Emergency decision-making deduction system and method

Abstract

The present invention discloses an emergency decision-making deduction system and method, which relates to the field of emergency management and intelligent decision-making technology, including receiving flood-related data to generate environmental data and social data, constructing a spatiotemporal disaster matrix and a social vulnerability map based on the environmental data and social data, determining a coupling data body based on the analysis of the spatiotemporal disaster matrix and the social vulnerability map, determining a disaster-social coupling network based on the coupling data body, and constructing an emergency decision-making action-knowledge body including a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix and an adaptive rule set through further analysis of the disaster-social coupling network. The emergency decision-making action-knowledge body is used to perform deduction to determine the optimal emergency decision-making plan including executable decision instructions, a resource allocation list and a plan deduction report. The present invention takes the construction of the emergency decision-making action-knowledge body as the deduction core, thereby improving the system's dynamic adaptability and intelligent decision-making capabilities in response to complex disaster scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management and intelligent decision-making, and in particular to an emergency decision-making deduction system and method. Background Art

[0002] With the frequent occurrence of extreme weather, flood disasters have shown characteristics such as suddenness, wide impact range and complex evolution path.

[0003] In the existing urban emergency management system, decision-making methods for flood disasters mostly rely on rule-based preset emergency plans. By analyzing historical hydrological data, geographic information, rainfall and other parameters, possible disasters are predicted and assessed. Although such methods have the functions of disaster monitoring and initial risk assessment to a certain extent, they mainly focus on changes at the physical environmental level and ignore the vulnerability of social structure and the complexity of response behavior. For example, some emergency systems only divide regional risk levels based on fixed geographic information and real-time water level changes. They lack in-depth perception of social factors such as population mobility, regional carrying capacity, and key node dependencies. As a result, evacuation route settings lack flexibility, making it difficult to cope with dynamically spreading disaster situations, resulting in inaccurate evacuation routes and delayed material dispatch. Summary of the Invention

[0004] In view of the above problems in the prior art, the present invention is proposed.

[0005] Therefore, the purpose of the present invention is to provide an emergency decision-making deduction system and method. The problem it aims to solve is that in the traditional urban emergency management system, when predicting and evaluating possible disasters, it mainly focuses on changes in the physical environment, ignoring the vulnerability of the social structure and the complexity of response behavior, resulting in a lack of flexibility in the setting of evacuation routes and difficulty in coping with the dynamic spread of disaster situations.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solution: an emergency decision-making deduction method, comprising:

[0007] S1, receiving flood-related data to generate environmental data and social data, and constructing a spatiotemporal disaster matrix and a social vulnerability map based on the environmental data and social data;

[0008] S2. Determine a coupled data volume based on the analysis of the spatiotemporal disaster matrix and social vulnerability map, determine the disaster-society coupling network based on the coupled data volume, and construct an emergency decision-making action knowledge body consisting of a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix, and an adaptive rule set through further analysis of the disaster-society coupling network;

[0009] S3. Use the emergency decision-making action knowledge body to conduct deductions to determine the optimal emergency decision-making plan that includes executable decision instructions, resource allocation lists, and plan deduction reports.

[0010] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of constructing the spatiotemporal disaster matrix and the social vulnerability map respectively includes:

[0011] Preprocess and divide flood-related data into environmental data and social data. Environmental data includes data related to geographical meteorology and hydrological conditions, while social data includes data related to population, facilities, and dependencies.

[0012] Analyze environmental data to determine the spatiotemporal disaster matrix, which includes disaster time, disaster spatial grid and disaster intensity information;

[0013] Based on the analysis of social data, a social vulnerability map is determined. The social vulnerability map includes a set of key area nodes, a set of dependency edges, and a set of dependency intensity weights.

[0014] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of analyzing the spatiotemporal disaster matrix and the social vulnerability map includes:

[0015] Analyze the spatiotemporal hazard matrix and social vulnerability map to determine the coupled data volume, which includes the spatiotemporal grid, hazard intensity, and social vulnerability score;

[0016] Based on the analysis of coupled data volumes, the flood diffusion feature set and the crowd flow feature set are determined;

[0017] Based on the analysis of flood diffusion and crowd flow feature sets, the disaster dynamic characteristics are determined; based on the coupled data volume, the disaster exposure and connectivity of each spatiotemporal grid node are determined; based on the analysis of the disaster exposure and the connectivity of each node, the comprehensive criticality score is determined;

[0018] A criticality identification threshold is preset, and key points whose comprehensive criticality scores are greater than the preset criticality identification threshold are determined as a key node list;

[0019] By analyzing the disaster dynamics characteristics and key node lists, the disaster-society coupling network is determined;

[0020] Analyze the disaster-society coupling network and determine the candidate strategy set.

