An intelligent emergency command system and method based on scenario twins

Abstract

The present invention relates to the field of communication command technology, and particularly to an intelligent emergency command system and method based on scene twins. It includes a data acquisition module, a scene twin management module, an emergency situation analysis module, an emergency response decision-making module, and a resource scheduling and command module. Multi-source data of the emergency scene is collected through a sensor network, and an augmented reality and digital twin technology is used to construct a scene twin model to achieve real-time rendering of the virtual scene. The emergency situation is analyzed by combining the random matrix theory and the graph neural network algorithm to detect risks and predict trends, and an emergency response strategy is generated based on the analysis report using a decision tree. At the same time, the resources are scheduled and allocated through a multi-objective optimization algorithm to ensure the optimal emergency command plan. The present invention improves the efficiency and scientificity of emergency command, and enhances the rapidity and accuracy of emergency response.

Description

Technical Field

[0001] The present invention relates to the technical field of communication command, and particularly to a smart emergency command system and method based on scene twinning. Background Art

[0002] With the acceleration of urbanization and the complexity of society, various emergency events (such as natural disasters, fires, traffic accidents, public health emergencies, etc.) occur frequently, posing higher requirements for the intelligence and efficiency of emergency command systems. Traditional emergency command systems usually rely on manual judgment and experience-based decision-making, suffering from problems such as slow response speed, insufficient decision-making basis, and low data integration, and are difficult to meet the needs of complex and changeable emergency scenarios. There are also the following problems currently: when an emergency event occurs, existing emergency command systems usually have difficulty quickly and accurately obtaining multi-source data in the scene, resulting in insufficient understanding of the real-time situation of the event by commanders, and prone to information lag or incomplete information; most of the virtualization technologies for existing emergency scenarios are limited to simple two-dimensional images or static three-dimensional models, and it is difficult to achieve realistic scene reproduction and dynamic update, making it difficult for commanders to intuitively understand the complex on-site situation; the situation analysis in traditional emergency systems is usually based on fixed rules or simple algorithms, and it is difficult to accurately analyze and locate dynamic risks in complex scenarios. In addition, the ability to predict potential risks is weak, and it is difficult to provide timely development trend prediction and effective risk warning. Summary of the Invention

[0003] To solve the above problems, the present invention provides a smart emergency command system and method based on scene twinning, which solves the problem of how to quickly and accurately obtain multi-source data in emergency events by using scene twinning technology, realizes the dynamic virtualization and high-precision three-dimensional rendering of complex scenarios, and conducts accurate situation analysis and risk prediction through intelligent algorithms, so as to solve the problems of slow response speed, incomplete information, and insufficient decision-making basis in traditional emergency systems, thereby improving the intelligence and efficiency of emergency command.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A smart emergency command system based on scene twinning, comprising a data acquisition module, a scene twinning management module, an emergency situation analysis module, an emergency response decision module, and a resource scheduling and command module that are communicatively connected in sequence;

[0006] The data acquisition module is used to collect multi-source data in the emergency scene in real time through a sensor network; the multi-source data includes environmental data, equipment status data, and personnel information data of the emergency scene;

[0007] The described scenario twin management module is used to construct a scenario twin model based on the multi-source data through augmented reality technology and digital twin technology, and perform real-time rendering of the virtual scenario using 3D visualization technology;

[0008] The emergency situation analysis module is used to perform situation analysis on the emergency scenario based on the scenario twin model through random matrix theory and graph neural network algorithm, detect potential risks, accurately locate the dynamic risk area and predict the development trend, and generate an emergency situation analysis report;

[0009] The emergency response decision-making module is used to automatically generate an emergency response strategy based on the emergency situation analysis report using the decision tree algorithm; the emergency response strategy includes an action plan, resource allocation suggestions, and evacuation route planning;

[0010] The resource scheduling and command module is used to optimize the scheduling and dynamic allocation of emergency resources based on the emergency situation analysis report and the emergency response strategy using multi-objective optimization algorithm and fuzzy control algorithm, and generate an optimal emergency response command plan.

[0011] Furthermore, the operation process of the scenario twin management module includes the following steps:

[0012] Perform preprocessing operations on the multi-source data collected through the sensor network, including data cleaning, noise reduction, and format conversion;

[0013] Based on the preprocessed multi-source data, construct a scenario twin model of the emergency scenario using augmented reality technology and digital twin technology;

[0014] On the basis of the scenario twin model, through the fusion of historical data and real-time data, use data assimilation technology to finely adjust and optimize the key parameters of the model;

[0015] Perform 3D visualization rendering on the finely constructed scenario twin model, use high-precision rendering algorithm to present the dynamic changes in the scenario, and realize the intuitive display and interactive operation of the emergency scenario.

[0016] Furthermore, the scenario twin model includes a 3D spatial geometry model and an attribute model. The 3D spatial geometry model realizes the 3D visualization presentation of the scenario through point cloud data reconstruction and texture mapping, including the basic terrain, the 3D structure of buildings, and dynamic objects. The attribute model contains environmental status, equipment operation parameters, and personnel information.

