Mountain torrent disaster early warning information multi-level distribution and response system and method
By combining edge computing and cloud-based intelligent analysis, a multi-level distribution and action feedback loop for flash flood disaster early warning information has been achieved, solving the problem of insufficient targeting of early warning information in traditional early warning systems and providing accurate early warning level classification and differentiated emergency response.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-29
- Publication Date
- 2026-04-03
AI Technical Summary
Traditional flash flood disaster early warning systems lack multi-level response mechanisms, resulting in weak targeting of early warning information and an inability to achieve hierarchical information distribution and action feedback loop.
The system employs edge computing terminals for risk area analysis, combines cloud-based intelligent analysis and command modules to classify early warning levels and generate emergency response strategies, and utilizes a two-way communication and response module to push and manage multi-level early warning signals and emergency strategies.
It has achieved multi-level distribution of flash flood disaster early warning information and closed-loop action feedback, accurately classified early warning levels, provided differentiated emergency response strategies, and ensured the receipt and feedback confirmation of early warning information.
Smart Images
Figure CN121789398A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of disaster early warning, and in particular to a multi-level distribution and response system and method for flash flood disaster early warning information. Background Technology
[0002] Traditional flash flood early warning systems often employ a one-size-fits-all broadcast approach, failing to adequately consider the varying risks faced by different areas (such as riverbanks, low-lying areas, and high-altitude regions). This results in weak targeting of warning information and unclear response instructions. Furthermore, existing systems lack effective feedback mechanisms, making it impossible to confirm whether warning information has been received and acted upon by key personnel, hindering closed-loop management of the warning-response process. Therefore, achieving intelligent early warning response with tiered information distribution and a closed-loop action feedback system has become a pressing issue. Summary of the Invention
[0003] The purpose of this application is to provide a multi-level distribution and response system and method for flash flood disaster early warning information, which can realize multi-level distribution of flash flood disaster early warning information and closed-loop early warning response with action feedback.
[0004] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a multi-level distribution and response system for flash flood disaster early warning information, including: an edge computing terminal, a cloud-based intelligent analysis and command module, multi-level user terminals and response nodes, and a two-way communication and response module; one of the aforementioned edge computing devices is deployed in each risk area; The edge computing terminal is used to determine the inundation information of the risk area based on the hydrological data of the risk area using a hydrological and hydrodynamic model, predict the probability of flash floods in the risk area using a flash flood disaster risk model, and issue an early warning signal based on the inundation information and the probability of flash floods. The cloud-based intelligent analysis and command module is used to fuse and analyze the early warning signals of all risk areas, and determine the early warning level and corresponding emergency response strategy for each risk area based on the analysis results. The multi-level user terminals and response nodes are used to receive early warning signals of different warning levels and corresponding emergency response strategies according to their levels. The two-way communication and response module is used to push warning signals of different warning levels and corresponding emergency response strategies to nodes of designated levels, and to receive response feedback information from the receiving personnel at each level of nodes, and to determine whether to push the signals a second time based on the response feedback information.
[0005] In one embodiment, the multi-level user terminal and response node includes: a first-level node, a second-level node, a third-level node, and a fourth-level node; The first-level node is used to receive early warning signals and corresponding emergency response strategies for all risk areas; the second-level node is used to receive early warning signals and corresponding emergency response strategies for the current risk area; the third-level node is used to receive early warning signals and corresponding emergency response strategies for specific locations; and the fourth-level node is used to broadcast early warning signals and corresponding emergency response strategies to households or individuals.
[0006] In one embodiment, the edge computing terminal includes: The inundation information determination unit is used to determine the inundation information of the risk area based on the hydrological data of the risk area and using a hydrological and hydrodynamic model. The hydrological and hydrodynamic model is obtained by calibrating the runoff generation and confluence model using historical data. The hydrological data includes rainfall and soil moisture. The historical data includes hydrological data and inundation information of the risk area at historical times. The inundation information includes inundation depth and flow velocity. The flash flood risk prediction unit is used to predict the probability of flash floods occurring in risk areas using a flash flood risk prediction model. The flash flood risk prediction model is obtained by training a machine learning model using training data. The training data includes: historical rainfall, soil moisture, environmental characteristics, and flash flood occurrence records of the risk area. The early warning signal generation unit is used to issue an early warning signal based on the inundation information and the probability of flash flood occurrence.
