A method for formulating emergency plans based on industrial parks

Abstract

The present invention discloses a method for formulating emergency plans based on industrial parks. It realizes data collection and preprocessing through distributed sensor networks and edge computing, builds a dynamic model of the park using multi-source heterogeneous data fusion and digital twin technology, analyzes risk propagation paths in combination with graph neural networks, and generates preliminary emergency suggestions based on knowledge graph matching of historical cases. The present invention further adopts resource scheduling optimization and path planning algorithms to formulate dynamic response plans, evaluates the effects of different plans through situation evolution prediction models, and finally generates the optimal emergency disposal plan and issues structured instructions. This method realizes the intelligent formulation of emergency plans for industrial parks, improves the accuracy of risk identification and the timeliness of emergency response, and provides strong support for park safety management.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial park emergency management, and in particular to an industrial park-based emergency plan formulation method. Background Art

[0002] As a vital vehicle for national economic development, industrial parks' safe operations and emergency management are crucial for ensuring production safety and environmental protection. As industrial parks expand in scale and become more complex, traditional single-source monitoring methods and isolated data processing approaches are no longer sufficient to meet the comprehensive awareness and rapid response needs of modern industrial parks. Current industrial park management systems are plagued by data silos, making it difficult to effectively integrate information collected by various sensor devices. This results in incomplete situational awareness and delayed risk warnings.

[0003] Industrial park security management faces the technical challenge of integrating heterogeneous data from multiple sources. The data generated by the park's geolocation sensors, environmental parameter monitoring equipment, and personnel tracking systems vary in format and update frequency. The lack of a unified data standard and processing framework hinders effective decision-making from these valuable information resources. These difficulties in data standardization and integration further imperfect the overall park situation model, which fails to accurately reflect the park's real-time status. This leads to deviations between risk assessment results and actual conditions, compromising the scientific nature and relevance of emergency response plans.

[0004] How to achieve efficient collection and fusion processing of multi-source heterogeneous data in a complex and ever-changing industrial park environment, 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 an emergency plan formulation method based on an industrial park.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for formulating an emergency plan based on an industrial park, comprising:

[0007] S1: Obtain the raw data collected by the distributed sensor network, use the edge computing node to timestamp, filter outliers and convert the format of the raw data to generate a standardized basic data stream;

[0008] S2: Based on the standardized basic data stream, a unified encoding conversion of multi-source heterogeneous data is performed through a preset data fusion model, wherein a hierarchical cache update strategy is set for high-frequency environmental data and low-frequency device status data, and a multi-dimensional associated data set is formed after fusion;

[0009] S3: Extract key status indicators from the fused multi-dimensional correlation data set, use time series analysis methods combined with the safety threshold range of the historical database to build a digital twin model;

[0010] S4: In the digital twin model, the relationship between entity nodes is analyzed through a graph neural network to identify potential risk propagation paths and generate a risk topology map;

[0011] S5: Based on the high-risk areas of the risk topology map, call the knowledge graph to perform historical case similarity matching, extract the disposal elements and generate preliminary emergency suggestions;

[0012] S6: Based on the preliminary emergency suggestions, a resource scheduling optimization algorithm is used to calculate an emergency resource allocation plan, and a path planning algorithm is used to generate a dynamic response roadmap;

[0013] 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;

[0014] S8: Generate structured emergency instructions based on the optimal emergency response plan, and push the corresponding instruction set of 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 response task according to the preset process.

[0015] Preferably, the raw data in S1 includes raw data generated by geographic location sensors, environmental parameter monitoring equipment, and personnel distribution tracking systems; the standardized basic data stream in S1 includes four basic fields: sensor identifier, measurement value, timestamp, and data quality mark; the calculation formula of the standardized basic data stream is:

[0016]

[0017] in, Represents a standardized basic data stream, represents the sensor identifier, Indicates the measured value, Indicates a timestamp, Indicates data quality mark, Represents the total number of data points.

[0018] Preferably, the JSON format is used as a unified data exchange standard in S2, and a hierarchical cache update strategy of 10Hz sampling of high-frequency environmental data and 1Hz sampling of low-frequency device status data 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 spatiotemporal association rules. The model first performs time alignment processing on the high-frequency environmental data and the low-frequency device status data, and then constructs a spatiotemporal association matrix to perform correlation analysis on data from different data sources at the same time or similar time. The calculation formula of the spatiotemporal association matrix is:

[0019]

[0020] Where, is the spatiotemporal correlation matrix Rank Column element, representing the data source and data sources the related situation; is the preset time threshold;

[0021] The data from different data sources are weighted and fused through the spatiotemporal correlation matrix to obtain a multidimensional correlation dataset. The calculation formula for the multidimensional correlation dataset is:

[0022]

[0023] in, Represents a multidimensional relational dataset, Indicates the Data from the data source, for The weight of the data source, weight Determined based on the reliability of the data source and the frequency of data updates. The total number of data sources.