[0021] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of determining the disaster-society coupling network includes:

[0022] Analyze the disaster dynamics characteristics to determine the disaster hotspot set; calculate the impact propagation intensity set based on the disaster hotspot set and the key node list;

[0023] A propagation impact threshold is preset, and the propagation impact strength set is compared with the preset propagation impact threshold, and the propagation impact strength greater than the preset propagation impact threshold is determined as the effective propagation path set;

[0024] Taking the disaster hotspot nodes as the starting point and the key nodes as the end point, the effective propagation path set as the edge set, and the corresponding impact propagation intensity as the edge weight, a disaster-society coupling network is constructed.

[0025] As a preferred solution of the emergency decision-making deduction method of the present invention, the process of determining the candidate strategy set includes:

[0026] Obtain real-time material inventory data and public opinion monitoring data. The real-time material inventory data includes material types, quantities, storage locations, and deployment status. The public opinion monitoring data includes but is not limited to public sentiment, emergency information needs, and focus information.

[0027] Analyze the disaster-society coupling network to determine the evacuation plan set; analyze the disaster-society coupling network and real-time material inventory data to determine the resource allocation plan;

[0028] Determine the information strategy set based on analysis of public opinion monitoring data and the disaster-society coupling network;

[0029] Analyze the evacuation plan set, resource allocation plan and information strategy set to determine the candidate strategy set.

[0030] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of analyzing the spatiotemporal disaster matrix and the social vulnerability map further includes:

[0031] Obtain real-time environmental update data to determine the latest disaster evolution map;

[0032] Analyze the candidate strategy set and the latest disaster evolution map to determine the effectiveness of each candidate strategy in the current disaster environment;

[0033] Analyze the execution results of each candidate strategy and the pre-stored historical strategy optimization case library to determine the corresponding strategy optimization recommendations;

[0034] Analyze the execution effect of each candidate strategy and the corresponding strategy optimization suggestions to determine the dynamic adjustment rule base.

[0035] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of further analyzing the disaster-society coupling network includes:

[0036] Analyze the disaster-society coupling network and determine the spatiotemporal knowledge graph;

[0037] Based on the analysis of the candidate strategy set and the execution effect of each candidate strategy, a multidimensional strategy evaluation matrix is ​​determined; based on the analysis of the execution effect of each candidate strategy and the dynamic adjustment rule base, an adaptive rule set is determined; the spatiotemporal knowledge graph, the multidimensional strategy evaluation matrix and the adaptive rule set are processed to generate an emergency decision-making action knowledge body.

[0038] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of determining the optimal emergency decision-making solution including executable decision instructions, resource allocation list and solution deduction report includes:

[0039] Analyze the spatiotemporal knowledge graph to determine the set of potential optimal strategic paths;

[0040] Calculate each path in the potential optimal strategy path set based on the multi-dimensional strategy evaluation matrix and determine the comprehensive score of each strategy path;

[0041] Set a threshold and determine the strategy paths whose comprehensive scores exceed the threshold as the candidate optimal solution subset;

[0042] Analyze the subset of candidate optimal solutions based on the adaptive rule set to determine the optimal emergency decision-making solution, which includes executable decision instructions, resource allocation list and solution deduction report;

[0043] Use blockchain evidence storage mechanism to timestamp signatures and hash records for access to multi-source data;

[0044] Leverage edge computing nodes to perform local preprocessing of multi-source data.

[0045] As a preferred solution of the emergency decision-making and deduction system of the present invention, the system includes: a data perception module for receiving flood-related data and generating a spatiotemporal disaster matrix and a social vulnerability map based on the flood-related data;

[0046] A construction module is used to analyze the spatiotemporal disaster matrix and social vulnerability map to determine the coupled data volume, determine the disaster-society coupling network based on the coupled data volume, and construct an emergency decision-making action knowledge body containing a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix, and an adaptive rule set through the disaster-society coupling network;

[0047] The output module is used to generate the optimal emergency decision-making plan through the emergency decision-making behavior knowledge body.

