Emergency plan making method based on industrial park

Abstract

The invention discloses an emergency plan making method based on an industrial park, and the method comprises the steps: achieving the data collection and preprocessing through a distributed sensor network and edge calculation, building a park dynamic model through the multi-source heterogeneous data fusion and digital twinning technology, analyzing a risk propagation path in combination with a graph neural network, and obtaining a risk plan. And a preliminary emergency suggestion is generated based on a knowledge graph matching historical case. According to the invention, a resource scheduling optimization and path planning algorithm is further adopted to formulate a dynamic response scheme, effects of different schemes are evaluated through a situation evolution prediction model, and finally an optimal emergency disposal scheme is generated and a structured instruction is issued. According to the method, intelligent formulation of the emergency plan of the industrial park is realized, the accuracy of risk identification and the timeliness of emergency response are improved, and powerful support is provided for safety management of the park.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management in industrial parks, and specifically to a method for formulating an emergency plan based on industrial parks. Background Art

[0002] As an important carrier of national economic development, the safe operation and emergency management of industrial parks are of decisive significance for ensuring production safety and environmental protection. With the expansion of the scale and the complexity of functions in industrial parks, traditional single monitoring means and isolated data processing methods are difficult to meet the needs of comprehensive perception and rapid response in modern industrial parks. Currently, there is a common phenomenon of data islands in industrial park management systems, and it is difficult to effectively integrate the information collected by various sensing devices, resulting in incomplete situation awareness and untimely risk warning in the park.

[0003] The safety management of industrial parks faces technical challenges in integrating multi-source heterogeneous data. Due to the different data formats and update frequencies of the geographical location sensors, environmental parameter monitoring devices, and personnel distribution tracking systems deployed in the park, and the lack of a unified data standard and processing framework, these valuable information resources cannot form effective decision support. This difficulty in data standardization and fusion processing further leads to an imperfect construction of the overall situation model of the park, which cannot accurately reflect the real-time situation of the park, resulting in a deviation between the risk assessment results and the actual situation, and affecting the scientificity and pertinence of the emergency plan.

[0004] How to achieve the efficient collection and fusion processing of multi-source heterogeneous data in the complex and changeable environment of industrial parks, and quickly generate accurate emergency response strategies based on comprehensive situation analysis has become a key issue in improving the safety management level of industrial parks. Summary of the Invention

[0005] In order to make up for the deficiencies of the prior art, the present invention proposes a method for formulating an emergency plan based on industrial parks.

[0006] The technical solution adopted by the present invention to solve its technical problems is: a method for formulating an emergency plan based on industrial parks, including: S1: Obtain the original data collected by the distributed sensing network, and use the edge computing node to perform timestamp marking, outlier filtering, and format conversion processing on the original data to generate a standardized basic data stream; S2: According to the standardized basic data stream, perform unified coding conversion of multi-source heterogeneous data through a preset data fusion model, and set a hierarchical caching update strategy for high-frequency environmental data and low-frequency device status data, and form a multi-dimensional associated data set after fusion; S3: Extract key status indicators from the fused multi-dimensional associated data set, and use the time series analysis method combined with the safety threshold range of the historical database to construct a digital twin model; S4: In the digital twin model, analyze the association relationships between entity nodes through a graph neural network, identify potential risk propagation paths, and generate a risk topology map; S5: According to the high-risk areas of the risk topology map, call the knowledge graph to perform historical case similarity matching, extract disposal elements, and generate preliminary emergency suggestions; S6: Based on the preliminary emergency suggestions, use a resource scheduling optimization algorithm to calculate the emergency resource allocation plan, and generate a dynamic response route map in combination with a path planning algorithm; S7: Input the dynamic response route map, resource allocation plan, and risk topology map into the situation evolution prediction model, simulate the risk control effects of different disposal plans, and select the optimal emergency disposal plan; S8: Generate structured emergency instructions according to the optimal emergency disposal plan, and push the corresponding instruction sets of the blockade range, evacuation route, and resource allocation parameters to the execution unit through the emergency command system. After receiving the instructions, the execution unit executes the emergency disposal task according to the preset process.