[0024] Preferably, the specific contents in S3 include: extracting key status indicators of temperature, pressure, and flow from a multidimensional correlated 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 that reflects the real-time status of the physical device. The digital twin model includes a device geometry model, state parameters, and operation rules;

[0025] The calculation formula of the timing analysis model is:

[0026]

[0027] in represents the number of autoregressive terms, represents the number of differences, represents the number of moving average items, express The number of time series values ​​at the moment, and are the autoregressive and moving average parameters, represents the white noise error term;

[0028] The device geometric model is constructed using three-dimensional modeling technology, including the shape, size, and position of the device. The mathematical expression of the device geometric model is:

[0029]

[0030] in, Represents the device geometry model, Represented as point coordinates in the device geometric space;

[0031] The calculation formula of the digital twin model is:

[0032]

[0033] in, Indicates time Digital twin models, Indicates real-time status parameters For the Status parameters; Indicates the set of running rules. For the By associating and mapping real-time status parameters and operating rules with the device geometric model, the mapping relationship between the physical world and the digital world is realized.

[0034] Preferably, the specific contents in S4 include: converting the device entities and their connection relationships in the digital twin model into graph structure data, analyzing the information transmission and influence relationship 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 map containing risk level marks, in which the nodes in the risk topology map represent device entities, the edges represent influence relationships, and the node colors represent risk levels.

[0035] Preferably, the specific contents in S5 include: based on the risk score of the high-risk area identified in the risk topology map being greater than 0.8, extracting the key risk feature vector, using the risk feature vector as a query condition to call the knowledge graph to perform historical case similarity matching, using the cosine similarity algorithm implemented by the scikit-learn library to screen historical cases with a similarity of more than 75%, extracting disposal elements to generate preliminary emergency recommendations, and the preliminary emergency recommendations include a resource requirement list and disposal area information.

[0036] Preferably, the specific contents in S6 include: inputting the resource requirement list and disposal area information in the preliminary emergency proposal into the resource scheduling optimization algorithm, using the PuLP library to implement the integer linear programming method to calculate the optimal resource allocation plan based on the current available emergency resource inventory status, and combining the A-star path planning algorithm to generate a dynamic response route map considering traffic conditions, the dynamic response route map including resource scheduling paths and time nodes;

[0037] The evaluation function calculation formula of the A-star path planning algorithm is:

[0038]

[0039] in, Indicates passing through the node from the starting point The estimated total cost to reach the destination, Indicates passing through the node from the starting point The actual cost, Represents a slave node heuristically estimated cost to the endpoint;

[0040] Actual cost stated By calculating from the starting point to the node The path length or time is determined by the path length, 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 speed information;

[0041] The heuristic estimates the cost The Euclidean distance method is used for calculation. The calculation formula of the Euclidean distance is:

[0042]

[0043] in, For nodes The coordinates of is the coordinate of the end point.

[0044] Preferably, the specific contents in S7 include: inputting the dynamic response roadmap, resource allocation plan and risk topology map into a situation evolution prediction model based on 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;

[0045] The comprehensive risk index is determined by three factors: the probability of risk occurrence, the degree of risk impact, and the difficulty of risk control. Its calculation formula is:

[0046]

[0047] in, is the comprehensive risk index, is the probability of risk occurrence, is the weight of the risk probability, is the risk impact degree, is the weight of the risk impact, To control the difficulty of risk, is the weight of risk control difficulty, the weight satisfies ;

[0048] Probability of risk occurrence Obtained through historical data statistics; risk impact Determine the quantitative indicators of losses, casualties, property losses and environmental damage caused by the risk; the difficulty of risk control Evaluate the resources, time and technical factors required for emergency response;

[0049] 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 strategy, response sequence and expected control effect.