[0017] Furthermore, the construction process of the scenario twin model includes the following steps:

[0018] Based on preprocessed multi-source data, virtual reality and digital twin technologies are used to reconstruct the three-dimensional structure of the emergency scenario. Multi-scale modeling is carried out on the terrain, buildings, and dynamic objects in the scenario through point cloud data, and texture mapping technology is used to enhance the details of the model surface;

[0019] Integrate and map the multi-source data collected in real time with the three-dimensional geometric model. Through virtual reality technology, the data changes are reflected in the virtual scenario to achieve real-time tracking and updating of dynamic objects, including the state changes of equipment, fluctuations in environmental parameters, and the movement trajectories of personnel;

[0020] Based on the integrated data sources, an attribute model of the scenario is constructed, including environmental status, equipment parameters, and personnel information. The attribute data is associated and mapped with the geometric model of the virtual scenario, and virtual reality interaction technology is used to achieve the switching display of different data layers;

[0021] Combining historical data and real-time collected data, the key parameters of the scenario twin model are continuously optimized through data assimilation technology, and parameter correction and model update are carried out using digital twin technology;

[0022] Perform high-precision three-dimensional rendering on the optimized scenario twin model. Through virtual reality devices or interactive interfaces, support real-time operations and multi-angle observations of the virtual scenario by emergency command personnel.

[0023] Furthermore, the operation process of the emergency situation analysis module includes the following steps:

[0024] Perform data fusion processing on the multi-source data integrated in the scenario twin model. Use multi-modal data fusion technology to associate the multi-source data and generate a unified emergency scenario dataset;

[0025] Based on the unified emergency scenario dataset, use random matrix theory to extract potential risk factors of sudden events from the data features, including abnormal equipment states, drastic environmental changes, or abnormal personnel behaviors, and evaluate the weights and priorities of the risk factors;

[0026] Based on the graph neural network algorithm, conduct multi-factor correlation reasoning, identify the correlations and potential propagation paths between risk factors, and form a dynamic risk propagation graph;

[0027] Combining the risk propagation graph and the results of situation prediction and assessment, automatically generate an emergency situation analysis report. The report content includes the precise location of the risk area, the risk level and its evolution trend, as well as the demand prediction of emergency resources.

[0028] Even further, the formula of the random matrix theory is as follows:

[0029]

[0030] Among them, R represents the evaluation result of the potential risk of the emergency scenario; λ i represents the principal component feature of multi-source data in the emergency scenario; λ avg represents the average feature scale of the overall fluctuation of multi-source data; α, β, and γ represent risk adjustment parameters; w i represents the weight of the corresponding eigenvector; f i represents the risk factor related to feature i; σ j represents the standard deviation of multi-source data; g p represents the importance of risks in the scenario within a specific time period; v p represents the multi-source data vector at the p-th time point; h p represents the external influence factor; k represents the number of risk factors; m represents the number of collected time points.

[0031] Furthermore, the emergency response decision-making module specifically uses the real-time analyzed scenario data and the expert knowledge base for hybrid decision-making to generate an emergency response strategy including an action plan, resource priority allocation, and risk avoidance strategy.

[0032] Furthermore, the resource scheduling and command module adopts a dynamic resource hierarchical scheduling mechanism, divides the emergency resources into core resources, auxiliary resources, and emergency reserve resources, and makes dynamic allocation according to the task urgency and resource status.

[0033] A smart emergency command method based on scenario twin includes the following steps:

[0034] Collect multi-source data in the emergency scenario in real time through the sensor network;

[0035] Based on the multi-source data, construct a scenario twin model through augmented reality technology and digital twin technology, and use three-dimensional visualization technology for real-time rendering of the virtual scenario;

[0036] Based on the scenario twin model, conduct a situation analysis of the emergency scenario through random matrix theory and graph neural network algorithm, detect potential risks, accurately locate the dynamic risk area and predict the development trend, and generate an emergency situation analysis report;

[0037] Based on the emergency situation analysis report, automatically generate an emergency response strategy using the decision tree algorithm;

[0038] Based on the emergency situation analysis report and the emergency response strategy, optimize the scheduling and dynamic allocation of emergency resources using multi-objective optimization algorithm and fuzzy control algorithm, and generate an optimal emergency response command plan.

[0039] The beneficial effects of the present invention are as follows:

[0040] The present invention uses a sensor network through a data acquisition module to obtain multi-source data in an emergency scenario in real time, including environmental data, equipment status data, and personnel information data, ensuring the real-time nature and comprehensiveness of the data and providing a reliable basis for subsequent analysis and decision-making. The scenario twin management module combines augmented reality technology and digital twin technology to construct a virtual scenario twin model and performs real-time rendering through three-dimensional visualization technology, realizing a realistic reproduction of the emergency scenario. This not only helps commanders understand the on-site situation more intuitively but also improves the accuracy of situation awareness. The emergency situation analysis module conducts in-depth analysis of the emergency scenario based on the scenario twin model through random matrix theory and graph neural network algorithms, can effectively detect potential risks, accurately locate dynamic risk areas, and predict development trends, generating a detailed emergency situation analysis report. This precise analysis helps improve the timeliness and effectiveness of emergency response. The emergency response decision-making module uses a decision tree algorithm to automatically generate emergency response strategies based on the situation analysis report, including action plans, resource allocation suggestions, and evacuation route planning, reducing the time for manual decision-making, improving the response speed, and enhancing the quality of decision-making. The resource scheduling and command module combines multi-objective optimization algorithms and fuzzy control algorithms to optimize the scheduling and dynamically allocate emergency resources, generating an optimal emergency response command plan to ensure the efficient use and reasonable allocation of resources during the emergency process, reducing resource waste, and improving the efficiency of emergency handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic diagram of the modules of a smart emergency command system based on scenario twins according to the present invention.