[0007] Preferably, in S1, the original data includes the original data generated by geographical location sensors, environmental parameter monitoring devices, and personnel distribution tracking systems; in S1, the standardized basic data stream contains four basic fields: sensor identifier, measurement value, timestamp, and data quality flag; the calculation formula for the standardized basic data stream is:

[0008] where, represents the standardized basic data stream, represents the sensor identifier, represents the measurement value, represents the timestamp, represents the data quality flag, represents the total number of data points.

[0009] Preferably, in S2, the JSON format is used as the unified data exchange standard, and a hierarchical caching update strategy of 10Hz sampling for high-frequency environmental data and 1Hz sampling for low-frequency device status data is adopted. The high-frequency environmental data is updated every 5 seconds, and the low-frequency device status data is updated every 1 minute; use a data fusion algorithm to perform correlation analysis on the data in the space-time dimension to obtain a multi-dimensional correlation data set; the calculation formula for the multi-dimensional correlation data set is:

[0010] In the formula, is the element in the row and column of the space-time correlation matrix, representing the data source and the data source The associated situation; is a preset time threshold;

[0011] Weighted fusion of data from different data sources is performed through a spatio-temporal association matrix to obtain a multi-dimensional association dataset; the calculation formula for the multi-dimensional association dataset is:

[0012] Among them, represents the multi-dimensional association dataset, represents the data of the th data source, is the weight of the th data source, and the weight is determined according to factors such as the reliability of the data source and the data update frequency,

[0013] Preferably, the specific content in S3 includes: extracting key state indicators such as temperature, pressure, and flow from the multi-dimensional association dataset, implementing the ARIMA time series analysis method using the statsmodels library in Python to identify the change trend of the data, combining the preset safety threshold range in the historical database, using the deviation detection algorithm to calculate the distance between the current state and the safety threshold, and constructing a digital twin model reflecting the real-time state of the physical device. The digital twin model includes the device geometric model, state parameters, and operation rules; The calculation formula for the time series analysis model is:

[0014] Among them, , represents the number of autoregressive terms, represents the number of differencing times, represents the number of moving average terms, represents the number of time series values at the and are the autoregressive and moving average parameters respectively, represents the white noise error term; The device geometric model is constructed using three-dimensional modeling technology and includes the shape, size, and position of the device. Its mathematical expression of the device geometric model is:

[0015] Among them, represents the device geometric model, represents the point coordinates in the device geometric space; The calculation formula for the digital twin model is:

[0016] Among them, the digital twin model representing time t, the device geometric model, representing real-time status parameters , and it is the th status parameter; representing the set of operation rules, , and it is the th operation rule; By associatively mapping the real-time status parameters and operation rules with the device geometric model, the mapping relationship between the physical world and the digital world is realized.

[0017] Preferably, the specific content in S4 includes: converting the device entities and their connection relationships in the digital twin model into graph structure data, analyzing the information transmission and influence relationships between entity nodes through the GCN graph convolutional neural network implemented by the PyTorch Geometric library, identifying potential risk propagation paths and generating a risk topology graph with risk level markings, where the nodes in the risk topology graph represent device entities, the edges represent influence relationships, and the node colors represent risk levels.

[0018] Preferably, the specific content in S5 includes: based on the risk score of the high-risk area identified in the risk topology graph being greater than 0.8, extracting key risk feature vectors, using the risk feature vectors as query conditions to call the knowledge graph to perform historical case similarity matching, screening historical cases with a similarity exceeding 75% using the cosine similarity algorithm implemented by the scikit-learn library, and extracting disposal elements to generate preliminary emergency suggestions, where the preliminary emergency suggestions include a resource requirement list and disposal area information.