[0050] Preferably, a structured emergency instruction XML document containing the blockade area coordinates, evacuation route node sequence, and resource allocation parameters is generated according to the optimal emergency response plan; and the corresponding instruction set is pushed to each execution unit through the secure communication channel of the emergency command system;

[0051] The calculation formula for the optimal emergency response plan is:

[0052]

[0053] in, represents the optimal emergency response plan, Represents the set of all possible disposal options, by minimizing the comprehensive risk index To select the best solution.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention discloses a method for formulating emergency plans based on industrial parks. It realizes data collection and preprocessing through distributed sensor networks and edge computing, builds a dynamic model of the park using multi-source heterogeneous data fusion and digital twin technology, analyzes risk propagation paths in combination with graph neural networks, and generates preliminary emergency suggestions based on knowledge graph matching of historical cases. The present invention further adopts resource scheduling optimization and path planning algorithms to formulate dynamic response plans, evaluates the effects of different plans through situation evolution prediction models, and finally generates the optimal emergency disposal plan and issues structured instructions. This method realizes the intelligent formulation of emergency plans for industrial parks, improves the accuracy of risk identification and the timeliness of emergency response, and provides strong support for park safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of an emergency plan development method based on an industrial park. DETAILED DESCRIPTION

[0057] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0058] like Figure 1 As shown, a method for formulating an emergency plan based on an industrial park includes:

[0059] S1: Obtain the raw data collected by the distributed sensor network, use the edge computing node to timestamp, filter outliers and convert the format of the raw data to generate a standardized basic data stream;

[0060] S2: Based on the standardized basic data stream, a unified encoding conversion of multi-source heterogeneous data is performed through a preset data fusion model, wherein a hierarchical cache update strategy is set for high-frequency environmental data and low-frequency device status data, and a multi-dimensional associated data set is formed after fusion;

[0061] S3: Extract key status indicators from the fused multi-dimensional correlation data set, use time series analysis methods combined with the safety threshold range of the historical database to build a digital twin model;

[0062] S4: In the digital twin model, the relationship between entity nodes is analyzed through a graph neural network to identify potential risk propagation paths and generate a risk topology map;

[0063] S5: Based on the high-risk areas of the risk topology map, call the knowledge graph to perform historical case similarity matching, extract the disposal elements and generate preliminary emergency suggestions;

[0064] S6: Based on the preliminary emergency suggestions, a resource scheduling optimization algorithm is used to calculate an emergency resource allocation plan, and a path planning algorithm is used to generate a dynamic response roadmap;

[0065] 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;

[0066] S8: Generate structured emergency instructions based on the optimal emergency response plan, and push the corresponding instruction set of 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 response task according to the preset process.

[0067] Preferably, the raw data in S1 includes raw data generated by geographic location sensors, environmental parameter monitoring equipment, and personnel distribution tracking systems; the standardized basic data stream in S1 includes four basic fields: sensor identifier, measurement value, timestamp, and data quality mark; the calculation formula of the standardized basic data stream is:

[0068]

[0069] in, Represents a standardized basic data stream, represents the sensor identifier, Indicates the measured value, Indicates a timestamp, Indicates data quality mark, Represents the total number of data points.

[0070] Preferably, the JSON format is used as a unified data exchange standard in S2, and a hierarchical cache update strategy of 10Hz sampling of high-frequency environmental data and 1Hz sampling of low-frequency device status data 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 spatiotemporal association rules. The model first performs time alignment processing on the high-frequency environmental data and the low-frequency device status data, and then constructs a spatiotemporal association matrix to perform correlation analysis on data from different data sources at the same time or similar time. The calculation formula of the spatiotemporal association matrix is:

[0071]

[0072] Where, is the spatiotemporal correlation matrix Rank Column element, representing the data source and data sources the related situation; is the preset time threshold;

[0073] The data from different data sources are weighted and fused through the spatiotemporal correlation matrix to obtain a multidimensional correlation dataset. The calculation formula for the multidimensional correlation dataset is:

[0074]

[0075] in, Multidimensional relational datasets, Indicates the Data from the data source, for The weight of the data source, weight Determined based on the reliability of the data source and the frequency of data updates. The total number of data sources.

[0076] Preferably, the specific contents in S3 include: extracting key status indicators of temperature, pressure, and flow from a multidimensional correlated 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 that reflects the real-time status of the physical device. The digital twin model includes a device geometry model, state parameters, and operation rules;

[0077] The calculation formula of the timing analysis model is:

[0078]

[0079] in, represents the number of autoregressive terms, represents the number of differences, represents the number of moving average items, express The number of time series values ​​at the moment, and are the autoregressive and moving average parameters, represents the white noise error term;

[0080] The device geometric model is constructed using three-dimensional modeling technology, including the shape, size, and position of the device. The mathematical expression of the device geometric model is:

[0081]

[0082] in, Represents the device geometry model, Represented as point coordinates in the device geometric space;

[0083] The calculation formula of the digital twin model is:

[0084]

[0085] in, Indicates time Digital twin models, Indicates real-time status parameters For the Status parameters; Indicates the set of running rules. For the By associating and mapping real-time status parameters and operating rules with the device geometric model, the mapping relationship between the physical world and the digital world is realized.

