Intelligent emergency command system and method based on scene twinning

Abstract

The invention relates to the technical field of communication command, in particular to an intelligent emergency command system and method based on scene twinning. Comprising a data acquisition module, a scene twinborn management module, an emergency situation analysis module, an emergency response decision module and a resource scheduling command module. Multi-source data of an emergency scene are collected through a sensor network, a scene twinning model is constructed by using augmented reality and digital twinning technologies, and real-time rendering of a virtual scene is realized. According to the method, emergency situation analysis, risk detection and trend prediction are performed in combination with a random matrix theory and a graph neural network algorithm, an emergency response strategy is generated by using a decision tree based on an analysis report, resources are scheduled and allocated through a multi-objective optimization algorithm, and an optimal emergency command scheme is ensured. According to the invention, the efficiency and scientificity of emergency command are improved, and the rapidity and accuracy of emergency response are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of communication command technology, and in particular to an intelligent emergency command system and method based on scenario twins. 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, which puts forward 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, and have problems such as slow response speed, insufficient decision-making basis, and low data integration, which makes it difficult to meet the needs of complex and changing emergency scenarios. At present, there are still the following problems: when an emergency occurs, the existing emergency command system usually finds it difficult to quickly and accurately obtain multi-source data in the scene, resulting in insufficient grasp of the real-time status of the event by the commander, and prone to information lag or incomplete information; the existing virtualization technology of emergency scenes is mostly limited to simple two-dimensional images or static three-dimensional models, which makes it difficult to achieve realistic scene reproduction and dynamic update, making it difficult for commanders to intuitively understand the complex situation on the scene; the situation analysis in traditional emergency systems is usually based on fixed rules or simple algorithms, which makes it difficult to accurately analyze and locate dynamic risks in complex scenes. In addition, the ability to predict potential risks is weak, making it difficult to provide development trend predictions and effective risk warnings in a timely manner. Summary of the invention

[0003] To solve the above problems, the present invention provides a smart emergency command system and method based on scene twins, which solves the problem of how to use scene twin technology to quickly and accurately acquire multi-source data in emergency events, realize dynamic virtualization and high-precision three-dimensional rendering of complex scenes, and perform accurate situation analysis and risk prediction through intelligent algorithms to solve the problems of slow response speed, incomplete information and insufficient decision-making basis of 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 scenario twins, comprising a data acquisition module, a scenario twin management module, an emergency situation analysis module, an emergency response decision module and a resource scheduling command module which are sequentially connected in communication;

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

[0007] 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 use three-dimensional visualization technology to perform real-time rendering of virtual scenes;

[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 dynamic risk areas and predict development trends, and generate an emergency situation analysis report;

[0009] The emergency response decision 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 deployment 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 emergency response strategy, and generate the optimal emergency response command plan by using a multi-objective optimization algorithm and a fuzzy control algorithm.

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

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

[0013] Based on pre-processed multi-source data, augmented reality technology and digital twin technology are used to build a scene twin model of the emergency scene;

[0014] Based on the scenario twin model, by integrating historical data and real-time data, data assimilation technology is used to fine-tune and optimize the key parameters of the model;

[0015] The carefully constructed scene twin model is rendered in three-dimensional visualization, and a high-precision rendering algorithm is used to present the dynamic changes in the scene, enabling intuitive display and interactive operation of emergency scenes.

[0016] Furthermore, the scene twin model includes a three-dimensional space geometry model and an attribute model. The three-dimensional space geometry model realizes three-dimensional visualization of the scene through point cloud data reconstruction and texture mapping, including basic terrain, three-dimensional structure of buildings and dynamic objects. The attribute model contains environmental status, equipment operating parameters and personnel information.

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

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

[0019] Integrate and map multi-source data collected in real time with 3D geometric models, and use virtual reality technology to reflect data changes in virtual scenes, so as to achieve real-time tracking and updating of dynamic objects, including equipment status changes, environmental parameter fluctuations, and personnel position movement trajectories;

[0020] Based on the integrated data source, the attribute model of the scene is constructed, including the environment status, equipment parameters and personnel information. The attribute data is associated and mapped with the geometric model of the virtual scene, and the virtual reality interaction technology is used to realize the switching display of different data layers.