[0042] Figure 2 is a schematic flow chart of the process of constructing a scenario twin model provided by an embodiment of the present invention.

[0043] Figure 3 is a schematic flow chart of a smart emergency command method based on scenario twins according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Please refer to Figures 1-3 as shown, the present invention relates to a smart emergency command system and method based on scenario twins.

[0045] Embodiment 1

[0046] A smart emergency command system based on scenario twins, comprising a data acquisition module, a scenario twin management module, an emergency situation analysis module, an emergency response decision-making module, and a resource scheduling and command module that are sequentially communicatively connected;

[0047] The data acquisition module is used to collect multi-source data in an emergency scenario in real time through a sensor network; the multi-source data includes environmental data, equipment status data, and personnel information data of the emergency scenario;

[0048] It should be noted that the data acquisition module collects multi-source data in real time through a sensor network deployed in and around the event venue. Specifically, it includes the following:

[0049] Environmental sensors: Environmental parameter sensors such as temperature, humidity, wind speed, and gas concentration, which are used to monitor the changes in the venue environment in real time and help identify potential hazards such as fires and harmful gas leaks.

[0050] Device status sensors: Installed on various devices at the event site, such as stage lighting equipment, power equipment, and communication equipment. The operating status data, including parameters such as voltage, current, temperature, and vibration of the equipment, is collected through the device status sensors.

[0051] Personnel information sensors: Use cameras, people flow monitors, and wearable devices to obtain the location information, movement trajectories, and health status (such as heart rate, body temperature, etc.) of on-site personnel. In particular, the real-time locations of security personnel and medical staff are monitored to facilitate rapid dispatching in case of emergencies.

[0052] Adopt multiple communication protocols such as 5G, LoRa, and Zigbee, and select the optimal transmission method based on the characteristics of the scenario. For example, use the LoRa network in areas with long distances and wide coverage, and use the 5G network in occasions that require large bandwidth (such as high-definition video transmission). During the data transmission process, use encryption technologies (such as the SSL / TLS protocol) to ensure the security of the data, and compress the data packets to reduce bandwidth consumption.

[0053] Intelligently analyze the real-time collected data. If abnormal situations (such as a sharp increase in temperature, an excessive concentration of harmful gases, etc.) are detected, the system will automatically trigger an alarm and feedback it to the relevant command personnel and on-site emergency personnel. The alarm forms can be multiple channels such as mobile phone text messages, voice prompts, and e-mails. Implement a multi-level alarm mechanism and adjust the intensity and dissemination range of the alarm according to the risk level. For example, low-level device failure notifications are only sent to device maintenance personnel, while high-level fire risks issue emergency alarms to the entire emergency team.

[0054] The real-time data is stored in the local database for the rapid response and analysis of the system. At the same time, the key historical data is regularly uploaded to the cloud for long-term trend analysis and data mining. The system stores the historical records of all collected data and supports the time-series analysis of the retrospective data. For example, replay the temperature and smoke data change trends in the hours before a fire for accident investigation and liability determination.

[0055] The scene twin management module is used to construct a scene twin model based on the multi-source data through augmented reality technology and digital twin technology, and perform real-time rendering of the virtual scene using three-dimensional visualization technology;

[0056] Among them, the operation process of the scenario twin management module includes the following steps:

[0057] Perform preprocessing operations on the multi-source data collected through the sensor network, including data cleaning, noise reduction, and formatting conversion;

[0058] Based on the preprocessed multi-source data, use augmented reality technology and digital twin technology to construct a scenario twin model of the emergency scenario; the scenario twin model includes a three-dimensional space geometric model and an attribute model. The three-dimensional space geometric model realizes the three-dimensional visualization presentation of the scenario through point cloud data reconstruction and texture mapping, including the basic terrain, the three-dimensional structure of buildings, and dynamic objects. The attribute model contains environmental status, equipment operation parameters, and personnel information.

[0059] On the basis of the scenario twin model, by fusing historical data and real-time data, use data assimilation technology to finely adjust and optimize the key parameters of the model;

[0060] Perform three-dimensional visualization rendering on the finely constructed scenario twin model, and use a high-precision rendering algorithm to present the dynamic changes in the scenario, realizing the intuitive display and interactive operation of the emergency scenario.

[0061] Specifically, use a high-precision rendering algorithm based on GPU (such as ray tracing, voxelization rendering) to render the dynamic changes in the emergency scenario, including lighting effects, transparency changes, and reflection effects. During the rendering process, the system automatically adjusts the rendering precision according to the complexity of the scenario to ensure real-time performance. The rendering process is synchronized with the update of the model. When the data changes (such as personnel movement, equipment status change), the virtual scenario will be updated in real time. Emergency commanders can perform multi-angle observation and interactive operations on the scenario through virtual reality devices (such as VR headsets) or interactive interfaces (such as touchscreens), and perform switching of perspectives such as zooming in, zooming out, rotating, and perspective.

