score, the judgment result regarding the relief activities, and information on equipment and materials held and personnel available at each branch or base, derived via a network based on the one or more pieces of information acquired, and provides traffic permit information or traffic restriction instructions to a traffic permit management system or entry management system for the disaster area. A road management support method as described in

[35]

[21] , characterized in that the computer automatically switches the operating mode from normal mode to disaster mode and changes the priority or notification format of the information to be output when the road disaster score derived based on the one or more pieces of information acquired via the network exceeds a predetermined threshold. A road management support method as described in

[36]

[21] , wherein the computer collects at least one of the following via a network: the actual occurrence of a road disaster, the results of the rescue activities, the traffic records of the rescue vehicles, the output or operational results of the support information, and information on the analysis results obtained by the analysis, and stores it as training data for an artificial intelligence model, and controls a model update process for retraining or updating the artificial intelligence model. A road management support method as described in

[37]

[21] , characterized in that the computer ensures the continuity of operation of the entire system by making the failure of some functions due to communication failures, power failures or equipment failures in the event of a disaster redundant by using other network routes (including satellite communication), alternative servers, or alternative processing mechanisms. A road management support method as described in

[38]

[21] , wherein the computer diagnoses the malfunction state or abnormal trend of the vehicle to be rescued based on vehicle diagnostic information obtained from a vehicle malfunction detection device that operates in cooperation with a vehicle malfunction detection device or portable information device connected to the vehicle to be rescued via a network, selects either on-site repair or towing as a rescue measure based on the diagnostic result, transmits the selection result or the diagnostic result to a portable information device carried by a field officer of the road service provider, and further transmits the diagnostic result or statistical information of the diagnostic result to a repair shop or automobile manufacturer at the towing destination via the road service provider's server, thereby contributing to fault response, analysis of fault causes, or consideration of measures to prevent recurrence. The road management support method according to any one of

[39] to

[38] , wherein the computer automatically translates the rescue request reception information including a foreign language through a network by a multilingual processing layer that utilizes a large language model, extracts attribute information regarding language type, speaker classification, urgency, or location description, uses the translation result for deriving the occurrence probability of a road disaster, the affected area of the road disaster, or the score of the road disaster, and automatically translates and outputs the output support information or passable route information to a foreign language speaker.

Advantages of the Invention

[0006] According to the present invention, by acquiring and analyzing information such as rescue request reception information from rescue requesters, rescue activity acquisition information obtained on-site by rescue vehicles, etc., and notification information from members, the disaster situation can be grasped quickly and accurately, enabling the acceleration of the initial response in rescue activities and the rationalization of prioritization. In addition, by analyzing elements such as the density of rescue requests, geographical concentration, and occurrence time zone, and evaluating the disaster occurrence probability and affected area, the optimal deployment plan of rescue vehicles and the selection of passable routes can be automated by AI (artificial intelligence), realizing the efficiency of rescue activities and the reduction of the burden on on-site team members. Furthermore, by visualizing judgment information, rescue availability, recommended routes, etc. based on the analysis results as a dashboard and having a function to directly notify members and on-site team members, it is possible to share the situation among all relevant parties and improve the accuracy and speed of rescue activities. In addition, by having a configuration that accumulates the implementation results of rescue activities as re-learning data for the AI model and continuously improves the accuracy of the model, highly accurate rescue support can be provided in the long term. As a result, in the road service business where it was conventionally difficult to grasp the situation until arriving at the site after a rescue request, it is possible to support rational and scientific judgment from before the rescue activity and achieve a quick and optimal disaster response.

Brief Description of the Drawings

[0007] [Figure 1] This is a system configuration diagram relating to the road disaster response support system of the present invention. [Figure 2] This is a flowchart illustrating an example of a patrol procedure. [Figure 3] This is a flowchart illustrating an example of a fixed-point camera setup. [Figure 4] This figure shows an example of a patrol situation. [Figure 5] This figure shows an example of the status of fiber optic cable surveys. [Figure 6] This is a diagram illustrating an example of satellite survey status. [Figure 7] This figure shows an example of weather conditions. [Figure 8] This is a diagram illustrating an example of vehicle driving conditions. [Figure 9] This flowchart illustrates an example of the flow of the road disaster response support system of the present invention. [Figure 10] This figure shows an example of the criteria used in the decision-making unit of the present invention. [Figure 11] This figure shows 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 Figures 1, 9, 10, and 11 are examples of the present invention and do not comprehensively illustrate all 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. [Modes for carrying out the invention]

[0008] An embodiment of the road disaster response support system of the present invention will be described below with reference to the drawings. Note that this embodiment described below does not unduly limit the technical concept of the present invention as described in the claims. Furthermore, not all of the configurations described in this embodiment are necessarily essential components of the present invention. In addition, each individual component constituting the feature group can also constitute an independent invention. The road disaster response support system according to the present invention, in the event of a disaster, collects at least one of the following: information received from road service operators regarding requests for assistance, information acquired through rescue vehicles, drive recorders, small unmanned aerial vehicles (drones), portable information devices, etc., including information received from members regarding member reports, and uses an AI model or the like to evaluate the likelihood and impact of a disaster and the priority of rescue activities. Based on these analysis results, the system outputs information to support the feasibility of rescue vehicle access, optimal routes, the necessity and priority of rescue operations, and the deployment of necessary equipment and personnel. Furthermore, this information can be displayed on the command center dashboard and directly notified to member terminals and field personnel terminals. Furthermore, the road disaster response support system can continuously improve the accuracy of disaster response by accumulating the results of rescue operations as training data for the AI ​​model, and by retraining and updating the model. In this specification, "support information" refers to the general term for information output by the Road Service Department based on analysis results obtained by the Analysis Department, etc., to support the execution of rescue activities. Support information may include at least the following: (1) Information regarding the deployment, reallocation, and operational plans of personnel, equipment, and rescue vehicles, (2) Decision on whether or not to pass, assessment of passability (either one or both), (3) Information on passable routes (which may include estimated arrival times and sections requiring caution), (4) At least one of the following judgments regarding the necessity, priority, and vehicle selection of rescue operations, (5) Proposed response information (may include recommended procedures, necessary permits, partners, etc.) (6) Metadata for display and notification used for the visualization and distribution of each of the above-mentioned pieces of information. Hereafter, unless otherwise specified, "support information" will be used to mean that it includes at least one of the above-mentioned pieces of information. Furthermore, in this invention, "rescue vehicle" means a vehicle owned by the road service provider and used to rescue members or general vehicles (which may include emergency vehicles), and includes tow trucks, service cars, transport vehicles, or vehicles equipped with vehicle rescue equipment. "Rescue request reception information" means information related to rescue requests transmitted to the road service provider by telephone or app from members or third parties, and includes the requester's contact information, location information, reason and circumstances for needing rescue, and information on the target vehicle. "Rescue activity acquisition information" means information acquired at the rescue activity site using a rescue vehicle or a drive recorder, small unmanned aerial vehicle camera (drone), portable information device, etc., and includes video data, still image data, audio data, location information, and on-site environment information. Furthermore, "information related to road disasters" is a comprehensive concept encompassing multiple types of information used in disaster relief activities and assessment of the road infrastructure situation, including information on receiving requests for assistance, information on acquired assistance activities, member reports, disaster information, road information, traffic information, and weather information. Paragraphs 0009 to 0069 primarily provide a basic explanation and design concept for road disaster response for road administrators (these can also be adapted and applied to inventions by road service providers), while paragraphs 0070 to 0207 primarily describe in detail embodiments of inventions by road service providers.

[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 the present invention, the road disaster response support system 600 may be configured to include a road clearing unit, an infrastructure maintenance unit, an improvement unit, a robotics unit, and a road service unit, with the road service unit and the like being responsible for acquiring, integrating, and distributing information regarding the 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 and vehicle conditions before and after a disaster, or to detect road conditions and vehicle conditions 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, or low) of the information, and adding labels such as "needs verification" or "pending" to the judgment results for 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, the system can provide 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 610, through the information provision unit, etc., with functions such as email notification to road administrators, data provision to road information board systems, data provision to road restoration visualization maps (which unify and display road restoration status, main damaged areas 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 to road users and residents (homepage, smartphone app, etc.), and it is not necessarily required to go through the information provision unit. An example of the function of disclosing information to 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 610, or the results of analysis by the analysis unit 620, or the necessity of road disaster response determined by the decision unit 630, or the road clearing implementation plan formulated by the road clearing unit, or the recovery status by the robotics unit, or the prediction of the necessity of road disaster response by the prediction unit, to a mobile terminal device (mobile phone, smartphone, tablet device, laptop computer, 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 conjunction with the analysis unit 620 and the improvement unit, and may be fed back into the infrastructure maintenance unit and the road clearing unit's pre-planning 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 620 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 disaster conditions, 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 pieces of information acquired by the information acquisition unit 610, such as satellite data, fiber optic data, and vehicle driving data. This allows for the quantitative identification of locations where the formation of subsurface cavities 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 610 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 620 that comprehensively analyzes the information by statistical analysis, threshold comparison, or rule-based inference; and a decision unit 630 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 620 can apply processing parameters that focus on cavity risk assessment and anomaly detection, while in disaster mode, the decision unit 630 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, the Road Disaster Response Support System 600 may be configured to dynamically change the judgment thresholds for risk score calculation and anomaly detection, or the prioritization logic for on-site survey target locations, depending on the normal operation mode and the 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] Up to this point, we have mainly provided a basic explanation of road disaster response for road administrators. In the following paragraphs, we will describe specific examples of how the Road Disaster Response Support System 600 can incorporate embodiments of inventions by road service providers (such as the Japan Automobile Federation). The Road Disaster Response Support System 600 is primarily used by road service providers to respond to rescue requests, and includes a configuration that includes a road service section for use by road service providers in actual rescue operations. The Road Service Department is a component responsible for the command, analysis, and optimization of the entire rescue operation based on rescue request information. It is responsible for dispatching rescue vehicles, determining road passability, and formulating or updating rescue operation plans. The road disaster response support system 600 includes an embodiment that can operate based on one or more pieces of information, including information on the receipt of a request for assistance. Specifically, in addition to rescue request reception information managed by the road service provider itself or its organization, the system acquires at least one type of rescue activity information obtained through the rescue vehicle, the drive recorder installed in the vehicle, portable information devices, vehicle abnormality detection devices, small unmanned aerial vehicle cameras (drones), and various sensor modules, and processes these in an integrated manner. Furthermore, member notification information transmitted via member terminals may be acquired and utilized in an integrated manner. Furthermore, if necessary, public data such as information on traffic restrictions provided by road administrators and administrative agencies (e.g., traffic restriction information, road closure information, traffic restriction information, road opening information, detour information) may be referenced as supplementary information. Based on the collected information, the analysis unit 620 analyzes the likelihood of road disasters or abnormalities in road infrastructure using non-AI analysis methods or AI (machine learning models, reinforcement learning, clustering, etc.). Based on the analysis results obtained by the Analysis Department 620, the Road Service Department makes the necessary decisions regarding the implementation of support information (the definition of terms is the same as in paragraph 0008) concerning rescue activities by road service providers. Based on these processing results, the following can be determined: whether rescue vehicles can pass, optimization of personnel or equipment deployment, determination of rescue activity priorities or selection of rescue vehicles, and selection of routes that rescue vehicles can take. These derived results are visualized on a dashboard installed in the command center of the road service provider and, as needed, transmitted to the portable information devices or member terminals of field personnel, supporting the rapid and accurate implementation of rescue operations. Furthermore, after relief operations, the collected performance data is stored as learning data by the improvement unit, and the system is configured to continuously improve the accuracy of analysis and the ability to optimize relief operation plans. Thus, the road disaster response support system 600 according to the present invention, centered on the road service department, consistently performs information gathering, situation assessment, support plan formulation, implementation, result reflection, and international response based on one or more pieces of information, including rescue request reception information.

