Chemical industry park emergency management method and system based on cloud edge end cooperation

Abstract

This invention relates to the field of chemical emergency management technology, and discloses a method and system for emergency management of chemical industrial parks based on cloud-edge-device collaboration. The system includes modules for data acquisition, processing, prediction, optimization, and strategy generation. The data acquisition module collects real-time data from multiple sources, including park environmental monitoring, equipment operation, and personnel location. The data processing module collaboratively processes the multi-source data to generate risk characteristic indicators such as risk probability distribution, event evolution trends, and impact range assessments. The prediction module dynamically analyzes a pre-trained emergency prediction model to generate predicted values ​​for emergencies and identifiers of key risk areas. The optimization module corrects the predicted values ​​based on cloud-edge-device data consistency verification. The strategy generation module generates a set of emergency response strategies, including evacuation route planning and resource scheduling schemes, based on the key risk area identifiers. The system leverages cloud-edge-device collaboration to improve efficiency and serve emergency management in chemical industrial parks.

Description

Technical Field

[0001] This invention relates to the field of chemical emergency management technology, specifically to a method and system for emergency management of chemical industrial parks based on cloud-edge-device collaboration. Background Technology

[0002] As the core area for the concentrated layout of the chemical industry, chemical industrial parks are home to a large number of production facilities, storage facilities, and transportation pipelines with flammable, explosive, and toxic properties. Their safe operation and emergency management have always been a key focus of the industry. With the continuous expansion of the scale of chemical industrial parks and the increasing complexity of the industrial chain, traditional emergency management models have gradually exposed many shortcomings and are no longer able to meet the dynamic and refined management needs of modern chemical industrial parks. At the data acquisition level, traditional systems often rely on single-type monitoring equipment, only able to acquire environmental parameters for localized areas or operational data from specific devices. This fails to provide comprehensive coverage of multi-dimensional information regarding the environment, equipment, and personnel within the chemical industrial park. Furthermore, inconsistencies in communication protocols and data formats among different monitoring devices hinder effective integration of multi-source data, creating "data silos" that cannot provide complete and coherent data support for subsequent risk analysis. In addition, some monitoring equipment is deployed at the park's periphery, where network bandwidth and transmission latency result in low real-time data upload efficiency and frequent data delays, preventing managers from timely understanding of the park's real-time situation. In terms of data processing and risk prediction, traditional systems typically employ a centralized data processing approach, transmitting all collected data to the cloud for unified analysis. This approach not only increases the computational burden on the cloud, leading to low data processing efficiency, but also makes data loss or corruption during transmission prone to occur, affecting the accuracy of risk analysis results. Furthermore, existing emergency prediction models are mostly trained based on fixed historical data, lacking dynamic adjustment capabilities. They cannot optimize the prediction models in real time based on changing working conditions, environmental conditions, and personnel flow within the park, resulting in significant deviations between the generated risk prediction results and the actual situation. This makes it difficult to accurately identify key risk areas and predict the probability and evolution trend of emergencies. In the emergency response strategy generation phase, traditional systems often rely on the experience of management personnel for decision-making, lacking scientific and systematic decision support tools. Due to the inability to accurately obtain detailed information on key risk areas and the real-time distribution of emergency resources within the park, the generated evacuation route planning and resource allocation schemes often contain unreasonable aspects. For example, evacuation routes may not adequately consider road congestion or the spread of dangerous areas, leading to low evacuation efficiency; resource allocation schemes may suffer from uneven resource distribution or excessively long dispatch routes, failing to achieve rapid deployment of emergency resources during emergencies and delaying the optimal emergency response time. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for emergency management of chemical industrial parks based on cloud-edge-device collaboration, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, this invention provides an emergency management system for chemical industrial parks based on cloud-edge-device collaboration, the system comprising: The data acquisition module is used to collect multi-source real-time data from the chemical industrial park, including environmental monitoring data, equipment operation data, and personnel location data. The data processing module is used to collaboratively process the multi-source real-time data to generate risk characteristic indicators, which include risk probability distribution, event evolution trend and impact range assessment. The prediction module is used to call a pre-trained emergency prediction model to perform dynamic prediction and analysis on the risk characteristic indicators, and generate prediction values ​​for emergencies and key risk area identifiers. The optimization module is used to collaboratively optimize and correct the predicted value of the emergency event, and generate an optimized predicted value of the emergency event. The collaborative optimization and correction is based on the consistency verification of cloud-edge-device data. The strategy generation module is used to generate an emergency response strategy set based on the key risk area identifiers. The emergency response strategy set includes evacuation route planning schemes and resource scheduling schemes.

[0005] Preferably, the data processing module performs collaborative processing on the multi-source real-time data to generate risk characteristic indicators, including: The environmental monitoring data is divided into multiple data segments according to the time series, and each data segment corresponds to a monitoring period; For each of the data segments, perform the following processing: Based on the equipment operation data, a three-dimensional equipment status topology structure of the chemical industrial park is constructed. The three-dimensional equipment status topology structure includes spatial data of temperature distribution field, pressure distribution field and concentration distribution field. The three-dimensional device state topology is correlated with the data segment to generate the risk field reconstruction result for the current monitoring period. The risk field reconstruction result includes a spatial matrix of risk probability components, risk intensity components, and risk propagation components. The risk field reconstruction results of multiple consecutive monitoring periods are cumulatively calculated to obtain the risk probability distribution, event evolution trend and impact range assessment.

[0006] Preferably, the step of performing correlation analysis on the three-dimensional device state topology and the data segment to generate the risk field reconstruction result for the current monitoring period includes: Based on the correspondence between the temperature distribution field and the temperature data in the data segment, a temperature-risk mapping equation is established, and the first distribution function of the risk probability component is obtained by solving the temperature-risk mapping equation. Based on the correlation characteristics between the pressure distribution field and the leakage pressure, a pressure risk calculation model is constructed, which includes dynamic parameters of material properties and environmental factors. By combining the spatial rate of change and diffusion coefficient of the concentration distribution field, an iterative calculation process for concentration risk is established, which includes a feedback mechanism for concentration increment and risk increment. The outputs of the first distribution function, the pressure risk calculation model, and the concentration risk iterative calculation process are spatially fused to generate three-dimensional risk field distribution data containing risk probability, risk intensity, and risk propagation components.

