Emergency expert resource overall management system and method

Abstract

The invention relates to the technical field of emergency management, and discloses an emergency expert resource overall management system and method, and the method comprises the steps: constructing a multi-dimensional expert capability portrait; chain-type evolution prediction of the accident is realized; pre-deployment of expert resources is realized; finally, prospective and dynamic overall management of expert resources is realized; a multi-dimensional expert ability portrait module in the invention adopts a knowledge graph technology and a deep learning model to carry out expert ability description and dynamic updating; the accident chain type evolution prediction module constructs a multi-path accident evolution prediction model by using a graph attention network; the expert resource pre-deployment module realizes prospective scheduling of expert resources based on Monte Carlo tree search and a sliding time window mechanism; the expert matching accuracy and the emergency response efficiency are improved, the accident control time is shortened, and powerful technical support is provided for safety production of chemical industrial parks.

Description

Technical Field

[0001] The present invention relates to the field of emergency management technology, and more specifically, to an emergency expert resource coordination management system and method. Background Art

[0002] As a concentrated area of ​​hazardous chemicals, chemical parks face complex safety risks and challenges of various emergencies. When dealing with chemical accidents, the efficient and coordinated management of expert resources has a decisive impact on the effect of accident handling. However, the current emergency expert dispatch management system in chemical parks has the following technical problems: Expert matching is not accurate. The existing expert dispatch system mainly relies on simple label matching or manual screening, lacking a multi-dimensional and accurate description of expert capabilities, resulting in irrational allocation of expert resources and low handling efficiency. Traditional methods usually use simple labels such as professional fields and titles for matching, which cannot take into account deep-level characteristics such as the actual experience, professional advantages, and historical case handling effects of experts, making it difficult to meet the needs of complex accident handling.

[0003] Lack of ability to predict the evolution of accidents. In traditional emergency management, the prediction of accident development trends mainly relies on expert experience. There is a lack of scientific evolution prediction models, and it is impossible to accurately predict the possible development path and probability distribution of accidents. This leads to passive response of expert dispatch and difficulty in forward-looking deployment of expert resources. Often, relevant experts are added only after the accident escalates, delaying the best time to deal with it.

[0004] Expert resource scheduling lags behind the development of accidents. Existing expert scheduling systems mostly adopt static scheduling modes, which cannot dynamically adjust the configuration of expert resources according to the evolution of accidents, resulting in delayed emergency response and inefficient resource utilization. At the same time, traditional systems lack evaluation of the collaborative effectiveness of expert teams, making it difficult to form an expert team with the best overall effectiveness.

[0005] Insufficient expert knowledge sharing and response preparation. In the traditional emergency response process, experts usually begin to understand the accident situation after arriving at the scene. The lack of prior preparation and knowledge sharing mechanism prolongs the expert's response time and decision-making cycle.

[0006] With the development of technologies such as artificial intelligence, knowledge graphs, and deep learning, new technical means have been provided to solve the above problems. It is necessary to develop an innovative emergency expert resource coordination management system and method to break through the passive response mode in traditional expert resource management, realize forward-looking and dynamic coordinated management of expert resources, and improve the scientificity, accuracy, and timeliness of emergency management in chemical parks. Summary of the invention

[0007] The present invention provides an emergency expert resource coordination management system and method, which solves technical problems existing in traditional emergency expert dispatch in related technologies, such as unreasonable expert resource allocation, insufficient accident evolution prediction capability, passive resource dispatch delay, etc.

[0008] The present invention provides a method for overall management of emergency expert resources, comprising the following steps: Construct a multi-dimensional expert capability portrait, including constructing a knowledge graph-based expert capability representation model, using a deep learning model to dynamically update expert capabilities, applying an improved weighted cosine similarity algorithm to calculate the matching degree between experts and emergency scenarios, and implementing a hierarchical and progressive expert recommendation strategy; Realize accident chain evolution prediction, including building a knowledge graph of chemical accident chain evolution, building a multi-path accident evolution prediction model based on graph attention network, and establishing an expert demand prediction model; Realize the pre-deployment of expert resources, including building an expert resource pre-deployment planning system based on Monte Carlo tree search, building an expert resource dynamic adjustment algorithm, introducing an expert collaborative relationship evaluation module based on graph neural network, and building an expert knowledge graph dynamic query system.

[0009] In a preferred embodiment, the construction of a knowledge graph-based expert capability representation model is to construct an expert capability portrait model based on knowledge graph technology, collect feature data including professional fields, processing cases, skill scores, response speed, collaboration capabilities, and professional qualification dimensions, and form an expert capability feature vector, wherein the knowledge graph model consists of three parts: entities, attributes, and relationships.

[0010] In a preferred embodiment, the formula for calculating the matching degree between experts and emergency scenarios using the improved weighted cosine similarity algorithm is: ; in, Indicates the matching degree between expert ability and emergency scenario, The first The value of the dimension, The first The value of the dimension, Indicates The weight coefficient of each dimension, Indicates the number of dimensions of the vector.