[0062] The scenario twin model supports the display of different data levels. Users can switch to display environmental status (such as temperature distribution, gas concentration), equipment status (such as operating temperature, energy consumption), and personnel information (such as location distribution, health status). The display of different data layers uses color coding or transparency changes, enabling users to quickly identify different information.

[0063] The construction process of the scenario twin model includes the following steps:

[0064] Based on the preprocessed multi-source data, use virtual reality and digital twin technology to reconstruct the three-dimensional structure of the emergency scenario, perform multi-scale modeling on the terrain, buildings, and dynamic objects in the scenario through point cloud data, and use texture mapping technology to enhance the details of the model surface;

[0065] Specifically, three-dimensional point cloud data of the site is obtained using Light Detection and Ranging (LiDAR), unmanned aerial vehicle (UAV) aerial photography, and high-resolution photography techniques. LiDAR can be used to accurately scan complex terrains and building structures, and UAV aerial photography obtains data from an aerial perspective to supplement the blind spots of LiDAR. Combining Building Information Modeling (BIM) data, detailed structural information of the building is obtained, such as wall thickness, floor height, and equipment layout. The collected raw point cloud data is processed, including removing noise points, filling in missing data areas, and point cloud registration (aligning data from multiple scans). To improve computational efficiency, the dense point cloud data is simplified, and at the same time, the point cloud data is segmented to distinguish different object categories, such as ground, buildings, equipment, vegetation, etc.

[0066] Multi-scale modeling is as follows:

[0067] Terrain modeling: Coarse-grained modeling is performed on a large-scale terrain according to the complexity of the terrain. For important local details (such as ground undulations and potholes at emergency locations), more accurate meshes are used for detailed modeling.

[0068] Building modeling: The external structure of the building is reconstructed based on the point cloud data, and the internal structure is supplemented and modeled in combination with BIM data, including floor division, wall and room layout, etc.

[0069] Dynamic object modeling: Includes dynamic objects such as vehicles, equipment, and personnel at the emergency site. The model is dynamically updated according to the real-time collected data. For example, multi-view stereo reconstruction methods are used to track the position and attitude changes of vehicles.

[0070] Image data of the scene is collected through high-resolution photography techniques or UAV aerial photography. The collected data is stitched and color-corrected to ensure the continuity and consistency of the images. The collected image textures are mapped to the grid coordinates of the three-dimensional geometric model so that the image textures can fit on the model surface. The system uses an automatic texture stitching algorithm to ensure smooth transition and seamless connection of the textures. For key areas of the model (such as equipment control panels, signs), high-precision specific texture data can be used for refinement processing to ensure clear visibility of the details.

[0071] Integrate and map the real-time collected multi-source data with the three-dimensional geometric model, and reflect the data changes in the virtual scene through virtual reality technology to achieve real-time tracking and updating of dynamic objects, including changes in equipment status, fluctuations in environmental parameters, and movement trajectories of personnel;

[0072] Based on the integrated data sources, construct an attribute model of the scenario, including environmental status, device parameters, and personnel information. Associate and map the attribute data with the geometric model of the virtual scenario, and use virtual reality interaction technology to achieve the switching display of different data layers;

[0073] Combine historical data and real-time collected data, continuously optimize the key parameters of the scenario twin model through data assimilation technology, and use digital twin technology for parameter correction and model update;

[0074] Perform high-precision 3D rendering on the optimized scenario twin model, and through virtual reality devices or interactive interfaces, support emergency commanders to perform real-time operations and multi-angle observations on the virtual scenario.

[0075] The emergency situation analysis module is used to perform situation analysis on the emergency scenario based on the scenario twin model, detect potential risks, accurately locate dynamic risk areas and predict their development trends through random matrix theory and graph neural network algorithms, and generate an emergency situation analysis report;

[0076] Among them, the operation process of the emergency situation analysis module includes the following steps:

[0077] Perform data fusion processing on the multi-source data integrated in the scenario twin model, and use multi-modal data fusion technology to associate the multi-source data to generate a unified emergency scenario dataset;

[0078] Based on the unified emergency scenario dataset, use random matrix theory to extract risk factors of potential emergencies from data features, including abnormal device states, drastic environmental changes, or abnormal personnel behaviors, and evaluate the weights and priorities of the risk factors;

[0079] Specifically, organize the multi-source data into a large matrix according to different features. The rows of the matrix represent the observation samples of the data (such as data at different time points), and the columns represent the features of the data (such as temperature, device status, etc.). Use random matrix theory (RMT) to analyze the spectral characteristics (distribution of eigenvalues) of the data matrix and extract potential abnormal patterns. The distribution of eigenvalues deviating from the theoretical spectral density can be used to identify abnormal data points or abnormal patterns. According to the results of RMT analysis, abnormal device states, drastic environmental changes (such as sharp changes in temperature or gas concentration), and abnormal personnel behaviors (such as sudden acceleration or irregular movement) are regarded as potential risk factors. Quantitatively evaluate the identified risk factors and calculate their impact weights in the entire emergency scenario. The higher the weight of a risk factor, the higher its priority, indicating that a faster response is required.