[0021] Combining historical data with real-time collected data, the key parameters of the scene twin model are continuously optimized through data assimilation technology, and digital twin technology is used to perform parameter correction and model update;

[0022] The optimized scene twin model is rendered in high-precision three-dimensional form, and through virtual reality equipment or interactive interfaces, it supports emergency command personnel in real-time operation and multi-angle observation of the virtual scene.

[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 multimodal data fusion technology to associate the multi-source data, and generate a unified emergency scenario data set;

[0025] Based on a unified emergency scenario data set, random matrix theory is used to extract risk factors for potential emergencies from data features, including abnormal equipment status, drastic environmental changes, or abnormal personnel behavior, and the weights and priorities of risk factors are evaluated;

[0026] Based on the graph neural network algorithm, multi-factor correlation reasoning is performed to identify the correlation and potential transmission paths between risk factors and form a dynamic risk transmission map;

[0027] Combining the risk propagation map with the results of situation prediction and assessment, an emergency situation analysis report is automatically generated, the report content includes the precise location of the risk area, the risk level and its evolution trend, and the demand forecast of emergency resources.

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

[0029]

[0030] Among them, R represents the assessment result of the potential risk of the emergency scenario; λ i Represents the principal component characteristics of multi-source data in emergency scenarios; avg represents the average characteristic scale of the overall volatility of multi-source data; α, β and γ represent risk adjustment parameters; w i represents the weight of the corresponding feature vector; f i represents the risk factor associated with feature i; σ j Indicates the standard deviation of multi-source data; g p Indicates the importance of the risk in the scenario within a specific time period; p represents the multi-source data vector at the pth time point; h p represents external influencing factors; k represents the number of risk factors; m represents the number of time points collected.

[0031] Furthermore, the emergency response decision module specifically utilizes the real-time analyzed scenario data and the expert knowledge base to make hybrid decisions and 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 to divide emergency resources into core resources, auxiliary resources and emergency reserve resources, and dynamically allocates them according to the urgency of the task and the status of the resources.

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

[0034] Collect multi-source data in emergency scenarios in real time through sensor networks;

[0035] Based on the multi-source data, a scene twin model is constructed through augmented reality technology and digital twin technology, and three-dimensional visualization technology is used for real-time rendering of virtual scenes;

[0036] Based on the scenario twin model, the situation analysis of emergency scenarios is carried out through random matrix theory and graph neural network algorithm to detect potential risks, accurately locate dynamic risk areas and predict development trends, and generate emergency situation analysis reports;

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

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

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

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

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

[0042] Figure 2 It is a flowchart of the scene twin model construction process provided by one embodiment of the present invention.

[0043] Figure 3 It is a flow chart of an intelligent emergency command method based on scenario twins of the present invention. DETAILED DESCRIPTION

[0044] See also Figure 1-3 As shown, the present invention relates to an intelligent emergency command system and method based on scene twins.

[0045] Example 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 module and a resource scheduling command module which are sequentially connected in communication;

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

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

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

[0050] Equipment status sensors: installed on various equipment at the event site, such as stage lighting equipment, power equipment, communication equipment, etc., to collect operating status data, including equipment voltage, current, temperature, vibration and other parameters.

[0051] Personnel information sensors: Use cameras, crowd monitors and wearable devices to obtain the location information, movement trajectory and health status (such as heart rate, body temperature, etc.) of on-site personnel, especially to monitor the real-time location of security personnel and medical staff so as to quickly dispatch them in emergency situations.

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

[0053] The data collected in real time is intelligently analyzed. If an abnormal situation is detected (such as a sharp rise in temperature, excessive concentration of harmful gases, etc.), the system will automatically trigger an alarm and feedback to the relevant commanders and on-site emergency personnel. The alarm can be in the form of mobile phone text messages, voice prompts, emails and other channels. A multi-level alarm mechanism is implemented to adjust the intensity and communication range of the alarm according to the risk level. For example, low-level equipment failure notifications are only sent to equipment maintenance personnel, while high-level fire risks issue emergency alerts to the entire emergency team.