[0011] In a preferred embodiment, the prediction formula for constructing a multi-path accident evolution prediction model based on a graph attention network is: ; in, Indicates that in the known Accident status at all times Under the condition of Accident status after time The probability of express The accident status at the moment, Indicates the future The accident status after time, Indicates The weight coefficient of the attention head, Indicates The transfer function of the attention head is Indicates environmental condition parameters, Represents the softmax activation function.

[0012] In a preferred embodiment, the expert demand forecasting model is established for the predicted multiple accident evolution paths, and the expert demand forecasting model is constructed to calculate the demand change trend of experts in different professional fields for each path, and the path professional mapping matrix is ​​used to calculate the expert demand change trend of experts in different professional fields for each path. , the accident path is mapped to the expert demand vector, and the comprehensive expert demand prediction result is: ; in, represents the comprehensive expert demand forecast results, Indicates The weight of the path, represents the number of predicted paths, Indicates The expert demand vector corresponding to the accident evolution path.

[0013] In a preferred embodiment, the expert resource dynamic adjustment algorithm adopts a sliding time window mechanism. Time according to Expert demand forecast within the time window, adjust expert resource allocation in advance, and the optimization formula is: ; in, express The best combination of experts at the moment, represents the set of all possible expert combinations, Indicates expert combination Status responsiveness, Indicates status The probability of occurrence, Indicates the size of the sliding time window.

[0014] In a preferred embodiment, the synergy efficiency calculation formula of the expert collaborative relationship evaluation module based on the graph neural network is: ; in, express Expert combination performance at all times, represents the weight coefficient, Indicates expert The synergy index, Indicates expert About the event responsiveness.

[0015] In a preferred embodiment, the knowledge push relevance calculation formula for constructing the expert knowledge graph dynamic query system is: ; in, Representing knowledge points and predicting accidents The correlation of represents the knowledge point vector, represents the predicted accident vector, and They represent the Euclidean norm of the vector respectively.

[0016] In a preferred embodiment, the expert and emergency scenario matching calculation, accident evolution prediction, expert demand prediction and resource dynamic adjustment algorithm are trained and optimized through deep learning methods, and data interaction between the models is carried out through standardized interfaces to achieve overall collaborative optimization of the system.

[0017] In a preferred embodiment, the emergency expert resource coordination management system includes a multi-dimensional expert capability portrait module, which is used to: construct a knowledge graph-based expert capability representation model to characterize expert capabilities in multiple dimensions; use a deep learning model to analyze expert historical case handling data in real time and dynamically update expert capability vectors; use an improved weighted cosine similarity algorithm to calculate the matching degree between experts and emergency scenarios; implement a hierarchical and progressive expert recommendation strategy to intelligently select the most suitable expert resources according to the type and severity of the accident; The accident chain evolution prediction module is used to: construct a knowledge graph of chemical accident chain evolution and establish evolutionary associations between accident types; build a multi-path accident evolution prediction model based on the graph attention network to predict the possible development path and probability of accidents; establish an expert demand prediction model to predict the type and number of experts required in the future based on the accident evolution path; The expert resource pre-deployment module is used to: plan the pre-deployment plan of expert resources based on the Monte Carlo tree search algorithm; build an expert resource dynamic adjustment algorithm to optimize the expert resource allocation in real time according to the actual development of the accident; introduce the expert collaborative relationship evaluation function based on the graph neural network to optimize the combined efficiency of the expert team; build an expert knowledge graph dynamic query system to provide experts with accident-related knowledge support.

[0018] The beneficial effects of the present invention are: The accuracy of expert matching is significantly improved: Through multi-dimensional expert capability portraits and an improved weighted cosine similarity algorithm, the system can accurately describe and match expert capabilities. Compared with traditional methods based on label matching or manual screening, the accuracy of expert matching is improved by 42%, and the utilization rate of expert resources is increased by 37%, which greatly reduces delays caused by the mismatch between expert capabilities and accident handling needs.

[0019] Breakthrough in accident evolution prediction capability: The multi-path accident evolution prediction model based on graph attention network can predict the possible accident development path and probability distribution in the next 4-24 hours, with a prediction accuracy of 83%. Compared with the empirical judgment of accident development trends in traditional emergency management, it is more scientific and reliable, and provides strong decision-making support for the pre-deployment of expert resources.

[0020] Pre-deployment of expert resources: Through the Monte Carlo tree search and sliding time window mechanism, the system can deploy expert resources in advance according to the prediction results of accident evolution, transform the scheduling of expert resources from passive response to active prevention, shorten the emergency response time by 65%, and effectively solve the problem of expert resource scheduling lagging behind accident development in the traditional model.

[0021] Improved collaborative efficiency of expert teams: The expert collaborative relationship evaluation module based on graph neural networks can analyze the collaborative effects of different expert combinations and maximize the overall efficiency of the expert team. Compared with the traditional single-professional expert selection method, the collaborative disposal efficiency of the expert team is improved by 56%, which better copes with the multidisciplinary cross-cutting problems in the process of chemical accident disposal.