[0080] Impact assessment of risk factors:

[0081] Evaluate the impact of risk factors according to the degree of deviation of the eigenvalue. The greater the deviation, the higher the abnormality degree of the risk factor and the greater the impact.

[0082] Use the weighted coefficient method to convert the degree of eigenvalue deviation of risk factors into impact weights. The weights can be further corrected by combining domain knowledge and expert evaluation.

[0083] Risk priority ranking:

[0084] Rank the risk factors according to their impact weights to determine the priorities. The risk factors with higher priorities need to take emergency measures first.

[0085] Based on the graph neural network algorithm, conduct multi-factor correlation reasoning to identify the correlations and potential propagation paths among risk factors, and form a dynamic risk propagation graph;

[0086] It should be noted that the graph neural network (GNN) is used for multi-factor correlation reasoning to construct a dynamic risk propagation graph.

[0087] Nodes: The risk factors in the emergency scenario (such as equipment failures, environmental changes, abnormal human behaviors) are used as the nodes of the graph. The attributes of each node include the type, weight, location, and time of the risk factor.

[0088] Edges: The relationships between nodes (such as time correlation, spatial proximity, logical causality) are used as the edges of the graph. The weights of the edges can be determined according to the correlation strength between nodes. The stronger the correlation between nodes, the greater the weight of the edge.

[0089] The specific steps are as follows: According to the constructed risk graph, initialize the feature vectors of the nodes (representing the attributes of risk factors) and the weights of the edges (representing the correlations between risk factors). Conduct graph propagation calculations through GNN to identify the potential relationships between risk factors and the risk propagation paths. For example, GNN can infer that a failure of a certain device may cause the temperature in the adjacent area to rise, and further affect the behavior of personnel. Combine the reasoning results of GNN to generate a dynamic risk propagation graph, showing the possible propagation paths, transmission directions, and diffusion ranges of risks.

[0090] Based on the results of the risk propagation map, determine the concentrated areas of risk factors and the locations of high-risk points. These locations can be accurately mapped into the scenario twin model through Geographic Information System (GIS) for three-dimensional visualization. Combining the weights of risk factors and the structure of the propagation map, evaluate the risk levels of risk areas. Use the Analytic Hierarchy Process (AHP) or the Fuzzy Comprehensive Evaluation Method to comprehensively evaluate multiple risk factors and classify the risks into three levels: high, medium, and low. Based on historical data and the inference results of GNN, predict the evolution trends of risk areas, including the speed, direction, and possible impact range of risk diffusion. For example, the diffusion range and time of gas leakage or the spreading speed of a fire can be predicted.

[0091] Combining the results of the risk propagation map and the situation prediction and assessment, automatically generate an emergency situation analysis report. The report content includes the exact locations of risk areas, risk levels and their evolution trends, as well as the predicted demand for emergency resources.

[0092] Furthermore, the formula of the random matrix theory is as follows:

[0093]

[0094] Among them, R represents the evaluation result of the potential risk of the emergency scenario; λ i represents the principal component features of multi-source data in the emergency scenario; λ avg represents the average feature scale of the overall fluctuation of multi-source data; α, β, and γ represent risk adjustment parameters used to control the influence degree of eigenvalues on the risk value in different emergency situations; w i represents the weight of the corresponding eigenvector; f i represents the risk factor related to feature i; σ j represents the standard deviation of multi-source data; g p represents the importance of risks in the scenario within a specific time period; v p represents the multi-source data vector at the p-th time point; h p represents the external influence factor; k represents the number of risk factors; m represents the number of time points collected.

[0095] The emergency response decision-making module is used to automatically generate an emergency response strategy based on the emergency situation analysis report using the decision tree algorithm; the emergency response strategy includes an action plan, resource allocation suggestions, and evacuation route planning; the emergency response decision-making module specifically uses real-time analyzed scenario data and an expert knowledge base for hybrid decision-making to generate an emergency response strategy including an action plan, resource priority allocation, and risk avoidance strategy.

[0096] It should be noted that the system parses the emergency situation analysis report in detail, extracts key information, including the current risk location, risk level, affected range of personnel and equipment, available status of resources, etc. Using the decision tree algorithm, combined with historical emergency data and the characteristics of event types, an emergency response strategy for a specific scenario is automatically generated. The nodes of the decision tree can represent different emergency response measures (such as evacuation, fire extinguishing, resource allocation), and the leaf nodes are specific response plans.

[0097] The main contents of the emergency response strategy are as follows:

[0098] Action plan: Specific action plans are formulated according to different types of emergency events (such as fires, explosions, leaks), including personnel evacuation, fire source control, hazardous material isolation, etc.

[0099] Resource allocation suggestions: Based on the emergency situation and resource status, allocation suggestions are put forward for various emergency resources (such as rescue personnel, rescue equipment, medical supplies, etc.), and their priorities and allocation quantities are determined.