[0007] Preferably, the prediction module invokes a pre-trained emergency prediction model to dynamically predict and analyze the risk characteristic indicators, generating predicted values ​​for emergencies and key risk area identifiers, including: The risk probability distribution is input into the first analysis layer of the emergency prediction model, and the coordinates of the risk concentration area and the risk amplitude change curve are determined by the risk concentration factor calculation module. The event evolution trend is input into the second analysis layer of the emergency prediction model to perform event chain damage accumulation calculation and generate predicted values ​​for the event chain trigger probability and expansion rate. The impact range assessment is input into the third analysis layer of the emergency prediction model, and the material consumption rate and personnel evacuation demand data within the impact range are calculated based on the impact diffusion model. By integrating the risk amplitude change curve, the trigger probability, and the material consumption rate, a comprehensive risk index for the chemical industrial park is generated. Based on the comparison result between the comprehensive risk index and a preset threshold, the predicted value of the emergency event is determined. Based on the spatial overlay results of the coordinates, the predicted expansion rate, and the personnel evacuation demand data, the geometric locations of the risk concentration area, the event expansion path, and the high-risk area are identified.

[0008] Preferably, the optimization module performs collaborative optimization and correction on the predicted value of the emergency event to generate an optimized predicted value of the emergency event, including: Extract historical data from the cloud center platform and real-time data from edge computing nodes, and calculate data difference values ​​and consistency indicators; Based on the data difference values, the risk probability distribution is corrected by data correction calculation to generate a corrected risk probability distribution; Based on the correlation between the consistency index and the model prediction characteristics, the prediction bias of the event evolution trend is corrected to generate a corrected event evolution trend. Based on the environmental change data in real-time data, the impact range assessment is dynamically adjusted to generate a corrected impact range assessment. The corrected risk probability distribution, event evolution trend, and impact scope assessment are input into the emergency prediction model for recalculation, generating optimized emergency event prediction values.

[0009] Preferably, the optimization module performs data correction calculations on the risk probability distribution based on the data difference values ​​to generate a corrected risk probability distribution, including: Obtain the initial risk probability of the chemical industrial park under baseline conditions and the data difference value, and establish a risk probability-data correlation function; The risk probability increment is calculated based on the risk probability-data association function, where the risk probability increment is the product of the data difference and the risk probability change. The risk probability increment is superimposed on the risk probability distribution calculation process to generate a risk probability distribution correction value that includes data correction; The risk probability distribution correction value is subjected to risk decay effect compensation processing, which is based on the product of historical risk decay curve and time decay factor.

[0010] Preferably, the strategy generation module generates an emergency response strategy set based on the key risk area identifiers, including: For the identified high-risk areas, the optimal evacuation route is calculated, which is achieved by adjusting personnel distribution and exit capacity. Based on the identifier of the event extension path, a resource scheduling scheme is constructed, which includes the selection of emergency material distribution areas and the optimized configuration of the distribution quantity; Based on the identification of the high-risk areas, a personnel rescue strategy is generated. The personnel rescue strategy dynamically adjusts the rescue frequency and rescue force according to the predicted population density. The optimal evacuation route, the resource scheduling scheme, and the personnel rescue strategy are prioritized to generate an emergency response strategy set that includes execution timing and implementation parameters.

[0011] Preferably, the strategy generation module constructs a resource scheduling scheme, including: Extract the geometric features of the event's expansion path, and calculate the path length and expansion direction; The coverage density of the material distribution points is selected based on the path length, and the coverage density is inversely proportional to the path length. The direction of application of the material transportation route is adjusted based on the expansion direction, so that the transportation direction and the expansion direction form a predetermined angle; The transportation speed is dynamically adjusted based on the urgency test data of material demand to ensure that materials arrive before the critical time limit for demand. Generate a scheduling parameter configuration table that includes coverage density, transport direction, and transport speed.

[0012] Preferably, the system further includes: The verification module is used to collect actual event data and response effect data of the chemical industrial park within a preset verification period; The error analysis module is used to perform deviation analysis processing on the actual event data and the predicted event value to generate a first error correction coefficient; The response data is compared with the expected results to generate a second error correction coefficient. The model optimization module is used to adjust the weight parameters of the emergency prediction model according to the first error correction coefficient and the second error correction coefficient, and generate an optimized emergency prediction model. The application module is used to apply the optimized emergency prediction model to subsequent emergency management tasks.

[0013] Preferably, the present invention also includes an emergency management method for chemical industrial parks based on cloud-edge-device collaboration, the method comprising all the modules and method flow of the emergency management system for chemical industrial parks based on cloud-edge-device collaboration as described above.

[0014] Compared with the prior art, the beneficial effects of the present invention are: In terms of data acquisition, the system's data acquisition module enables comprehensive real-time collection of multi-source data, including environmental monitoring data, equipment operation data, and personnel location data within the chemical industrial park. This breaks through the limitations of traditional single-data acquisition systems, ensuring that managers can obtain comprehensive real-time information about the park. Simultaneously, this module is compatible with the communication protocols of different types of monitoring equipment, performing preliminary integration and format standardization of the collected multi-source data, avoiding the creation of "data silos" and laying a solid data foundation for subsequent collaborative data processing. Furthermore, relying on a cloud-edge-device collaborative architecture, edge nodes can perform local preprocessing of the collected real-time data, uploading only critical and abnormal data to the cloud. This effectively reduces data transmission volume, alleviates network bandwidth pressure, improves data transmission efficiency, and ensures data real-time performance, enabling managers to promptly grasp dynamic changes within the park.