[0054] Real-time data is stored in the local database for rapid response and analysis of the system. At the same time, key historical data is uploaded to the cloud regularly for long-term trend analysis and data mining. The system stores the history of all collected data and supports time series analysis of retrospective data. For example, the trend of temperature and smoke data changes a few hours before the fire can be replayed to investigate the cause of the accident and determine responsibility.

[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 use three-dimensional visualization technology to perform real-time rendering of virtual scenes;

[0056] The operation process of the scene twin management module includes the following steps:

[0057] Preprocess the multi-source data collected through the sensor network, including data cleaning, noise reduction and format conversion;

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

[0059] Based on the scenario twin model, by integrating historical data and real-time data, data assimilation technology is used to fine-tune and optimize the key parameters of the model;

[0060] The carefully constructed scene twin model is rendered in three-dimensional visualization, and a high-precision rendering algorithm is used to present the dynamic changes in the scene, enabling intuitive display and interactive operation of emergency scenes.

[0061] Specifically, a high-precision rendering algorithm based on GPU (such as ray tracing and voxel rendering) is used to render dynamic changes in emergency scenes, including light and shadow effects, transparency changes, and reflection effects. During the rendering process, the system automatically adjusts the rendering accuracy according to the complexity of the scene to ensure real-time performance. The rendering process is synchronized with the update of the model. When the data changes (such as personnel movement and equipment status changes), the virtual scene will be updated in real time. Emergency commanders can observe and interact with the scene from multiple angles through virtual reality devices (such as VR headsets) or interactive interfaces (such as touch screens), and switch perspectives such as zooming in, zooming out, rotating, and perspective.

[0062] The scene 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). Different data layers are displayed using color coding or transparency changes, allowing users to quickly identify different information.

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

[0064] Based on the pre-processed multi-source data, virtual reality and digital twin technology are used to reconstruct the three-dimensional structure of the emergency scene, multi-scale modeling of the terrain, buildings, and dynamic objects in the scene is carried out through point cloud data, and texture mapping technology is used to enhance the details of the model surface;

[0065] Specifically, laser radar (LiDAR), drone aerial photography and high-resolution photography are used to obtain three-dimensional point cloud data on site. LiDAR can be used to accurately scan complex terrain and building structures, and drone aerial photography obtains data from a high-altitude perspective to supplement the blind spots of LiDAR. Combined with building information model (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 data missing areas, and point cloud registration (aligning data from multiple scans). In order to improve computing efficiency, the dense point cloud data is simplified and segmented to distinguish different object categories, such as ground, buildings, equipment, vegetation, etc.

[0066] The details of multi-scale modeling are as follows:

[0067] Terrain modeling: Coarse-grained modeling is performed on large-scale terrain according to the complexity of the terrain. For important local details (such as ground undulations and potholes at emergency sites), more precise grids 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 by modeling in combination with BIM data, including floor division, wall and room layout, etc.

[0069] Dynamic object modeling: including dynamic objects such as vehicles, equipment and personnel at the emergency scene. The model is dynamically updated based on real-time collected data, such as using multi-view stereo reconstruction methods to track the position and posture changes of vehicles.

[0070] The image data of the scene is collected through high-resolution photography technology or drone aerial photography. The collected data is stitched and color corrected to ensure the continuity and consistency of the image. The collected image texture is mapped to the grid coordinates of the 3D geometric model so that the image texture can fit on the model surface. The system uses an automatic texture stitching algorithm to ensure smooth transition and seamless connection of the texture. For key areas of the model (such as equipment control panels and signboards), high-precision specific texture data can be used for refinement to ensure that the details are clearly visible.

[0071] Integrate and map multi-source data collected in real time with 3D geometric models, and use virtual reality technology to reflect data changes in virtual scenes, so as to achieve real-time tracking and updating of dynamic objects, including equipment status changes, environmental parameter fluctuations, and personnel position movement trajectories;

[0072] Based on the integrated data source, the attribute model of the scene is constructed, including the environment status, equipment parameters and personnel information. The attribute data is associated and mapped with the geometric model of the virtual scene, and the virtual reality interaction technology is used to realize the switching display of different data layers.