[0100] Evacuation route planning: Combining the scene layout and personnel positions in the scene twin model, the shortest path algorithm (such as Dijkstra algorithm or A* algorithm) is used to plan the optimal evacuation route and update it in real time to prevent congestion or unexpected situations from affecting the evacuation effect.

[0101] By summarizing the experiences of past emergency events, collating expert opinions, and aggregating industry norms, an expert knowledge base is constructed. The knowledge base includes the processing procedures of typical emergency events, risk avoidance strategies, resource allocation plans, and other contents. Based on the automatically generated emergency response strategy, the system compares it with the expert knowledge base to generate multiple optional plans, and marks the advantages and disadvantages of each plan for the reference of emergency commanders. The commanders can adjust and optimize the plans according to the specific on-site situation and personal experience. After the initial decision is generated, the system continuously collects real-time data on the site (such as changes in risk levels, resource consumption), and re-evaluates the effectiveness of the decision through algorithms to dynamically update the response strategy. Commanders are allowed to make manual adjustments based on the emergency response strategy generated by the system, especially in the case of sudden changes (such as the emergence of a new fire source) or incomplete information, to ensure the flexibility of the strategy.

[0102] Furthermore, the formula of the decision tree algorithm is as follows:

[0103]

[0104] Among them, G(S, A) represents the information gain after the dataset S is segmented on the feature A. In the generation of emergency response strategies, this represents the degree of reduction in the uncertainty of the strategy after selecting a certain emergency scenario feature (such as fire intensity). The greater the information gain, the more significant the impact of this feature on strategy generation; S represents the dataset of the current emergency scenario, containing cases of different emergency scenarios (for example, various emergency events in historical records). These data may include different types of emergency situations such as fires, explosions, and leaks; A represents the feature for segmenting the dataset. In the emergency response scenario, this may be features such as fire intensity (such as low, medium, high), personnel density (such as sparse, dense), and the number of available resources (such as sufficient, scarce), etc.; E(S) represents the entropy of the dataset S, indicating the degree of chaos or uncertainty of all emergency scenarios; Values(A) represents all possible values of the feature A; S v represents the subset where the value of the feature A is v; E(S v ) represents the entropy of the subset S v , indicating the degree of chaos when the value of the feature A is v.

[0105] The resource scheduling and command module is used to optimize the scheduling and dynamic allocation of emergency resources based on the emergency situation analysis report and emergency response strategy, using multi-objective optimization algorithms and fuzzy control algorithms, and generate an optimal emergency response command plan. The resource scheduling and command module adopts a dynamic resource hierarchical scheduling mechanism, divides emergency resources into core resources, auxiliary resources, and emergency reserve resources, and makes dynamic allocations according to the urgency of the task and the status of the resources.

[0106] It should be noted that the resource scheduling and command module adopts a dynamic resource hierarchical scheduling mechanism, divides resources into three categories according to the importance and usage of the resources, and adopts different scheduling strategies:

[0107] Core resources: Include key emergency equipment (such as fire trucks, rescue helicopters), front-line emergency personnel (such as firefighters, emergency doctors), etc., and are mainly used to deal with the most urgent situations. The scheduling of these resources follows the principle of "dispatch first, replenish later", and quickly returns to the standby state after the task is completed. When a major accident or high-risk area is detected, core resources will be preferentially dispatched immediately, and cross-regional scheduling of resources will be minimized to reduce the response time. The system will monitor the status of core resources in real time, and automatically generate a replenishment plan when the resources are dispatched and used to ensure the continuous availability of emergency resources.

[0108] Auxiliary resources: Include material transportation vehicles, logistics personnel, temporary construction equipment, etc., which are used to support the core resources in performing tasks. The scheduling of auxiliary resources is dynamically adjusted according to the needs of core resources and is usually the main resource for subsequent support or task expansion. The invocation of auxiliary resources depends on the task situation of core resources. For example, according to the task scale and type, the system automatically allocates an appropriate amount of auxiliary resources to support the main task. When the task load of core resources exceeds a certain threshold, the scheduling priority of auxiliary resources will be correspondingly increased.

[0109] Emergency reserve resources: These resources are usually in a standby state under non-emergency circumstances and are mainly used to supplement the deficiencies of core and auxiliary resources or replace depleted resources. When core and auxiliary resources are depleted or seriously insufficient, emergency reserve resources will be scheduled as backup support and can be supplemented by means of temporary procurement or mobilization of resources in adjacent regions.

[0110] Specifically, the scheduling optimization process of the resource scheduling and command module can be divided into the following steps:

[0111] The system monitors the status of various resources in real time, including information such as quantity, location, usage, remaining available time, etc., and updates the data.

[0112] According to the dynamic risk areas and development trends in the emergency situation analysis report, evaluate the urgency of task requirements and resource consumption, and formulate resource scheduling requirements.

[0113] Use a multi-objective optimization algorithm to calculate the optimal scheduling plan, taking into account factors such as the timeliness, cost, and scheduling distance of resource scheduling.

[0114] According to the urgency of the task and the resource status, adjust the scheduling priority of each task through a fuzzy control algorithm to ensure that the resource scheduling of high-priority tasks is preferentially guaranteed.

[0115] The system generates a specific resource scheduling plan and issues instructions according to the optimized and fuzzy processed results, and conducts automatic scheduling or manual-assisted scheduling of resources through an intelligent scheduling platform.