[0015] In the data processing stage, the data processing module collaboratively processes multi-source real-time data. Through cloud-edge-device collaboration, some data processing tasks are distributed to edge nodes for localized processing, reducing the computational burden on the cloud and significantly improving data processing efficiency. During processing, this module can deeply explore the correlations between multi-source data and accurately extract risk characteristic indicators, including risk probability distribution, event evolution trends, and impact scope assessment, providing a comprehensive and accurate analytical basis for subsequent risk prediction. Compared to traditional centralized data processing methods, this collaborative processing model not only improves the timeliness and accuracy of data processing but also reduces the possibility of data loss or corruption during data transmission, further ensuring the reliability of risk analysis results. In terms of risk prediction, the prediction module calls upon a pre-trained emergency prediction model to dynamically predict and analyze risk characteristic indicators. Leveraging the advantages of a cloud-edge-device collaborative architecture, it can acquire real-time updates from multiple sources within the park and adjust and optimize the pre-trained model in real time based on this dynamic data, ensuring the model maintains high prediction accuracy. Through this dynamic prediction and analysis approach, the system can accurately generate predicted values ​​for emergencies and identify key risk areas, helping managers to identify potential safety hazards in advance, grasp the probability of emergencies and their possible evolution trends, as well as the specific location and severity of key risk areas, providing clear direction for subsequent emergency preparedness work. The optimization module further refines the predicted values ​​for emergencies through a collaborative optimization and correction method based on cloud-edge-device data consistency verification. This effectively eliminates prediction deviations caused by factors such as data transmission delays and data errors. During the cloud-edge-device data consistency verification process, the system cross-validates the data processed by the cloud and edge nodes to ensure data accuracy and consistency. Based on the verification results, the predicted values ​​for emergencies are then corrected, generating optimized predictions that better reflect the actual situation in the park and provide a more reliable reference for emergency decision-making. In terms of emergency response strategy generation, the strategy generation module generates a set of emergency response strategies, including evacuation route planning schemes and resource scheduling schemes, based on key risk area identification, real-time distribution of emergency resources within the park, road traffic conditions, and personnel evacuation needs. When generating evacuation route planning schemes, this module fully considers factors such as the spread of key risk areas, road congestion, and population density to plan safe and efficient evacuation routes, ensuring that personnel can quickly and orderly evacuate dangerous areas. When generating resource scheduling schemes, it rationally allocates and rapidly dispatches emergency resources based on the severity of the emergency, the location of key risk areas, and the availability of emergency resources, shortening the transportation time of emergency resources and ensuring that emergency resources can be promptly deployed to the emergency response, improving emergency response efficiency and minimizing casualties and property losses caused by the emergency. Attached Figure Description

[0016] Figure 1 This is a sequence diagram of the cloud-edge-device collaborative emergency management system for chemical industrial parks as described in this invention; Figure 2 A flowchart for generating risk characteristic indicators for the data processing module; Figure 3 A flowchart for dynamic predictive analysis in the prediction module; Figure 4 The flowchart for optimizing module collaboration has been revised. Figure 5 A flowchart for constructing a resource scheduling scheme. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides an emergency management system for chemical industrial parks based on cloud-edge-device collaboration. The system includes: a data acquisition module, a data processing module, a prediction module, an optimization module, and a strategy generation module.

[0019] The data acquisition module collects multi-source real-time data through a sensor network deployed within the chemical industrial park, including environmental monitoring data, equipment operation data, and personnel location data. Environmental monitoring data covers information such as temperature, pressure, and gas concentration; equipment operation data includes equipment status parameters and operation logs; and personnel location data is obtained through positioning terminals. The data processing module collaboratively processes the multi-source real-time data to generate risk characteristic indicators, including risk probability distribution, event evolution trends, and impact range assessments. The prediction module uses a pre-trained emergency prediction model to dynamically predict and analyze the risk characteristic indicators, generating predicted values ​​for emergencies and identifying key risk areas. The optimization module performs collaborative optimization and correction of the predicted values ​​for emergencies based on cloud-edge-device data consistency verification, generating optimized predicted values. The strategy generation module generates a set of emergency response strategies based on the key risk area identifications, including evacuation route planning schemes and resource scheduling schemes. The system achieves global data aggregation and model training through a cloud center platform, real-time data processing and preliminary analysis are performed by edge nodes, and terminal devices execute data acquisition and response command issuance, forming a collaborative architecture.

[0020] Example 1: See Figure 2 The data acquisition module continuously acquires multi-source real-time data from the chemical industrial park through a widely deployed sensor network. Environmental monitoring data comes from gas concentration sensors, temperature and humidity sensors, and pressure transmitters installed in key areas. These devices collect parameters such as the concentration of specific volatile organic compounds in the atmosphere, changes in equipment surface temperature, and pressure fluctuations inside pipelines several times per second. Equipment operation data is obtained from the distributed control system (DCS) and programmable logic controller (PLC), including real-time operating status, start-stop frequency, and current load data of key equipment such as pumps, valves, and reactors. Personnel location data is collected through positioning tags worn by workers and Bluetooth beacons or UWB base stations deployed in the plant area, tracking the distribution and movement of personnel within the plant area in real time.

[0021] After the data processing module starts, it first performs time-series partitioning on the environmental monitoring data. The system divides the continuous real-time data stream into discrete data segments at fixed time intervals, such as every five minutes as a monitoring cycle. Each data segment contains the readings of all sensors within that time period, forming a timestamped multidimensional data snapshot. For each data segment, the system synchronously processes equipment operation data. Based on the equipment's geographical location information, type, and current operating parameters, a three-dimensional equipment status topology is constructed. This topology not only includes the spatial distribution relationship of the equipment but also generates continuous temperature, pressure, and concentration distribution fields through data mapping algorithms. The temperature distribution field uses interpolation algorithms to transform discrete point temperature measurement data into a temperature gradient distribution map of the entire park space. The pressure distribution field simulates and calculates a pressure propagation model based on pipeline network topology and fluid dynamics principles. The concentration distribution field combines atmospheric diffusion models and real-time concentration monitoring values ​​to generate the spatial concentration distribution of hazardous gases.