[0073] Combining historical data with real-time collected data, the key parameters of the scene twin model are continuously optimized through data assimilation technology, and digital twin technology is used to perform parameter correction and model update;

[0074] The optimized scene twin model is rendered in high-precision three-dimensional form, and through virtual reality equipment or interactive interfaces, it supports emergency command personnel in real-time operation and multi-angle observation of the virtual scene.

[0075] 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 dynamic risk areas and predict development trends, and generate an emergency situation analysis report;

[0076] 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, use multimodal data fusion technology to associate the multi-source data, and generate a unified emergency scenario data set;

[0078] Based on a unified emergency scenario data set, random matrix theory is used to extract risk factors for potential emergencies from data features, including abnormal equipment status, drastic environmental changes, or abnormal personnel behavior, and the weights and priorities of risk factors are evaluated;

[0079] Specifically, multi-source data are organized into a large matrix according to different features. The rows of the matrix represent the observed samples of the data (such as data at different time points), and the columns represent the features of the data (such as temperature, equipment status, etc.). The spectral characteristics (distribution of eigenvalues) of the data matrix are analyzed using random matrix theory (RMT) to extract potential abnormal patterns. The distribution of eigenvalues ​​that deviate from the theoretical spectral density can be used to identify abnormal data points or abnormal patterns. According to the results of the RMT analysis, abnormal equipment status, drastic environmental changes (such as drastic changes in temperature or gas concentration), and abnormal personnel behavior (such as sudden acceleration or irregular movement) are regarded as potential risk factors. The identified risk factors are quantitatively evaluated and their impact weights in the entire emergency scenario are calculated. Risk factors with higher weights have higher priorities, indicating that a faster response is needed.

[0080] Impact assessment of risk factors:

[0081] The impact of the risk factor is evaluated based on the degree of deviation of the characteristic value. The greater the deviation, the higher the abnormality of the risk factor and the greater the impact.

[0082] The weighted coefficient method is used to convert the deviation of the characteristic value of the risk factor into an impact weight. The weight can be further corrected by combining domain knowledge and expert evaluation.

[0083] Risk Prioritization:

[0084] Sort the risk factors by their impact weights and determine their priorities. Risk factors with higher priorities require emergency measures to be taken first.

[0085] Based on the graph neural network algorithm, multi-factor correlation reasoning is performed to identify the correlation and potential transmission paths between risk factors and form a dynamic risk transmission map;

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

[0087] Node: The risk factors in emergency scenarios (such as equipment failure, environmental changes, and abnormal personnel behavior) are used as nodes of the graph. The attributes of each node include the type, weight, location, and time of the risk factor.

[0088] Edge: The relationship between nodes (such as temporal association, spatial proximity, logical causality) is the edge of the graph. The weight of the edge can be determined according to the strength of the association between the nodes. The stronger the association, the greater the weight of the edge.

[0089] The specific steps are as follows: Based on the constructed risk graph, initialize the node feature vector (indicating the attributes of the risk factor) and the edge weight (indicating the correlation between risk factors). Use GNN to perform graph propagation calculations to identify the potential relationships between risk factors and the risk propagation paths. For example, GNN can infer that the failure of a certain device may cause the temperature in the adjacent area to rise and further affect the behavior of personnel. Combined with the reasoning results of GNN, a dynamic risk propagation graph is generated to show the possible propagation path, transmission direction and diffusion range of the risk.

[0090] According to the results of the risk propagation map, the locations of risk factor concentration areas and high-risk points are determined. These locations can be accurately mapped to the scene twin model through the Geographic Information System (GIS) for three-dimensional visualization. Combined with the weight of the risk factor and the structure of the propagation map, the risk level of the risk area is evaluated. Use the analytic hierarchy process (AHP) or fuzzy comprehensive evaluation method to comprehensively evaluate multiple risk factors and divide the risks into three levels: high, medium, and low. Based on historical data and the reasoning results of GNN, the evolution trend of the risk area is predicted, including the speed, direction, and possible scope of risk diffusion. For example, the diffusion range and time of a toxic gas leak, or the speed of fire spread, can be predicted.

[0091] Combining the risk propagation map with the results of situation prediction and assessment, an emergency situation analysis report is automatically generated, the report content includes the precise location of the risk area, the risk level and its evolution trend, and the demand forecast of emergency resources.