[0071] In this embodiment, rescue request information includes, for example, rescue requests with location information sent by members via a dedicated smartphone app or call center. This information includes the requester's current location, vehicle information, date and time of the request, details of the request (e.g., dead battery, running out of gas, flat tire, wheel coming off / falling off, flooding / submersion, recovery from snow / mud, vehicle towing / transportation, rescue due to extreme weather, rescue due to disaster, fall, accident, etc.), road / traffic conditions, and disaster / damage situation. This data is transmitted in real time to a management server and used to determine whether a rescue vehicle can be dispatched and to prioritize the request.

[0072] One example of acquiring information for rescue operations is to have a drone fly ahead to the vicinity of the site before rescue vehicles arrive to acquire aerial video of the road closure situation. Artificial intelligence (AI) then analyzes the images to detect obstacles such as flooding and fallen trees. The acquired information is used as a factor in determining in advance whether rescue vehicles can pass through.

[0073] The information acquisition process in the Road Disaster Response Support System 600 targets at least one of the following: information received from rescue requests, information acquired from rescue activities, and information reported by members, including information received from rescue requests. For example, by combining location information and request details obtained when a rescue request is received from a member with video and still image data acquired from drive recorders or small unmanned aerial vehicles (drones) mounted on rescue vehicles, the situation at the rescue site can be grasped quickly and in detail. Furthermore, this information can be collected during or before rescue operations begin, allowing for pattern analysis of the concentration of rescue requests, geographical distribution, and time of occurrence, which can then be used for scoring and prioritizing road disasters. These analysis results can be used by the Road Service Department to determine whether or not to dispatch rescue vehicles and to prioritize their deployment, and the system may be configured to dynamically update the rescue plan in conjunction with the analysis unit 620. Furthermore, the rescue request information can include attribute information such as the type of vehicle to be rescued, the nature of the rescue, and the condition of the person requesting the rescue, enabling more detailed and accurate decision-making when formulating rescue activity plans. This information acquisition function allows for the development of more accurate rescue plans between the time of the rescue request and arrival at the scene, thereby enabling a faster initial response. Furthermore, rescue request information and rescue activity acquisition information may also be input into the AI ​​estimation module in the road service department and used as a decision support model that reflects past dispatch history and regional characteristics.

[0074] The analysis process can be structured in two stages: first, each acquired piece of information is individually analyzed by type, and then an integrated analysis process is performed to evaluate the geographical and temporal concentration. For example, the individual analysis might involve aggregating the frequency of rescue requests by time period, detecting the presence or absence of road disasters from rescue activity information, and then integrating these to calculate a risk score for a specific area.

[0075] Next, the process for generating support information necessary for rescue operations will be described. In this embodiment, support information is generated to assist in planning the deployment of rescue vehicles and personnel based on density scores and analysis results. The generated support information may be managed under the integrated control of the Road Service Department and used to support decision-making such as dispatching orders to each base, planning vehicle deployment, and determining rescue routes. This support information includes elements such as the types and quantities of equipment and materials held by each base or branch, the skills and deployment status of available personnel, and the expected time period and area of ​​operation for relief efforts. By comprehensively considering these factors, it is possible to derive the optimal combination of vehicles and personnel for each rescue request and rescue operation, and this can be applied to support coordination plans between multiple locations and wide-area personnel movement plans. The Road Service Department provides support for rescue operations through the generation, output, visualization, and distribution of the above-mentioned support information.

[0076] The specific processing flow for generating support information can be structured as follows: (1) calculating the rescue request density score, (2) acquiring information on available personnel and equipment at each base, (3) sequentially performing calculations to determine the priority of response by combining multiple factors, and (4) selecting rescue vehicles and proposing the optimal rescue route.

[0077] Furthermore, this embodiment includes a process that selects a response method suitable for rescue activities from among multiple response methods based on support information and rescue request details, and generates response proposal information regarding the execution of each response method. The response methods include selecting the type of rescue vehicle, selecting the route to the scene, determining the method of transporting necessary equipment and materials, and deciding whether to activate a wide-area support system. The proposed response information is generated by the Road Service Department, taking into account priority scores, the operational status of each branch, geographical conditions, and traffic restriction information. It is displayed on the dashboard of the rescue command center and is also configured to notify field personnel and relevant personnel via their mobile devices. This enables faster and more efficient overall disaster response. The generated response proposal information may be distributed to the dashboard and field terminals via the road service department's command function, and may be configured to be immediately reflected in dispatch orders, vehicle selection, and arrival route determination.

[0078] As a concrete example of the response suggestion information generation process, it is possible to set a rule that limits the type of rescue vehicle to large tow trucks for requests with a high priority score, and conversely, prioritizes the allocation of small service cars for minor rescue requests. It is also possible to include a process that recalculates recommended routes based on traffic regulation information and reflects the latest congestion information.

[0079] A possible workflow for selecting response measures would involve the following steps: (1) assessing the urgency of the rescue request, (2) calculating the estimated time required to reach the scene, (3) determining the passability of candidate routes, (4) deciding on the final configuration of vehicles, personnel, and equipment, and (5) distributing information to on-site personnel and the command center.

[0080] Furthermore, this embodiment includes a process for learning or updating the AI ​​model used in rescue operations, using either rescue request reception information, rescue site information, or member notification information. This allows for the accumulation of information on the progress of rescue operations and actual results obtained after rescue operations as learning data, thereby improving the accuracy of judgment, priority determination, and optimal route selection in subsequent disaster responses. This learning or model update control is automatically triggered by information such as rescue operation results and rescue vehicle traffic data, enabling continuous and optimal rescue support.

[0081] In AI model training, for example, by extracting past cases where rescue vehicles had difficulty passing and training the model with features such as obstacles, road conditions, weather conditions, and time of day, it becomes possible to improve accuracy and predict with high certainty whether roads will become impassable when similar conditions occur in the future.

[0082] An effective trigger for model updates is a configuration that automatically collects logs of rescue operation results, instances where administrators have flagged a model as needing correction on the dashboard, and logs of unexpected road closures. Once a certain threshold of logs has been accumulated, the retraining process is automatically initiated.

[0083] Furthermore, this embodiment includes a process for visualizing and providing various information and analysis results related to rescue operations on a dashboard screen installed at a management base such as a rescue command center. This dashboard can display integrated information such as a disaster score based on the density of rescue requests, the results of rescue operation decisions, passable routes, and the deployment status of equipment and personnel at branches and bases, thereby accelerating the command center's ability to grasp the overall situation of rescue operations and supporting decision-making. Furthermore, if necessary, the same information can be transmitted to portable information devices used by field personnel and to members' mobile devices, enabling accurate and timely information sharing between the field and management bases. The dashboard management and updates may be handled by an integrated control module in the road service department, and the configuration may allow for centralized monitoring throughout the command center.

[0084] A concrete example of a dashboard would be a heatmap function that displays each rescue request location as a marker on a map and colors them according to the density of requests. This would allow operators to intuitively grasp the rescue request situation across a wide area and immediately decide to concentrate resources on areas where requests are concentrated.

[0085] The dashboard update process automatically updates at regular intervals (for example, every 30 seconds) when information is acquired, reflecting rescue requests, road passability, and the progress of rescue vehicles in real time. This configuration allows for commands to always be based on the latest situation. The system could be configured so that the latest information obtained through automatic dashboard updates is immediately reflected by the road service department in revisions to dispatch instructions (such as redeployment and route recalculation).

[0086] Furthermore, this embodiment can be equipped with a communication function that transmits information such as the estimated arrival time of rescue vehicles, traffic restriction information, and road disaster status to the member's mobile terminal based on acquired and analyzed rescue request reception information, rescue activity acquisition information, and passable route information. This allows the member requesting rescue to understand the progress of rescue vehicles and the status of rescue activities in real time, contributing to reducing anxiety while waiting and facilitating smooth handover.

[0087] By utilizing push notification functions and other features to send notifications automatically when the estimated arrival time or progress of rescue vehicles changes, we can provide real-time reassurance to those making requests.

[0088] The system calculates the estimated arrival time of rescue vehicles by dynamically updating the ETA (Estimated Time of Arrival) using the latest traffic and vehicle location information, enabling accurate progress management.

[0089] Furthermore, this embodiment can include a function to transmit acquired and analyzed information on rescue request receipts, rescue activity acquisitions, impassable areas, the status of rescue support implementation, and estimated arrival times of rescue vehicles to available external systems, including road administrators, administrative agencies, or disaster response organizations (fire departments, police, Self-Defense Forces, disaster medical assistance teams, etc.). This enables administrative agencies and other support organizations to immediately grasp the situation in the disaster area and to quickly make decisions regarding wide-area recovery work and traffic restrictions.

[0090] For transmitting information to external systems, a configuration can be adopted that allows for bidirectional communication with disaster information management systems and traffic control systems owned by road administrators and administrative agencies, using standard API interfaces (e.g., REST API or WebSocket). This enables real-time mutual updating of rescue operation status and traffic control status. External update information obtained through API integration is used by the Road Service Department to update dispatch priorities, road access permits, and route instructions.

[0091] The transmitted information should include not only the details of the incident, but also detailed information that supports decision-making, such as the impact scope score calculated from the analysis results and information on recommended routes for lifting restrictions. This structure can improve the accuracy of administrative decision-making.

[0092] Furthermore, this embodiment can be equipped with a function to optimize the coordinated deployment of personnel and equipment across multiple branches or municipalities, as well as the securing of accommodations and support bases, based on information regarding equipment and personnel available at each branch or base, a score of relief activities based on analysis results, and judgment results regarding relief activities. In addition, by providing information on traffic permits and traffic restriction instructions in the disaster-stricken area to the traffic permit management system and the entry management system according to these optimization results, the efficiency of relief activities and the accuracy of disaster area management can be improved. Here, the Road Service Department integrates the scoring results from the Analysis Department 620 and the availability information of each base, and based on the results of the optimization process, it makes the final decision on the allocation of personnel and equipment and issues traffic orders.

[0093] Regarding the wide-area coordinated deployment plan for personnel and equipment, the system can perform multivariate analysis of disaster scores and availability information for each base to list branches and municipalities that can provide support in order of priority, and automatically present recommended plans.

[0094] Furthermore, by managing hotels, inns, and public facilities in disaster-stricken areas as potential accommodation bases and automatically coordinating the process of securing accommodations when planning the dispatch of support personnel, the practical feasibility of disaster response can be ensured.

[0095] Furthermore, in this embodiment, when the score related to rescue activities exceeds a predetermined threshold, the system's operating mode is automatically switched from normal mode to disaster mode, and the priority of output information and notification format can be changed according to the progress of rescue activities and surrounding conditions. This enables flexible operation according to the scale and impact of the disaster, and can improve the accuracy of initial response and rescue support. After switching to disaster mode, the Road Service Department may have priority control authority and may be configured to aggregate inputs from the Analysis Department 620 and other systems to make integrated decisions regarding dispatch orders, redeployment, and traffic control orders.

[0096] In controlling the switching of operating modes, not only scores exceeding a predetermined threshold, but also a sudden increase in the number of rescue requests and the results of geographical cluster analysis (patterns in which requests are concentrated in a narrow area in a short period of time) can be used as additional trigger conditions. This enables flexible mode switching according to the actual situation on site.

[0097] In the disaster mode after the switch, the dashboard UI is automatically simplified, and the layout is changed to make it easier for command center operators to grasp only priority information, thus incorporating features that enhance operational responsiveness.