[0022] The risk of accidents expanding is greatly reduced: through forward-looking deployment of expert resources, the system can organize expert intervention in advance in the early stages of the accident, effectively control the scope of the accident expansion, and reduce the probability of secondary disasters by about 74%, realizing the transformation of the emergency management model from mending the fold after the sheep have been lost to preparing for a rainy day. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for coordinating and managing emergency expert resources according to the present invention; Figure 2 A flowchart for constructing a multi-dimensional expert capability portrait in an embodiment of the present invention; Figure 3 This is a flowchart of accident chain evolution prediction in an embodiment of the present invention; Figure 4 This is a flowchart of expert resource pre-deployment in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0025] At least one embodiment of the present invention discloses a method for coordinating and managing emergency expert resources, such as Figure 1-4 As shown, the following steps are included: Step 100, construct a multi-dimensional expert capability portrait, including constructing a knowledge graph-based expert capability representation model, using a deep learning model to dynamically update expert capabilities, applying an improved weighted cosine similarity algorithm to calculate the matching degree between experts and emergency scenarios, and implementing a hierarchical and progressive expert recommendation strategy; It includes the following sub-steps: Step 101, construct a knowledge graph-based expert capability representation model; Based on the knowledge graph technology, an expert capability portrait model is constructed, and feature data of at least 15 dimensions including professional fields, processing cases, skill scores, response speed, collaboration capabilities, professional qualifications, etc. are collected to form an expert capability feature vector. Among them, the professional field is further subdivided into dimensions such as hazardous chemical types, process types, and equipment types. The processing case dimension includes sub-dimensions such as the number of cases, case type distribution, and case complexity, making the expert capability portrait more comprehensive and detailed. The knowledge graph model consists of three parts: entities, attributes, and relationships. Entities include expert nodes and capability nodes, attributes include expert basic information and capability scores, and relationships include the mastery relationship between experts and capabilities. In specific applications, taking chemical leakage accident disposal experts as an example, the system will construct professional capability nodes including organic leakage disposal capabilities, gas diffusion simulation capabilities, and poisoning emergency disposal capabilities, and establish associations with expert nodes. The strength of the association is dynamically adjusted according to the expert's historical case disposal situation.

[0026] Step 102, using deep learning models to dynamically update expert capabilities; The expert's historical case handling text and field literature contributions are input into the text analysis model based on the transformer architecture to extract the technical expertise and knowledge points shown by the experts in handling different types of accidents, and the extracted technical expertise and knowledge points are mapped to the corresponding dimensions of the expert's ability vector to achieve dynamic updates of the expert's ability portrait. The text analysis model includes an input layer, a multi-head self-attention layer, a feedforward neural network layer, and an output layer. The multi-head self-attention layer processes the text in parallel through 8 attention heads to capture different semantic relationships. In the application example, when the system inputs a case description such as "XX expert successfully handled a propylene leakage accident in a petrochemical plant and used negative pressure adsorption and water curtain isolation technology", the model can automatically extract key capability labels such as propylene leakage handling, negative pressure adsorption technology, and water curtain isolation technology, and update them to the corresponding expert's ability vector.

[0027] Step 103, using the improved weighted cosine similarity algorithm to calculate the matching degree between the expert and the emergency scenario; Based on the expert capability vector and the scenario requirement vector, the matching degree is calculated by using the improved weighted cosine similarity algorithm. The calculation formula is: ; in, Indicates the matching degree between expert ability and emergency scenario, The first The value of the dimension, The first The value of the dimension, Indicates The weight coefficient of each dimension, Indicates the number of dimensions of the vector. Weight coefficient Dynamically adjust according to the characteristics and handling requirements of different types of accidents. For example, for fire and explosion accidents, the weights of dimensions related to firefighting and explosion mechanisms will be increased; for leakage accidents, the weights of dimensions related to material properties and diffusion models will be increased. The improved weighted cosine similarity algorithm is different from the traditional cosine similarity algorithm. It introduces weight coefficients to reflect the importance of different dimensions in different scenarios.

[0028] In actual implementation, the weight coefficient is optimized through historical matching effect feedback, and the gradient descent method is used to iteratively update the weight value, so that the matching result is more in line with actual needs. For example, in a chlorine gas leak accident in a chemical plant, the system will automatically increase the weight of toxic gas disposal, respiratory protection and other dimensions, and reduce the weight of fire protection, explosion protection and other dimensions, so as to more accurately screen out experts with experience in gas leak disposal.

[0029] Step 104, implement a hierarchical and progressive expert recommendation strategy; Construct an expert recommendation strategy model, which dynamically adjusts the priority of expert recommendation according to factors such as event level (general, large, major, and extremely major), geographical location (within the park, local, surrounding cities, and nationwide), and expert availability (on-the-job, on-duty, vacation, etc.), in accordance with the principle of proximity and the law of emergency management of graded response. For high-level accidents, the system automatically expands the scope of expert screening and increases the weight of professional matching; for low-level accidents, the system gives priority to experts with closer geographical locations to improve response speed. The expert recommendation strategy model is implemented using a decision tree structure. The first-level nodes are branched based on the event level, the second-level nodes are branched based on the geographical scope, and the third-level nodes are branched based on professional matching. The leaf nodes are specific expert sets. In the application example, taking a styrene leakage accident in a chemical park as an example, when the system determines that it is a large-level accident, it first screens experts from within the park and within 50 kilometers of the surrounding area, and gives priority to experts with rich experience in handling organic chemical leakage; when the situation escalates to a major accident, the system automatically expands the search scope to the entire province and begins to include national-level expert resources for matching recommendations.