[0022] The system correlates the 3D equipment status topology with data segments from the current monitoring period. In temperature field analysis, the system establishes a mapping between temperature and equipment overheating risk. By analyzing the changing trends and absolute values ​​of the temperature gradient, it calculates the probability distribution of risk caused by temperature anomalies in each spatial unit, i.e., the risk probability component. In pressure field analysis, the system considers the pressure resistance limit of the equipment material, the current pressure value, and historical pressure fluctuation patterns to construct a dynamic pressure risk calculation model. This model integrates dynamic parameters such as material fatigue characteristics and environmental corrosion factors to calculate the intensity of leakage risk caused by pressure, forming a risk intensity component. In concentration field analysis, the system executes an iterative calculation process based on the spatial rate of change of concentration distribution and the known diffusion coefficient. This process simulates the propagation path and impact range of hazardous substances through a feedback loop of concentration increment and risk increment, generating a risk propagation component.

[0023] The calculation of these three components is not carried out in isolation, but involves data interaction and mutual correction. For example, high-temperature regions may accelerate gas diffusion, thereby affecting the distribution of the concentration field and the calculation results of the risk propagation component; if a leak occurs in a high-pressure region, it will immediately change the local concentration field, thus affecting the reconstruction of the entire risk field.

[0024] After completing the analysis of a single monitoring cycle, the system spatially fuses the generated risk probability component, risk intensity component, and risk propagation component to form the three-dimensional risk field distribution data for the current cycle. This data volume is stored in a gridded format, with each grid cell containing quantified values ​​for risk probability, risk intensity, and risk propagation direction. The system accumulates the risk field reconstruction results for multiple consecutive monitoring cycles. The risk probability distribution is generated by the spatiotemporal superposition of risk probability components within consecutive cycles, reflecting the accumulation and evolution patterns of risk probability within the park. The event evolution trend identifies the migration path and evolutionary patterns of risks by analyzing the directional and intensity changes of the risk propagation component. The impact range assessment calculates the potentially affected physical areas and the degree of impact based on the spatial distribution and cumulative effect of the risk intensity component.

[0025] The entire process is completed within a cloud-edge-device collaborative architecture. Edge computing nodes are responsible for the initial processing of real-time data and the rapid construction of 3D topology structures, while the cloud center platform undertakes complex model calculations and the cumulative analysis of large-scale data. Terminal devices provide continuous data acquisition and result feedback. Through this collaborative processing model, the system can efficiently transform massive amounts of raw data into high-quality risk characteristic indicators, providing a data foundation for subsequent prediction and response.

[0026] Example 2: See Figure 3 In the operation of the emergency management system in the chemical industrial park, the prediction module plays a crucial role in conducting in-depth analysis of risk characteristic indicators and generating forward-looking judgments. This module calls upon a pre-trained emergency prediction model to dynamically predict and analyze the risk probability distribution, event evolution trend, and impact scope assessment generated by the data processing module. Ultimately, it outputs quantified emergency event prediction values ​​and identifies key risk areas that require focused attention.

[0027] The emergency prediction model employs a multi-layered analytical architecture, with the first layer specifically handling risk probability distribution data. This layer's built-in risk concentration factor calculation module uses a spatial clustering algorithm to scan the entire park's risk probability grid data. This module can identify outliers or abnormal areas with significantly higher risk probabilities than surrounding areas, calculating the geometric center coordinates of these areas as the coordinates of the risk concentration regions. Simultaneously, this module extracts the risk probability values ​​of these areas over continuous time series and generates risk amplitude variation curves using curve fitting techniques. These curves reflect the trend of risk levels changing over time in the risk concentration regions, serving as a crucial basis for judging the development trend of risks.

[0028] The second analysis layer focuses on processing event evolution trend data. This layer performs cumulative damage calculations for event chains, an analytical method that simulates the chain reactions of sudden events. Based on a historical case library and physical models, the system constructs a sequence of event chains that may occur within the chemical industrial park, such as chain reaction models of equipment leaks leading to fires, fires leading to explosions, and explosions causing the spread of hazardous substances. This analysis layer inputs current event evolution trend data into these event chain models to calculate the trigger probability of each potential event chain, i.e., the likelihood of the event chain occurring. Simultaneously, this layer also calculates the expansion rate of each link in the event chain, i.e., the predicted speed at which the event progresses from one link to the next. These calculations take into account current environmental conditions, equipment status, and risk propagation characteristics, making the prediction results dynamically adaptable.

[0029] The third analytical layer processes the impact range assessment data. This layer is based on an impact diffusion model, which integrates knowledge from multiple disciplines, including fluid mechanics, atmospheric diffusion theory, and personnel behavior models. The model first calculates the boundary and intensity distribution of the impact range based on risk characteristic indicators, and then calculates the material consumption rate within the impact range. The material consumption rate considers the intensity and temporal distribution of demand for emergency resources such as fire extinguishing materials, protective equipment, and medical supplies. Simultaneously, the model also calculates personnel evacuation demand data, including the number of people needing evacuation, the urgency of the evacuation, and dynamic changes in demand during the evacuation process. These calculations provide quantitative basis for emergency resource allocation and personnel evacuation decisions.

[0030] The system integrates the risk amplitude change curve generated by the first analysis layer, the event chain trigger probability generated by the second analysis layer, and the material consumption rate generated by the third analysis layer to generate a comprehensive risk index for the chemical industrial park. This integration process is not a simple data overlay but considers the mutual influence and weighting relationships between the indicators. For example, if a high-risk amplitude area also has a high event trigger probability and high material consumption demand, its comprehensive risk index will be significantly improved. The system compares this comprehensive risk index with a preset threshold and generates a predicted value for emergencies based on the comparison results. This predicted value is a quantitative risk level indicator that reflects the overall risk level currently faced by the chemical industrial park.