[0098] Furthermore, in this embodiment, the system can collect and store data on the actual disaster situation, the results of rescue operations, and the movement records of rescue vehicles. The collected data and analyzed results can then be used as training data for the artificial intelligence model, allowing for the model to be retrained or updated. This configuration enables more accurate judgment and support in future disaster responses, continuously improving response capabilities. Furthermore, the collection of this performance data and the execution of the retraining process may be controlled in cooperation between the Road Service Unit and the Analysis Unit 620.

[0099] In retraining an AI model, the learning efficiency can be improved by comparing the rescue vehicle's passability determination results with actual travel results (such as arrival time and travel logs), and focusing on adding misjudgment cases as training data.

[0100] Furthermore, the timing of retraining can be either performed in a batch process after multiple disaster response events have been completed, or performed sequentially after a certain number of new data points have been acquired.

[0101] Furthermore, this embodiment includes security processing to prevent unauthorized access and tampering with communication content, and to perform authentication processing for information transmitted and received between external systems, members' mobile terminals, and portable information devices used by field personnel. This makes it possible to deter the leakage and misuse of important information during disaster response and ensure the security of information.

[0102] In security processing, "selective encryption" is used, which prioritizes encryption only for highly important information such as rescue request reception information and rescue activity acquisition information, thereby achieving both responsiveness and security strength.

[0103] Furthermore, by employing two-way authentication on the terminals of rescue vehicles and field personnel to verify the certificates of the communication partners (command center and central system), security against man-in-the-middle attacks and other threats can be enhanced.

[0104] Furthermore, in this embodiment, even if some functions stop due to communication failures, power failures, equipment failures, etc., during a disaster, the functions can be made redundant by using other network routes, alternative servers, and alternative processing mechanisms, thereby ensuring the continuity of operation for the entire system. With this configuration, rescue support functions can be maintained without interruption even in emergencies, making it possible to continue on-site activities and information sharing among stakeholders.

[0105] In a redundant configuration, the main server normally handles rescue operations, and a failover mechanism can be incorporated that automatically switches to the standby server after a failure is detected.

[0106] Furthermore, by installing standby servers in multiple geographically separated data centers, it is possible to adopt a configuration that ensures the continued operation of the system even if the disaster itself affects the region including the data centers.

[0107] Furthermore, in this embodiment, information such as the actual circumstances of a disaster, the results of rescue operations, and the traffic records of rescue vehicles are collected, and the accumulated information and analysis results are used as training data for an artificial intelligence model. This improves the accuracy of analysis, such as the assessment of the probability of a disaster occurring and the scope of its impact, as well as the determination of priorities in rescue operations. As a result, in the event of future disasters, more sophisticated decision-making support will be possible, reflecting past examples. Furthermore, the collection of this performance data and the control of AI model learning may be managed in cooperation with the Road Service Department and the Analysis Department 620, and the system may be configured to automatically reflect dispatch records and on-site logs into the learning target. Furthermore, the stored information may also include output results or operational results of support information output by the Road Service Unit (such as dispatch plans, redistribution results, route instructions, the difference between the estimated arrival time and the actual arrival time, and notification logs). The Improvement Unit may be configured to improve the optimization accuracy of the output related to at least one of the following: the accuracy of road disaster determination, the accuracy of passability determination, and the accuracy of support information output by the Road Service Unit, based on this information and the analysis results of the Analysis Unit 620.

[0108] In analyzing rescue operations, we not only analyze rescue request information, rescue activity acquisition information, and member reports individually, but also integrate these multiple pieces of information and perform correlation analysis to identify the concentration of rescue requests, the extent of damage, and priority areas for assistance.

[0109] Correlation analysis allows for, for example, using geographic clustering to assess the risk of a region as higher when multiple requests are received from members in the same area, or to analyze the similarity of the reported content using natural language processing to estimate that they are the same event.

[0110] Furthermore, in this embodiment, in preparation for situations where some functions stop due to communication failures, power failures, or equipment failures during a disaster, the system can be configured to ensure redundancy of each function by using alternative network paths, alternative servers, or alternative processing mechanisms, thereby enabling the entire system to continue operating. This configuration makes it possible to minimize service interruptions even in emergencies.

[0111] When requesting information, it is desirable to include detailed information such as the condition of the damaged vehicle (overturned, flooded, stuck in snow, etc.), road conditions (snow depth, flood depth, fallen objects, etc.), and local weather conditions (presence or absence of snowfall / rainfall, temperature, wind speed, etc.).

[0112] By obtaining this detailed information from the moment a rescue request is received, it becomes possible to assess the risk factors and difficulty of operations at the disaster site in advance, and to reflect this in the preparation of rescue personnel and vehicles, as well as the selection of equipment.

[0113] Furthermore, by acquiring the current location and travel history of rescue vehicles in real time and comparing them with information on passable routes, it is possible to avoid the risk of road closures during the journey and assist in switching to alternative routes.

[0114] By integrating and analyzing rescue vehicle travel data and member reports, the system can continuously update information on passable and impassable sections and share this information with administrators and other rescue teams, thereby supporting smooth rescue operations throughout the disaster-stricken area. These update details may be consolidated under the integrated management of the Road Service Department and automatically synchronized to other locations, branches, and government agency terminals. The Road Service Department may also be configured to reflect the results of the integrated analysis performed by the Analysis Unit 620 in the overall command.

[0115] Furthermore, video and audio data from rescue operations can be used to quantify the damage and determine the level of danger through image recognition and audio analysis using AI models. For example, it can detect road cracks and fallen trees from video, and estimate on-site noise levels and the urgency of those requesting rescue from audio, contributing to highly accurate disaster scoring. The analysis may be configured as an AI analysis process performed by the analysis unit 620, and the analysis results may be reflected in the command system via the road service unit.

[0116] The AI ​​analysis results are automatically reflected on a dashboard used by rescue workers and the command center, allowing for real-time updates of decision-making information in response to changing circumstances. The dashboard update process is executed by the integrated control function of the road service unit, and the configuration may dynamically control the priority and update frequency of the displayed content according to the situation. A two-stage control configuration may be adopted in which the Road Service Unit automatically updates the UI in response to an analysis update event by the Analysis Unit 620.

[0117] Furthermore, the system can be configured in either a way that the analysis unit 620 processes the collected information individually during relief operations, or a way that integrates and comprehensively analyzes multiple pieces of information. This allows for a flexible design that can be operated according to the scale of the disaster and the local conditions.

[0118] Furthermore, by utilizing location information included in rescue request reception information and rescue activity acquisition information, a configuration can be adopted that visualizes rescue request locations, rescue vehicle locations, passable routes, and obstacle locations on a GIS (Geographic Information System). This visualization of geographic information allows for the intuitive formulation of rescue activity priorities and wide-area support plans. The configuration may also include the Road Service Department visualizing the geographical analysis results generated by the Analysis Department 620 on a dashboard.

[0119] Furthermore, the dashboard displays a list of the operational status and equipment / personnel deployment status of each branch / base, in addition to the disaster score. By providing information that supports coordination between multiple branches and personnel shift planning, it can streamline command operations in wide-area and complex relief efforts. The operational information of each branch / base may be collected and evaluated by the analysis unit 620, and then integrated, visualized, and output as a command by the road service unit.

[0120] Furthermore, the AI ​​(artificial intelligence) automatically recalculates the optimal route in accordance with the progress of rescue operations and notifies field personnel and the command center of the revised route in real time, enabling a system that minimizes delays and allows rescue operations to continue even in emergencies. The results of this recalculation process may be automatically reflected in the road service department's command system, enabling the immediate issuance of redeployment orders and personnel notifications based on the revised route. The AI ​​recalculation process may be performed by the analysis unit 620, and the results may be reflected in the distribution and command by the road service unit in a two-tiered control configuration. Furthermore, the Road Service Unit integrates and controls commands, distributions, notifications, etc., based on the analysis results of the Analysis Unit 620, and the means of achieving this may include rule-based systems, mathematical optimization, heuristics, or machine learning. Furthermore, the optimization process in the road service department does not necessarily require AI; it may be configured to use machine learning models in combination as needed. This allows for the selection of flexible control methods in response to changing circumstances and resource constraints, enabling autonomous rescue commands that are suited to the actual operating environment. Furthermore, the road service department may adopt a configuration that utilizes AI (artificial intelligence) in the process of optimizing the dispatch, redistribution, and route selection of rescue vehicles. For example, a learning-based assignment method may be used to optimize the correspondence between rescue vehicles and request locations. Alternatively, a configuration may be adopted that uses reinforcement learning or a bandit algorithm to perform multi-objective optimization that simultaneously considers waiting time, distance traveled, and rescue priority. Furthermore, a configuration may be adopted in which the road service unit's learning-type heuristics recalculate multi-vehicle route optimization (dynamic VRP) in real time, using the road access information and disaster score output by the analysis unit 620 as input. In addition, a learning model that estimates the ETA (Estimated Time of Arrival) and arrival confidence may be used to dynamically optimize the notification frequency and timing to field personnel terminals or member terminals according to demand, congestion, and communication conditions. Furthermore, the improvement unit may collect the results of the recalculation process and the dispatch, redistribution, and notification control as learning data, and perform control to retrain or update the learning heuristics or prediction models (such as estimated arrival time, arrival confidence, and notification timing estimation) related to the optimization process of the road service unit, in addition to the passability determination model in the analysis unit 620.

[0121] Furthermore, in determining passability based on rescue request information and rescue activity data, the system can incorporate a configuration that combines external data such as weather and meteorological information, traffic restriction information, and road damage information, and uses AI (artificial intelligence) to perform a multifaceted assessment. This allows for the formulation of advanced rescue plans that go beyond conventional simple route determination and can respond to complex road conditions during disasters.

[0122] Furthermore, the system can be equipped with an algorithm that immediately reflects newly arising rescue requests during rescue operations, as well as changes in the status of existing requests (cancellations, location changes, etc.), and dynamically reallocates rescue vehicles and personnel and updates routes. This allows for the effective use of rescue resources and maximizes the efficiency of rescue operations. These redistribution and route update processes are performed by the integrated control function of the Road Service Department, and the configuration may be set up to automatically reflect coordinated commands and redeployment commands between multiple locations.

[0123] Furthermore, by incorporating a data management function that saves time-series data from the receipt to the completion of rescue requests and systematically organizes and manages it as training data for future AI models, it becomes possible to improve the performance of AI models over the long term.

[0124] Furthermore, in determining the priority of rescue requests, the system can be equipped with a mechanism to dynamically change the priority by scoring based on factors such as the urgency of the requester in the rescue request information (e.g., the degree of danger to life and physical safety), the traffic importance of the request location on the road network (e.g., whether it is a main road or a local road), and the number of simultaneous requests and the operational status of branch offices.

[0125] Furthermore, by providing a dashboard screen that visualizes multiple evaluation data points, such as disaster scores, traffic impact, and rescue operation schedules, in a multidimensional manner, enabling comprehensive decision-making in the command center, it becomes possible to grasp the situation in an integrated manner between the field and management bases. This dashboard can function as an integrated control screen for the road service department, reflecting changes in rescue status and priority in real time, and may be configured to be shared throughout the entire command center.

[0126] In addition, by collecting on-site video, audio, and environmental data obtained during rescue operations in real time, continuously updating the disaster situation, and using this data as feedback to the AI ​​model, the accuracy and responsiveness of rescue operations can be further improved.

[0127] Furthermore, the acquired information can be analyzed individually for each rescue request, or multiple rescue requests that are geographically and temporally close can be analyzed together. This allows for flexible scoring and priority evaluation to be performed according to the different disaster response needs of each request and region.