[0019] Preferably, the specific content in S6 includes: inputting the resource requirement list and disposal area information in the preliminary emergency suggestions into the resource scheduling optimization algorithm, calculating the optimal resource allocation plan based on the current available emergency resource inventory status using the integer linear programming method implemented by the PuLP library, and at the same time generating a dynamic response roadmap considering traffic conditions in combination with the A* path planning algorithm, where the dynamic response roadmap includes resource scheduling paths and time nodes; The calculation formula of the evaluation function of the A* path planning algorithm is:

[0020] Among them, represents the estimated total cost from the starting point passing through node to the end point, represents the actual cost from the starting point passing through node , represents the heuristic estimated cost from node to the end point; The actual cost is determined by calculating the path length or time from the starting point to the node . The path length is calculated based on the distance information in the map data, and the time can be calculated according to the traffic conditions and vehicle driving speed information; The heuristic estimated cost is calculated using the Euclidean distance method. The calculation formula for the Euclidean distance is:

[0021] where, is the coordinate of the node , and is the coordinate of the end point.

[0022] Preferably, the specific content in S7 includes: inputting the dynamic response roadmap, resource allocation plan, and risk topology map into the situation evolution prediction model of Monte Carlo simulation implemented based on the NumPy library, simulating the risk control effects of different disposal plans within 4 hours, and selecting the optimal emergency disposal plan by minimizing the comprehensive risk index; The comprehensive risk index is comprehensively determined by three factors: the risk occurrence probability, the risk impact degree, and the risk control difficulty. Its calculation formula is:

[0023] where, is the comprehensive risk index, is the risk occurrence probability, is the weight of the risk occurrence probability, is the risk impact degree, is the weight of the risk impact degree, is the risk control difficulty, is the weight of the risk control difficulty, and the weights satisfy ; The risk occurrence probability is obtained through historical data statistics; the risk impact degree is determined according to the losses caused by the risk, and the quantitative indicators of casualties, property losses, and environmental damage; the risk control difficulty is evaluated according to the resources, time, and technical factors required for emergency disposal; By simulating different disposal plans, calculate the comprehensive risk index of each plan, and select the plan with the smallest comprehensive risk index as the optimal emergency disposal plan. The optimal emergency disposal plan includes resource allocation strategies, response timings, and expected control effects.

[0024] Preferably, generate a structured emergency instruction XML document containing the coordinates of the blocked area, the sequence of evacuation route nodes, and resource allocation parameters according to the optimal emergency response plan; push the corresponding instruction set to each execution unit through the secure communication channel of the emergency command system; The calculation formula for the optimal emergency response plan is:

[0025] Among them, represents the optimal emergency response plan, represents the set of all possible response plans, and the optimal plan is selected by minimizing the comprehensive risk index

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention discloses a method for formulating an emergency plan based on an industrial park, which realizes data collection and preprocessing through a distributed sensing network and edge computing, constructs a dynamic model of the park by using multi-source heterogeneous data fusion and digital twin technology, analyzes the risk propagation path by combining graph neural networks, and generates preliminary emergency suggestions based on historical cases matched by a knowledge graph. The present invention further adopts resource scheduling optimization and path planning algorithms to formulate a dynamic response plan, evaluates the effects of different plans through a situation evolution prediction model, and finally generates an optimal emergency response plan and issues a structured instruction. This method realizes the intelligent formulation of the emergency plan for the industrial park, improves the accuracy of risk identification and the timeliness of emergency response, and provides strong support for the safety management of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic flowchart of a method for formulating an emergency plan based on an industrial park. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] As Figure 1 shown, a method for formulating an emergency plan based on an industrial park includes: S1: Obtain the original data collected by the distributed sensing network, and use the edge computing node to perform timestamp marking, outlier filtering, and format conversion processing on the original data to generate a standardized basic data stream; ​S2: According to the standardized basic data stream, perform unified encoding conversion of multi-source heterogeneous data through a preset data fusion model, where a hierarchical caching update strategy for high-frequency environmental data and low-frequency device status data is set, and a multi-dimensional associated data set is formed after fusion; S3: Extract key status indicators from the fused multi-dimensional associated data set, and construct a digital twin model by using a time series analysis method in combination with the security threshold range of the historical database; S4: In the digital twin model, analyze the association relationship between entity nodes through a graph neural network, identify potential risk propagation paths, and generate a risk topology map; S5: According to the high-risk areas of the risk topology map, call the knowledge graph to perform historical case similarity matching, extract disposal elements, and generate preliminary emergency suggestions; S6: Based on the preliminary emergency suggestions, use a resource scheduling optimization algorithm to calculate an emergency resource allocation plan, and generate a dynamic response roadmap in combination with a path planning algorithm; S7: Input the dynamic response roadmap, resource allocation plan, and risk topology map into a situation evolution prediction model, simulate the risk control effects of different disposal plans, and select the optimal emergency disposal plan; S8: Generate structured emergency instructions according to the optimal emergency disposal plan, and push the corresponding instruction sets of the blockade range, evacuation route, and resource allocation parameters to the execution unit through the emergency command system. After receiving the instructions, the execution unit executes the emergency disposal task according to the preset process.