[0031] In generating key risk area identifiers, the system spatially overlays the risk concentration area coordinates obtained from the first analysis layer, the event spread rate prediction values ​​obtained from the second analysis layer, and the personnel evacuation demand data obtained from the third analysis layer. This overlay analysis is performed on a geographic information system platform. Through spatial mapping and correlation analysis, it identifies the specific location and extent of the risk concentration area, the most likely path and direction of event spread, and the high-risk areas with the greatest impact on personnel evacuation demand. These identification results not only include geometric location information but also include risk attribute data, such as metadata like risk level, impact degree, and urgency level.

[0032] The entire predictive analysis process is dynamic. The emergency prediction model continuously updates its predictions based on real-time input risk characteristic indicators, ensuring that predicted values ​​for emergencies and identification of key risk areas reflect the latest risk status of the chemical industrial park. This dynamic prediction capability enables the emergency management system to promptly detect changing risk trends, providing a window of opportunity for preventative and responsive measures. The prediction module relies on computing resources provided by a cloud-edge-device collaborative architecture. Complex model calculations and big data analysis are completed on the cloud platform, edge nodes are responsible for real-time data preprocessing and preliminary analysis, and terminal devices provide continuous data acquisition and visualization of prediction results. Through this distributed computing model, the system can complete complex predictive analysis tasks within a reasonable timeframe, meeting the timeliness requirements of emergency management.

[0033] The output format of the forecast results is specially designed to include both numerical and non-numerical data. Emergency event predictions are presented as quantified risk indices, facilitating automated system assessment and response. Key risk area identifiers are displayed in a spatial data format, enabling integration with geographic information systems for visualization and spatial analysis. This multi-format output design allows the forecast results to meet diverse application needs, supporting both automated system decision-making and managerial monitoring and intervention.

[0034] Example 3: See Figure 4 In the operation of the emergency management system in the chemical industrial park, the optimization module plays a crucial role in refining the predicted values ​​of emergencies output by the prediction module. Based on a cloud-edge-device collaborative architecture and a data consistency verification mechanism, this module achieves collaborative optimization and correction of the initial prediction values ​​through comparison of multi-source data and dynamic adjustment of model parameters, generating more accurate and reliable optimized emergency event prediction values.

[0035] After the optimization module starts, it first performs data extraction and difference analysis. The module extracts historical case data similar to the current operating conditions from the historical database of the cloud center platform. This data includes complete records of risk probability distribution, event evolution patterns, and actual impact range in past events. Simultaneously, the module obtains the latest real-time monitoring data from edge computing nodes distributed throughout the park, including instantaneous values ​​of environmental parameters, equipment status, and personnel distribution. The system calculates the data difference value between historical and real-time data. This difference value quantifies the degree of deviation between the current situation and historical patterns using statistical methods. The consistency index is calculated by analyzing the time-series characteristics of the real-time data stream and the degree of agreement with historical data patterns, reflecting the degree of matching between current data and historical experience.

[0036] Based on the data discrepancy values, the module performs data correction calculations on the risk probability distribution. The system establishes a risk probability-data correlation function, which describes the quantitative relationship between data discrepancies and changes in risk probability. Using this function, the module calculates the risk probability increment, and the calculation process can be expressed by the following formula:

[0037] in: It represents the increment of risk probability and is a dimensionless risk correction quantity. It is the environmental adaptability coefficient, with a value ranging from 0.8 to 1.2, used to adjust the correction intensity; It is the data difference value, calculated by the standard deviation between real-time data and historical data; It is the rate of change of risk probability over time, reflecting the dynamic characteristics of the risk situation.

[0038] After adding this risk probability increment to the original risk probability distribution calculation process, the module further performs risk decay effect compensation processing. This processing is based on the product of historical risk decay curves and time decay factors. The historical risk decay curves describe the natural decay law of risk levels under similar risk scenarios, while the time decay factor dynamically adjusts the compensation intensity according to the interval between the current time and the time of risk occurrence.

[0039] Regarding the correction of event evolution trends, the module analyzes the correlation between consistency indices and model prediction characteristics. The system establishes a prediction bias correction model, which analyzes the magnitude and trend of consistency indices to identify potential systematic biases in the current prediction model. By dynamically adjusting the time-series parameters of the event evolution trend, including recalibrating the estimated event trigger time and correcting the predicted rate of event expansion, a corrected event evolution trend is generated. This correction process considers not only the degree of data consistency but also the impact of real-time environmental conditions on the event evolution pattern.

[0040] For dynamic adjustments to the impact range assessment, the module focuses on real-time environmental change data. This data includes atmospheric parameters such as wind speed, wind direction, temperature gradient, and humidity distribution, as well as spatial constraints such as terrain features and building layout. The system recalculates the propagation paths and impact boundaries of hazardous substances through environmental parameter interpolation algorithms and spatial diffusion model reconstruction. For example, when a change in wind direction is detected, the system immediately adjusts the predicted direction of the impact range; when the temperature gradient indicates the formation of an inversion layer, the system corrects the vertical diffusion parameters. This dynamic adjustment ensures that the impact range assessment can promptly reflect changes in environmental conditions.

[0041] After completing all corrections, the module re-inputs the corrected risk probability distribution, event evolution trend, and impact scope assessment into the emergency prediction model. Because the input data has undergone multi-level optimization and correction, the recalculated emergency prediction values ​​have higher accuracy and reliability. This optimized prediction not only reflects the current actual risk situation but also takes into account the impact of historical experience data and environmental changes.