[0048] In summary, the present invention has at least one of the following beneficial effects:

[0049] 1. The present invention analyzes flood-related data, constructs a spatiotemporal disaster matrix and a social vulnerability map, systematically integrates disaster evolution data and social system response capabilities, and achieves deep coupling of disaster information and social structure. Through coupled data body modeling, disaster exposure analysis and key node extraction, it constructs a disaster-society coupling network, accurately identifies high-risk transmission paths and weak points in social structure, and not only has stronger dynamic perception capabilities, but can also formulate more locally adaptable and goal-oriented response strategy paths based on the actual dependencies and transmission intensity between regions, significantly improving the scientific nature and implementation effect of disaster response measures.

[0050] 2. The present invention, by constructing an emergency decision-making action-aware body as the deduction core, integrates the spatiotemporal knowledge graph, the multi-dimensional strategy evaluation matrix and the adaptive rule set, and can realize intelligent strategy screening and adjustment based on real-time data, candidate strategy sets and environmental changes. During the emergency deduction process, the strategy path can be scored and corrected in multiple dimensions through the adaptive rule set, and the strategy content, node sequence and resource allocation method can be dynamically adjusted, and finally a visualization solution including execution instructions, deployment lists and graphical processes is output, which greatly improves the rapid response capability and deployment efficiency in multiple scenarios, and significantly improves the system's dynamic adaptability and intelligent decision-making capabilities in response to complex disaster scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0052] Figure 1 Schematic diagram of the structure of an emergency decision-making deduction system and method of the present invention;

[0053] Figure 2 It is a schematic diagram of the process of the present invention;

[0054] Figure 3 A flow chart of the emergency decision-making behavior knowledge body is constructed for the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] The embodiment of the present invention discloses an emergency decision-making deduction system and method.

[0057] Example 1

[0058] See also Figure 1 and Figure 3 As shown, the emergency decision-making deduction method described in this embodiment includes:

[0059] S1, receiving flood-related data to generate environmental data and social data, and constructing a spatiotemporal disaster matrix and a social vulnerability map based on the environmental data and social data;

[0060] The process of constructing the spatiotemporal hazard matrix and social vulnerability map respectively includes:

[0061] Preprocess and divide flood-related data into environmental data and social data. Environmental data includes data related to geographical meteorology and hydrological conditions, while social data includes data related to population, facilities, and dependencies.

[0062] Analyze environmental data to determine the spatiotemporal disaster matrix, which includes disaster time, disaster spatial grid and disaster intensity information;

[0063] Based on the analysis of social data, a social vulnerability map is determined. The social vulnerability map includes a set of key area nodes, a set of dependency edges, and a set of dependency intensity weights.

[0064] It should be explained that flood-related data includes meteorological monitoring data (such as rainfall, radar echoes, air pressure and wind speed), geographic information data (topography, land use, river distribution), social operation data (population density, traffic conditions, infrastructure distribution), hydrological monitoring data (real-time status of bridges, pipeline networks, sluices, etc.), and public opinion and social media data (user-posted ground disaster information, help requests, etc.). Flood-related data is synchronized and accessed using a unified time index, and then cleaned and integrated to form environmental data and social data.

[0065] Based on environmental data, the target area is divided into regular spatial grids, and the disaster intensity (including water accumulation depth, runoff velocity, risk level, etc.) on each spatial grid is analyzed and extracted, thereby forming a three-dimensional spatiotemporal disaster matrix containing time-space-intensity; based on social data, key regional nodes (hospitals, schools, transportation hubs, etc.) are identified, and a graph structure is constructed with regional nodes as points and functions or dependencies as edges. The dependency edge set reflects the interdependence between nodes, and the dependency intensity weight combines social vulnerability factors such as population structure, aging rate, and resource accessibility to form a social vulnerability map.

[0066] It needs to be explained that the spatiotemporal disaster matrix describes the distribution and severity of flood disasters from the perspective of the natural environment, and the social vulnerability map reflects the vulnerability of the social environment in the face of disasters.