[0030] Step 200, realizing accident chain evolution prediction, including constructing a knowledge graph of chemical accident chain evolution, building a multi-path accident evolution prediction model based on graph attention network, and establishing an expert demand prediction model; It includes the following sub-steps: Step 201, constructing a knowledge graph of chemical accident chain evolution; By integrating the historical accident case library (including more than 5,000 chemical accident records at home and abroad), the chemical reaction mechanism database and process safety data, an evolutionary path network covering various types of chemical accidents is constructed. The knowledge graph contains nodes (accident status) and edges (state transition relationships), forming a directed weighted graph structure. Node attributes include accident type, hazard level, impact range, etc.; edge attributes include transition probability, influencing factors, triggering conditions, etc. The graph structure is as follows: ; in, represents the accident evolution knowledge graph, represents a node set (accident status), represents the edge set (state transition relationship), Represents a collection of node attributes. Represents a set of edge attributes. The construction process of the knowledge graph includes six steps: data collection, data cleaning, ontology construction, entity extraction, relationship extraction, and graph storage. Neo4j graph database is used for storage and query. In the application example, taking the chemical tank leakage accident as an example, the graph will include multiple evolution paths such as initial leakage → liquid pool formation → cloud formation → explosion and initial leakage → liquid pool formation → environmental pollution. Each path is marked with transition probability and influencing factors, such as temperature, pressure, surrounding flammable materials, etc.

[0031] Step 202, construct a multi-path accident evolution prediction model based on graph attention network; The accident evolution prediction model is constructed using the Graph Attention Network (GAT) technology, which takes the current monitoring data and the initial accident type as input to generate possible accident development paths and probability distributions within the next 4-24 hours. The core calculation formula of the prediction model is: ; in, Indicates that in the known Accident status at all times Under the condition of Accident status after time The probability of express The accident status at the moment, Indicates the future The accident status after time, Indicates The weight coefficient of the attention head, Indicates The transfer function of the attention head is Indicates environmental condition parameters (including temperature, pressure, humidity, wind direction, wind speed, etc.), represents the softmax activation function. Attention weight Calculated as follows: ; in, represents the attention weight, represents the attention vector, represents the weight matrix, and Represents node characteristics, represents the feature concatenation operation, Representation Node The graph attention network model consists of three layers of GAT, including input layer, hidden layer and output layer. The hidden layer contains 64 hidden units, and the multi-head attention mechanism is used to enhance the model's expression ability. In specific applications, for example, when an ammonia leak occurs, the system inputs the current ammonia concentration, leakage rate, ambient temperature, wind direction and speed and other parameters. The model will predict multiple evolution paths that may occur in the next 4-24 hours, such as concentration diffusion-local exceeding the standard-personnel poisoning, gas diffusion-encountering open flames-explosion, etc., and give the probability of each path.

[0032] Step 203, establishing an expert demand forecasting model; Based on the predicted multiple accident evolution paths, an expert demand prediction model is constructed to calculate the changing trend of the demand for experts in different professional fields for each path. , mapping the accident path to the expert demand vector: ; in, represents the expert demand vector corresponding to the accident path, represents the path professional mapping matrix, Represents the path probability vector. Each element of the expert demand vector represents the degree of demand for experts in a specific professional field. The larger the value, the more urgent the demand. By taking the weighted sum of the expert demand vectors of all possible paths, the comprehensive expert demand prediction result is obtained: ; in, represents the comprehensive expert demand forecast results, Indicates The weight of each path (usually equal to the path probability), Indicates the number of predicted paths. The expert demand prediction model uses a three-layer fully connected neural network to realize the path professional mapping function. The input layer corresponds to the accident evolution state characteristics, the hidden layer contains 128 neurons, and the output layer corresponds to the degree of expert demand in each professional field.

[0033] In the application example, for example, when the system predicts that a petrochemical plant has a 70% probability of developing along the path of tank leakage → liquid pool diffusion → environmental pollution, and a 30% probability of developing along the path of tank leakage → cloud formation → explosion, the model will automatically calculate the expert demand combination under different paths. For the first path, the system derives a higher demand for environmental monitoring experts, pollution disposal experts, and emergency monitoring experts; for the second path, the system derives a higher demand for explosion safety experts, fire experts, and medical rescue experts. Through probability weighted aggregation, a vector containing the degree of demand for various types of experts is generated, providing a basis for the subsequent pre-deployment of expert resources.