[0128] Furthermore, information such as the feasibility of rescue operations and recommended routes based on the analysis results can be linked to terminals used by on-site personnel to provide navigation support. This allows personnel to select appropriate entry routes at disaster sites and carry out rescue operations safely and quickly. This navigation support information may be configured to be transmitted to the field service personnel's terminal via the road service department's command system.

[0129] Furthermore, by providing timely notifications to members requesting assistance regarding the progress of rescue vehicles, estimated arrival times, and recommended waiting locations, it is possible to support them in optimizing their own actions, thereby facilitating rescue operations and increasing the sense of security for those requesting assistance.

[0130] Furthermore, when acquiring information on rescue requests, rescue activities, and member reports, attribute data included in each piece of information (such as the time of the request, location coordinates, road section identifier, road type, and traffic volume indicator) is added and managed as metadata. By allowing these attributes to be referenced during subsequent integrated analysis and rescue plan formulation, the searchability, traceability, and accuracy of the analysis can be improved.

[0131] Furthermore, statistical analysis using various attribute information allows for a multifaceted understanding of the trends in rescue requests, including time series, regional breakdown, and road type. This information can then be used for pre-disaster training and disaster prevention planning during normal times, and for optimizing the deployment strategy of relief resources during disasters.

[0132] In addition, this embodiment can include a process that performs image analysis on the cloud or elsewhere on still and video data acquired from rescue operation sites, and automatically classifies disaster elements such as snow accumulation, flooding, fallen trees, and road collapses. The results of this analysis can be used for quantitative assessment of the degree of disaster impact, prioritization of rescue routes, and support for the selection of equipment and materials, contributing to rapid and accurate decision-making in response.

[0133] Furthermore, these automated classification processes can generate heatmaps by combining time and location information, visualizing areas where disasters are concentrated, thereby intuitively supporting the understanding of the extent of the damage.

[0134] Furthermore, in this embodiment, audio data included in rescue activity acquisition information can also be analyzed, and for example, it can be equipped with audio analysis processing that estimates the urgency and danger level from the voices of those being rescued or those making the call. This makes it possible to grasp the situation at the scene from multiple perspectives and assist in determining the priorities of rescue activities.

[0135] Furthermore, these audio analysis results are integrated with video analysis results, location information, and call content, and used as indicators for comprehensive rescue scoring and danger ranking. This allows the rescue command center to quantitatively grasp the urgency of the situation on the ground and quickly decide on a response plan.

[0136] Furthermore, in this embodiment, video data and still image data acquired as rescue activity information are analyzed using image recognition technology to detect specific disaster elements such as obstacles on roads, flooding, sinkholes, and collapsed bridges. The results of this analysis are used to determine whether rescue vehicles can pass and to assess risks when formulating rescue activity plans.

[0137] In addition, the detection results obtained through image recognition can be integrated and analyzed with other acquired information (such as rescue request reception information, member report information, and traffic restriction information), and visualized on a dashboard by plotting the location and extent of obstacles on a map. This allows the rescue command center and field personnel to intuitively grasp the disaster situation spatially and quickly select appropriate evacuation and rescue routes.

[0138] Furthermore, in this embodiment, when retraining the artificial intelligence model based on the results of rescue operations, it is possible to separately manage not only the rescue request data from normal times but also the specific rescue request reception information and rescue operation results from large-scale disasters, and to maintain separate models for normal times and disaster times. This makes it possible to switch to the appropriate model according to the different rescue patterns in normal times and disaster times, thereby optimizing the accuracy of the judgment. Retraining can be performed in batch processing all at once after the completion of disaster response, or using an online learning method that responds to rescue results sent sequentially from the field, supporting the maintenance of the accuracy necessary for rescue operations. The execution control of model switching and retraining may be managed in cooperation with the analysis unit 620 and the road service unit, and the configuration may be such that the judgment results of the road service unit are given priority when in disaster response mode. Furthermore, the scope of segmented management is not limited to the analysis unit 620; the models used in the road service unit for dispatching, redistribution, route selection, and notification optimization may also be configured to switch between and maintain / operate normal-time models and disaster-time models.

[0139] Furthermore, in this embodiment, a dashboard screen installed in a rescue command center or the like overlays acquired rescue request information, rescue activity information, support information, road passability determination results, and passable route information onto a map, enabling real-time visualization of the current location, direction of travel, and estimated arrival time of rescue vehicles. The map screen can integrate features such as color-coding to indicate the priority of rescue requests, a pop-up function to display detailed rescue request information, and a list view showing the operational status of multiple locations, allowing administrators to intuitively grasp the situation and quickly issue appropriate instructions. The updates and output control of this dashboard may be managed by the integrated control module of the road service department, and it may be configured to function as a central command screen for the entire rescue operation.

[0140] Furthermore, in this embodiment, when training the AI ​​model with the results of rescue operations, the travel history of rescue vehicles, and the analysis results, the system is configured to store, along with the details of the rescue request and on-site situation data, historical information on the determination of passability, route information used in rescue operations, and the time required until the completion of the rescue. By retraining the AI ​​model using this set of information as training data, it is possible to achieve highly accurate determination of the necessity of rescue, priority evaluation, and route selection in future disasters. Moreover, the trained model is gradually reflected in the operational environment, and continuous performance improvement is achieved through model updates. Furthermore, the learning storage may include the output results or operational results of support information output by the Road Service Department (vehicle dispatch, redistribution, route instructions, notification history, arrival records, etc.), and this information set may be used as training data to retrain or update the optimization-related models used by the Road Service Department.

[0141] Furthermore, in this embodiment, the configuration may include an AI model management server to manage the training and updating of the AI ​​model. The AI ​​model management server is responsible for centrally managing the entire training process, including registering training datasets, version control, monitoring training progress, and verifying training results. The AI ​​model management server may be configured to manage not only the models used in the analysis unit 620, but also the models used in the road service unit, such as dispatch, redistribution, route selection, and notification optimization. This prevents tampering with or incorrect registration of training data, ensures the reliability of the learning process, and makes it possible to maintain a certain level of quality in the AI ​​models used in rescue operations. The AI ​​model management server may be operated in conjunction with the integrated control module of the road service department, and may be configured to allow the rescue command center and administrators to directly monitor and control the status of the models. Furthermore, the server may control the collection of performance data, the creation of training datasets, the management of conditions for triggering retraining, and the determination of whether the model is applicable to operational use, thereby providing centralized execution management of model update processes (retraining or updating).

[0142] Furthermore, in this embodiment, a process for verifying the operation of the AI ​​model may be included after retraining or updating the AI ​​model. In this operation verification process, a simulated rescue scenario is executed using the updated AI model, and the validity of the judgment results and rescue plan is evaluated. The evaluation is performed automatically based on past actual disaster data and standardized test cases, and if the evaluation result does not meet predetermined criteria, control can be implemented to prevent the model from being applied to field operations. The evaluation can include models for the road service department (vehicle dispatching, redistribution, route selection, notification optimization, etc.). The system can automatically evaluate the validity of past disaster cases or simulated rescue scenarios using indicators such as waiting time, distance traveled, on-time arrival rate, rescue priority achievement rate, and notification effectiveness. If the criteria are not met, the system may be configured to withhold operational application. This makes it possible to objectively verify the quality after updating the AI ​​model and improve the reliability of the entire rescue support system. The verification results may be automatically reflected in the command and control system of the road service department and used for operational decisions regarding the rescue support system and for approving model updates.

[0143] Furthermore, this embodiment may include a history management process for managing the operational history of the AI ​​model. This history management process stores the version of the AI ​​model's training data, the date and time of retraining and updates, the content of the updates, and the results of post-update operational verification in chronological order, making it available for administrators to review as needed. The history management information is important because it allows for a centralized understanding of the AI ​​model's operational status and enables explanation of the model's behavior during disaster response and post-disaster verification. This ensures strict change management of the AI ​​model and guarantees transparency and reliability in disaster relief operations. Furthermore, for models used in the road service department, the following may be stored as historical management information: versions related to dispatching, redistribution, route selection, and notification optimization; the origin of the learning data; update details; and evaluation items related to the operational status after the update (waiting time, distance traveled, service provision rate, ETA error, notification open rate, arrival consistency, etc.). This configuration may be useful for post-event verification and rollback operations.

[0144] Furthermore, this embodiment may include a process for automatically switching and failovering AI models. This process automatically switches to a predefined alternative AI model if the AI ​​model detects an anomaly during relief operations or if the model's response delay exceeds an acceptable range. During the switch, the system takes over the relief request information and analysis results up to the present, allowing for continuous processing without affecting support activities. This reduces the risk of system downtime caused by the AI ​​model and enhances the stability and reliability of disaster response support.

[0145] Furthermore, this embodiment may include a process for automatically evaluating the analysis accuracy of the AI ​​model and the accuracy of rescue plan formulation using activity performance data acquired when rescue operations are completed. This process compares actual data such as the content of the rescue request, the arrival time of rescue vehicles, the actual rescue completion time, and local environmental factors with the inference results of the AI ​​model and quantitatively calculates errors and discrepancies. The evaluation results can be used to determine whether or not model updates are necessary and to prioritize them, contributing to the continuous maintenance and improvement of model accuracy.

[0146] Furthermore, in this embodiment, in order to monitor the progress of rescue operations in real time, the system may include a process to aggregate and reflect on a management screen the current location of rescue vehicles, the time spent at the scene, the response status, and the contact status with those who requested rescue. This process allows the command center and administrators to grasp the progress of multiple rescue requests at a glance, and to immediately request additional support or give instructions in the event of delays or problems. This supports the smooth progress of the entire rescue operation. The monitoring results may be reflected in real time on the road service department's dashboard, allowing administrators to centrally monitor the progress of all rescue requests.

[0147] Furthermore, this embodiment may include a process for automatically re-evaluating and changing the priority of rescue requests in response to additional information collected during rescue operations, new rescue requests, changes in road conditions, worsening weather, etc. This priority change process enables the command center to update the support plan in response to changes in the situation, thereby improving the overall efficiency of rescue operations and ensuring the safety of those requesting rescue.

[0148] Furthermore, this embodiment may include a process for predicting the time required for a rescue vehicle to arrive at the scene. This prediction is made considering the current location information of the rescue vehicle, road traffic conditions, traffic regulation information, weather conditions, etc., and can calculate the estimated arrival time of the rescue vehicle with high accuracy. The calculated estimated arrival time can be notified to member terminals, on-site personnel's mobile devices, or relevant organizations, which can be used to facilitate the smooth implementation of rescue operations and to coordinate standby at the scene.

[0149] Furthermore, this embodiment may include a process for automatically recording a series of processes in rescue operations. This process automatically records, in chronological order, the time of receipt of the rescue request, the departure time of the rescue vehicle, the arrival time at the scene, the start and end times of the rescue operation, acquired video and image data, and the activity history of the rescue vehicle and on-site personnel, thereby automatically generating a rescue operation report. The generated report can be used by the operator's command center or management department for progress management and quality improvement of rescue operations, and can be used as documentation to be submitted to relevant organizations as needed.

[0150] Furthermore, in this embodiment, the system may be equipped with a process to automatically detect anomalies in the progress of rescue operations or on-site conditions based on various data acquired during rescue operations, and to urgently re-evaluate the analysis results and priorities. Examples of anomalies include unexpected road closures, significant delays in the arrival of rescue vehicles, and sudden deterioration of weather conditions. When these are detected, the system can dynamically recalculate support information and notify the command center and on-site personnel to encourage appropriate responses. The results of this re-evaluation can be immediately reflected in the command system of the road service department, and the system may be configured to automatically reconstruct dispatch orders and support information.