[0042] The entire optimization and correction process operates efficiently within a cloud-edge-device collaborative architecture. Edge nodes are responsible for the rapid acquisition and initial processing of real-time data, the cloud platform handles complex data correction calculations and model re-runs, and terminal devices provide real-time display and feedback of the optimization results. This distributed processing model ensures both the accuracy of the optimization calculations and meets the timeliness requirements of emergency management. The output of the optimization module adopts a structured data format, including optimized prediction values ​​of emergencies and their related confidence indices. This data provides more reliable input for subsequent emergency strategy generation and also provides high-quality labeled data for the system's continuous learning. Through this iterative optimization mechanism, the predictive capability of the emergency management system can continuously improve with the accumulation of operating time. Various correction parameters and adjustment records generated during the optimization process are completely saved in the system log, providing important reference materials for subsequent system performance analysis and model improvement. The system periodically performs statistical analysis on these optimization records to identify operational patterns with significant optimization effects and correction methods that need improvement, thereby continuously refining the optimization algorithm and correction strategy.

[0043] Example 4: See Figure 5 In the operation process of the emergency management system in the chemical industrial park, the strategy generation module generates a set of actionable emergency response strategies based on the key risk area identifiers output by the prediction module. This module calculates the optimal evacuation routes, constructs resource scheduling plans, and generates personnel rescue strategies for different types of key risk area characteristics, ultimately integrating them into a complete set of strategies including execution sequences.

[0044] When the system identifies a high-risk area, the strategy generation module initiates evacuation route calculations. This calculation is based on real-time acquired population distribution heatmaps and a building structure database. The population distribution heatmap, generated from location terminal data, displays the population density distribution in different areas; the building structure database contains the exit locations, widths, passage capacities, and connectivity relationships of each building. The system establishes a weighted directed graph model, where nodes represent evacuation safety points or key intersections, and edge weights combine path length, population density congestion coefficients, and exit capacity limiting factors. An improved Dijkstra's algorithm is used to traverse and calculate multiple alternative routes from the high-risk area to the safe area, while satisfying the instantaneous passage capacity constraint at the exits. The system monitors the congestion status of each route in real time. When the population flow on a route approaches the design capacity threshold, the weight of that route in the recommended plan is automatically reduced, while the priority of alternative routes is increased. The final generated evacuation route plan includes the main evacuation route, alternative evacuation routes, and the coordinates of the corresponding diversion control points.

[0045] For identifying the event expansion path, the module constructs a resource scheduling scheme. First, it extracts the geometric features of the event expansion path, including path length, expansion direction angle, expansion rate, and path width variation rate. The system determines the coverage density configuration principle of material distribution points based on the path length: high-density distribution points (spacing ≤ 50 meters) are set for paths within 200 meters, medium density (spacing 50-100 meters) for paths between 200-500 meters, and low density (spacing 100-150 meters) for paths exceeding 500 meters. The expansion direction angle is used to plan the angle of the material transportation route, ensuring that the transportation route forms an angle between 75° and 105° with the expansion direction. This angle design ensures that the transportation route avoids being parallel to the risk expansion direction while effectively covering the areas on both sides of the expansion path.

[0046] The urgency of material needs is determined using a three-tiered assessment system: Level 1 urgency areas (expected to be affected within 30 minutes) are equipped with high-speed transportation (≥60km / h); Level 2 urgency areas (30-60 minutes) use medium-speed transportation (40-60km / h); and Level 3 areas (>60 minutes) use conventional transportation speed (≤40km / h). The system generates a scheduling parameter configuration table to guide on-site execution; see Table 1.

[0047] Table 1: Event Extension Path Resource Scheduling Parameter Configuration Table

[0048] For high-risk areas, the module generates personnel rescue strategies. The system predicts population density trends over the next 15-30 minutes by integrating historical pedestrian flow data and real-time location information. The population density prediction model considers factors such as weekday patterns, current on-duty personnel, and regional functional attributes, outputting a population density index for high-risk areas. Rescue levels are categorized based on the density index: areas with an index ≥ 0.8 activate a high-frequency rescue mode (rescue team response within 5 minutes); areas with an index between 0.5 and 0.8 use a medium-frequency mode (rescue within 10 minutes); and areas with an index < 0.5 implement a routine patrol mode (rescue within 15 minutes). Rescue force allocation uses a dynamic proportional algorithm, with one rescue team allocated per 100 people as a baseline. For every 20% increase in population density, 0.5 additional rescue teams are added, while also considering the distribution of special population groups and adjusting medical resource allocation.

[0049] After generating the three core strategies, the module performs a priority ranking process. The ranking is based on a weighted average of a risk level coefficient (0-1) and an urgency coefficient (0-1), where the risk level coefficient is derived from the risk assessment data of the prediction module, and the urgency coefficient is calculated based on the event's development rate. The system establishes a strategy execution timeline matrix, placing evacuation route execution first (T+0 time slot), resource scheduling initiation at T+5 minutes, and personnel rescue initiation at T+10 minutes. Each strategy item is associated with a set of implementation parameters; for example, the evacuation route plan includes a sequence of diversion control instructions, the resource scheduling plan includes transport vehicle numbers and a material list, and the personnel rescue strategy includes rescue team numbers and equipment configuration.

[0050] The final emergency response strategy set is stored using a hierarchical data structure. The top layer contains strategy type indexes, the middle layer contains spatiotemporal execution parameters, and the bottom layer contains specific operational instructions. This data structure is transmitted to the cloud platform command center and edge execution terminals via standard interfaces. The command center displays a panoramic view of the strategy on a large screen, and specific execution instructions are pushed to mobile terminals, forming a complete closed loop for strategy implementation. The system updates the strategy set every 2 minutes, and automatically triggers a strategy regeneration process when the risk area identifier changes.