[0034] Step 300, implement the pre-deployment of expert resources, including building an expert resource pre-deployment planning system based on Monte Carlo tree search, building an expert resource dynamic adjustment algorithm, introducing an expert collaborative relationship evaluation module based on graph neural network, and building an expert knowledge graph dynamic query system; It includes the following sub-steps: Step 301, building an expert resource pre-deployment planning system based on Monte Carlo tree search; For the predicted multiple accident evolution paths, the Monte Carlo Tree Search (MCTS) algorithm is used to build an expert resource deployment planning system. By previewing the response effects of different expert combinations on various possible accident paths in a simulation environment, the resource scheduling strategy is optimized. The core steps of the MCTS algorithm include: selection, expansion, simulation, and backpropagation. The upper confidence bound (UCB) formula is used in the selection stage to balance exploration and utilization: ; in, represents the upper confidence bound, Representation Node The cumulative return of Representation Node The number of visits, Indicates the number of visits to the parent node. Represents the exploration parameter. By repeatedly executing the MCTS algorithm, the system can find the expert resource deployment plan with the best average performance under various possible situations. The system consists of four parts: state representation module, action space definition module, reward calculation module and search control module. The state is represented by the combination of the current available expert set and the accident prediction state, and the action space is all possible choices for the expert combination. In specific applications, for example, in the face of a chlorine gas leakage accident, the system will simulate the response effects of different expert combinations (such as gas leakage expert + toxicology expert + medical expert vs. gas leakage expert + equipment maintenance expert + safety management expert) under different evolutionary paths, and identify the expert combination plan with the best expected effect through a large number of simulation iterations.

[0035] Step 302, construct an expert resource dynamic adjustment algorithm; According to the different stages of accident evolution, an algorithm for dynamic adjustment of expert resources is constructed to achieve automatic optimization of the composition of the expert team. Time according to Forecast expert demand within the time window and adjust expert resource allocation in advance: ; in, express The best combination of experts at the moment, represents the set of all possible expert combinations, Indicates expert combination Status responsiveness, Indicates status The probability of occurrence, Indicates the size of the sliding time window. The size of the sliding time window is dynamically adjusted according to the type of accident. For fast-developing accident types (such as explosions), the time window is small (1-4 hours); for slowly evolving accident types (such as leakage diffusion), the time window is large (8-24 hours). The dynamic adjustment algorithm is implemented using a recursive optimization method, which includes four steps: initialization, evaluation, optimization, and update.

[0036] In the scenario of the application example, such as a sulfuric acid leakage accident in a chemical plant, the system deploys a combination of leakage control experts and chemical disposal experts in the initial stage. As the situation monitoring shows that the leakage may flow into nearby water bodies, the system will adjust the expert combination 4 hours in advance, adding water pollution treatment experts and environmental monitoring experts to prepare for possible subsequent water pollution and realize the pre-deployment of expert resources.

[0037] Step 303, introduce the expert collaborative relationship evaluation module based on graph neural network; The graph neural network (GNN) technology is used to build an expert collaborative relationship evaluation model to analyze the collaborative effects of different expert combinations in real time in responding to specific accident evolution paths, maximizing the overall effectiveness of the expert team. The collaborative effectiveness calculation formula is: ; in, express Expert combination performance at all times, represents the weight coefficient, Indicates expert The synergy index, Indicates expert About the event Synergy Index Computation through graph neural network: ; in, represents the synergy index, Representation and Experts The feature vector of the set of experts with collaborative relationships, represents the expert collaboration adjacency matrix, represents the graph neural network operation, Represents the activation function. The expert collaborative relationship evaluation model is implemented using a two-layer graph convolutional network (GCN), in which the first layer has 32 convolution kernels, the second layer has 16 convolution kernels, and finally the collaborative index is output through the fully connected layer. The collaborative relationship adjacency matrix between experts is constructed based on three dimensions: historical joint disposal cases, degree of interdisciplinary intersection, and professional complementarity. In a specific scenario, for example, a reactor explosion occurred in a chemical plant. The system will evaluate the collaborative effectiveness of two different combinations of process safety experts + pressure vessel experts + explosion-proof experts and process safety experts + structural engineering experts + fire protection experts. The model will take into account factors such as the collaborative experience of process safety experts and other experts, the complementarity of professional knowledge, etc., so as to recommend an expert team combination with higher overall collaborative effectiveness, rather than just a simple superposition of the capabilities of a single expert.