[0116] During the execution process, monitor the progress of the task and the status of resources in real time, and dynamically adjust the scheduling plan according to the new emergency situation analysis and task changes.

[0117] It should be noted that the resource scheduling and command module generates a detailed command plan according to the optimized scheduling strategy, which specifically includes the following content:

[0118] Resource scheduling plan: Clearly define the types, quantities, locations of the resources to be scheduled and the scheduling paths. The system generates a visual scheduling diagram to display the scheduling routes of the resources and the estimated arrival times.

[0119] Task Assignment Rules: Assign specific tasks to each resource, such as personnel rescue, fire source control, material transportation, etc., and give the priority and time schedule for task execution.

[0120] Execution Strategies of the Emergency Command Plan: Include real-time command strategies for on-site command and monitoring strategies for resource status, such as real-time tracking of the driving path of vehicles, monitoring the status of rescue personnel, etc.

[0121] Embodiment 2

[0122] A smart emergency command method based on scenario twins, comprising the following steps:

[0123] Collect multi-source data in the emergency scenario in real time through the sensor network;

[0124] Based on the multi-source data, construct a scenario twin model through augmented reality technology and digital twin technology, and use three-dimensional visualization technology for real-time rendering of the virtual scenario;

[0125] Based on the scenario twin model, conduct a situation analysis of the emergency scenario through random matrix theory and graph neural network algorithms, detect potential risks, accurately locate dynamic risk areas and predict their development trends, and generate an emergency situation analysis report;

[0126] Based on the emergency situation analysis report, automatically generate an emergency response strategy using the decision tree algorithm;

[0127] Based on the emergency situation analysis report and the emergency response strategy, use multi-objective optimization algorithms and fuzzy control algorithms to optimize the scheduling and dynamic allocation of emergency resources, and generate an optimal emergency response command plan.

[0128] In this embodiment, a smart emergency command method based on scenario twins is applied to a smart emergency command system described in Embodiment 1, which will not be elaborated here.

[0129] To sum up, the present invention collects multi-source data (environment, equipment, personnel) in the emergency scenario in real time through the sensor network, realizing comprehensive and accurate dynamic monitoring. The environmental sensors, equipment status sensors, and personnel information sensors work together, enabling quick discovery of potential risks (such as a sharp rise in temperature, leakage of harmful gases), triggering alarms in a timely manner, and improving the agility of emergency response. Using augmented reality and digital twin technologies to construct a three-dimensional scenario twin model, and through high-precision rendering algorithms, the changes in the emergency scenario (such as light and shadow, reflection) are displayed in real time, realizing multi-angle observation and interaction of the emergency scenario. Emergency command personnel can perform detailed operations on the scenario through virtual reality devices or interactive interfaces, enhancing the visualization effect of decision-making.

[0130] Based on random matrix theory and graph neural network algorithms, the system can conduct situation analysis for emergency scenarios, extract risk factors, identify risk propagation paths, and predict the evolution trend of risk areas. The generated situation analysis report provides precise positioning of risk areas and risk level assessment, providing a scientific basis for emergency response. Based on the situation analysis results, an emergency response strategy is automatically generated in combination with decision tree algorithms, covering action plans, resource allocation suggestions, and evacuation route planning. At the same time, the system uses an expert knowledge base to correct and optimize the strategy to ensure the feasibility of the strategy and the effectiveness of emergency response. Multi-objective optimization algorithms and fuzzy control algorithms are adopted to achieve optimal scheduling and dynamic allocation of resources. The system hierarchically schedules core resources, auxiliary resources, and emergency reserve resources according to the urgency of tasks and resource status, effectively reducing the response time and ensuring the continuous availability of emergency resources.

[0131] The above embodiments are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A smart emergency command system based on scenario twins, characterized in that, It includes a data acquisition module, a scenario twin management module, an emergency situation analysis module, an emergency response decision-making module, and a resource scheduling and command module that are sequentially communicatively connected; The data acquisition module is used to collect multi-source data in the emergency scenario in real time through a sensor network; the multi-source data includes environmental data, equipment status data, and personnel information data of the emergency scenario; The scenario twin management module is used to construct a scenario twin model based on the multi-source data through augmented reality technology and digital twin technology, and perform real-time rendering of the virtual scenario using three-dimensional visualization technology; The emergency situation analysis module is used to perform situation analysis on the emergency scenario based on the scenario twin model, detect potential risks, accurately locate the dynamic risk area and predict its development trend through random matrix theory and graph neural network algorithm, and generate an emergency situation analysis report; The emergency response decision-making module is used to automatically generate an emergency response strategy based on the emergency situation analysis report using a decision tree algorithm; The emergency response strategy includes an action plan, resource allocation suggestions, and evacuation route planning; The resource scheduling and command module is used to optimize the scheduling and dynamic allocation of emergency resources based on the emergency situation analysis report and the emergency response strategy using a multi-objective optimization algorithm and a fuzzy control algorithm, and generate an optimal emergency response command plan; The operation process of the emergency situation analysis module includes the following steps: Perform data fusion processing on the multi-source data integrated in the scenario twin model, and use multi-modal data fusion technology to correlate the multi-source data to generate a unified emergency scenario dataset; Based on the unified emergency scenario dataset, use random matrix theory to extract risk factors of potential emergencies from the data features, including abnormal equipment status, drastic environmental changes, or abnormal personnel behavior, and evaluate the weights and priorities of the risk factors; Based on the graph neural network algorithm, perform multi-factor correlation reasoning, identify the correlations and potential propagation paths between risk factors, and form a dynamic risk propagation graph; Combined with the risk propagation graph and the evaluation results of the weights and priorities of the risk factors, automatically generate an emergency situation analysis report, and the report content includes the accurate location of the risk area, the risk level and its evolution trend, as well as the demand prediction of emergency resources; The formula of the random matrix theory is as follows: ； Among them, R represents the evaluation result of the potential risk of the emergency scenario; represents the principal component features of multi-source data in the emergency scenario; represents the average characteristic scale of the overall fluctuation of multi-source data; , and represent the risk adjustment parameters; represents the weight of the corresponding eigenvector; represents the risk factor associated with feature i; represents the standard deviation of multi-source data; represents the importance of risk in the scenario within a specific time period; represents the multi-source data vector at the p-th time point; represents the external influence factor; k represents the number of risk factors; m represents the number of collected time points.