[0007] In one embodiment, the cloud-based intelligent analysis and command module includes: A dynamic risk grid generation unit is used to generate a dynamic risk grid map of the entire region based on the early warning signals of all risk areas; each grid in the dynamic risk grid map corresponds to a risk index; the risk index is determined based on the probability of flash flood occurrence. The early warning level classification and response strategy generation unit is used to determine the early warning level and corresponding emergency response strategy for each risk area based on the dynamic risk grid map and the flooding information.
[0008] In one embodiment, the early warning level classification and response strategy generation unit includes: The Level 1 Warning Issuing Subunit is used to issue a Level 1 warning if the risk index is greater than the first set risk threshold, the flooding depth is greater than the first set depth value, or the flow velocity is greater than the set flow velocity value. The Level 2 Early Warning Issuing Subunit is used to issue a Level 2 early warning if the risk index is greater than the second set risk threshold and less than or equal to the first set risk threshold, or if the flooding depth is greater than or equal to the second set depth value and less than or equal to the first set depth value. The Level 3 Early Warning Issuance Subunit is used to issue a Level 3 early warning if the risk index is greater than the third set risk threshold and less than or equal to the second set risk threshold, or if the flooding depth is less than the second set depth value. The Level 4 warning unit is used to issue a Level 4 warning if the risk index is less than or equal to the third set risk threshold. The emergency response strategy generation subunit is used to generate corresponding emergency response strategies for different warning levels.
[0009] In one embodiment, the bidirectional communication and response module includes: The information push unit is used to push the Level 1 warning and corresponding emergency response strategy to the Level 3 and Level 4 nodes, the Level 2 warning and corresponding emergency response strategy to the Level 2 and Level 3 nodes, the Level 3 warning and corresponding emergency response strategy to the Level 2 and Level 3 nodes, and the Level 4 warning and corresponding emergency response strategy to the Level 1 and Level 2 nodes. The response feedback information receiving and monitoring unit is used to receive response feedback information from the receiving personnel at each level node, and determine whether to push the response feedback information a second time.
[0010] In one embodiment, the cloud-based intelligent analysis and command module further includes: The escape route generation unit generates escape routes based on a dynamic risk grid map.
[0011] In one embodiment, the bidirectional communication and response module further includes: The route push unit is used to push the escape route to the corresponding level of node.
[0012] In one embodiment, the escape route generation unit includes: The risk map generation sub-unit is used to overlay a dynamic risk raster map with an electronic map to generate a risk map. The route generation subunit is used to generate an escape route based on the risk map using a path planning algorithm; the escape route includes at least one route.
[0013] Secondly, this application provides a multi-level distribution and response method for flash flood disaster early warning information, including: Obtain hydrological data for each risk area; Based on the hydrological data, a hydrological and hydrodynamic model is used to determine the inundation information of the risk area, a flash flood disaster risk model is used to predict the probability of flash floods in the risk area, and an early warning signal is generated based on the inundation information and the probability of flash floods. The warning signals from all risk areas are integrated and analyzed, and the warning level and corresponding emergency response strategy for each risk area are determined based on the analysis results. Push warning signals of different warning levels and corresponding emergency response strategies to nodes at designated levels; It receives response feedback information from the receiving personnel at each level of the multi-level user terminals and response nodes, and determines whether to perform a secondary push based on the response feedback information.
[0014] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a multi-level distribution and response system and method for flash flood disaster early warning information. It analyzes different risk areas and issues early warning signals through edge computing terminals, accurately classifies early warning levels through a cloud-based intelligent analysis and command module, and implements different emergency responses based on different early warning levels. A two-way communication and response module pushes early warning signals and corresponding emergency response strategies of different levels to designated nodes. Multi-level user terminals and response nodes receive the information hierarchically, realizing multi-level distribution of flash flood disaster early warning information. The two-way communication and response module also receives feedback information from personnel at each level to determine whether to perform a secondary push, achieving a closed-loop early warning response with action feedback. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This application provides a schematic diagram of the structure of a multi-level distribution and response system for flash flood disaster early warning information. Figure 2 This is a flowchart illustrating a multi-level distribution and response method for flash flood disaster early warning information provided in an embodiment of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0019] Disaster early warning requires different emergency response strategies based on the risk situation in different regions. Traditional early warning systems often do not take into account multi-level response mechanisms. Therefore, there is an urgent need for an intelligent early warning and response system that can achieve accurate risk assessment, hierarchical information distribution, differentiated response measures, and a closed-loop feedback mechanism.