[0038] Step 304, construct an expert knowledge graph dynamic query system; Based on knowledge graph technology, an expert knowledge dynamic query system is constructed. According to the predicted accident evolution path, the system activates and pushes historical processing cases and knowledge points of relevant experts in advance, shortening the expert response preparation time. The relevance calculation formula of knowledge push is: ; in, Representing knowledge points and predicting accidents The correlation of represents the knowledge point vector, represents the predicted accident vector, and Respectively represent the Euclidean norm of the vector. The system is more sensitive to correlation than the threshold The knowledge points are pushed: ; Among them, push decision indicates whether the system pushes the final decision result of a specific knowledge point to the expert; Representing knowledge points and predicting accidents 's relevance; Indicates the relevance threshold preset by the system, which is used to filter high-value knowledge points; Indicates that a push operation is performed when the relevance between a knowledge point and a predicted accident exceeds a threshold; Indicates that when the relevance does not exceed the threshold, the push operation will not be performed to prevent irrelevant information from interfering with expert decision-making The expert knowledge graph dynamic query system includes a knowledge point vectorization module, a relevance calculation module, a threshold control module and a push interface module. The knowledge point vectorization is implemented through the BERT pre-trained language model, which converts the text knowledge points into a 768-dimensional vector representation. In the application example, for example, when the system predicts that an ammonium nitrate explosion may occur in a chemical plant, it will automatically retrieve knowledge points related to the ammonium nitrate explosion from the knowledge base, such as the decomposition temperature of ammonium nitrate, the calculation method of the explosion shock wave parameters, and the historical case analysis of ammonium nitrate explosions, etc., calculate the relevance of these knowledge points to the current predicted accident, and push the highly relevant knowledge points to the explosion safety experts who are about to be dispatched, so that the experts can understand the key information before arriving at the scene and shorten the response time.

[0039] Technical effect: This implementation method has fully verified its technical advantages and practical value in application examples, improved the scientificity, accuracy and timeliness of emergency management in chemical parks, and provided strong technical support for safe production in chemical parks.

[0040] This implementation example is used in the emergency management system of a large petrochemical industrial park. The industrial park covers an area of ​​about 15 square kilometers and has more than 40 hazardous chemical production and storage companies, involving a variety of hazardous chemicals such as flammable, explosive, toxic and harmful, and there is an obvious safety risk correlation between the companies. The park has about 120 resident emergency management experts and about 500 external expert resources available, covering multiple professional fields such as chemical industry, fire protection, environmental protection, and medical care.

[0041] An ethylene tank leakage accident occurred in a company in the petrochemical park, posing a serious threat to the safety management of the park. The initial state of the accident was that the ethylene tank valve failure caused gas leakage, and the gas concentration at the leakage point exceeded the standard. Under the traditional emergency management model, the emergency command center usually immediately mobilizes experts in the fields of leakage disposal and chemical safety to deal with it, but this method has problems such as low expert matching and inability to foresee the development trend of the accident.

[0042] After adopting the emergency expert resource coordinated management method provided in this implementation method, the system works according to the following process: Example of constructing a multi-dimensional expert capability profile: The system first extracts expert information from the expert resource library and constructs a multi-dimensional capability portrait. Taking an expert in the field of leak disposal as an example, the knowledge graph-based capability portrait constructed by the system is shown in Table 1: Table 1: Example of a multi-dimensional capability profile of a leak management expert;

[0043] Based on these multi-dimensional capability portraits, the system uses an improved weighted cosine similarity algorithm to calculate the matching degree between the expert and the current ethylene leak accident scenario. In this process, the system automatically assigns higher weights to dimensions such as gas leak, flammability, and emergency control according to the characteristics of the accident. The calculation results are shown in Table 2: Table 2: Calculation results of the matching degree between different experts and ethylene leakage accident scenarios;

[0044] By comparison, it can be seen that the weighted cosine similarity algorithm significantly improves the matching degree of experts in the fields of leakage disposal and flammable safety, while reducing the matching degree of environmental monitoring experts who are less relevant to the current scenario, making expert matching more accurate. At the same time, based on the initial judgment of the incident as a larger level accident, the system automatically expands the expert search range to within the park and within 50 kilometers around it, and gives priority to recommending experts with high matching degree and short response time.

[0045] Example of accident chain evolution prediction: After determining the initial expert team, the system starts the accident chain evolution prediction module and builds an accident evolution prediction model based on the data of the current ethylene leak accident (including leakage rate, ethylene concentration, ambient temperature 27°C, southeast wind level 4, etc.). The system extracts the accident evolution path related to ethylene leak from the chemical accident chain evolution knowledge graph and uses the graph attention network for multi-path prediction. The prediction results are shown in Table 3: Table 3: Prediction results of the evolution path of the ethylene leakage accident in the next 12 hours;

[0046] Based on the above prediction results, the system further analyzes the impact of different evolution paths on expert demand. For path P1 (explosion path), it is necessary to deploy explosion prevention, firefighting and medical rescue experts in advance; for path P2 (poisoning path), it is necessary to deploy toxicology, medical and environmental monitoring experts in advance; for path P3 (control path), it is necessary to maintain continuous investment in leakage disposal experts. The expert demand forecast generated by the system is shown in Table 4: Table 4: Expert demand prediction under different evolution paths;

[0047] The comprehensive demand is calculated by the weighted sum of the demand of each path, and the weight is the probability of occurrence of the path. As can be seen from Table 4, leakage disposal experts have a high demand in each path, while the demand for medical rescue experts increases significantly when the accident may cause explosion or poisoning. This prediction-based expert demand analysis provides a scientific basis for subsequent pre-deployment.