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

[0093]

[0094] Among them, R represents the assessment result of the potential risk of the emergency scenario; λ i Represents the principal component characteristics of multi-source data in emergency scenarios; avg represents the average characteristic scale of the overall fluctuation of multi-source data; α, β and γ represent risk adjustment parameters, which are used to control the influence of characteristic values ​​on risk values ​​under different emergency situations; w i represents the weight of the corresponding feature vector; f i represents the risk factor associated with feature i; σ j Indicates the standard deviation of multi-source data; g p Indicates the importance of the risk in the scenario within a specific time period; p represents the multi-source data vector at the pth time point; h p represents external influencing factors; k represents the number of risk factors; m represents the number of time points collected.

[0095] The emergency response decision 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 emergency response decision module specifically uses real-time analysis scenario data and an expert knowledge base to make hybrid decisions to generate an emergency response strategy including an action plan, resource priority allocation, and a risk avoidance strategy.

[0096] It should be noted that the system analyzes the emergency situation analysis report in detail and extracts key information, including the current risk location, risk level, the scope of impact of personnel and equipment, the available status of resources, etc. The decision tree algorithm is used to automatically generate emergency response strategies for specific scenarios in combination with the characteristics of historical emergency data and event types. The nodes of the decision tree can represent different emergency response measures (such as evacuation, fire fighting, and 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: Develop specific action plans based on different types of emergency events (such as fire, explosion, leakage), including personnel evacuation, fire source control, isolation of hazardous substances, etc.

[0099] Resource allocation suggestions: Based on the emergency situation and resource status, make allocation suggestions for various emergency resources (such as rescue personnel, rescue equipment, medical supplies, etc.) and determine their priority and allocation quantity.

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

[0101] By summarizing the experience of previous emergency events, collating expert opinions and summarizing industry norms, an expert knowledge base is constructed. The knowledge base includes the handling process of typical emergency events, risk avoidance strategies, resource allocation plans, etc. Based on the automatically generated emergency response strategy, the system compares it with the expert knowledge base, generates multiple optional plans, and marks the advantages and disadvantages of each plan for reference by emergency commanders. Commanders can adjust and optimize the plan according to specific on-site conditions and personal experience. After the system generates a preliminary decision, it continuously collects real-time data on the scene (such as changes in risk levels and resource consumption), and re-evaluates the effectiveness of the decision through algorithms, and dynamically updates 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 new fire sources) 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 of the dataset S after segmentation on feature A. In emergency response strategy generation, this indicates the degree to which the uncertainty of the strategy is reduced after selecting a certain emergency scenario feature (such as fire intensity). The greater the information gain, the more significant the impact of the feature on strategy generation; S represents the dataset of the current emergency scenario, which contains cases of different emergency scenarios (for example, various emergency events in historical records). These data may include different types of emergency situations such as fire, explosion, and leakage; A represents the characteristics of the segmented dataset. In the emergency response scenario, this may be characteristics such as fire intensity (such as low, medium, and high), personnel density (such as sparse, dense), and the number of available resources (such as sufficient, scarce); E(S) represents the entropy of the dataset S, which represents the degree of chaos or uncertainty of all emergency scenarios; Values(A) represents all possible values ​​of feature A; S v represents a subset of feature A whose value is v; E(S v ) represents the subset S v The entropy represents the degree of disorder when the feature A takes the value 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 a multi-objective optimization algorithm and a fuzzy control algorithm to generate an optimal emergency response command plan. The resource scheduling and command module adopts a dynamic resource hierarchical scheduling mechanism to divide emergency resources into core resources, auxiliary resources and emergency reserve resources, and dynamically allocate them 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, which divides resources into three categories according to their importance and usage, and adopts different scheduling strategies:

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

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

[0109] Emergency reserve resources: These resources are usually on standby in non-emergency situations and are mainly used to supplement the shortage of core and auxiliary resources or replace exhausted resources. When core and auxiliary resources are exhausted or seriously insufficient, emergency reserve resources will be dispatched as backup support and can be supplemented by temporary procurement or mobilization of resources from adjacent areas.

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

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

[0112] Based on the dynamic risk areas and development trends in the emergency situation analysis report, assess 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, the scheduling priority of each task is adjusted through the fuzzy control algorithm to ensure that the resource scheduling of high-priority tasks is given priority.