[0030] Preferably, in S1, the original data includes the original data generated by geographical location sensors, environmental parameter monitoring devices, and personnel distribution tracking systems; in S1, the standardized basic data stream includes four basic fields: sensor identifier, measurement value, timestamp, and data quality mark; the calculation formula of the standardized basic data stream is:

[0031] Wherein, represents the standardized basic data stream, represents the sensor identifier, represents the measurement value, represents the timestamp, represents the data quality mark, represents the total number of data points.

[0032] Preferably, in S2, the JSON format is adopted as the unified data exchange standard, and a hierarchical caching update strategy for high-frequency environmental data sampled at 10 Hz and low-frequency device status data sampled at 1 Hz is used. The high-frequency environmental data is updated once every 5 seconds, and the low-frequency device status data is updated once every 1 minute. The data fusion model is a fusion algorithm model based on spatio-temporal association rules. This model first performs time alignment processing on the high-frequency environmental data and the low-frequency device status data, and then constructs a spatio-temporal association matrix to perform association analysis on the data from different data sources at the same time or close times. The calculation formula for the spatio-temporal association matrix is:

[0033] In the formula, is the element in the th row and th column of the spatio-temporal association matrix, representing the association situation between data source and data source ; is the preset time threshold; The data from different data sources is weighted and fused through the spatio-temporal association matrix to obtain a multi-dimensional association data set. The calculation formula for the multi-dimensional association data set is:

[0034] Among them, represents the multi-dimensional association data set, represents the data of the th data source, is the weight of the th data source. The weight is determined according to factors such as the reliability of the data source and the data update frequency, is the total number of data sources.

[0035] Preferably, the specific content in S3 includes: extracting key status indicators such as temperature, pressure, and flow from the multi-dimensional association data set, using the statsmodels library in Python to implement the ARIMA time series analysis method to identify the change trend of the data, combining the preset safety threshold range in the historical database, using the deviation detection algorithm to calculate the distance between the current state and the safety threshold, and constructing a digital twin model reflecting the real-time state of the physical device. The digital twin model includes the device geometric model, state parameters, and operation rules; The calculation formula for the time series analysis model is:

[0036] Among them , represents the number of autoregressive terms, represents the number of differencing times, represents the number of moving average terms, Represents The number of time series values at a moment, and Are the autoregressive and moving average parameters respectively, Represents the white noise error term; The device geometric model is constructed using three-dimensional modeling technology, including the shape, size, and position of the device. Its mathematical expression of the device geometric model is:

[0037] Among them, Represents the device geometric model, Represents the point coordinates in the device geometric space; The calculation formula of the digital twin model is:

[0038] Among them, Represents the digital twin model at time t, Represents the device geometric model, Represents the real-time state parameter , is the th state parameter; Represents the set of operation rules, , is the th operation rule; By associating and mapping the real-time state parameter and the operation rule with the device geometric model, the mapping relationship between the physical world and the digital world is realized.