[0048] Example of pre-deployment of expert resources: Based on the accident evolution prediction results, the system uses the Monte Carlo tree search algorithm to simulate the response effects of different expert combinations on each evolution path, simulating 5,000 different expert combinations and response strategies. By analyzing the simulation results, the system constructs an expert resource pre-deployment plan based on a sliding time window, as shown in Table 5: Table 5: Expert resource pre-deployment plan based on sliding time window;

[0049] In the process of resource deployment, the system also introduces an expert collaborative relationship evaluation module to calculate the collaborative effectiveness of different expert combinations through graph neural networks. The system analyzes the historical collaborative experience and professional complementarity of multiple expert combinations to generate collaborative effectiveness evaluation results, as shown in Table 6: Table 6: Results of synergy effectiveness evaluation of different expert combinations;

[0050] As can be seen from Table 6, the C1 combination (leakage disposal + flammable safety + chemical safety) has the highest synergy, which is much higher than the simple superposition of single capabilities. This is because the experts in this combination have experience in jointly handling similar accidents many times, and their professional cross-complementarity is strong. Based on this, the system gives priority to the C1 combination as the initial response team.

[0051] In addition, the system also pushes relevant knowledge and historical cases to the expert team based on the knowledge graph dynamic query technology. The system retrieves knowledge points and cases related to ethylene leakage from the knowledge base, calculates the relevance to the current accident, and pushes highly relevant content to the corresponding experts in advance. Through this knowledge push mechanism, experts can understand the professional knowledge and historical experience that are highly relevant to the current accident before rushing to the scene, significantly shortening the expert response preparation time.

[0052] Technical effect verification: In the process of handling the ethylene leakage accident, by comparing with the traditional emergency expert dispatch method, the method provided by this embodiment has achieved significant technical effects in the application example. The following focuses on verifying two core technical effects: one is the improvement of expert matching accuracy, and the other is the improvement of emergency response efficiency brought about by the pre-deployment of expert resources.

[0053] Verification of the effect of improving expert matching accuracy: The comparison of expert matching accuracy between this method and the traditional label matching-based method is shown in Table 7: Table 7: Comparative analysis of expert matching accuracy:

[0054] As can be seen from Table 7, this method has achieved significant improvements in all matching indicators, especially the expert scene matching accuracy has increased by 42%, and the expert resource utilization has increased by 36.9%. This is mainly due to the application of multi-dimensional expert capability portraits and improved weighted cosine similarity algorithm, which enables the system to more accurately characterize expert capabilities and match them with emergency scenario requirements.

[0055] During the accident handling process, the number of technical solution adjustments due to the mismatch between expert expertise was 8 times under the traditional method, while it was only 2 times under this method, which greatly reduced the delay caused by the mismatch between expert capabilities and accident handling needs.

[0056] Verification of the effect of pre-deployment of expert resources: The comparison of the emergency response efficiency between this method and the traditional passive response mode is shown in Table 8: Table 8: Comparative analysis of the effect of pre-deployment of expert resources;

[0057] As can be seen from Table 8, this method has achieved significant improvements in various response indicators, especially the average expert response time has been shortened by 64.5%, and the accident expansion rate has been reduced by 74.0%. This is mainly due to the expert resource pre-deployment strategy based on accident evolution prediction, which enables the system to organize expert intervention in advance at the early stage of the accident and transform the expert resource scheduling from passive response to active prevention.

[0058] In the case of the ethylene leak accident, the system predicted that the accident might develop along the explosion path in 8 hours, and dispatched explosion-proof safety and fire rescue experts 4 hours in advance. When the accident developed to the critical point, key experts had completed preparations on site and implemented targeted measures, successfully avoiding the escalation of the accident and the occurrence of secondary disasters. In the traditional model, such experts are usually dispatched urgently after the accident has already shown signs of explosion, and it is often difficult for them to arrive in time to implement intervention.

[0059] In addition, the efficiency of expert collaborative disposal has increased by 56.4%, thanks to the expert collaborative relationship evaluation module based on graph neural network, which enables the system to form an expert team with the best collaborative efficiency, rather than simply superimposing the capabilities of individual experts. In the application example, this improvement in collaborative efficiency is directly reflected in the enhanced consistency of expert team decision-making, the improvement of program implementation efficiency and the improvement of emergency disposal quality.

[0060] In summary, this implementation method has fully verified its technical advantages and practical value in application examples, significantly improved the scientificity, accuracy and timeliness of emergency management in chemical parks, and provided strong technical support for safe production in chemical parks.

[0061] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation mode. The above-mentioned specific implementation mode is merely illustrative and not restrictive. Under the guidance of this embodiment, ordinary technicians in this field can also make more forms of equivalent embodiments, all of which are within the protection of this embodiment.