[0020] This embodiment provides a multi-level distribution and response system for flash flood disaster early warning information, which is based on edge computing to solve the problems of traditional early warning systems such as crude response strategies, indiscriminate information transmission, and lack of feedback mechanisms.
[0021] In one exemplary embodiment, such as Figure 1 As shown, a multi-level distribution and response system for flash flood disaster early warning information is provided, including: an edge computing terminal, a cloud-based intelligent analysis and command module, multi-level user terminals and response nodes, and a two-way communication and response module; one of the edge computing devices is deployed in each risk area. The edge computing terminal, the cloud-based intelligent analysis and command module, the two-way communication and response module, and the multi-level user terminals and response nodes are connected sequentially.
[0022] The edge computing terminal is used to determine the inundation information of the risk area based on the hydrological data of the risk area using a hydrological and hydrodynamic model, predict the probability of flash floods in the risk area using a flash flood disaster risk model, and issue an early warning signal based on the inundation information and the probability of flash floods.
[0023] The cloud-based intelligent analysis and command module is used to fuse and analyze the early warning signals of all risk areas, and determine the early warning level and corresponding emergency response strategy for each risk area based on the analysis results.
[0024] The multi-level user terminals and response nodes are used to receive early warning signals of different warning levels and corresponding emergency response strategies according to their hierarchical levels.
[0025] The two-way communication and response module is used to push warning signals of different warning levels and corresponding emergency response strategies to nodes of designated levels, and to receive response feedback information from the receiving personnel at each level of nodes, and to determine whether to push the signals a second time based on the response feedback information.
[0026] In another exemplary embodiment of this application, the multi-level user terminal and response node includes: a first-level node, a second-level node, a third-level node, and a fourth-level node.
[0027] The first-level node is used to receive early warning signals and corresponding emergency response strategies for all risk areas; the second-level node is used to receive early warning signals and corresponding emergency response strategies for the current risk area; the third-level node is used to receive early warning signals and corresponding emergency response strategies for specific locations; and the fourth-level node is used to broadcast early warning signals and corresponding emergency response strategies to households or individuals.
[0028] In another exemplary embodiment of this application, the edge computing terminal includes: The inundation information determination unit is used to determine the inundation information of the risk area based on the hydrological data of the risk area and using a hydrological and hydrodynamic model. The hydrological and hydrodynamic model is obtained by calibrating the runoff generation and confluence model using historical data. The hydrological data includes rainfall and soil moisture. The historical data includes hydrological data and inundation information of the risk area at historical times. The inundation information includes inundation depth and flow velocity.
[0029] The flash flood risk prediction unit is used to predict the probability of flash floods occurring in risk areas using a flash flood risk prediction model. The flash flood risk prediction model is obtained by training a machine learning model using training data. The training data includes: historical rainfall, soil moisture, environmental characteristics, and flash flood occurrence records in the risk area.
[0030] The early warning signal generation unit is used to issue an early warning signal based on the inundation information and the probability of flash flood occurrence.
[0031] In another exemplary embodiment of this application, the cloud-based intelligent analysis and command module includes: The dynamic risk grid generation unit is used to generate a dynamic risk grid map of the entire region based on the early warning signals of all risk areas; each grid in the dynamic risk grid map corresponds to a risk index; the risk index is determined based on the probability of flash flood occurrence.
[0032] The early warning level classification and response strategy generation unit is used to determine the early warning level and corresponding emergency response strategy for each risk area based on the dynamic risk grid map and the flooding information.
[0033] In another exemplary embodiment of this application, the early warning level classification and response strategy generation unit includes: The Level 1 Warning Issuing Subunit is used to issue a Level 1 warning (particularly severe) if the risk index exceeds a first set risk threshold, the inundation depth exceeds a first set depth value, or the flow velocity exceeds a set flow velocity value. The first set risk threshold can be set to 3, and the first set depth value can be set to 1.5 meters.