[0039] Preferably, the specific content in S4 includes: converting the device entities and their connection relationships in the digital twin model into graph structure data, analyzing the information transmission and influence relationships between entity nodes through the GCN graph convolutional neural network implemented by the PyTorch Geometric library, identifying potential risk propagation paths and generating a risk topology graph with risk level markings. In the risk topology graph, nodes represent device entities, edges represent influence relationships, and node colors represent risk levels.

[0040] Preferably, the specific content in S5 includes: based on the risk score of the high-risk area identified in the risk topology graph being greater than 0.8, extracting key risk feature vectors, using the risk feature vectors as query conditions to call the knowledge graph to perform historical case similarity matching, screening historical cases with a similarity exceeding 75% using the cosine similarity algorithm implemented by the scikit-learn library, and extracting disposal elements to generate preliminary emergency suggestions. The preliminary emergency suggestions include a resource requirement list and disposal area information.

[0041] Preferably, the specific content in S6 includes: inputting the resource requirement list and the information of the disposal area in the preliminary emergency suggestion into the resource scheduling optimization algorithm, calculating the optimal resource allocation plan by using the integer linear programming method with the PuLP library based on the current available emergency resource inventory status, and simultaneously generating a dynamic response roadmap considering the traffic conditions by combining the A* path planning algorithm. The dynamic response roadmap includes the resource scheduling path and time nodes; The calculation formula of the evaluation function of the A* path planning algorithm is:

[0042] Among them, represents the estimated total cost from the starting point through the node to the end point, represents the actual cost from the starting point through the node , represents the heuristic estimated cost from the node to the end point; The actual cost is determined by calculating the path length or time from the starting point to the node . The path length is calculated according to the distance information in the map data, and the time can be calculated according to the traffic conditions and vehicle driving speed information; The heuristic estimated cost is calculated by using the Euclidean distance method. The calculation formula of the Euclidean distance is:

[0043] Among them, is the coordinate of the node , is the coordinate of the end point.

[0044] Preferably, the specific content in S7 includes: inputting the dynamic response roadmap, the resource allocation plan and the risk topology map into the situation evolution prediction model of Monte Carlo simulation implemented based on the NumPy library, simulating the risk control effects of different disposal plans within 4 hours, and selecting the optimal emergency disposal plan by minimizing the comprehensive risk index; The comprehensive risk index is comprehensively determined by three factors: the risk occurrence probability, the risk impact degree and the risk control difficulty. Its calculation formula is:

[0045] Among them, is the comprehensive risk index, is the risk occurrence probability, is the weight of the risk occurrence probability, is the risk impact degree, is the weight of the risk impact degree, is the risk control difficulty is the weight of the risk control difficulty, and the weight satisfies ; Probability of risk occurrence is obtained through historical data statistics; Degree of risk impact is determined according to the losses caused by the risk, and the quantitative indicators of casualties, property losses and environmental damage; Risk control difficulty is evaluated according to the resources, time and technical factors required for emergency response; By simulating different disposal plans, calculate the comprehensive risk index of each plan, and select the plan with the smallest comprehensive risk index as the optimal emergency disposal plan. The optimal emergency disposal plan includes resource allocation strategies, response time sequences and expected control effects.

[0046] Preferably, generate a structured emergency instruction XML document containing the coordinates of the blocked area, the sequence of evacuation route nodes and resource allocation parameters according to the optimal emergency disposal plan; Push the corresponding instruction set to each execution unit through the secure communication channel of the emergency command system; The calculation formula of the optimal emergency disposal plan is:

[0047] where, represents the optimal emergency disposal plan, represents the set of all possible disposal plans, and the optimal plan is selected by minimizing the comprehensive risk index to select the optimal plan.

[0048] In a specific implementation manner, based on the emergency plan formulation method for industrial parks, the standardized processing of raw data is the basic link. The raw data generated by geographical location sensors, environmental parameter monitoring devices and personnel distribution tracking systems may have different formats. By standardizing the basic data stream, it is unified into structured data containing sensor identifiers, measurement values, timestamps and data quality marks.