[0151] Furthermore, in this embodiment, in order to enhance support based on local conditions during rescue operations, the system may be equipped with a function to remotely control cameras mounted on rescue vehicles or small unmanned aerial vehicles (drones) from a management base such as a command center. This function allows operators at the management base to grasp the detailed situation in real time before and after arrival at the site. In addition, the results of video and image acquisition through remote control can be used to revise rescue plans and provide accurate instructions to on-site personnel.

[0152] Furthermore, this embodiment may include a communication function that enables real-time communication via voice calls and chat messages between field personnel during rescue operations and the management base or members. This allows for the rapid sharing of detailed information and changes in the situation at the disaster site, enabling rescue operations to proceed with appropriate decision-making support. Voice and text data are recorded as rescue activity history and can be used for later verification and as training data for AI models.

[0153] Furthermore, this embodiment may include a function for managing access rights to rescue activity acquisition information and various analysis results. This allows for setting access rights according to the user's role, such as rescue command center administrators, field personnel, and cooperating organizations, and provides only the minimum necessary information. This reduces the risk of information leakage and operational errors, enabling each user to safely and smoothly carry out rescue activities in accordance with their role.

[0154] Furthermore, in this embodiment, when notifying members and field personnel of various information related to disaster response, a function may be provided to automatically control the notification frequency according to the situation. For example, if rescue operations are stalled, the system may periodically notify them of the progress, and if the situation changes rapidly, it may notify them immediately, allowing for flexible adjustment of the notification interval according to the situation. This enables users to receive necessary information at the appropriate time, preventing confusion and anxiety caused by information overload.

[0155] Furthermore, in this embodiment, a function can be provided to automatically retry notifications after a certain period of time has elapsed in case of communication failures or temporary connection problems when notifying member terminals or field personnel terminals. This function minimizes the omission of important information notifications even in emergencies and ensures that necessary information is reliably delivered to users. The number of retries and the interval may be dynamically changed according to the disaster situation and network conditions.

[0156] Furthermore, in this embodiment, location information from member terminals or field team member terminals can be utilized to optimize notification timing based on the current location of each terminal. For example, it is possible to send arrival information when a rescue vehicle is about to arrive, or to notify users of passable routes before they approach traffic jams or restricted areas, thereby enabling timely information provision tailored to the user's location. This configuration facilitates user behavior during rescue operations and while waiting, and supports situational awareness.

[0157] Furthermore, in this embodiment, the AI ​​model can analyze a variety of meteorological data, such as precipitation, snow depth, temperature, road surface temperature, humidity, and wind speed, in order to predict disaster occurrence and evaluate road conditions. This enables anomaly detection based on complex conditions without relying on a single meteorological element, contributing to improved accuracy in understanding road disaster risks and formulating relief plans.

[0158] Furthermore, this embodiment includes a process that trains an AI model with case information such as past rescue activity history, road disaster occurrence status, rescue vehicle arrival records, and damage extent, and automatically generates a rescue plan by referring to past cases when a similar disaster situation occurs. This makes it possible to utilize experiential knowledge as data and formulate a rapid and appropriate rescue plan based on past response results.

[0159] Furthermore, this embodiment includes a process for continuously updating the road disaster risk assessment model using performance data accumulated through relief activities, such as the content of relief requests, the extent of disaster damage, and the results of relief responses. This improves the accuracy of risk assessments that take into account regional characteristics, weather conditions, and the aging deterioration of road infrastructure, thereby supporting more accurate decision-making in future relief activities and the formulation of road management plans. The updated road disaster risk assessment model may be incorporated into the road service department's rescue plan development process and used to determine future deployment priorities.

[0160] Furthermore, in this embodiment, information can be shared in real time with other disaster response organizations such as government agencies, police, fire departments, and the Self-Defense Forces during relief operations or planning, and cooperation functions based on standardized data formats and communication protocols can be provided. This configuration enables accurate and immediate exchange of location information, disaster progress, and the deployment status of relief equipment and personnel among each organization, realizing wide-area and integrated disaster response.

[0161] Furthermore, in this embodiment, the artificial intelligence model used to support relief activities can be equipped with a function to manage update and learning history based on various learning data, including past disaster response results, and to clearly manage the model version. This function makes it possible to track which disaster cases a particular model version was learned from and updated based on, to verify the process of accuracy improvement, and to roll back to a previous version if necessary.

[0162] Furthermore, this embodiment includes a function to automatically pause the model update process during rescue operations. This prevents unexpected fluctuations in AI judgment results during on-site response, thereby ensuring the stability and consistency of rescue operations. The system is configured to automatically resume the model update process after the rescue operations are completed.

[0163] Furthermore, in this embodiment, the road disaster response support system 600 can be equipped with a process for managing the history of mode switching, such as switching from normal mode to disaster mode and returning from disaster mode to normal mode. This makes it possible to visualize the timeline of rescue activities and the entire disaster response, and to refer to the switching history when analyzing past disaster cases, so that it can be reflected in future disaster response plans.

[0164] Furthermore, this embodiment includes a process for periodically monitoring the operational status of various modules (information acquisition, analysis, support information generation, communication, etc.) in the road disaster response support system 600 and saving it as an operational status log. This makes it possible to check the operational status of each module during disaster response and post-disaster reviews, identify the location of failures and the causes of processing delays, and utilize this information for operational improvements and system maintenance.

[0165] Furthermore, this embodiment can include a configuration that integrates and manages collected rescue request information and rescue activity acquisition information in chronological order, and stores it as a disaster time-series database. This configuration makes it possible to reproduce the entire process from the occurrence of a disaster to the completion of rescue along a timeline, and can be used as reference information for verifying past cases and formulating future rescue plans.

[0166] In this embodiment, the system can be equipped with a function to display information on passable routes and the current location of rescue vehicles in map format on the screen of a member's terminal or a field team member's terminal. This display function can be configured to allow for an intuitive understanding of route conditions by indicating road blockages and traffic restrictions with icons and color coding, thereby supporting members and team members in making quick decisions to respond on-site. Furthermore, by displaying the calculated estimated arrival time on the map, those requesting assistance while waiting can visually grasp the progress of the rescue vehicles, thereby increasing their sense of security and promoting a smooth handover. This map display may be linked to the road service department's dashboard and configured to be updated synchronously on the command center and field terminals.

[0167] Furthermore, this embodiment allows for the acquisition of information regarding the types and quantities of equipment and materials necessary for relief operations in conjunction with the inventory management systems of each branch and base, and to reflect this information in the relief plan. This enables real-time tracking of the location, quantity, and availability of necessary equipment and materials, and allows for the automatic formulation of a plan for transporting equipment and materials from the most suitable branch or base. The transport plan is optimized according to traffic restriction information and road disaster scores, thereby improving the speed and efficiency of relief operations.

[0168] Furthermore, this embodiment includes a configuration that allows for real-time tracking of the rescue vehicle's movement as it en route to the scene, and displays the vehicle's current location and progress on the control room's dashboard. This enables the control room to understand the estimated arrival time of the rescue vehicle and issue route change instructions in response to traffic congestion, road closures, etc., thereby minimizing delays in the overall rescue operation.

[0169] Furthermore, this embodiment can be equipped with a schedule optimization function that takes into account the expected arrival times of multiple rescue vehicles and support units to avoid simultaneous entry into the scene and to coordinate their arrival sequentially. This schedule optimization allows for efficient progress of work at the scene and avoids congestion in the delivery of rescue equipment and securing work space. This schedule optimization process is executed by the command and control module of the road service department and may be configured to dynamically readjust the arrival order of multiple vehicles.

[0170] Furthermore, this embodiment can include a configuration that monitors external factors such as local traffic conditions and weather changes in real time after the dispatch of rescue vehicles, predicts the impact of these factors on rescue operations, and automatically proposes changes to the rescue vehicle's route, recalculations of estimated arrival times, and requests for alternative support as needed. This configuration enables flexible responses to changes in the on-site environment.

[0171] Furthermore, this embodiment includes a function that allows information acquired by each base and command center in relation to relief activities to be managed separately by region and base. This makes it possible to individually grasp the situation in each region and optimize the support system on a regional basis, even when relief activities are carried out simultaneously in multiple regions, such as during a large-scale disaster, thereby improving the overall efficiency of the activities. Regional management information may be consolidated into the integrated control system of the Road Service Department and used for coordination and decision-making among regional bases.

[0172] Furthermore, this embodiment can be equipped with a function to manage various information used in rescue operations in chronological order, allowing a series of records from the rescue request to the completion of the rescue to be viewed in a timeline format. This timeline includes the time the rescue request was received, the time of arrival at the scene, the start and completion times of the work, and records of the rescue results, enabling the command center and managers to easily grasp the progress of the rescue operation and support rapid decision-making.

[0173] Furthermore, in this embodiment, when multiple rescue requests occur simultaneously, the system can be equipped with a function to manage an independent response status for each rescue request and formulate parallel rescue operation plans according to priority. This function enables the system to respond to multiple requests in the optimal order, even in situations where requests are concentrated in a short period of time, such as immediately after a disaster, thereby improving the efficiency of on-site response and preventing confusion.

[0174] Furthermore, this embodiment can be equipped with a function to generate progress management information linked to each rescue request and share it with the rescue command center, field personnel, and member terminals. The progress management information includes the departure, arrival, and completion times of rescue vehicles, the work performed at the site, and the completion status of each stage, allowing all parties involved to accurately grasp the progress of the rescue operation and improve the accuracy and speed of coordination.

[0175] Furthermore, this embodiment can be equipped with a function to manage the history of rescue requests. This history information includes the content of the request, the time required for the response, the route and activities of the rescue vehicles, and the results of various decisions. By accumulating and referring to past cases, it is possible to expedite and optimize initial responses and the deployment of equipment and personnel in similar situations.

[0176] Furthermore, this embodiment can include a function to aggregate information on rescue requests received from members during a disaster and generate a heat map based on geographical distribution. This heat map is used to visually grasp the scale of the disaster and the degree of localized concentration of damage, helping command centers and administrators quickly determine priority response areas and efficiently deploy rescue vehicles and personnel.

[0177] Furthermore, this embodiment includes a function that analyzes text data (reasons for rescue, situation description, etc.) included in rescue request information from members using natural language processing technology, and scores the urgency based on the content and expression of the utterances. This makes it possible to quantify the urgency of the requester, which is difficult to grasp from mere location and number information alone, and to more appropriately determine the priority of rescue activities.

[0178] Furthermore, this embodiment can include a configuration that automatically extracts information such as the level of chaos at the scene and the degree of danger in the surrounding environment from audio and video data acquired during rescue operations using speech recognition and video analysis, and utilizes this information to support rescue operations. This information is displayed in real time on a dashboard and is also used to optimize the support plan.

[0179] Furthermore, this embodiment includes a configuration that allows field personnel to transmit text and voice feedback regarding the rescue situation through portable information devices they carry during rescue operations. This feedback is immediately reflected in support plans, rescue operation priorities, and route information, accelerating decision-making in the command center and information sharing with other field personnel. The feedback information may be immediately reflected in the integrated dashboard of the Road Service Department and used for dispatch decisions at all locations.

[0180] Furthermore, this embodiment includes a function to record the situation during and after rescue operations, automatically recording the location history of rescue vehicles, time spent at the site, video and audio logs during the operation, and road infrastructure condition data acquired during the operation, all with timestamps. This recorded information can be used for post-disaster recovery plan development, administrative reports, and training feedback, and can also be used as training data when retraining AI models. This makes it possible to improve the quality and efficiency of rescue operations by utilizing detailed logs of on-site activities.

[0181] Furthermore, this embodiment includes a function to switch communication routes related to rescue operations. Normally, it uses existing mobile lines, but in the event of base station failures or communication congestion during disasters, it can automatically switch to alternative routes such as satellite communication, mesh networks, or Wi-Fi Direct communication. This enables the continued transmission and reception of important information between rescue vehicles, the command center, field personnel, and members even in emergencies, reducing delays in activities and information disruptions caused by the instability of the communication environment during disasters.