[0034] The Level 2 warning issuing subunit is used to issue a Level 2 warning (severe) if the risk index is greater than the second set risk threshold and less than or equal to the first set risk threshold, or if the flooding depth is greater than or equal to the second set depth value and less than or equal to the first set depth value. The second set risk threshold can be set to 2, and the second set depth value can be set to 0.5 meters.
[0035] The Level 3 Early Warning Issuance Subunit is used to issue a Level 3 (Severe) early warning if the risk index is greater than the third set risk threshold and less than or equal to the second set risk threshold, or if the flooding depth is less than the second set depth value. The third set risk threshold can be set to 1.
[0036] The Level 4 warning unit is used to issue a Level 4 warning (general) if the risk index is less than or equal to the third set risk threshold.
[0037] The emergency response strategy generation subunit is used to generate corresponding emergency response strategies for different warning levels.
[0038] In another exemplary embodiment of this application, the bidirectional communication and response module includes: The information push unit is used to push Level 1 warnings and corresponding emergency response strategies to Level 3 and Level 4 nodes, Level 2 warnings and corresponding emergency response strategies to Level 2 and Level 3 nodes, Level 3 warnings and corresponding emergency response strategies to Level 2 and Level 3 nodes, and Level 4 warnings and corresponding emergency response strategies to Level 1 and Level 2 nodes.
[0039] The response feedback information receiving and monitoring unit is used to receive response feedback information from the receiving personnel at each level node, and determine whether to push the response feedback information a second time.
[0040] In another exemplary embodiment of this application, the cloud-based intelligent analysis and command module further includes: an escape route generation unit, which generates escape routes based on a dynamic risk grid map.
[0041] In another exemplary embodiment of this application, the two-way communication and response module further includes: a route pushing unit, used to push the escape route to the corresponding level of node.
[0042] In another exemplary embodiment of this application, the escape route generation unit includes: a risk map generation subunit, used to overlay a dynamic risk raster map with an electronic map to generate a risk map; and a route generation subunit, used to generate an escape route based on the risk map using a path planning algorithm; the escape route includes at least one route.
[0043] Among them, the path planning algorithm can use A Algorithms, such as Dijkstra's algorithm.
[0044] The following section provides a more detailed introduction to each part of the aforementioned multi-level distribution and response system for flash flood disaster early warning information.
[0045] This embodiment of the flash flood disaster early warning information multi-level distribution and response system analyzes different risk areas through edge computing terminals, accurately classifies early warning levels, and implements different emergency responses according to different levels. Its core lies in the architecture of "edge-cloud collaboration" and "human-machine interaction." The main functional modules of the system include: (1) Edge computing terminal: Deployed in the field of small watersheds, it performs edge perception and decision-making, and is specifically responsible for: ① Data aggregation: Real-time collection and processing of sensor data such as water level, rainfall, and soil moisture. ② Preliminary risk assessment: Running the built-in hydrological and hydrodynamic model and flash flood disaster risk model to quickly and preliminarily calculate the danger level of the current monitoring area. ③ Early warning triggering: Generating and issuing early warning signals based on the preliminary assessment results.
[0046] (2) Cloud-based intelligent analysis and command module: As the "brain" of the system, it receives data from various edge computing terminals and is responsible for: ① Comprehensive risk fusion analysis: Integrating data from all edge computing terminals and combining it with historical disaster data and forecast data to conduct comprehensive risk analysis across the entire region. ② Precise delineation of early warning levels: Based on the fusion analysis results, dynamically and accurately delineating early warning levels for different regions. ③ Generation of response strategies: Matching preset, differentiated emergency response strategies to each early warning level.
[0047] (3) Multi-level user terminals and response nodes: These receive and execute early warning information, forming the periphery of the response network. Specifically, they include: ① Level 1 nodes (such as the city / county-level command center of a province): receiving the overall risk situation and conducting macro-level command and resource allocation. ② Level 2 nodes (township / street leaders): receiving detailed early warning and response instructions for their jurisdiction and organizing and coordinating responses. ③ Level 3 nodes (village cadres, community monitoring and prevention personnel, school or enterprise leaders, etc.): receiving early warning information, risk maps, and action guidelines down to specific locations (a village entrance, a bridge) through a dedicated APP, and being responsible for on-site organization and feedback. ④ Level 4 nodes (in-home alarms, broadcasts): issuing the most direct alarms (audio-visual alarms, voice broadcasts) to the general public.