[0067] S2. Determine a coupled data volume based on the analysis of the spatiotemporal disaster matrix and social vulnerability map, determine the disaster-society coupling network based on the coupled data volume, and construct an emergency decision-making action knowledge body consisting of a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix, and an adaptive rule set through further analysis of the disaster-society coupling network;

[0068] The process of analyzing the spatiotemporal hazard matrix and social vulnerability map includes:

[0069] Analyze the spatiotemporal hazard matrix and social vulnerability map to determine the coupled data volume, which includes the spatiotemporal grid, hazard intensity, and social vulnerability score;

[0070] Based on the analysis of coupled data volumes, the flood diffusion feature set and the crowd flow feature set are determined;

[0071] Based on the analysis of flood diffusion and crowd flow feature sets, the disaster dynamic characteristics are determined; based on the coupled data volume, the disaster exposure and connectivity of each spatiotemporal grid node are determined; based on the analysis of the disaster exposure and the connectivity of each node, the comprehensive criticality score is determined;

[0072] A criticality identification threshold is preset, and key points whose comprehensive criticality scores are greater than the preset criticality identification threshold are determined as a key node list;

[0073] By analyzing the disaster dynamics characteristics and key node lists, the disaster-society coupling network is determined;

[0074] Analyze the disaster-society coupling network and determine the candidate strategy set.

[0075] It should be explained that the coupled data volume is the result of matching and fusing the disaster intensity in the spatiotemporal disaster matrix with the social vulnerability score in the social vulnerability map according to the spatial grid and time index, obtaining the risk expression of each spatiotemporal grid unit in the dual dimension of disaster and society. The hydrodynamic model simulates the propagation process of floods in different terrains to obtain flood diffusion characteristics (such as wavefront propagation speed, flood arrival time, inundation duration, etc.). At the same time, combined with the migration trends of the population (mined from social media location data, traffic congestion index, and mobile communication data), the flow characteristics of the population (such as flow direction, evacuation path, dense areas, etc.) are extracted. The two together constitute the dynamic characteristics of the disaster.

[0076] It should be explained that the coupled data body is analyzed to determine the degree to which each spatiotemporal node is directly affected in the disaster scenario and quantify it to form a disaster exposure metric, while the connectivity represents the structural importance of the node in the entire social dependency relationship. The two are weighted and fused to form a comprehensive criticality score. By analyzing the areas severely affected by previous flood disasters, the comprehensive criticality score range of the nodes in these areas is used as the criticality identification threshold, and compared with the comprehensive criticality score to obtain a list of critical nodes.

[0077] The process of identifying the hazard-society coupling network includes:

[0078] Analyze the disaster dynamics characteristics to determine the disaster hotspot set; calculate the impact propagation intensity set based on the disaster hotspot set and the key node list;

[0079] A propagation impact threshold is preset, and the propagation impact strength set is compared with the preset propagation impact threshold, and the propagation impact strength greater than the preset propagation impact threshold is determined as the effective propagation path set;

[0080] Taking the disaster hotspot nodes as the starting point and the key nodes as the end point, the effective propagation path set as the edge set, and the corresponding impact propagation intensity as the edge weight, a disaster-society coupling network is constructed.

[0081] It should be explained that the disaster intensity (such as water depth and diffusion rate) and population flow density of each spatiotemporal grid node are obtained from the coupled data volume, and their change curves in continuous time series are analyzed to form the disaster intensity evolution curve and population density change curve of the node. By analyzing the change rate of these curves, areas with significant disaster changes and long-term high exposure values ​​are identified. The two types of areas are merged to form a disaster hotspot set;

[0082] Key area nodes with comprehensive criticality scores greater than the threshold are obtained from the social vulnerability map. Using a graph traversal algorithm, starting from each disaster hotspot node, all reachable paths connected to the key nodes are searched. Each path consists of a set of grid points with variable path length and topological structure. For each path, the propagation impact intensity value is calculated based on factors such as the average disaster intensity along the path length, cumulative exposure, and population density weighting to form an impact propagation intensity set. Referring to the actual situation of impact propagation in historical disaster events, the propagation range and consequences of disasters under different impact intensities are analyzed to determine the propagation impact threshold. Through traversal, paths with actual influence in the disaster diffusion process are screened out as the effective propagation path set, forming a directed weighted graph structure with nodes as disaster hotspots, edges as impact paths, and weights as propagation intensity, namely the disaster-society coupling network.