[0182] Furthermore, this embodiment includes a function that uses AI (artificial intelligence) to perform real-time analysis of on-site video and still image data acquired during rescue operations, and automatically detects road obstacles, flooded areas, signs of collapse, etc., that appear in the video. The detection results are immediately provided to the command center and on-site personnel as support information necessary for rescue operations, and can be used to identify dangerous areas, decide on route changes, and arrange for additional equipment and materials.

[0183] Furthermore, this embodiment includes a function that utilizes driving data from the rescue vehicle during its operation to automatically analyze changes in road conditions and driving environment from data such as speed changes, sudden stops, and vibration data. The results of this analysis can detect risk signs such as road freezing and sinkholes at an early stage and provide warnings to the rescue command center and subsequent rescue vehicles, thereby preventing secondary disasters during rescue operations. The analysis results may be transmitted to the road service department's operation monitoring system and visualized as real-time warnings.

[0184] Furthermore, this embodiment includes a function that uses audio data acquired during rescue operations to transcribe voice reports from on-site personnel into text in real time. The transcribed information is automatically reflected on the dashboard, allowing the command center to immediately grasp the situation on the ground. In addition, the audio content can be saved to a database and used to improve future disaster response. This multi-agent management function may be integrated into the integrated control screen of the road service department, creating a configuration that centrally controls the activity information of each entity. Furthermore, this transcribed information will also be used as training data by the improvement unit, contributing to improving the accuracy of the passability determination model of the analysis unit 620 and the optimization-related models of the road service unit.

[0185] Furthermore, this embodiment includes a function that performs AI (artificial intelligence) image analysis on video data acquired during rescue operations, automatically identifying the presence or absence of road blockages and the types of obstacles at the scene. The identification results are clearly visualized on a dashboard, supporting immediate consideration of countermeasures according to the type and scale of obstacles. In addition, the image analysis results are saved as history and can be used for later disaster response evaluation and as training data.

[0186] Furthermore, in this embodiment, in order to efficiently process multiple rescue requests received simultaneously during large-scale disasters when rescue requests are concentrated, the system includes a function to map rescue request reception information in real time and analyze and visualize the geographical concentration of request locations using AI (artificial intelligence). This allows the rescue command center to grasp the distribution of simultaneous rescue requests and quickly make wide-area priority decisions and efficient resource allocations.

[0187] Furthermore, this embodiment includes a process for collecting and recording information such as the operational status, movement history, and rescue completion time of rescue vehicles, and for automatically generating operational history for each vehicle. This allows for a quantitative understanding of the operational rate and response performance of each rescue vehicle, which can be used for business management and optimization of future vehicle deployment plans.

[0188] Furthermore, this embodiment includes a process for estimating secondary disasters and additional risks that may occur during rescue operations, based on collected rescue activity information and on-site environmental data. This process makes it possible to identify areas where risks such as rockfalls and floods have increased, and to generate warning information that contributes to ensuring safety during rescue operations, which can then be provided to on-site personnel and the command center.

[0189] Furthermore, this embodiment includes a process that considers the physical characteristics of the rescue vehicle, such as its size, turning radius, height, and load capacity, when evaluating passable routes during rescue operations. This allows for a detailed determination of passability for each rescue vehicle, rather than relying solely on static information such as road width and gradient, and enables the selection of a route optimized for each vehicle.

[0190] Furthermore, in this embodiment, when determining the priority of relief activities, weighting can be applied according to the urgency in the affected area. For example, requests for relief in areas where saving lives is a priority or near medical facilities can be given a high weight in the scoring system, while requests in areas with limited traffic impact can be given a lower weight, allowing for flexible prioritization according to the situation.

[0191] In this embodiment, when sharing collected and analyzed information such as disaster conditions, relief activity status, and road infrastructure status between support bases, other municipalities, and related businesses during a disaster, the system can include a process to apply access control according to the authority and role of the information recipient. For example, it is possible to provide each branch manager with comprehensive information covering a wide area, field personnel with information limited to their assigned area, and external businesses with only the minimum necessary information, thereby supporting smooth cooperation while preventing information leaks and misuse. This access control can be implemented using means such as ID / password authentication, digital certificates, and terminal authentication, achieving both information security and operational efficiency in the field.

[0192] In this embodiment, notification control processing can be provided to dynamically adjust the content and frequency of information to be notified according to the disaster situation and the progress of relief activities. For example, if the damage is limited, progress will be notified at the normal frequency, and if the damage expands and the priority of relief activities changes, the notification frequency can be increased to relevant parties such as requesters, field personnel, and the command center, and the system can be switched to prioritize the transmission of information of high urgency. This function makes it possible to provide necessary information in a timely manner without excess or deficiency, and to improve the accuracy of situational judgment at the field and management bases.

[0193] Furthermore, this embodiment can include a process for automatically generating progress reports based on information collected during rescue operations and sharing the progress with the requester, field personnel, command center, and administrative agencies. The progress report can include the location of rescue vehicles, start and end times of operations, activity details, rescue results, and next work schedule, and the report can be provided in various formats such as map display, timeline display, and text summary. This function can reduce information disparities among stakeholders and promote faster situation sharing and decision-making.

[0194] In addition, in this embodiment, in preparation for the case where multiple support teams and external contractors operate simultaneously during rescue operations, a multi-agent management function can be provided to integrally manage the progress, location, and operating status of each activity entity. This can avoid duplication of work among entities and insufficient support in important areas, enabling optimal command and control of the entire rescue operation. The multi-agent management function can dynamically update and display the activity status of each entity on a visualization dashboard, assisting the command room to issue instructions from an overall perspective.

[0195] Furthermore, in this embodiment, a driving monitoring function can be provided to collect the driving logs and vehicle status data of rescue vehicles in real time and manage the vehicle health status, remaining fuel, estimated travel time, etc. during rescue operations in conjunction with the analysis results. This function can reduce the risk of vehicle troubles and fuel depletion during rescue, enabling prompt arrangement of alternative vehicles and route changes as needed. Also, this information can be provided to the dashboard and on-site team terminals, improving the safety and reliability of rescue operations.

[0196] In addition, in this embodiment, a wide-area relocation function can be provided to redistribute personnel, resources, and equipment among branches based on the priority of rescue operations and the operating status of rescue vehicles. This function optimizes the cooperation among multiple branches in real time in response to changes in the scale of disasters and sudden increases in the number of requests, enabling rapid concentration of human and material resources in specific areas. This makes it possible to maximize the utilization of limited resources and improve the efficiency of the entire rescue operation. The wide-area relocation process may be commanded by the central control module of the load service department and configured to automatically adjust the resource movement among branches.

[0197] Furthermore, this embodiment may include a process to support the selection and preparation of equipment and materials to be loaded onto the rescue vehicle, depending on the disaster situation and the content of the rescue request. This process comprehensively analyzes the condition of the vehicle to be rescued (the vehicle of the member being rescued), the details of the rescue, the geographical conditions of the planned activity site, and the anticipated work content, and automatically suggests the type and quantity of equipment and materials required. This improves work efficiency at the site, reduces the transport of unnecessary equipment and materials, and enables rapid rescue operations.

[0198] The Road Disaster Response Support System 600 is configured to acquire and cache road information in advance using Street View images, in order to improve the efficiency of rescue operations during disasters. Roadside video and surrounding environment images acquired during normal times are stored chronologically in cloud storage, and by comparing and analyzing them with the latest video acquired after a disaster, abnormal areas such as road deformation, sinkholes, fallen trees, and flooding can be automatically detected. This could also be integrated with roadside footage captured by small unmanned aerial vehicles (UAVs) operated by road service providers. Street View information can be obtained by linking with existing map APIs such as Google Maps, and Street View images from Google Maps or similar sources may also be used. The analysis unit 620 or the road service unit extracts the differences between these normal-time images and post-disaster images and uses them to predict passability and identify dangerous areas.

[0199] The Road Disaster Response Support System 600 is equipped with a mechanism that automatically sends SMS (Short Message Service) to mobile terminal devices carried by road service members in the vicinity of the disaster area immediately after a disaster occurs, and obtains the current location information of the members through a location information acquisition link included in the SMS. This configuration allows for the accurate and smooth sharing of rescue location information, even in areas without landmarks or where the user is unfamiliar with the terrain. The acquired location information is automatically linked to the rescue request reception information and used by the Road Service Department to estimate rescue location information, the distribution density of those requiring rescue, and traffic flow. Based on this location information, the Road Service Department dynamically optimizes the dispatch of rescue vehicles and the planning of travel routes, thereby improving the efficiency of rescue operations and accelerating on-site decision-making.

[0200] The portable information devices carried by rescue personnel incorporate the functions of a vehicle anomaly detection device (which may also be a vehicle diagnostic unit, fault detection module, mobile diagnostic adapter, etc.) developed independently by the road service provider. (Note that the vehicle anomaly detection device may be integrated with the portable information device, or it may be a separate vehicle anomaly detection device that communicates with the portable information device.) This function acquires vehicle information such as engine status, power system, oil pressure, temperature, vibration, attitude angle, and error codes in real time via the vehicle's diagnostic port (OBD) or Bluetooth communication, and performs fault diagnosis on a portable information device (tasks that can be performed on-site based on on-site judgment, such as simple repairs, parts replacement, and restart operations, can be performed immediately). The diagnostic results are automatically sent to the server, and the Road Service Department analyzes the diagnostic information obtained via the portable information device to immediately determine the malfunction status and abnormal trends of the vehicle being assisted (the member's vehicle being assisted). The results of this determination are then sent to the portable information device and presented to the on-site personnel to support emergency response or towing decisions. Furthermore, diagnostic results are aggregated and learned from on the cloud, and individual diagnostic results are sent to the towing location, such as a repair shop, for use in troubleshooting. Statisticalized diagnostic information is sent to automobile manufacturers and others for use in analyzing failure trends and considering measures to prevent recurrence. This allows for both rapid response on-site and the effective use of overall failure data and the development of advanced measures to prevent recurrence.

[0201] The road disaster response support system 600 is equipped with a multilingual processing layer that automatically translates rescue request information (including voice, text, and chat) entered in foreign languages, extracts and adds attribute information such as language type, speaker category, urgency level, and location description, and then inputs it into the analysis unit 620. A large-scale language model (LLM) is used for translation, and a confidence score for translation accuracy is also generated, which is used in the analysis unit 620 to derive the probability of road disaster occurrence, impact area, and score. The multilingual processing layer is designed to handle two-way communication with foreign language speakers during rescue requests, and uses speech synthesis and dialogue translation engines to enable field personnel and operators to respond in multiple languages ​​immediately. Furthermore, the support information, passable route information, and suggested response information generated by the Road Service Department are automatically translated into foreign languages ​​and then transmitted to the mobile devices, member terminals, and in-vehicle terminals of foreign language speakers. This will enable consistent rescue operations even in tourist areas and around airports where there are many foreign drivers.

[0202] The Road Disaster Response Support System 600 integrates an SMS-based location information acquisition mechanism, a vehicle abnormality detection device, and a multilingual processing layer to automatically process the entire information flow from rescue request to rescue completion. For example, by comparing the location information of the SMS recipient with the location information of the towing destination, the system can calculate the optimal dispatch route or towing route for the local rescue vehicle and issue a command accordingly. Furthermore, cached Street View information from before the disaster occurs is automatically overlaid on real-time footage acquired after the disaster, predicting impassable areas and alternative routes. This result is transmitted to portable information devices carried by field personnel and visualized as a warning for restricted areas, rockfall risk zones, and other dangerous locations.