[0086] Preferably, the specific contents in S4 include: converting the device entities and their connection relationships in the digital twin model into graph structure data, analyzing the information transmission and influence relationship 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 map containing risk level marks, in which the nodes in the risk topology map represent device entities, the edges represent influence relationships, and the node colors represent risk levels.

[0087] Preferably, the specific contents in S5 include: based on the risk score of the high-risk area identified in the risk topology map being greater than 0.8, extracting the key risk feature vector, using the risk feature vector as a query condition to call the knowledge graph to perform historical case similarity matching, using the cosine similarity algorithm implemented by the scikit-learn library to screen historical cases with a similarity of more than 75%, extracting disposal elements to generate preliminary emergency recommendations, and the preliminary emergency recommendations include a resource requirement list and disposal area information.

[0088] Preferably, the specific contents in S6 include: inputting the resource requirement list and disposal area information in the preliminary emergency proposal into the resource scheduling optimization algorithm, using the PuLP library to implement the integer linear programming method to calculate the optimal resource allocation plan based on the current available emergency resource inventory status, and combining the A-star path planning algorithm to generate a dynamic response route map considering traffic conditions, the dynamic response route map including resource scheduling paths and time nodes;

[0089] The evaluation function calculation formula of the A-star path planning algorithm is:

[0090]

[0091] in, Indicates passing through the node from the starting point The estimated total cost to reach the destination, Indicates passing through the node from the starting point The actual cost, Represents a slave node heuristically estimated cost to the endpoint;

[0092] Actual cost stated By calculating from the starting point to the node The path length or time is determined by the path length, 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 speed information;

[0093] The heuristic estimates the cost The Euclidean distance method is used for calculation. The calculation formula of the Euclidean distance is:

[0094]

[0095] in, For nodes The coordinates of is the coordinate of the end point.

[0096] Preferably, the specific contents in S7 include: inputting the dynamic response roadmap, resource allocation plan and risk topology map into a situation evolution prediction model based on 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;

[0097] The comprehensive risk index is determined by three factors: the probability of risk occurrence, the degree of risk impact, and the difficulty of risk control. Its calculation formula is:

[0098]

[0099] in, is the comprehensive risk index, is the probability of risk occurrence, is the weight of the risk probability, is the risk impact degree, is the weight of the risk impact, To control the difficulty of risk, is the weight of risk control difficulty, the weight satisfies ;

[0100] Probability of risk occurrence Obtained through historical data statistics; risk impact Determine the quantitative indicators of losses, casualties, property losses and environmental damage caused by the risk; the difficulty of risk control Evaluate the resources, time and technical factors required for emergency response;

[0101] 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 strategy, response sequence and expected control effect.

[0102] Preferably, a structured emergency instruction XML document containing the blockade area coordinates, evacuation route node sequence, and resource allocation parameters is generated according to the optimal emergency response plan; and the corresponding instruction set is pushed to each execution unit through the secure communication channel of the emergency command system;

[0103] The calculation formula for the optimal emergency response plan is:

[0104]

[0105] in, represents the optimal emergency response plan, Represents the set of all possible disposal options, by minimizing the comprehensive risk index To select the best solution.

[0106] In one specific implementation, standardization of raw data is a fundamental step in developing an emergency plan for an industrial park. The raw data generated by geolocation sensors, environmental parameter monitoring equipment, and personnel tracking systems may come in various formats. By standardizing the underlying data streams, the data is unified into structured data containing sensor identifiers, measurement values, timestamps, and data quality markers.

[0107] For example, the data record of a temperature sensor has an identifier of T001, a measured value of 25.5 degrees, a timestamp of 2023-10-01 08:00:00, and a data quality mark of high.

[0108] This standardization facilitates subsequent data fusion and analysis, and improves the efficiency and accuracy of data processing.

[0109] In terms of data exchange and hierarchical caching, the JSON format is adopted as the unified standard, and 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.

[0110] Environmental data such as air quality is updated every 5 seconds to ensure real-time performance, while device status such as switch status is updated once a minute to reduce system load.

[0111] This hierarchical strategy effectively balances real-time performance and resource usage, ensuring stable system operation.