[0048] (4) Two-way communication and response module: Integrated into the user terminal (APP, in-home alarm) to achieve closed-loop management of early warning information. Specifically: ① Information distribution: Pushes early warning information and response instructions to designated responsible persons. ② Response feedback: Receiving personnel must confirm receipt of information within a limited time and can report the on-site situation ("transferred", "road interrupted", etc.). ③ Status monitoring: The platform displays the information delivery status and feedback status in real time, and automatically provides secondary reminders or escalation notifications to those who do not respond.
[0049] The process by which the cloud-based intelligent analysis and command module performs risk area analysis and early warning level classification is as follows.
[0050] Specific methods for risk area analysis include: data layer overlay, model calculation, and dynamic risk raster generation.
[0051] Data layer overlay: On the GIS map, real-time hydrological data (water level and flow calculated by edge computing terminals), static environmental factors (elevation, slope, distance from the river channel, land use type) and disaster-bearing body information (population distribution, houses, important facilities) are spatially overlaid.
[0052] Model calculation: A calibrated runoff generation and confluence model from a hydrological and hydrodynamic model is used to simulate the flood inundation range, depth, and velocity. Machine learning models (random forest, neural network models, etc.) are combined with historical disaster data to refine model parameters, resulting in a flash flood risk model to assess the probability and impact of flash floods.
[0053] Dynamic risk raster generation: Based on the overlaid GIS map and the above calculation results, a global, gridded dynamic risk raster map is generated. Each raster (10m×10m) has a risk index, which comprehensively reflects the degree of danger of that location under current conditions.
[0054] Criteria for classifying warning levels: Based on a dynamic risk grid, specific thresholds are set to quantify risks into different warning levels, as shown in Table 1.
[0055] Table 1. Classification of Warning Levels
[0056] The system can dynamically adjust the warning level based on real-time data and distribute information to different levels of management personnel or equipment (such as village cadres, community monitoring and prevention personnel, etc.); it also introduces a response function to ensure that the recipients of the warning information can confirm receipt and provide feedback on the action.
[0057] The cloud-based intelligent analysis and command module in this embodiment can also provide risk maps and escape route recommendations based on geographic information systems, and guide grassroots personnel to respond to flash floods in a timely manner through a two-way communication and response module.
[0058] The methods for recommending escape routes specifically include: generating a risk map and generating escape routes.
[0059] Generate a risk map: Overlay a dynamic risk grid map with a basic electronic map, and use different colors (red, orange, yellow, and blue) to intuitively display the real-time warning level of each area, forming a risk heat map.
[0060] Generate escape routes: Input the start point, end point, and constraints. Start point: Current location of personnel (obtained via APP location) or a preset settlement. End point: Preset safe zone or emergency shelter. Constraints: Real-time risk grid map (as cost surface), road network, bridge load-bearing capacity, and terrain slope.
[0061] Determine the path planning algorithm. An improved optimal path algorithm (A) is adopted. The algorithm (or Dijkstra's algorithm) sets the cost of paths traversing high-risk areas (red / orange) to be extremely high, while setting the cost of paths traversing low-risk or safe areas to be relatively low.
[0062] Dynamic avoidance. Path planning algorithms automatically plan a path with the lowest total cost. Essentially, it avoids high-risk areas as much as possible and chooses a relatively safe and fast route to a safe point.
[0063] Multiple route options. The system can generate 1 to 3 alternative routes for the same starting point and clearly mark them on the risk map. It also provides key information such as route length, estimated travel time, and risk level along the route to help frontline staff make decisions.
[0064] The multi-level distribution and response system for flash flood disaster early warning information in this embodiment is based on edge computing to achieve multi-level distribution and response of early warning information. Specifically, through the system structure composed of edge computing terminals, cloud intelligent analysis and command modules, multi-level user terminals and response nodes, and two-way communication and response modules, it realizes closed-loop management from risk perception, level classification, strategy generation to accurate information dissemination and action feedback.