[0083] The process of determining a candidate policy set includes:

[0084] Obtain real-time material inventory data and public opinion monitoring data. The real-time material inventory data includes material types, quantities, storage locations, and deployment status. The public opinion monitoring data includes but is not limited to public sentiment, emergency information needs, and focus information.

[0085] Analyze the disaster-society coupling network to determine the evacuation plan set; analyze the disaster-society coupling network and real-time material inventory data to determine the resource allocation plan;

[0086] Determine the information strategy set based on analysis of public opinion monitoring data and the disaster-society coupling network;

[0087] Analyze the evacuation plan set, resource allocation plan and information strategy set to determine the candidate strategy set.

[0088] It should be explained that by connecting with the information management department, real-time material inventory data (types, quantities, storage locations, available status, etc. of various emergency materials) is obtained in real time, and public opinion monitoring data (public concern about disasters, emotional attitudes, demand information, etc.) is collected through social platforms. Based on the impact propagation intensity and disaster intensity of nodes in the disaster-social coupling network, the optimal evacuation path is generated by combining traffic accessibility analysis and the shortest path algorithm, and a set of evacuation plans is determined. Each evacuation plan includes an evacuation route, path load estimation and evacuation priority; combined with the location of the material warehouse, allocation radius, traffic capacity and key node priority, a material distribution-demand matching diagram is established, and a resource allocation plan is output. Each resource allocation plan includes an allocation path, delivery quantity and estimated time; through the public's cognition and demand for disasters in the public opinion monitoring data and the propagation trend of disasters in the disaster-social coupling network, targeted publicity content is formulated. The publicity content includes content type, publishing platform and push frequency, and finally a candidate strategy set including evacuation plans, resource allocation plans and information strategies is formed.

[0089] The process of analyzing the spatiotemporal hazard matrix and social vulnerability map also includes:

[0090] Obtain real-time environmental update data to determine the latest disaster evolution map;

[0091] Analyze the candidate strategy set and the latest disaster evolution map to determine the effectiveness of each candidate strategy in the current disaster environment;

[0092] Analyze the execution results of each candidate strategy and the pre-stored historical strategy optimization case library to determine the corresponding strategy optimization recommendations;

[0093] Analyze the execution effect of each candidate strategy and the corresponding strategy optimization suggestions to determine the dynamic adjustment rule base.

[0094] It should be explained that real-time environmental update data can be obtained by automatically connecting to the meteorological and hydrological platform. Based on the environmental update data, the original spatiotemporal disaster matrix is ​​updated in time series, new or significantly changed disaster event nodes are identified, and the two-dimensional flood evolution model is applied to generate the latest disaster evolution map. The latest disaster evolution map reflects the current disaster development trend and its potential impact range. Each strategy in the candidate strategy set is simulated or simulated in the current disaster evolution map, and multiple evaluation indicators such as evacuation success rate, resource delivery rate, material coverage timeliness, public opinion guidance accuracy, public response level and social stability index are output. These multiple evaluation indicators are used to form the execution effect of each candidate strategy. The historical strategy optimization case library stores strategy response records under different spatiotemporal disaster backgrounds and social vulnerability conditions, including corresponding environmental parameters, key decision paths and proven effective strategy modification methods. The execution effects of the current strategies are matched with the historical strategy optimization case library for similarity, and targeted strategy optimization suggestions are generated based on the matching results. The strategy optimization suggestions include adjusting key nodes of the evacuation path, rearranging material allocation paths, and increasing the information frequency or release channels in specific areas.

[0095] It should be explained that the classification of dynamic adjustment rules includes strategy start and termination rules, strategy parameter adjustment rules and strategy switching rules. Strategy start and termination rules are used to define under what disaster evolution conditions a strategy is activated or terminated; strategy parameter adjustment rules are used to identify adjustable parameters and their adjustment ranges in strategy execution, such as evacuation priority and deployment frequency; strategy switching rules are used to automatically switch to alternative plans when it is detected that the effectiveness of the current strategy has declined.