[0203] With the above configuration, the Road Disaster Response Support System 600 integrates disaster response information managed by administrative agencies with actual driving data, member information, and diagnostic information held by private road service providers, enabling simultaneous support for rescue operations and evaluation of road infrastructure health. Furthermore, the Analysis Department 620 and the Road Service Department comprehensively manage acquired diagnostic information and member information, which are then used as learning data by the Improvement Department. The improvement department will update its analysis models for each type of disaster to improve the accuracy of predicting the occurrence of rescue requests and determining passability in the event of the next disaster. As a result, the Road Disaster Response Support System 600 functions as a comprehensive relief support system that integrates pre-disaster information acquisition, real-time response during a disaster, and post-disaster learning and improvement. Furthermore, a multilingual dashboard has been built that includes guidance functions for foreign language speakers, allowing operators to monitor rescue progress, traffic restrictions, and rescue completion reports on a single screen while viewing translated information. This enables the overall optimization of disaster response.

[0204] These components are managed by an integrated data control module running on a cloud server, and continuous operation is guaranteed by a redundant configuration using satellite communication paths, even in the event of a communication line failure or power outage. The Road Disaster Response Support System 600 simultaneously achieves faster rescue operations, reduced burden of on-site decision-making, support for foreigners, and continuous data utilization, resulting in a significant improvement in response capability, reliability, and responsiveness compared to conventional disaster response systems. In particular, by integrating and utilizing a group of devices independently operated by road service providers (such as drive recorders, vehicle anomaly detection devices, and small unmanned aerial vehicle cameras), it is possible to construct a practical, operational system that differs from government-led disaster response systems. With the above configuration, the Road Disaster Response Support System 600 enables the visualization, optimization, and international response of relief activities during natural disasters, and realizes immediate and practical support at disaster sites. Furthermore, the above functions constitute an expanded embodiment of this system.

[0205] The Road Disaster Response Support System 600 operates using a software program that executes these components on a computer. By coordinating the operation of each component via a communication network, it can comprehensively manage disaster response, relief, and traffic support.

[0206] With the above configuration, according to the road disaster response support system 600 of the present embodiment, by collecting and analyzing various information held by road service providers to support rescue activities, a series of processes from the rescue request to the on-site response can be enhanced and streamlined. Furthermore, by having continuous model improvement by AI (artificial intelligence), a wide-area function for coordinating the deployment of personnel, resources, and equipment, and a redundancy function during communication failures, etc., a road disaster response that is faster and more accurate than before can be achieved. As a result, both administrative agencies and private operators can achieve an integrated and sustainable road disaster response.

[0207] In the present invention, the "portable terminal device" includes, for example, mobile phones, smartphones, tablet terminals, notebook computers, game machines, etc., and means a general-purpose information terminal carried by members of the road service provider. On the other hand, the "portable information device" includes, for example, business tablet terminals used for on-site work, rugged portable computers, dedicated terminals for disaster response, etc., and means a business information device carried by on-site team members of the road service provider. Also, a dashboard is a UI screen that integrally displays a plurality of indicators on a map or in a list format, and users can intuitively grasp support information, judgment results, etc. Note that each process and function in the present embodiment may be configured as an information acquisition unit 610, an analysis unit 620, a road service unit, an improvement unit, etc. described in the claims.

[0208] <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 equipped with a road service unit for acquiring and processing information related to the status of road services, and may be configured to communicate with each information provision server. The road disaster response support system 600 may be configured so that each functional unit, including the analysis unit and the road service unit, communicates with each other and operates in cooperation with other components such as the information acquisition unit and the improvement unit. 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), a TPU (Tensor Processing Unit), or an AI accelerator (such as a processor dedicated to neural network inference) 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. Furthermore, this embodiment integrates rescue request reception information, rescue activity acquisition information, and member notification information held by a road service provider, and has a configuration that can be applied to enable the road service provider to carry out rescue request response operations itself. For this reason, it can also be materialized as an embodiment of the invention by the road service provider itself. Furthermore, the Road Disaster Response Support System 600 may be built and operated within the organization of a road service provider, or it may be configured in a way that the road service provider entrusts its management to an external ICT vendor. Furthermore, it may be provided in a cloud environment or as a SaaS (Software as a Service) model, and may include a configuration that acquires information from at least one or more sources, including internal systems, external systems, or field terminals. This approach allows for flexible implementation depending on the operating environment and contract type, and enables expansion while maintaining compatibility with existing road service infrastructure.

[0209] 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 and judgment results, processing for formulating rescue activity plans based on acquired rescue request reception information, rescue activity acquisition information, and member notification information, integrated analysis processing of various sensing information (camera images, drone images, vehicle driving information, etc.), learning improvement processing of AI (artificial intelligence) models, and implementation configurations for visualization functions via a dashboard and feedback functions for rescue activities, which are also included in the technical scope of the present invention. Furthermore, the road disaster response support system 600 according to the present invention may be configured to include an integrated control mechanism centered on the road service unit, and to comprehensively control the dispatch of rescue operations, the allocation of equipment and personnel, the transmission of information to on-site personnel terminals, and the aggregation and synchronization of rescue progress based on the analysis results generated by the analysis unit. This makes it possible to provide integrated support for a series of rescue activities, from the formulation of rescue plans to on-site response and post-incident analysis, in accordance with the command system of the road service provider. Therefore, the present invention can be modified, altered, or substituted in various ways without departing from its essence. [Explanation of Symbols]

[0210] 100: Patrol status server 200: Fiber Optic Survey Status Provision Server 300: Satellite Survey Status Server 400: Weather information server 500: Vehicle driving status provision 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. Information on the receipt of rescue requests related to responses to rescue requests caused by natural disasters, managed by road service providers. Furthermore, rescue activity information concerning road disaster conditions or road infrastructure conditions obtained by at least one of the following: the road service provider's rescue vehicle, the drive recorder installed in the rescue vehicle, the road service provider's small unmanned aerial vehicle camera, or the road service provider's portable information device. An information acquisition unit that acquires one or more pieces of information, including the aforementioned rescue request acceptance information, An analysis unit analyzes the possibility of a road disaster occurring or an abnormality in road infrastructure based on one or more pieces of information acquired by the information acquisition unit, It includes a Road Service Department that provides support for rescue operations by the said Road Service provider, The Road Service Unit generates and outputs support information regarding the implementation of the rescue activities by the Road Service Provider, using the analysis results analyzed by the Analysis Unit. A road disaster response support system characterized in that the support information includes information on at least one of the following: whether or not the rescue activities are necessary, the priority of the rescue activities, the selection of response means or rescue vehicles to be used for the rescue activities, or how to improve the efficiency of the rescue activities.

2. A road disaster response support system according to claim 1, Based on the one or more pieces of information acquired by the information acquisition unit, the analysis unit performs the analysis based on the information acquired by the information acquisition unit. Based on at least one of the following included in either the rescue request reception information or the rescue activity acquisition information, the road disaster content, geographical concentration, and time of occurrence, A road disaster response support system characterized by its ability to evaluate the probability of a road disaster occurring or the extent of its impact.

3. A road disaster response support system according to claim 1, Based on the one or more pieces of information acquired by the information acquisition unit, the analysis unit performs the analysis based on the information acquired by the information acquisition unit. Based on the information, geographical details, and number of occurrences related to road disasters included in either the aforementioned rescue request reception information or the aforementioned rescue activity acquisition information, A road disaster response support system characterized by calculating the density of the aforementioned rescue requests or rescue activities, and deriving a road disaster score based on the density.

4. A road disaster response support system according to claim 3, The Road Service Department, based on the road disaster score derived by the Analysis Department based on the density, as well as information on the equipment and materials held at each branch or base and information on the personnel available to respond, A road disaster response support system characterized by generating and outputting the aforementioned support information for optimizing the allocation of personnel or equipment.

5. A road disaster response support system according to claim 1, Based on the one or more pieces of information acquired by the information acquisition unit, the analysis unit performs the analysis based on the information acquired by the information acquisition unit. The contents related to road disasters included in either the aforementioned rescue request reception information or the aforementioned rescue activity acquisition information are analyzed by artificial intelligence. Based on the analysis results, the Road Service Department determines whether the road service provider's rescue vehicle is passable. A road disaster response support system characterized by generating and outputting at least one of the following based on the determination result: whether or not the rescue activities are necessary, the priority of the rescue activities, or the selection of rescue vehicles to be used for the rescue activities.

6. A road disaster response support system according to claim 5, The aforementioned information acquisition unit acquires information on traffic regulations managed by road administrators or administrative agencies (for example, traffic regulation information, road closure information, traffic restriction information, road opening information, detour information), Based on the rescue location information, support base information, and the traffic restriction information, the analysis unit will: Furthermore, based on the results of analyzing the rescue activity information, the passability will be evaluated. A road disaster response support system characterized in that the road service unit selects a passable route for the rescue vehicle based on the evaluation results from the analysis unit.

7. A road disaster response support system according to claim 1, The aforementioned information acquisition unit sends an SMS to the member of the road service provider. A road disaster response support system characterized by having a function to acquire location information from a mobile terminal device carried by the member.

8. A road disaster response support system according to claim 1, The road disaster response support system, based on one or more pieces of information acquired by the information acquisition unit, The road disaster score derived by the analysis unit, and the support information, the judgment results regarding the rescue activities, or the passable route information output by the road service unit, A road disaster response support system characterized by having a dashboard that visualizes at least one of the following.

9. A road disaster response support system according to claim 4, Based on the road disaster score derived by the analysis unit and the support information output by the road service unit, Select the appropriate response method from among multiple response methods for the aforementioned rescue operation. A road disaster response support system characterized by generating and outputting response proposal information regarding the implementation of the selected response measures based on the selected response measures.

10. A road disaster response support system according to claim 1, The improvement unit, based on at least one of the following: the one or more pieces of information acquired by the information acquisition unit, the analysis results from the analysis unit, and the output results or operational results of the support information output by the road service unit, Using at least one of the aforementioned rescue request reception information and rescue activity acquisition information as training data, Perform training or updating of artificial intelligence models, A road disaster response support system characterized by being configured to improve the optimization accuracy of output related to at least one of the following: the accuracy of determining road disasters, the accuracy of determining whether a road is passable, and the accuracy of the support information output by the road service unit.

11. A road disaster response support system according to claim 1, The road disaster response support system, based on one or more pieces of information acquired by the information acquisition unit, At least one of the following, output by the Road Service Department, regarding the decision results concerning the rescue operation, passable route information, or proposed response information, It is equipped with a communication function that transmits to a portable information device carried by the field personnel of the road service provider, Furthermore, the road disaster response support system is characterized by retrieving Street View images corresponding to the passable route and automatically overlaying them on the display screen of the portable information device.

12. A road disaster response support system according to claim 1, The road disaster response support system, based on one or more pieces of information acquired by the information acquisition unit, At least one of the following, output by the analysis unit or the road service unit, is used: information regarding traffic restrictions, the status of road disasters, the estimated arrival time of the rescue vehicle, or information on passable routes. A road disaster response support system characterized by having a function to transmit information to a mobile terminal device carried by a member of the road service provider.

13. A road disaster response support system according to claim 1, The road disaster response support system, based on one or more pieces of information acquired by the information acquisition unit, At least one of the following pieces of information output by the analysis unit or the road service unit, regarding the status of road disasters, impassable sections, the status of rescue operations, or the estimated arrival time of rescue vehicles, A road disaster response support system characterized by having a function to transmit data to an external system that can be used by road administrators, administrative agencies, or disaster response agencies.