[0065] The multi-level distribution and response system for flash flood disaster early warning information in this embodiment combines geographic information with emergency response. Specifically, by overlaying real-time hydrological data, static environmental factors, and disaster-bearing body information, a dynamic risk raster map is generated using hydrological and hydrodynamic models and machine learning models. Based on this map, an improved optimal path algorithm is used to dynamically generate the optimal escape route that can avoid high-risk areas.
[0066] The multi-level distribution and response system for flash flood disaster early warning information in this embodiment can also achieve adaptive risk assessment and decision-making. Specifically, the system can dynamically update the risk grid map based on real-time monitoring data and automatically adjust the early warning level and corresponding emergency response strategy accordingly, so as to achieve adaptive linkage between early warning and response.
[0067] Based on the same inventive concept, this application also provides a method for multi-level distribution and response of flash flood warning information, implemented using the aforementioned multi-level distribution and response system for flash flood warning information. The solution provided by this method is similar to the implementation scheme described in the above system. Therefore, the specific limitations of one or more embodiments of the multi-level distribution and response method for flash flood warning information provided below can be found in the limitations of the multi-level distribution and response system for flash flood warning information described above, and will not be repeated here.
[0068] In one exemplary embodiment, such as Figure 2 As shown, a multi-level distribution and response method for flash flood disaster early warning information is provided, including: Step 201: Obtain hydrological data for each risk area.
[0069] Step 202: Based on the hydrological data, the inundation information of the risk area is determined by using a hydrological and hydrodynamic model, the probability of flash floods in the risk area is predicted by using a flash flood disaster risk model, and an early warning signal is generated based on the inundation information and the probability of flash floods.
[0070] Step 203: Perform fusion analysis on the early warning signals of all risk areas, and determine the early warning level and corresponding emergency response strategy for each risk area based on the analysis results.
[0071] Step 204: Push the warning signals of different warning levels and the corresponding emergency response strategies to the nodes of the designated level.
[0072] Step 205: Receive response feedback information from the receiving personnel of each level of the multi-level user terminal and response node, and determine whether to perform a second push based on the response feedback information.
[0073] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0074] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A multi-level distribution and response system for flash flood disaster early warning information, characterized in that, The multi-level distribution and response system for flash flood disaster early warning information includes: an edge computing terminal, a cloud-based intelligent analysis and command module, multi-level user terminals and response nodes, and a two-way communication and response module; one edge computing device is deployed in each risk area. The edge computing terminal is used to determine the inundation information of the risk area based on the hydrological data of the risk area using a hydrological and hydrodynamic model, predict the probability of flash floods in the risk area using a flash flood disaster risk model, and issue an early warning signal based on the inundation information and the probability of flash floods. The cloud-based intelligent analysis and command module is used to fuse and analyze the early warning signals of all risk areas, and determine the early warning level and corresponding emergency response strategy for each risk area based on the analysis results. The multi-level user terminals and response nodes are used to receive early warning signals of different warning levels and corresponding emergency response strategies according to their levels. The two-way communication and response module is used to push warning signals of different warning levels and corresponding emergency response strategies to nodes of designated levels, and to receive response feedback information from the receiving personnel at each level of nodes, and to determine whether to push the signals a second time based on the response feedback information.
2. The multi-level distribution and response system for flash flood disaster early warning information according to claim 1, characterized in that, The multi-level user terminals and response nodes include: first-level nodes, second-level nodes, third-level nodes and fourth-level nodes; The first-level node is used to receive early warning signals and corresponding emergency response strategies for all risk areas; the second-level node is used to receive early warning signals and corresponding emergency response strategies for the current risk area; the third-level node is used to receive early warning signals and corresponding emergency response strategies for specific locations; and the fourth-level node is used to broadcast early warning signals and corresponding emergency response strategies to households or individuals.
3. The multi-level distribution and response system for flash flood disaster early warning information according to claim 1, characterized in that, The edge computing terminal includes: The inundation information determination unit is used to determine the inundation information of the risk area based on the hydrological data of the risk area and using a hydrological and hydrodynamic model. The hydrological and hydrodynamic model is obtained by calibrating the runoff generation and confluence model using historical data. The hydrological data includes rainfall and soil moisture. The historical data includes hydrological data and inundation information of the risk area at historical times. The inundation information includes inundation depth and flow velocity. The flash flood risk prediction unit is used to predict the probability of flash floods occurring in risk areas using a flash flood risk prediction model. The flash flood risk prediction model is obtained by training a machine learning model using training data. The training data includes: historical rainfall, soil moisture, environmental characteristics, and flash flood occurrence records of the risk area. The early warning signal generation unit is used to issue an early warning signal based on the inundation information and the probability of flash flood occurrence.