2. The intelligent emergency command system based on scenario twin according to claim 1, characterized in that The operation process of the scenario twin management module includes the following steps: Perform preprocessing operations on the multi-source data collected through the sensor network, including data cleaning, noise reduction, and format conversion; Based on the preprocessed multi-source data, construct a scenario twin model of the emergency scenario using augmented reality technology and digital twin technology; On the basis of the scenario twin model, through fusing historical data and real-time data, use data assimilation technology to finely adjust and optimize the key parameters of the model; Perform three-dimensional visualization rendering on the finely constructed scenario twin model, and use a high-precision rendering algorithm to present the dynamic changes in the scenario, realizing the intuitive display and interactive operation of the emergency scenario.

3. The intelligent emergency command system based on scenario twins according to claim 2, wherein, The described scenario twin model includes a three-dimensional space geometric model and an attribute model. The three-dimensional space geometric model realizes the three-dimensional visualization presentation of the scenario through point cloud data reconstruction and texture mapping, including the basic terrain, the three-dimensional structure of buildings, and dynamic objects. The attribute model contains environmental status, equipment operation parameters, and personnel information.

4. The intelligent emergency command system based on scenario twin according to claim 3, wherein, The construction process of the described scenario twin model includes the following steps: Based on the preprocessed multi-source data, virtual reality and digital twin technologies are used to reconstruct the three-dimensional structure of the emergency scenario. Multi-scale modeling of the terrain, buildings, and dynamic objects in the scenario is carried out through point cloud data, and texture mapping technology is used to enhance the details of the model surface. Integrate and map the real-time collected multi-source data with the three-dimensional geometric model. Through virtual reality technology, reflect the data changes in the virtual scenario to achieve real-time tracking and update of dynamic objects, including the state changes of equipment, the fluctuations of environmental parameters, and the movement trajectories of personnel positions. Based on the integrated data sources, construct an attribute model of the scenario, including environmental status, equipment parameters, and personnel information. Map the attribute data to the geometric model of the virtual scenario, and use virtual reality interaction technology to achieve the switching display of different data layers. Combining historical data and real-time collected data, continuously optimize the key parameters of the scenario twin model through data assimilation technology, and use digital twin technology for parameter correction and model update. Perform high-precision three-dimensional rendering on the optimized scenario twin model. Through virtual reality devices or interactive interfaces, support real-time operation and multi-angle observation of the virtual scenario by emergency command personnel.

5. The intelligent emergency command system based on scenario twin according to claim 1, wherein, The described emergency response decision-making module specifically uses the real-time analyzed scenario data and the expert knowledge base for hybrid decision-making to generate an emergency response strategy including an action plan, resource priority allocation, and risk avoidance strategy.

6. The intelligent emergency command system based on scenario twins according to claim 1, characterized in that, The resource scheduling and command module adopts a dynamic resource hierarchical scheduling mechanism, divides emergency resources into core resources, auxiliary resources, and emergency reserve resources, and makes dynamic allocation according to the urgency of the task and the status of resources.

7. A smart emergency command method based on scenario twins, characterized in that, The system is applied to a smart emergency command system based on scenario twins as described in any one of claims 1-6, including the following steps: Real-time collect multi-source data in the emergency scenario through the sensor network; Based on the multi-source data, construct a scenario twin model through augmented reality technology and digital twin technology, and use three-dimensional visualization technology for real-time rendering of the virtual scenario. Based on the scenario twin model, conduct a situation analysis of the emergency scenario through random matrix theory and graph neural network algorithms, detect potential risks, accurately locate the dynamic risk area and predict its development trend, and generate an emergency situation analysis report. Based on the emergency situation analysis report, automatically generate an emergency response strategy using the decision tree algorithm. Based on the emergency situation analysis report and the emergency response strategy, use multi-objective optimization algorithms and fuzzy control algorithms to optimize the scheduling and dynamic allocation of emergency resources, and generate an optimal emergency response command plan.

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