[0049] Exemplarily, the data record of a temperature sensor is identifier T001, measurement value 25.5 degrees, timestamp 2023-10-01 08:00:00, and the data quality mark is high.

[0050] This standardization facilitates subsequent data fusion and analysis, improving the efficiency and accuracy of data processing.

[0051] In terms of data exchange and hierarchical caching, the JSON format is adopted as the unified standard, and the high-frequency environmental data and low-frequency device status data are sampled at 10Hz and 1Hz respectively, with update frequencies of 5 seconds and 1 minute.

[0052] Environmental data such as air quality is updated every 5 seconds to ensure real-time, while device status such as switch status is updated every minute to reduce system load.

[0053] This hierarchical strategy effectively balances real-time and resource occupancy, ensuring stable system operation.

[0054] Specifically, when constructing a digital twin model, indicators such as temperature, pressure, and flow are extracted from a multi-dimensional associated dataset, and the change trend is identified through time series analysis methods.

[0055] Exemplarily, the temperature of a certain device has been rising continuously for 3 hours, from 20 degrees to 35 degrees, exceeding the safety threshold of 30 degrees. The system calculates the risk of exceeding the standard by 5 degrees through a deviation detection algorithm, and then updates the digital twin model to map the real-time status of the device. This model can intuitively reflect the status of physical devices and facilitate early warning. In risk propagation analysis, a risk topology map is generated using a graph convolutional neural network.

[0056] Exemplarily, a chemical equipment node is marked as high-risk due to abnormal temperature and colored red, and the pipeline nodes connected to it are marked yellow due to the influence relationship, indicating potential risk propagation paths. This visualization method helps managers quickly locate the risk source and improve decision-making efficiency.

[0057] Exemplarily, in the emergency advice generation process, high-risk areas are identified based on the risk topology map, and feature vectors are extracted and matched with historical cases.

[0058] Suppose the risk score of a certain area is 0.85, and the system screens out past cases with a similarity of 80% and generates advice including fire extinguisher requirements and evacuation areas. This method can quickly reuse experience and reduce decision-making time.

[0059] In resource scheduling optimization, a dynamic response route is generated by combining the A* path planning algorithm.

[0060] Exemplarily, for an emergency vehicle from the warehouse to the accident site, a path that avoids congested roads is selected, and the estimated time is 15 minutes. This optimization ensures that resources arrive quickly and improves the response speed.

[0061] Finally, in the situation evolution prediction and instruction generation, by simulating the effects of different scenarios, the disposal scenario with the lowest comprehensive risk is selected, and a structured instruction document is generated.

[0062] Exemplarily, the instruction includes blocking an area with a radius of 500 meters and an evacuation route to ensure precise and efficient execution. This full-process method significantly improves the scientificity and operability of emergency management.

[0063] The present invention discloses an intelligent emergency plan formulation method based on industrial parks, which realizes data collection and preprocessing through a distributed sensing network and edge computing, constructs a dynamic model of the park by using multi-source heterogeneous data fusion and digital twin technology, analyzes the risk propagation path by combining graph neural networks, and generates preliminary emergency suggestions based on knowledge graph matching of historical cases. The present invention further formulates a dynamic response plan by using resource scheduling optimization and path planning algorithms, evaluates the effects of different plans through a situation evolution prediction model, and finally generates an optimal emergency disposal plan and issues structured instructions. This method realizes the intelligent formulation of industrial park emergency plans, improves the accuracy of risk identification and the timeliness of emergency response, and provides strong support for park safety management.