[0096] As a preferred embodiment of the emergency decision-making deduction method of the present invention, the process of further analyzing the disaster-society coupling network includes:

[0097] Analyze the disaster-society coupling network and determine the spatiotemporal knowledge graph;

[0098] Based on the analysis of the candidate strategy set and the execution effect of each candidate strategy, a multidimensional strategy evaluation matrix is ​​determined; based on the analysis of the execution effect of each candidate strategy and the dynamic adjustment rule base, an adaptive rule set is determined; the spatiotemporal knowledge graph, the multidimensional strategy evaluation matrix and the adaptive rule set are processed to generate an emergency decision-making action knowledge body.

[0099] It should be explained that disaster hotspot nodes, effective propagation path sets, and social structure vulnerabilities are obtained through the disaster-society coupling network, and the disaster propagation status and resource allocation plan are jointly characterized by the flood diffusion feature set and the disaster dynamics characteristics to form a spatiotemporal knowledge graph. The spatiotemporal knowledge graph includes nodes and edges. The nodes include disaster event nodes and social target nodes, and the edges include time series edges (evolution paths), spatial propagation edges (influence paths), and strategy action edges (the action relationship between strategies and nodes). Each node edge is accompanied by attribute information; the candidate strategy set is a row, and the execution effect of each candidate strategy is a column to form a multidimensional strategy evaluation matrix. The execution effect of each strategy is compared with the dynamic rule library to generate an adaptive rule set containing trigger conditions, correction content, and application object rule table; the spatiotemporal knowledge graph, the multidimensional strategy evaluation matrix, and the adaptive rule set are integrated to form an emergency decision-making action knowledge body with strategy identification-execution evaluation-optimization adjustment-decision output;

[0100] S3. Use the emergency decision-making action knowledge body to conduct deductions and determine the optimal emergency decision-making plan including executable decision instructions, resource allocation list and plan deduction report;

[0101] The process of determining the optimal emergency decision-making plan, which includes executable decision instructions, resource allocation lists, and plan simulation reports, includes:

[0102] Analyze the spatiotemporal knowledge graph to determine the set of potential optimal strategic paths;

[0103] Calculate each path in the potential optimal strategy path set based on the multi-dimensional strategy evaluation matrix and determine the comprehensive score of each strategy path;

[0104] Set a threshold and determine the strategy paths whose comprehensive scores exceed the threshold as the candidate optimal solution subset;

[0105] Analyze the subset of candidate optimal solutions based on the adaptive rule set to determine the optimal emergency decision-making solution, which includes executable decision instructions, resource allocation list and solution deduction report;

[0106] Use blockchain evidence storage mechanism to timestamp signatures and hash records for access to multi-source data;

[0107] Leverage edge computing nodes to perform local preprocessing of multi-source data.

[0108] What needs to be explained is that from the spatiotemporal knowledge graph, dynamic disaster information such as the trend of flood level changes and the expansion range of the inundated area is extracted. At the same time, the location, operation status and correlation information of key nodes in the social system (such as hospitals, schools, transportation hubs, etc.) with the disaster area are extracted, and multiple possible response paths are formed through analysis. Each response path is composed of several emergency response nodes and strategies. Multiple strategy paths constitute a potential optimal strategy path set. The execution effect of each strategy path in the potential optimal strategy path set is weighted and fused to construct a comprehensive score for each strategy path. The comprehensive scores of each strategy path of all strategies are arranged in descending order, and the paths with the highest comprehensive scores are screened to form a subset of candidate optimal solutions. Through adaptive rules The set matches and adjusts the rules of the candidate optimal solution subsets one by one to identify whether there are redundant links, suboptimal strategy nodes or passive response behaviors. If the correction rule is triggered, the strategy path is structurally optimized and re-evaluated according to the rule suggestions. Finally, the correction path with the highest score is output as the optimal emergency decision-making plan. The optimal emergency decision-making plan includes an executable decision instruction set, a resource allocation list and a plan deduction report. The executable decision instruction set specifies the operation actions of each time and space node, such as "evacuation start", "water pump start", "public opinion intervention", etc. The resource allocation list lists the resource types, quantities, allocation sources and allocation paths involved. The plan deduction report traces the deduction path based on the knowledge graph to form a visual response flow chart and expected effect diagram;

[0109] It needs to be explained that in the data access stage, all access data are timestamped and hashed by introducing a blockchain evidence storage mechanism. A hash fingerprint is generated for each data when it is accessed into the system and stored in the blockchain ledger to ensure subsequent traceability and non-tampering, thereby guaranteeing the authenticity and credibility of the data source. Edge computing nodes are deployed near the disaster area or mobile command nodes to perform local preliminary processing of the original data, including data screening, denoising, anomaly detection, data format standardization, etc., to achieve local response to high-frequency data at the front end and realize local emergency decision-making and deduction assistance functions in the event of a network outage.