[0115] Based on the results of optimization and fuzzy processing, the system generates a specific resource scheduling plan and issues instructions, and performs automatic or manually assisted scheduling of resources through an intelligent scheduling platform.

[0116] During the execution process, the progress of the task and the status of resources are monitored in real time, and the scheduling plan is dynamically adjusted based on new emergency situation analysis and task changes.

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

[0118] Resource scheduling plan: clearly define the type, quantity, location and scheduling path of the resources to be scheduled. The system generates a visual scheduling diagram to display the resource scheduling route and estimated arrival time.

[0119] Task allocation details: 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 strategy of emergency command plan: including real-time command strategy for on-site command and monitoring strategy for resource status, such as real-time tracking of vehicle driving paths, monitoring of the status of rescue personnel, etc.

[0121] Example 2

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

[0123] Collect multi-source data in emergency scenarios in real time through sensor networks;

[0124] Based on the multi-source data, a scene twin model is constructed through augmented reality technology and digital twin technology, and three-dimensional visualization technology is used for real-time rendering of virtual scenes;

[0125] Based on the scenario twin model, the situation analysis of emergency scenarios is carried out through random matrix theory and graph neural network algorithm to detect potential risks, accurately locate dynamic risk areas and predict development trends, and generate emergency situation analysis reports;

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

[0127] Based on the emergency situation analysis report and emergency response strategy, a multi-objective optimization algorithm and a fuzzy control algorithm are used to optimize the scheduling and dynamic allocation of emergency resources to 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 based on scenario twins described in Example 1, which will not be repeated here.

[0129] In summary, the present invention collects multi-source data (environment, equipment, personnel) in emergency scenes in real time through a sensor network to achieve comprehensive and accurate dynamic monitoring. Environmental sensors, equipment status sensors, and personnel information sensors work together to quickly detect potential risks (such as a sharp rise in temperature, leakage of harmful gases), trigger alarms in a timely manner, and improve the agility of emergency response. A three-dimensional scene twin model is constructed using augmented reality and digital twin technology, and changes in emergency scenes (such as light and shadow, reflection) are displayed in real time through a high-precision rendering algorithm to achieve multi-angle observation and interaction of emergency scenes. Emergency commanders can perform detailed operations on the scene through virtual reality devices or interactive interfaces to enhance the visualization of decision-making.

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

[0131] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field 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 module and a resource dispatching and command module which are sequentially connected in communication; The data acquisition module is used to collect multi-source data in the emergency scene in real time through the sensor network; the multi-source data includes environmental data, equipment status data and personnel information data of the emergency scene; 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 use three-dimensional visualization technology to perform real-time rendering of virtual scenes; 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 dynamic risk areas and predict development trends, and generate an emergency situation analysis report; The emergency response decision 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 action plans, resource deployment recommendations 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 emergency response strategy, and generate the optimal emergency response command plan by using a multi-objective optimization algorithm and a fuzzy control algorithm.

2. According to claim 1, a scenario twin-based intelligent emergency command system is characterized in that: The operation process of the scene twin management module includes the following steps: Preprocess the multi-source data collected through the sensor network, including data cleaning, noise reduction and format conversion; Based on pre-processed multi-source data, augmented reality technology and digital twin technology are used to build a scene twin model of the emergency scene; Based on the scenario twin model, by integrating historical data and real-time data, data assimilation technology is used to fine-tune and optimize the key parameters of the model; The carefully constructed scene twin model is rendered in three-dimensional visualization, and a high-precision rendering algorithm is used to present the dynamic changes in the scene, enabling intuitive display and interactive operation of emergency scenes.

3. According to claim 2, a scenario twin-based intelligent emergency command system is characterized in that: The scene twin model includes a three-dimensional space geometry model and an attribute model. The three-dimensional space geometry model realizes three-dimensional visualization of the scene through point cloud data reconstruction and texture mapping, including basic terrain, three-dimensional structure of buildings and dynamic objects. The attribute model contains environmental status, equipment operation parameters and personnel information.