4. The multi-level distribution and response system for flash flood disaster early warning information according to claim 2, characterized in that, The cloud-based intelligent analysis and command module includes: A dynamic risk grid generation unit is used to generate a dynamic risk grid map of the entire region based on the early warning signals of all risk areas; each grid in the dynamic risk grid map corresponds to a risk index; the risk index is determined based on the probability of flash flood occurrence. The early warning level classification and response strategy generation unit is used to determine the early warning level and corresponding emergency response strategy for each risk area based on the dynamic risk grid map and the flooding information.
5. The multi-level distribution and response system for flash flood disaster early warning information according to claim 4, characterized in that, The early warning level classification and response strategy generation unit includes: The Level 1 Warning Issuing Subunit is used to issue a Level 1 warning if the risk index is greater than the first set risk threshold, the flooding depth is greater than the first set depth value, or the flow velocity is greater than the set flow velocity value. The Level 2 Early Warning Issuing Subunit is used to issue a Level 2 early warning if the risk index is greater than the second set risk threshold and less than or equal to the first set risk threshold, or if the flooding depth is greater than or equal to the second set depth value and less than or equal to the first set depth value. The Level 3 Early Warning Issuance Subunit is used to issue a Level 3 early warning if the risk index is greater than the third set risk threshold and less than or equal to the second set risk threshold, or if the flooding depth is less than the second set depth value. The Level 4 warning unit is used to issue a Level 4 warning if the risk index is less than or equal to the third set risk threshold. The emergency response strategy generation subunit is used to generate corresponding emergency response strategies for different warning levels.
6. The multi-level distribution and response system for flash flood disaster early warning information according to claim 5, characterized in that, The bidirectional communication and response module includes: The information push unit is used to push the Level 1 warning and corresponding emergency response strategy to the Level 3 and Level 4 nodes, the Level 2 warning and corresponding emergency response strategy to the Level 2 and Level 3 nodes, the Level 3 warning and corresponding emergency response strategy to the Level 2 and Level 3 nodes, and the Level 4 warning and corresponding emergency response strategy to the Level 1 and Level 2 nodes. The response feedback information receiving and monitoring unit is used to receive response feedback information from the receiving personnel at each level node, and determine whether to push the response feedback information a second time.
7. The multi-level distribution and response system for flash flood disaster early warning information according to claim 4, characterized in that, The cloud-based intelligent analysis and command module also includes: The escape route generation unit generates escape routes based on a dynamic risk grid map.
8. The multi-level distribution and response system for flash flood disaster early warning information according to claim 7, characterized in that, The two-way communication and response module also includes: The route push unit is used to push the escape route to the corresponding level of node.
9. The multi-level distribution and response system for flash flood disaster early warning information according to claim 7, characterized in that, The escape route generation unit includes: The risk map generation sub-unit is used to overlay a dynamic risk raster map with an electronic map to generate a risk map. The route generation subunit is used to generate an escape route based on the risk map using a path planning algorithm; the escape route includes at least one route.
10. A multi-level distribution and response method for flash flood disaster early warning information, characterized in that, The multi-level distribution and response method for flash flood disaster early warning information includes: Obtain hydrological data for each risk area; Based on the hydrological data, a hydrological and hydrodynamic model is used to determine the inundation information of the risk area, a flash flood disaster risk model is used to predict the probability of flash floods in the risk area, and an early warning signal is generated based on the inundation information and the probability of flash floods. The warning signals from all risk areas are integrated and analyzed, and the warning level and corresponding emergency response strategy for each risk area are determined based on the analysis results. Push warning signals of different warning levels and corresponding emergency response strategies to nodes at designated levels; It receives response feedback information from the receiving personnel at each level of the multi-level user terminals and response nodes, and determines whether to perform a secondary push based on the response feedback information.