[0110] In this embodiment, by analyzing flood-related data, constructing a spatiotemporal disaster matrix and a social vulnerability map, systematically integrating disaster evolution data and social system response capabilities, and realizing deep coupling of disaster information and social structure, a disaster-society coupling network is constructed through coupled data body modeling, disaster exposure analysis, and key node extraction. High-risk transmission paths and weak points in social structure are accurately identified, which not only enhances dynamic perception capabilities, but also enables the formulation of more locally adaptable and goal-oriented response strategy paths based on the actual dependencies and transmission intensity between regions, significantly improving the scientific nature and implementation effectiveness of disaster response measures. ; Taking the construction of emergency decision-making action knowledge body as the core of the deduction, integrating spatiotemporal knowledge graph, multi-dimensional strategy evaluation matrix and adaptive rule set, it can realize intelligent strategy screening and adjustment based on real-time data, candidate strategy set and environmental changes. During the emergency deduction process, the strategy path can be scored and corrected in multiple dimensions through the adaptive rule set, and the strategy content, node sequence and resource allocation method can be dynamically adjusted. Finally, a visual solution including execution instructions, deployment list and graphical process is output, which greatly improves the rapid response capability and deployment efficiency in multiple scenarios, and significantly improves the system's dynamic adaptability and intelligent decision-making ability to deal with complex disaster scenarios.

[0111] Example 2

[0112] See also Figure 2 As shown, the emergency decision-making deduction system described in this embodiment includes:

[0113] The data perception module is used to receive flood-related data and generate a spatiotemporal disaster matrix and a social vulnerability map based on the flood-related data;

[0114] A construction module is used to analyze the spatiotemporal disaster matrix and social vulnerability map to determine the coupled data volume, determine the disaster-society coupling network based on the coupled data volume, and construct an emergency decision-making action knowledge body containing a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix, and an adaptive rule set through the disaster-society coupling network;

[0115] The output module is used to generate the optimal emergency decision-making plan through the emergency decision-making behavior knowledge body.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An emergency decision-making deduction method, characterized in that: include: S1, receiving flood-related data to generate environmental data and social data, and constructing a spatiotemporal disaster matrix and a social vulnerability map based on the environmental data and social data; S2. Analyze the spatiotemporal disaster matrix and social vulnerability map to determine the coupled data volume, determine the disaster-society coupling network based on the coupled data volume, and construct an emergency decision-making action knowledge body by analyzing the disaster-society coupling network, which includes a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix, and an adaptive rule set; S3. Use the emergency decision-making action knowledge body to conduct deductions and determine the optimal emergency decision-making plan including executable decision instructions, resource allocation list and plan deduction report; The process of constructing the spatiotemporal disaster matrix and social vulnerability map respectively includes: Preprocess and divide flood-related data into environmental data and social data. Environmental data includes data related to geographical meteorology and hydrological conditions, while social data includes data related to population, facilities, and dependencies. Analyze environmental data to determine the spatiotemporal disaster matrix, which includes disaster time, disaster spatial grid and disaster intensity information; Analyze social data to determine the social vulnerability map, which includes a set of key area nodes, a set of dependency edges, and a set of dependency intensity weights; The process of analyzing the spatiotemporal hazard matrix and social vulnerability map includes: Analyze the spatiotemporal hazard matrix and social vulnerability map to determine the coupled data volume, which includes the spatiotemporal grid, hazard intensity, and social vulnerability score; Analyze the coupled data volume to determine the flood diffusion feature set and the crowd flow feature set; Analyze the flood diffusion feature set and the crowd flow feature set to determine the disaster dynamics characteristics; calculate based on the coupled data volume to determine the disaster exposure and connectivity of each spatiotemporal grid node; analyze the disaster exposure and connectivity of each node to determine the comprehensive criticality score; A criticality identification threshold is preset, and key points whose comprehensive criticality scores are greater than the preset criticality identification threshold are determined as a key node list; By analyzing the disaster dynamics characteristics and key node lists, the disaster-society coupling network is determined; Analyze the disaster-society coupling network and determine the candidate strategy set.