Claims

1. A method for the coordinated management of emergency expert resources, characterized in that: The following steps are involved: Construct a multi-dimensional expert capability portrait, including constructing a knowledge graph-based expert capability representation model, using a deep learning model to dynamically update expert capabilities, applying an improved weighted cosine similarity algorithm to calculate the matching degree between experts and emergency scenarios, and implementing a hierarchical and progressive expert recommendation strategy; Realize accident chain evolution prediction, including building a knowledge graph of chemical accident chain evolution, building a multi-path accident evolution prediction model based on graph attention network, and establishing an expert demand prediction model; Realize the pre-deployment of expert resources, including building an expert resource pre-deployment planning system based on Monte Carlo tree search, building an expert resource dynamic adjustment algorithm, introducing an expert collaborative relationship evaluation module based on graph neural network, and building an expert knowledge graph dynamic query system.

2. The method for overall management of emergency expert resources according to claim 1 is characterized in that: The construction of the knowledge graph-based expert capability representation model is to construct an expert capability portrait model based on the knowledge graph technology, collect feature data including professional fields, processing cases, skill scores, response speed, collaboration capabilities, and professional qualification dimensions, and form an expert capability feature vector, wherein the knowledge graph model consists of three parts: entities, attributes, and relationships.

3. The method for overall management of emergency expert resources according to claim 1, characterized in that: The calculation formula for calculating the matching degree between experts and emergency scenarios using the improved weighted cosine similarity algorithm is: ； in, Indicates the matching degree between expert ability and emergency scenario, The first The value of the dimension, The first The value of the dimension, Indicates The weight coefficient of each dimension, Indicates the number of dimensions of the vector.

4. The method for overall management of emergency expert resources according to claim 1, characterized in that: The prediction formula for constructing a multi-path accident evolution prediction model based on graph attention network is: ； in, Indicates that in the known Accident status at all times Under the condition of Accident status after time The probability of express The accident status at the moment, Indicates the future The accident status after time, Indicates The weight coefficient of the attention head, Indicates The transfer function of the attention head is Indicates environmental condition parameters, Represents the softmax activation function.

5. The method for overall management of emergency expert resources according to claim 1, characterized in that: The expert demand prediction model is established for the predicted multiple accident evolution paths, and the expert demand prediction model is constructed to calculate the demand change trend of experts in different professional fields for each path, and the path professional mapping matrix is ​​used to calculate the demand change trend of experts in different professional fields for each path. , the accident path is mapped to the expert demand vector, and the comprehensive expert demand prediction result is: ； in, represents the comprehensive expert demand forecast results, Indicates The weight of the path, represents the number of predicted paths, Indicates The expert demand vector corresponding to the accident evolution path.

6. The method for overall management of emergency expert resources according to claim 1, characterized in that: The algorithm for constructing dynamic adjustment of expert resources adopts a sliding time window mechanism. Time according to Expert demand forecast within the time window, adjust expert resource allocation in advance, and the optimization formula is: ； in, express The best combination of experts at the moment, represents the set of all possible expert combinations, Indicates expert combination Status responsiveness, Indicates status The probability of occurrence, Indicates the size of the sliding time window.

7. The method for coordinating and managing emergency expert resources according to claim 1, characterized in that: The collaborative efficiency calculation formula of the expert collaborative relationship evaluation module based on graph neural network is: ； in, express Expert combination performance at all times, represents the weight coefficient, Indicates expert The synergy index, Indicates expert About Events responsiveness.

8. The method for overall management of emergency expert resources according to claim 1, characterized in that: The calculation formula for knowledge push relevance of constructing the expert knowledge graph dynamic query system is: ； in, Representing knowledge points and predicting accidents The correlation of represents the knowledge point vector, represents the predicted accident vector, and They represent the Euclidean norm of the vector respectively.

9. The method for overall management of emergency expert resources according to claim 1, characterized in that: The expert and emergency scenario matching calculation, accident evolution prediction, expert demand prediction and resource dynamic adjustment algorithm are trained and optimized through deep learning methods, and the models interact with each other through standardized interfaces to achieve overall coordinated optimization of the system.

10. An emergency expert resource coordination management system, characterized in that: include: The multi-dimensional expert capability profiling module is used to: construct a knowledge graph-based expert capability representation model to characterize expert capabilities in multiple dimensions; Use deep learning models to analyze experts' historical case handling data in real time and dynamically update expert capability vectors; use an improved weighted cosine similarity algorithm to calculate the matching degree between experts and emergency scenarios; Implement a hierarchical and progressive expert recommendation strategy, intelligently select the most suitable expert resources based on the type and severity of the accident; The accident chain evolution prediction module is used to: construct a knowledge graph of chemical accident chain evolution and establish evolutionary associations between accident types; build a multi-path accident evolution prediction model based on the graph attention network to predict the possible development path and probability of accidents; establish an expert demand prediction model to predict the type and number of experts required in the future based on the accident evolution path; The expert resource pre-deployment module is used to: plan the pre-deployment plan of expert resources based on the Monte Carlo tree search algorithm; build an expert resource dynamic adjustment algorithm to optimize the expert resource allocation in real time according to the actual development of the accident; introduce the expert collaborative relationship evaluation function based on the graph neural network to optimize the combined efficiency of the expert team; build an expert knowledge graph dynamic query system to provide experts with accident-related knowledge support.

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