[0064] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for formulating an emergency plan based on an industrial park, characterized in that, Including: S1: Obtain the original data collected by the distributed sensing network, and use edge computing nodes to perform timestamp marking, outlier filtering, and format conversion processing on the original data to generate a standardized basic data stream; S2: According to the standardized basic data stream, perform unified encoding conversion of multi-source heterogeneous data through a preset data fusion model, where a hierarchical caching update strategy for high-frequency environmental data and low-frequency device status data is set, and after fusion, a multi-dimensional associated data set is formed; S3: Extract key status indicators from the fused multi-dimensional associated data set, and use time series analysis methods combined with the security threshold range of the historical database to construct a digital twin model; S4: In the digital twin model, analyze the association relationship between entity nodes through a graph neural network, identify potential risk propagation paths, and generate a risk topology map; S5: According to the high-risk areas of the risk topology map, call the knowledge graph to perform historical case similarity matching, and extract disposal elements to generate preliminary emergency suggestions; S6: Based on the preliminary emergency suggestions, use a resource scheduling optimization algorithm to calculate an emergency resource allocation plan, and combine it with a path planning algorithm to generate a dynamic response roadmap; S7: Input the dynamic response roadmap, resource allocation plan, and risk topology map into the situation evolution prediction model, simulate the risk control effects of different disposal plans, and select the optimal emergency disposal plan; S8: Generate a structured emergency instruction according to the optimal emergency disposal plan, and push a corresponding instruction set of the blockade range, evacuation route, and resource allocation parameters to the execution unit through the emergency command system. After receiving the instruction, the execution unit executes the emergency disposal task according to the preset process.

2. The method for formulating an emergency plan based on an industrial park according to claim 1, wherein In S1, the original data includes the original data generated by geographical location sensors, environmental parameter monitoring devices, and personnel distribution tracking systems; in S1, the standardized basic data stream contains four basic fields: sensor identifier, measurement value, timestamp, and data quality mark; the calculation formula for the standardized basic data stream is: Among them, represents the standardized basic data stream, represents the sensor identifier, represents the measured value, represents the timestamp, represents the data quality flag, represents the total number of data points.

3. The method for formulating an emergency plan based on an industrial park according to claim 1, wherein, In S2, the JSON format is used as the unified data exchange standard, and a hierarchical caching update strategy for high-frequency environmental data sampled at 10Hz and low-frequency device status data sampled at 1Hz is adopted. The high-frequency environmental data is updated once every 5 seconds, and the low-frequency device status data is updated once every 1 minute; the data fusion model is a fusion algorithm model based on spatio-temporal association rules. This model first performs time alignment processing on high-frequency environmental data and low-frequency device status data, and then constructs a spatio-temporal association matrix to perform association analysis on the data of different data sources at the same time or close times. The calculation formula for the spatio-temporal association matrix is: In the formula, is the element at the -th row and the -th column of the spatio-temporal correlation matrix, representing the correlation between data source and data source ; is a preset time threshold; Perform weighted fusion on the data of different data sources through the spatio-temporal association matrix to obtain a multi-dimensional associated data set; the calculation formula for the multi-dimensional associated data set is: Among them, Multi-dimensional associated data set, represents the data of the th data source, is the weight of the th data source, and the weight is determined according to the reliability of the data source and the data update frequency factor, where is the total number of data sources.

4. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that The specific content in S3 includes: extracting key status indicators of temperature, pressure, and flow from the multi-dimensional correlation dataset, implementing the ARIMA time series analysis method using the statsmodels library in Python to identify the change trend of the data, combining the preset safety threshold range in the historical database, using the deviation detection algorithm to calculate the distance between the current state and the safety threshold, and constructing a digital twin model reflecting the real-time state of the physical device. The digital twin model includes the device geometric model, state parameters, and operation rules; The calculation formula of the time series analysis model is: Among them represents the number of autoregressive terms, represents the number of differencing times, represents the number of moving average terms, represents the number of time series values at the and are autoregressive and moving average parameters respectively, represents the white noise error term; The device geometric model is constructed using 3D modeling technology, including the shape, size, and position of the device. Its mathematical expression of the device geometric model is: Among them, represents the device geometric model, which is represented as the point coordinates in the device geometric space; The calculation formula of the digital twin model is: Among them, represents the digital twin model of time , represents the device geometric model represents the real-time state parameter , which is the th state parameter; represents the set of operation rules , , which is the th operation rule; By associating and mapping the real-time state parameters and operation rules with the device geometric model, the mapping relationship between the physical world and the digital world is realized.