2. The emergency decision-making deduction method according to claim 1, characterized in that: The process of determining the disaster-society coupling network includes: Analyze the disaster dynamics characteristics to determine the disaster hotspot set; calculate the impact propagation intensity set based on the disaster hotspot set and the key node list; A propagation impact threshold is preset, and the propagation impact strength set is compared with the preset propagation impact threshold, and the propagation impact strength greater than the preset propagation impact threshold is determined as the effective propagation path set; Taking the disaster hotspot nodes as the starting point and the key nodes as the end point, the effective propagation path set as the edge set, and the corresponding impact propagation intensity as the edge weight, a disaster-society coupling network is constructed.

3. The emergency decision-making deduction method according to claim 1, characterized in that: The process of determining the candidate policy set includes: Obtain real-time material inventory data and public opinion monitoring data. The real-time material inventory data includes material types, quantities, storage locations, and deployment status. The public opinion monitoring data includes but is not limited to public sentiment, emergency information needs, and focus information. Analyze the disaster-society coupling network to determine the evacuation plan set; analyze the disaster-society coupling network and real-time material inventory data to determine the resource allocation plan; Determine the information strategy set based on analysis of public opinion monitoring data and the disaster-society coupling network; The evacuation plan set, resource allocation plan and information strategy set are analyzed to determine the candidate strategy set.

4. The emergency decision-making deduction method according to claim 1, characterized in that: The process of analyzing the spatiotemporal disaster matrix and social vulnerability map further includes: Obtain real-time environmental update data to determine the latest disaster evolution map; Analyze the candidate strategy set and the latest disaster evolution map to determine the effectiveness of each candidate strategy in the current disaster environment; Analyze the execution results of each candidate strategy and the pre-stored historical strategy optimization case library to determine the corresponding strategy optimization recommendations; Analyze the execution effect of each candidate strategy and the corresponding strategy optimization suggestions to determine the dynamic adjustment rule base.

5. The emergency decision-making deduction method according to claim 1, characterized in that: The process of analyzing the disaster-society coupling network includes: Analyze the disaster-society coupling network and determine the spatiotemporal knowledge graph; Based on the analysis of the candidate strategy set and the execution effect of each candidate strategy, a multidimensional strategy evaluation matrix is ​​determined; based on the analysis of the execution effect of each candidate strategy and the dynamic adjustment rule base, an adaptive rule set is determined; the spatiotemporal knowledge graph, the multidimensional strategy evaluation matrix and the adaptive rule set are processed to generate an emergency decision-making action knowledge body.

6. The emergency decision-making deduction method according to claim 5, characterized in that: The process of determining the optimal emergency decision-making plan including executable decision instructions, resource allocation list and plan deduction report includes: Analyze the spatiotemporal knowledge graph to determine the set of potential optimal strategic paths; Calculate each path in the potential optimal strategy path set based on the multi-dimensional strategy evaluation matrix and determine the comprehensive score of each strategy path; Set a threshold and determine the strategy paths whose comprehensive scores exceed the threshold as the candidate optimal solution subset; Analyze the subset of candidate optimal solutions based on the adaptive rule set to determine the optimal emergency decision-making solution, which includes executable decision instructions, resource allocation list and solution deduction report; Use blockchain evidence storage mechanism to timestamp signatures and hash records for access to multi-source data; Leverage edge computing nodes to perform local preprocessing of multi-source data.

7. An emergency decision-making deduction system, used to execute an emergency decision-making deduction method according to any one of claims 1 to 6, characterized in that: include: The data perception module is used to receive flood-related data and generate a spatiotemporal disaster matrix and a social vulnerability map based on the flood-related data; A construction module is used to analyze the spatiotemporal disaster matrix and social vulnerability map to determine the coupled data volume, determine the disaster-society coupling network based on the coupled data volume, and construct an emergency decision-making action knowledge body containing a candidate strategy set, a spatiotemporal knowledge map, a multidimensional strategy evaluation matrix, and an adaptive rule set through the disaster-society coupling network; The output module is used to generate the optimal emergency decision-making plan through the emergency decision-making behavior knowledge body.

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