4. According to claim 3, a scenario twin-based intelligent emergency command system is characterized in that: The construction process of the scene twin model includes the following steps: Based on the pre-processed multi-source data, virtual reality and digital twin technology are used to reconstruct the three-dimensional structure of the emergency scene, multi-scale modeling of the terrain, buildings, and dynamic objects in the scene is carried out through point cloud data, and texture mapping technology is used to enhance the details of the model surface; Integrate and map multi-source data collected in real time with 3D geometric models, and use virtual reality technology to reflect data changes in virtual scenes, so as to achieve real-time tracking and updating of dynamic objects, including equipment status changes, environmental parameter fluctuations, and personnel position movement trajectories; Based on the integrated data source, the attribute model of the scene is constructed, including the environment status, equipment parameters and personnel information. The attribute data is associated and mapped with the geometric model of the virtual scene, and the virtual reality interaction technology is used to realize the switching display of different data layers. Combining historical data with real-time collected data, the key parameters of the scene twin model are continuously optimized through data assimilation technology, and digital twin technology is used to perform parameter correction and model update; The optimized scene twin model is rendered in high-precision three-dimensional form, and through virtual reality equipment or interactive interfaces, it supports emergency command personnel in real-time operation and multi-angle observation of the virtual scene.

5. According to claim 1, a scenario twin-based intelligent emergency command system is characterized in that: 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, use multimodal data fusion technology to associate the multi-source data, and generate a unified emergency scenario data set; Based on a unified emergency scenario data set, random matrix theory is used to extract risk factors for potential emergencies from data features, including abnormal equipment status, drastic environmental changes, or abnormal personnel behavior, and the weights and priorities of risk factors are evaluated; Based on the graph neural network algorithm, multi-factor correlation reasoning is performed to identify the correlation and potential transmission paths between risk factors and form a dynamic risk transmission map; Combining the risk propagation map with the results of situation prediction and assessment, an emergency situation analysis report is automatically generated, the report content includes the precise location of the risk area, the risk level and its evolution trend, and the demand forecast of emergency resources.

6. According to claim 5, a scenario twin-based intelligent emergency command system is characterized in that: The formula of the random matrix theory is as follows: Among them, R represents the assessment result of the potential risk of the emergency scenario; λ i Represents the principal component characteristics of multi-source data in emergency scenarios; avg represents the average characteristic scale of the overall volatility of multi-source data; α, β and γ represent risk adjustment parameters; w i represents the weight of the corresponding feature vector; f i represents the risk factor associated with feature i; σ j Indicates the standard deviation of multi-source data; g p Indicates the importance of the risk in the scenario within a specific time period; p represents the multi-source data vector at the pth time point; h p represents external influencing factors; k represents the number of risk factors; m represents the number of time points collected.

7. According to claim 1, a scenario twin-based intelligent emergency command system is characterized in that: The emergency response decision module specifically uses real-time analysis of scenario data and an expert knowledge base to make hybrid decisions and generate an emergency response strategy including an action plan, resource priority allocation, and risk avoidance strategy.

8. According to claim 1, a scenario twin-based intelligent emergency command system is characterized in that: The resource scheduling and command module adopts a dynamic resource hierarchical scheduling mechanism to divide emergency resources into core resources, auxiliary resources and emergency reserve resources, and dynamically allocates them according to the urgency of the task and the status of the resources.

9. A smart emergency command method based on scenario twins, characterized in that: The system is applied to a scenario twin-based intelligent emergency command system as described in any one of claims 1 to 8, comprising the following steps: Collect multi-source data in emergency scenarios in real time through sensor networks; Based on the multi-source data, a scene twin model is constructed through augmented reality technology and digital twin technology, and three-dimensional visualization technology is used for real-time rendering of virtual scenes; Based on the scenario twin model, the situation analysis of emergency scenarios is carried out through random matrix theory and graph neural network algorithm to detect potential risks, accurately locate dynamic risk areas and predict development trends, and generate emergency situation analysis reports; Based on the emergency situation analysis report, automatically generate an emergency response strategy using a decision tree algorithm; Based on the emergency situation analysis report and emergency response strategy, a multi-objective optimization algorithm and a fuzzy control algorithm are used to optimize the scheduling and dynamic allocation of emergency resources to generate an optimal emergency response command plan.

Images