5. The method for formulating an emergency plan based on an industrial park according to claim 1, wherein, The specific content in S4 includes: converting the device entities and their connection relationships in the digital twin model into graph structure data, analyzing the information transmission and influence relationships between entity nodes through the GCN graph convolutional neural network implemented by the PyTorch Geometric library, identifying potential risk propagation paths, and generating a risk topology graph with risk level markings. In the risk topology graph, nodes represent device entities, edges represent influence relationships, and node colors represent risk levels.

6. The method for formulating an emergency plan based on an industrial park according to claim 1, wherein, The specific content in S5 includes: based on the risk score of the high-risk area identified in the risk topology graph being greater than 0.8, extracting key risk feature vectors, using the risk feature vectors as query conditions to call the knowledge graph to perform historical case similarity matching, screening historical cases with a similarity exceeding 75% using the cosine similarity algorithm implemented by the scikit-learn library, and extracting disposal elements to generate preliminary emergency suggestions. The preliminary emergency suggestions include a resource requirement list and disposal area information.

7. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that The specific content in S6 includes: inputting the resource requirement list and disposal area information in the preliminary emergency suggestions into the resource scheduling optimization algorithm, based on the current available emergency resource inventory status, using the integer linear programming method implemented by the PuLP library to calculate the optimal resource allocation plan, and at the same time combining the A* path planning algorithm to generate a dynamic response roadmap considering traffic conditions. The dynamic response roadmap includes resource scheduling paths and time nodes; The calculation formula of the evaluation function of the A* path planning algorithm is: Among them, represents the estimated total cost from the starting point through node to the end point, represents the actual cost from the starting point through node , represents the heuristic estimated cost from node to the end point; The actual cost is determined by calculating the path length or time from the starting point to the node . The path length is calculated based on the distance information in the map data, and the time can be calculated based on the traffic conditions and vehicle driving speed information; The heuristic estimated cost It is calculated by the method of Euclidean distance. The calculation formula of Euclidean distance is: Among them, where is the node coordinates, are the coordinates of the end point.

8. A method for formulating an emergency plan based on an industrial park according to claim 1, wherein The specific content in S7 includes: jointly inputting the dynamic response roadmap, resource allocation plan, and risk topology graph into the situation evolution prediction model based on the Monte Carlo simulation implemented by the NumPy library, simulating the risk control effects of different disposal plans within 4 hours, and selecting the optimal emergency disposal plan by minimizing the comprehensive risk index; The comprehensive risk index is comprehensively determined by three factors: the probability of risk occurrence, the degree of risk impact, and the difficulty of risk control. Its calculation formula is: Among them, is the comprehensive risk index, is the risk occurrence probability, is the weight of the risk occurrence probability, is the risk impact degree, is the weight of the risk impact degree, is the risk control difficulty, is the weight of the risk control difficulty, and the weights satisfy ; Probability of risk occurrence Obtained by statistical analysis of historical data; Degree of risk impact Determined according to the losses caused by the risk, and the quantitative indicators of casualties, property losses and environmental damage; Difficulty of risk control Evaluated according to the resources, time and technical factors required for emergency response; By simulating different disposal plans, calculating the comprehensive risk index of each plan, and selecting the plan with the smallest comprehensive risk index as the optimal emergency disposal plan. The optimal emergency disposal plan includes resource allocation strategies, response timings, and expected control effects.

9. The method for formulating an emergency plan based on an industrial park according to claim 1, wherein Generate a structured emergency instruction XML document containing the coordinates of the blockade area, the sequence of evacuation route nodes, and resource allocation parameters according to the optimal emergency response plan; push the corresponding instruction set to each execution unit through the secure communication channel of the emergency command system; The calculation formula for the optimal emergency response plan is: Among them, represents the optimal emergency response plan, represents the set of all possible response plans, and the optimal plan is selected by minimizing the comprehensive risk index ​

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