[0051] Example 5: The verification module operates according to a preset verification cycle, which is dynamically adjusted based on the seasonal characteristics and risk profiles of the park's production activities. The verification cycle is typically shortened during high-risk periods such as major maintenance periods and extreme weather seasons. Within each verification cycle, the module collects actual event data from the chemical industrial park through a multi-source data acquisition system. This data originates from the park's event reporting system, historical records from the sensor network, video analysis records from surveillance cameras, and manually entered emergency response logs. The actual event data details structured information such as the event type, location, start time, duration, impact range, and handling process. Simultaneously, the module collects response effectiveness data, including quantitative indicators such as the mobilization and arrival time of emergency resources, the efficiency of evacuation command execution, the actual completion time of personnel evacuation, and the deployment effectiveness of rescue forces. All this data carries precise timestamps and spatial coordinates, ensuring accurate comparison with the system's predicted output.

[0052] The error analysis module receives the actual data collected by the verification module and performs a refined comparison with the system's predicted output. First, the module aligns the actual event data with the predicted event values ​​temporally and spatially to ensure the consistency of the comparison. The module employs a multi-dimensional deviation analysis method, calculating the prediction error of event occurrence time and duration in the time dimension; the location and area deviations of the impact range in the spatial dimension; and the evaluation differences of event level in the intensity dimension. These calculations generate a first error correction coefficient, a multi-dimensional vector reflecting the accuracy of the prediction model in different aspects. Simultaneously, the module compares the response effect data with the system's expected effect indicators to examine the degree to which the actual implementation effect of the emergency strategy conforms to the design objectives. This comparison includes timeliness analysis of resource scheduling, effectiveness assessment of evacuation routes, and verification of the rationality of rescue force allocation, ultimately generating a second error correction coefficient, which quantifies the degree of deviation between the strategy implementation effect and the expectations.

[0053] The model optimization module adjusts the internal parameters of the emergency prediction model based on two correction coefficients output by the error analysis module. This module employs an incremental learning algorithm, progressively adjusting the weight parameters while maintaining the overall model structure. The first error correction coefficient primarily adjusts the weights of risk identification-related parameters, including sensitivity parameters for risk probability calculation, prediction parameters for event evolution trends, and boundary parameters for impact range assessment. The second error correction coefficient mainly affects the parameter settings related to strategy generation, including the cost function weights for evacuation route planning, time constraint parameters for resource scheduling, and priority parameters for rescue force allocation. The parameter adjustment process uses a small-step iterative approach, making only minor adjustments each time to avoid performance fluctuations caused by large single adjustments. Simulation verification is performed after each adjustment to ensure the adjustment direction is correct and does not introduce new biases. Through this refined parameter optimization, an optimized emergency prediction model is generated. The new model maintains its original advantages while correcting previously identified prediction biases and execution errors.

[0054] The application module is responsible for deploying the optimized model to the actual operating environment. This module employs a canary release strategy, initially testing the new model in select areas to compare the performance differences between the old and new models. During the trial run, the system runs both models in parallel, but only uses the output of the old model as the basis for decision-making, while recording the prediction output of the new model for performance evaluation. After thorough validation, the new model is gradually expanded to the entire park. The module also establishes a version management system to record each model optimization in detail, including modification content, modification time, and reasons for modification, retaining historical versions for rollback when needed. The optimized model is applied to subsequent emergency management tasks, including real-time risk monitoring, emergency event prediction, and emergency strategy generation.

[0055] The entire implementation process forms a complete closed-loop optimization mechanism. The system collects actual operating data through the verification module, identifies existing problems through the error analysis module, improves model performance through the model optimization module, and finally implements the improvements in practical applications through the application module. This mechanism enables the system to continuously learn from actual operation and constantly adapt to the dynamically changing environmental conditions of the chemical industrial park. The system's predictive accuracy and strategy effectiveness gradually improve with the accumulation of operating time, forming a virtuous cycle of self-improvement.

[0056] The system also establishes a knowledge accumulation mechanism, transforming the experience gained from each optimization and adjustment into structured knowledge stored in a knowledge base. This knowledge includes risk characteristics for specific seasons, risk change patterns under special weather conditions, and risk models under different production states. This knowledge is not only used for model optimization but also provides decision-making references for emergency management personnel, helping them better understand system behavior and improve emergency response capabilities. Through this system implementation, the chemical industrial park emergency management system has achieved a transformation from static pre-setting to dynamic optimization, and from single prediction to closed-loop improvement, enabling the system to better cope with complex and ever-changing emergency management needs and providing continuous improvement technical support for the safe operation of the chemical industrial park.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An emergency management system for chemical industrial parks based on cloud-edge-device collaboration, characterized in that, The system includes: The data acquisition module is used to collect multi-source real-time data from the chemical industrial park, including environmental monitoring data, equipment operation data, and personnel location data. The data processing module is used to collaboratively process the multi-source real-time data to generate risk characteristic indicators, which include risk probability distribution, event evolution trend and impact range assessment. The prediction module is used to call a pre-trained emergency prediction model to perform dynamic prediction and analysis on the risk characteristic indicators, and generate prediction values ​​for emergencies and key risk area identifiers. The optimization module is used to collaboratively optimize and correct the predicted value of the emergency event, and generate an optimized predicted value of the emergency event. The collaborative optimization and correction is based on the consistency verification of cloud-edge-device data. The strategy generation module is used to generate an emergency response strategy set based on the key risk area identifiers. The emergency response strategy set includes evacuation route planning schemes and resource scheduling schemes.

2. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 1, characterized in that, The data processing module collaboratively processes the multi-source real-time data to generate risk characteristic indicators, including: The environmental monitoring data is divided into multiple data segments according to the time series, and each data segment corresponds to a monitoring period; For each of the data segments, perform the following processing: Based on the equipment operation data, a three-dimensional equipment status topology structure of the chemical industrial park is constructed. The three-dimensional equipment status topology structure includes spatial data of temperature distribution field, pressure distribution field and concentration distribution field. The three-dimensional device state topology is correlated with the data segment to generate the risk field reconstruction result for the current monitoring period. The risk field reconstruction result includes a spatial matrix of risk probability components, risk intensity components, and risk propagation components. The risk field reconstruction results of multiple consecutive monitoring periods are cumulatively calculated to obtain the risk probability distribution, event evolution trend and impact range assessment.

3. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 2, characterized in that, The step of performing correlation analysis and processing between the three-dimensional device state topology and the data segment to generate the risk field reconstruction result for the current monitoring period includes: Based on the correspondence between the temperature distribution field and the temperature data in the data segment, a temperature-risk mapping equation is established, and the first distribution function of the risk probability component is obtained by solving the temperature-risk mapping equation. Based on the correlation characteristics between the pressure distribution field and the leakage pressure, a pressure risk calculation model is constructed, which includes dynamic parameters of material properties and environmental factors. By combining the spatial rate of change and diffusion coefficient of the concentration distribution field, an iterative calculation process for concentration risk is established, which includes a feedback mechanism for concentration increment and risk increment. The outputs of the first distribution function, the pressure risk calculation model, and the concentration risk iterative calculation process are spatially fused to generate three-dimensional risk field distribution data containing risk probability, risk intensity, and risk propagation components.

4. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 1, characterized in that, The prediction module invokes a pre-trained emergency prediction model to dynamically predict and analyze the risk characteristic indicators, generating predicted values ​​for emergencies and identifiers of key risk areas, including: The risk probability distribution is input into the first analysis layer of the emergency prediction model, and the coordinates of the risk concentration area and the risk amplitude change curve are determined by the risk concentration factor calculation module. The event evolution trend is input into the second analysis layer of the emergency prediction model to perform event chain damage accumulation calculation and generate predicted values ​​for the event chain trigger probability and expansion rate. The impact range assessment is input into the third analysis layer of the emergency prediction model, and the material consumption rate and personnel evacuation demand data within the impact range are calculated based on the impact diffusion model. By integrating the risk amplitude change curve, the trigger probability, and the material consumption rate, a comprehensive risk index for the chemical industrial park is generated. Based on the comparison result between the comprehensive risk index and a preset threshold, the predicted value of the emergency event is determined. Based on the spatial overlay results of the coordinates, the predicted expansion rate, and the personnel evacuation demand data, the geometric locations of the risk concentration area, the event expansion path, and the high-risk area are identified.

5. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 1, characterized in that, The optimization module performs collaborative optimization and correction on the predicted value of the emergency event to generate an optimized predicted value of the emergency event, including: Extract historical data from the cloud center platform and real-time data from edge computing nodes, and calculate data difference values ​​and consistency indicators; Based on the data difference values, the risk probability distribution is corrected by data correction calculation to generate a corrected risk probability distribution; Based on the correlation between the consistency index and the model prediction characteristics, the prediction bias of the event evolution trend is corrected to generate a corrected event evolution trend. Based on the environmental change data in real-time data, the impact range assessment is dynamically adjusted to generate a corrected impact range assessment. The corrected risk probability distribution, event evolution trend, and impact scope assessment are input into the emergency prediction model for recalculation, generating optimized emergency event prediction values.

6. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 5, characterized in that, The optimization module performs data correction calculations on the risk probability distribution based on the data difference values ​​to generate a corrected risk probability distribution, including: Obtain the initial risk probability of the chemical industrial park under baseline conditions and the data difference value, and establish a risk probability-data correlation function; The risk probability increment is calculated based on the risk probability-data association function, where the risk probability increment is the product of the data difference and the risk probability change. The risk probability increment is superimposed on the risk probability distribution calculation process to generate a risk probability distribution correction value that includes data correction; The risk probability distribution correction value is subjected to risk decay effect compensation processing, which is based on the product of historical risk decay curve and time decay factor.

7. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 4, characterized in that, The strategy generation module generates a set of emergency response strategies based on the key risk area identifiers, including: For the identified high-risk areas, the optimal evacuation route is calculated, which is achieved by adjusting personnel distribution and exit capacity. Based on the identifier of the event extension path, a resource scheduling scheme is constructed, which includes the selection of emergency material distribution areas and the optimized configuration of the distribution quantity; Based on the identification of the high-risk areas, a personnel rescue strategy is generated. The personnel rescue strategy dynamically adjusts the rescue frequency and rescue force according to the predicted population density. The optimal evacuation route, the resource scheduling scheme, and the personnel rescue strategy are prioritized to generate an emergency response strategy set that includes execution timing and implementation parameters.

8. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 7, characterized in that, The strategy generation module constructs a resource scheduling scheme, including: Extract the geometric features of the event's expansion path, and calculate the path length and expansion direction; The coverage density of the material distribution points is selected based on the path length, and the coverage density is inversely proportional to the path length. The direction of application of the material transportation route is adjusted based on the expansion direction, so that the transportation direction and the expansion direction form a predetermined angle; The transportation speed is dynamically adjusted based on the urgency test data of material demand to ensure that materials arrive before the critical time limit for demand. Generate a scheduling parameter configuration table that includes coverage density, transport direction, and transport speed.

9. The emergency management system for chemical industrial parks based on cloud-edge-device collaboration according to claim 1, characterized in that, The system also includes: The verification module is used to collect actual event data and response effect data of the chemical industrial park within a preset verification period; The error analysis module is used to perform deviation analysis processing on the actual event data and the predicted event value to generate a first error correction coefficient; The response data is compared with the expected results to generate a second error correction coefficient. The model optimization module is used to adjust the weight parameters of the emergency prediction model according to the first error correction coefficient and the second error correction coefficient, and generate an optimized emergency prediction model. The application module is used to apply the optimized emergency prediction model to subsequent emergency management tasks.

10. An emergency management method for chemical industrial parks based on cloud-edge-device collaboration, characterized in that, It includes all modules and method flows of the chemical industrial park emergency management system based on cloud-edge-device collaboration as described in any one of claims 1 to 9.

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