[0112] Specifically, when building a digital twin model, indicators such as temperature, pressure, and flow are extracted from multidimensional correlated data sets, and change trends are identified through time series analysis methods.

[0113] For example, if the temperature of a device rises continuously for three hours, from 20°C to 35°C, exceeding the safety threshold by 30°C, the system uses a deviation detection algorithm to calculate a risk of exceeding the limit by 5°C. This system then updates the digital twin model to map the device's real-time status. This model intuitively reflects the physical device status and facilitates early warning. In risk propagation analysis, a graph convolutional neural network is used to generate a risk topology map.

[0114] For example, a chemical equipment node is marked as high-risk in red due to abnormal temperature, while its connected pipeline nodes are marked in yellow due to their impact, highlighting the potential risk transmission path. This visualization method helps managers quickly locate the source of risk and improve decision-making efficiency.

[0115] For example, in the emergency advice generation stage, high-risk areas are identified based on the risk topology map, and feature vectors are extracted to match historical cases.

[0116] For example, if a certain area has a risk score of 0.85, the system will filter past cases with an 80% similarity and generate recommendations, including fire extinguisher requirements and evacuation zones. This approach allows for rapid experience reuse and reduces decision-making time.

[0117] In resource scheduling optimization, the A-star path planning algorithm is combined to generate dynamic response routes.

[0118] For example, an emergency vehicle can travel from the warehouse to the accident site in 15 minutes, avoiding congested roads. This optimization ensures that resources are deployed quickly and improves response speed.

[0119] Finally, in situation evolution prediction and instruction generation, by simulating the effects of different plans, the disposal plan with the lowest overall risk is selected and a structured instruction document is generated.

[0120] For example, the directive includes a 500-meter radius for blocking off areas and evacuation routes, ensuring precise and efficient execution. This comprehensive approach significantly enhances the scientific nature and operability of emergency management.

[0121] The present invention discloses a method for formulating intelligent emergency plans based on industrial parks. It realizes data collection and preprocessing through distributed sensor networks and edge computing, builds a dynamic model of the park using multi-source heterogeneous data fusion and digital twin technology, analyzes risk propagation paths in combination with graph neural networks, and generates preliminary emergency suggestions based on knowledge graph matching of historical cases. The present invention further adopts resource scheduling optimization and path planning algorithms to formulate dynamic response plans, evaluates the effects of different plans through situation evolution prediction models, and finally generates the optimal emergency disposal plan and issues structured instructions. This method realizes the intelligent formulation of emergency plans for industrial parks, improves the accuracy of risk identification and the timeliness of emergency response, and provides strong support for park safety management.

[0122] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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: include: S1: Obtain the raw data collected by the distributed sensor network, use the edge computing node to timestamp, filter outliers and convert the format of the raw data to generate a standardized basic data stream; S2: Based on the standardized basic data stream, a unified encoding conversion of multi-source heterogeneous data is performed through a preset data fusion model, wherein a hierarchical cache update strategy is set for high-frequency environmental data and low-frequency device status data, and a multi-dimensional associated data set is formed after fusion; S3: Extract key status indicators from the fused multi-dimensional correlation data set, use time series analysis methods combined with the safety threshold range of the historical database to build a digital twin model; S4: In the digital twin model, the relationship between entity nodes is analyzed through a graph neural network to identify potential risk propagation paths and generate a risk topology map; S5: Based on the high-risk areas of the risk topology map, call the knowledge graph to perform historical case similarity matching, extract the disposal elements and generate preliminary emergency suggestions; S6: Based on the preliminary emergency suggestions, a resource scheduling optimization algorithm is used to calculate an emergency resource allocation plan, and a path planning algorithm is used 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 structured emergency instructions based on the optimal emergency response plan, and push corresponding instruction sets of blockade scope, evacuation routes and resource allocation parameters to the execution unit through the emergency command system. After receiving the instructions, the execution unit executes the emergency response task according to the preset process; In S2, the JSON format is used as a unified data exchange standard, and a hierarchical cache update strategy is adopted, with high-frequency environmental data sampled at 10Hz and low-frequency device status data sampled at 1Hz. High-frequency environmental data is updated every 5 seconds, and low-frequency device status data is updated every 1 minute. The data fusion model is a fusion algorithm model based on spatiotemporal association rules. The model first performs time alignment processing on high-frequency environmental data and low-frequency device status data, and then constructs a spatiotemporal association matrix to perform correlation analysis on data from different data sources at the same time or similar time. The calculation formula of the spatiotemporal association matrix is: Where, is the spatiotemporal correlation matrix Rank Column element, representing the data source and data sources the related situation; is the preset time threshold; The data from different data sources are weighted and fused through the spatiotemporal correlation matrix to obtain a multidimensional correlation dataset. The calculation formula for the multidimensional correlation dataset is: in, Represents a multidimensional relational dataset, Indicates the Data from the data source, for The weight of the data source, weight Determined based on the reliability of the data source and the frequency of data updates. The total number of data sources.

2. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that: The raw data in S1 includes the raw data generated by the geographic location sensor, environmental parameter monitoring equipment, and personnel distribution tracking system. The standardized basic data stream in S1 contains four basic fields: sensor identifier, measurement value, timestamp, and data quality mark. The calculation formula of the standardized basic data stream is: in, Represents a standardized basic data stream, represents the sensor identifier, Indicates the measured value, Indicates a timestamp, Indicates data quality mark, Represents the total number of data points.

3. 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 such as temperature, pressure, and flow from multidimensional correlated data sets, using the statsmodels library in Python to implement the ARIMA time series analysis method to identify data change trends, 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 that reflects the real-time status of the physical device. The digital twin model includes the device geometry model, status parameters, and operating rules; The calculation formula of the timing analysis model is: in, represents the number of autoregressive terms, represents the number of differences, represents the number of moving average items, express The number of time series values ​​at the moment, and are the autoregressive and moving average parameters, 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. The mathematical expression of the device geometric model is: in, Represents the device geometry model, Represented as point coordinates in the device geometric space; The calculation formula of the digital twin model is: in, Indicates time Digital twin models, Indicates real-time status parameters belong , For the Status parameters; Indicates the set of running rules. For the By associating and mapping real-time status parameters and operating rules with the device geometric model, the mapping relationship between the physical world and the digital world is realized.

4. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that: The specific contents of S4 include: converting the device entities and their connection relationships in the digital twin model into graph structure data, analyzing the information transmission and influence relationship 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 map containing risk level markers. In the risk topology map, nodes represent device entities, edges represent influence relationships, and node colors represent risk levels.

5. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that: The specific contents of S5 include: based on the high-risk areas identified in the risk topology map with a risk score 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, using the cosine similarity algorithm implemented by the scikit-learn library to screen historical cases with a similarity of more than 75%, extracting disposal elements to generate preliminary emergency recommendations, and the preliminary emergency recommendations include a list of resource requirements and disposal area information.

6. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that: The specific contents of S6 include: inputting the resource demand list and disposal area information in the preliminary emergency proposal into the resource scheduling optimization algorithm, using the PuLP library to implement the integer linear programming method based on the current available emergency resource inventory status to calculate the optimal resource allocation plan, and combining it with the A-star path planning algorithm to generate a dynamic response route map that takes into account traffic conditions. The dynamic response route map includes resource scheduling paths and time nodes; The evaluation function calculation formula of the A-star path planning algorithm is: in, Indicates passing through the node from the starting point The estimated total cost to reach the destination, Indicates passing through the node from the starting point The actual cost, Represents a slave node heuristically estimated cost to the endpoint; Actual cost stated By calculating from the starting point to the node The path length or time is determined by the path length, 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 speed information; The heuristic estimates the cost The Euclidean distance method is used for calculation. The calculation formula of the Euclidean distance is: in, For nodes The coordinates of is the coordinate of the end point.

7. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that: The specific contents of S7 include: inputting the dynamic response roadmap, resource allocation plan and risk topology map into the situation evolution prediction model based on 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 determined by three factors: the probability of risk occurrence, the degree of risk impact, and the difficulty of risk control. Its calculation formula is: in, is the comprehensive risk index, is the probability of risk occurrence, is the weight of the risk probability, is the risk impact degree, is the weight of the risk impact, To control the difficulty of risk, is the weight of risk control difficulty, the weight satisfies ; Probability of risk occurrence Obtained through historical data statistics; risk impact Determine the quantitative indicators of losses, casualties, property losses and environmental damage caused by the risk; the difficulty of risk control Evaluate 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 strategy, response sequence and expected control effect.

8. The method for formulating an emergency plan based on an industrial park according to claim 1, characterized in that: Generate a structured emergency instruction XML document containing the blockade area coordinates, evacuation route node sequence, and resource allocation parameters based on the optimal emergency response plan; and 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: in, represents the optimal emergency response plan, Represents the set of all possible disposal options, by minimizing the comprehensive risk index To select the best solution.

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