14. A road disaster response support system according to claim 1, The road disaster response support system, based on one or more pieces of information acquired by the information acquisition unit, Based on the road disaster score, the judgment results regarding the rescue activities, and information on the equipment and resources held at each branch or base, and the information on the personnel available, output by the aforementioned analysis unit or road service unit, To optimize the coordinated deployment of such personnel or equipment across multiple branches or multiple municipalities, and to optimize the securing of accommodation or support bases for such personnel, A road disaster response support system characterized by having a control linkage function that provides traffic permit information or traffic restriction instructions to a traffic permit management system or an entry management system for a disaster-stricken area.

15. A road disaster response support system according to claim 1, The road disaster response support system, based on one or more pieces of information acquired by the information acquisition unit, If the road disaster score derived by the analysis unit exceeds a predetermined threshold, A road disaster response support system characterized by having a control function that automatically switches the operating mode of the road disaster response support system from normal mode to disaster mode and changes the priority or notification format of the information to be output.

16. A road disaster response support system according to claim 1, The road disaster response support system collects at least one of the following: the actual status of a road disaster that has occurred, the results of the rescue activities, the traffic records of the rescue vehicles, the output results or operational results of the support information output by the road service department, and information regarding the analysis results obtained by the analysis department. The data is stored as training data for the artificial intelligence model used in the analysis unit or the road service unit, A road disaster response support system characterized by having a function to control a model update process for retraining or updating the aforementioned artificial intelligence model.

17. A road disaster response support system according to claim 1, The aforementioned road disaster response support system will respond to the failure of some functions caused by communication failures, power failures, or equipment failures during a disaster. A road disaster response support system characterized by having a configuration that ensures the continuity of operation of the entire system by providing redundancy in case of failure of the function through other network routes (including satellite communications), alternative servers, or alternative processing mechanisms.

18. A road disaster response support system according to claim 1, The Road Service Unit, based on vehicle diagnostic information obtained from a vehicle abnormality detection device connected to the vehicle being assisted or from a vehicle abnormality detection device that operates in conjunction with a portable information device, Diagnosing the malfunction condition or abnormal trend of the vehicle to be rescued, Based on the diagnostic results, a rescue method will be selected, either on-site repair or towing. The selection results or diagnostic results are transmitted to a portable information device carried by the field personnel of the road service provider. Furthermore, the diagnostic results or statistical information of those diagnostic results are transmitted via the road service provider's server. Send the message to the towing repair shop or car manufacturer. A road disaster response support system characterized by providing information to the repair shop or automobile manufacturer for use in troubleshooting, analyzing the causes of failures, or considering measures to prevent recurrence.

19. A road disaster response support system according to any one of claims 1 to 18, The aforementioned road disaster response support system includes a multilingual processing layer that utilizes a large-scale language model, The aforementioned distress request information, including foreign languages, will be automatically translated. Extract attribute information related to language type, speaker category, urgency, or location description, The translation results are input into the analysis unit and used to derive the probability of road disaster occurrence, the scope of impact of road disaster, or the road disaster score, A road disaster response support system characterized by automatically translating and outputting the support information or passable route information output by the road service unit for foreign language speakers.

20. A program for causing a computer to function as a road disaster response support system as described in claim 1, The program is installed on the computer. (i) at least one of the functions described in claims 2, 3, 5, 7, 8, and 10 to 18, Or (ii) at least one of the following functions (1) through (3): A program characterized by including a sequence of instructions for executing either or both: (1) A function to generate and output support information for optimizing the allocation of personnel or equipment based on road disaster scores, as well as information on equipment and materials held and personnel available at each branch or base. (2) A function to acquire information on traffic restrictions managed by road administrators or administrative agencies, evaluate the passability based on rescue location information, support base information and traffic restriction information, and further evaluate the passability in accordance with the analysis results of rescue activity information, and select a passable route for rescue vehicles based on the evaluation results. (3) A function that selects a response method suitable for relief activities from among multiple response methods based on the road disaster score and support information, and generates and outputs response proposal information regarding the implementation of said response method.

21. A computer-based road management support method, The aforementioned computer, via the network, Information on the receipt of rescue requests related to responses to rescue requests caused by natural disasters, managed by road service providers. Furthermore, rescue activity information concerning road disaster conditions or road infrastructure conditions obtained by at least one of the following: the road service provider's rescue vehicle, the drive recorder installed in the rescue vehicle, the road service provider's small unmanned aerial vehicle camera, or the road service provider's portable information device. Obtain one or more pieces of information from among the above, including the information on the receipt of the request for assistance, Based on the one or more pieces of information obtained, the possibility of a road disaster occurring or an abnormality in road infrastructure is analyzed. Using the analysis results obtained above, support information regarding the implementation of rescue activities by the road service provider is generated and output. A road management support method characterized in that the support information includes information on at least one of the following: whether or not the rescue activities are necessary, the priority of the rescue activities, the selection of the means of response or the rescue vehicles to be used for the rescue activities, or how to improve the efficiency of the rescue activities.

22. A road management support method according to claim 21, The computer, via the network, based on the one or more pieces of information acquired, Based on at least one of the following included in either the rescue request reception information or the rescue activity acquisition information, the road disaster content, geographical concentration, and time of occurrence, A road management support method characterized by evaluating the probability of occurrence of a road disaster or the extent of impact of a road disaster.

23. A road management support method according to claim 21, The computer, via the network, based on the one or more pieces of information acquired, Based on the information, geographical details, and number of occurrences related to road disasters included in either the aforementioned rescue request reception information or the aforementioned rescue activity acquisition information, A road management support method characterized by calculating the density of the aforementioned rescue requests or rescue activities, and deriving a road disaster score based on the density.

24. A road management support method according to claim 23, The computer, via the network, based on the score of the road disaster derived based on the density, as well as information on the equipment and materials held at each branch or base and information on the personnel available to respond, A road management support method characterized by generating and outputting support information for optimizing the placement of such personnel or equipment.

25. A road management support method according to claim 21, The computer, via the network, based on the one or more pieces of information acquired, The contents related to road disasters included in either the aforementioned rescue request reception information or the aforementioned rescue activity acquisition information are analyzed by artificial intelligence. Based on the analysis results, it is determined whether the road service provider's rescue vehicle is passable. A road management support method characterized by generating and outputting at least one of the following based on the determination result: whether or not the rescue activity is necessary, the priority of the rescue activity, or the selection of the rescue vehicle to be used for the rescue activity.

26. A road management support method according to claim 25, The computer acquires information regarding traffic regulations managed by road administrators or administrative agencies via the network (e.g., traffic regulation information, road closure information, traffic restriction information, road clearing information, detour information), Based on rescue location information, support base information, and the aforementioned traffic restriction information, Furthermore, based on the results of analyzing the rescue activity information, the passability will be evaluated. A road management support method characterized by selecting a passable route for the rescue vehicle based on the evaluation results.

27. A road management support method according to claim 21, The aforementioned computer sends an SMS to a member of the road service provider via the network. A road management support method characterized by acquiring location information from a mobile terminal device carried by the member.

28. A road management support method according to claim 21, The computer outputs, via the network, based on the one or more pieces of information acquired, A road management support method characterized by visualizing and displaying as a dashboard at least one of the following: road disaster score, support information, decision results regarding rescue activities, and passable route information.

29. A road management support method according to claim 24, The computer, via the network, based on the road disaster score and the support information, From among multiple response methods, select the response method most suitable for the aforementioned rescue operation. A road management support method characterized by generating and outputting proposed response information regarding the implementation of the selected response means.

30. A road management support method according to claim 21, The computer, via the network, based on at least one of the following: the one or more pieces of information acquired, the analysis results, and the output results or operational results of the support information, Using at least one of the aforementioned rescue request reception information and rescue activity acquisition information as training data, Perform training or updating of artificial intelligence models, A road management support method characterized by improving the optimization accuracy of output related to at least one of the following: the accuracy of determining road disasters, the accuracy of determining whether a road is passable, and the accuracy of the support information.

31. A road management support method according to claim 21, The computer outputs, via the network, based on the one or more pieces of information acquired, The results of the aforementioned rescue operations, information on passable routes, or information on proposed responses, at least one of these, The information is transmitted to the portable information device carried by the field personnel of the road service provider, Furthermore, the road management support method is characterized by retrieving a Street View image corresponding to the passable route and automatically overlaying it on the display screen of the portable information device.

32. A road management support method according to claim 21, The computer outputs, via the network, based on the one or more pieces of information acquired, Information regarding traffic restrictions, the status of road disasters, the estimated arrival time of the aforementioned rescue vehicles, or information on passable routes, at least one of these, A road management support method characterized by transmitting data to a mobile terminal device carried by a member of the road service provider.

33. A road management support method according to claim 21, The computer outputs, via the network, based on the one or more pieces of information acquired, Information regarding the occurrence of road disasters, impassable sections, the status of the aforementioned rescue operations, or the estimated arrival time of the aforementioned rescue vehicles, at least one of these, A road management support method characterized by transmitting data to an external system usable by road administrators, administrative agencies, or disaster response agencies.

34. A road management support method according to claim 21, The computer outputs, via the network, based on the one or more pieces of information acquired, Based on the road disaster score, the results of the assessment regarding the aforementioned relief activities, and information on the equipment and materials held at each branch or base, and information on the personnel available to respond, To optimize the coordinated deployment of such personnel or equipment across multiple branches or multiple municipalities, and to optimize the securing of accommodation or support bases for such personnel, A road management support method characterized by providing traffic permit information or traffic restriction instructions to a traffic permit management system or an entry management system for disaster-stricken areas.

35. A road management support method according to claim 21, The computer, via the network, derives the following based on the one or more pieces of information obtained: If the road disaster score exceeds a predetermined threshold, A road management support method characterized by automatically switching the operating mode from normal mode to disaster mode and changing the priority or notification format of the information to be output.

36. A road management support method according to claim 21, The aforementioned computer, via the network, The following information is collected: the actual circumstances of a road disaster, the results of the rescue activities, the traffic records of the rescue vehicles, the output or operational results of the support information, and the analysis results obtained through analysis. In addition to being stored as training data for artificial intelligence models, A road management support method characterized by controlling a model update process for retraining or updating the artificial intelligence model.

37. A road management support method according to claim 21, The aforementioned computer will respond to the failure of certain functions caused by communication failures, power failures, or equipment failures during a disaster. A road management support method characterized by ensuring the continuity of operation of the entire system by making the failure of the function redundant through other network routes (including satellite communications), alternative servers, or alternative processing mechanisms.

38. A road management support method according to claim 21, The aforementioned computer, via the network, Based on vehicle diagnostic information obtained from a vehicle abnormality detection device connected to the vehicle being rescued, or from a vehicle abnormality detection device that works in conjunction with a portable information device, Diagnosing the malfunction condition or abnormal trend of the vehicle to be rescued, Based on the diagnostic results, a rescue method will be selected, either on-site repair or towing. The selection results or diagnostic results are transmitted to a portable information device carried by the field personnel of the road service provider. Furthermore, the diagnostic results or statistical information of those diagnostic results are transmitted via the road service provider's server. Send the message to the towing repair shop or car manufacturer. A road management support method characterized by providing information to the repair shop or automobile manufacturer for use in dealing with breakdowns, analyzing the causes of breakdowns, or considering measures to prevent recurrence.

39. A road management support method according to any one of claims 21 to 38, The aforementioned computer, via the network, By utilizing a large-scale language model in the multilingual processing layer, The aforementioned distress request information, including foreign languages, will be automatically translated. Extract attribute information related to language type, speaker category, urgency, or location description, The translation results will be used to derive the probability of road disasters occurring, the scope of impact of road disasters, or the road disaster score, A road management support method characterized by automatically translating the outputted support information or passable route information for foreign language speakers and outputting it.

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