A dynamic deduction and intelligent optimization system and method for emergency plans of a chemical enterprise

Abstract

The present application relates to the technical field of chemical production safety, in particular to a kind of chemical enterprise emergency plan dynamic deduction and intelligent optimization system and method, including data acquisition and storage module, dynamic risk assessment module, preplan optimization module and system integration module;The dynamic risk assessment model module exports the real-time risk quantization evaluation result of chemical production environment by deeply mining the internal correlation of data;The preplan optimization module carries out dynamic deduction according to the real-time risk quantization evaluation result, resource allocation and rescue path of the response process of emergency plan, and predicts the implementation effect of preplan.The present application effectively solves the problems that emergency plan lacks full-process comprehensive deduction and is difficult to dynamically adapt to instantaneous changes in accident scene in the prior art through the collaborative operation of data acquisition and storage module, dynamic risk assessment model module, preplan optimization module and enterprise existing system integration module.

Description

Technical Field

[0001] This invention relates to the field of chemical safety production technology, specifically to a dynamic simulation and intelligent optimization system and method for emergency response plans in chemical enterprises. Background Technology

[0002] Safety management is always a core issue in chemical production. Chemical production involves high temperatures and pressures, flammable and explosive substances, and toxic and hazardous materials. The production systems are complex and have a high density of risk sources. Accidents such as leaks, fires, and explosions can easily cause significant casualties, property damage, and environmental pollution. Therefore, developing scientific and effective emergency plans is a crucial link in the safety assurance system of chemical enterprises. With the rapid development of technologies such as the Industrial Internet and intelligent sensing, risk management in chemical scenarios has gradually shifted from traditional manual monitoring to data-driven intelligent approaches, and related technologies have made certain progress.

[0003] In the existing technology, some intelligent solutions have been developed for risk management in chemical industry scenarios. For example, Chinese patent application CN113554318A discloses an integrated system and method for intelligent risk management and control of chemical industrial parks with three-dimensional visualization. This system collects environmental and production information through a real-time monitoring module, assesses disaster types using a coupled risk assessment algorithm, and obtains the optimal evacuation route accordingly. It realizes risk monitoring and route planning at the park level, and provides technical reference for risk management in the chemical industry.

[0004] However, existing technologies still fall short of meeting the actual needs of chemical enterprises for dynamic adaptation of emergency plans to complex accident scenarios, exhibiting significant shortcomings: First, traditional emergency plans are mostly in static text form. Even systems like those disclosed in CN113554318A, which can plan evacuation routes, focus primarily on optimizing a single aspect, lacking comprehensive simulation of all elements in the plan, such as complex response processes, personnel task allocation, and multi-departmental collaboration mechanisms, and cannot fully anticipate connection issues during plan execution. Second, existing technologies have not constructed a closed-loop mechanism of "real-time risk data - full-process simulation - dynamic plan optimization," failing to deeply integrate real-time risk assessment results, simulation feedback, and expert strategies, making it difficult to adjust plans in real time according to instantaneous changes at the accident site (such as sudden changes in equipment status or migration of hazardous areas). Finally, existing solutions lack the ability to dynamically verify the adaptability of rescue resource allocation and response processes. When accident scenarios exceed the preset scope, static or partially optimized plans are prone to resource mismatch and response delays, failing to guarantee the efficiency and relevance of emergency response. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a dynamic simulation and intelligent optimization system and method for emergency response plans in chemical enterprises.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A dynamic simulation and intelligent optimization system for emergency response plans in chemical enterprises includes a data acquisition and storage module, a dynamic risk assessment module, a plan optimization module, and a system integration module; The data acquisition and storage module collects equipment operating parameters, meteorological conditions, and personnel distribution information in real time through high-precision sensors, meteorological monitoring stations, and personnel positioning systems deployed within the chemical enterprise. It also retrieves historical accident data from the enterprise database through a secure connection channel and aggregates the above data to a data storage center using distributed storage technology. The dynamic risk assessment model module adopts a machine learning model with a deep learning neural network or a hybrid model architecture. It performs supervised learning training based on historical accident data, receives real-time data transmitted by the data acquisition and storage module, outputs real-time risk quantitative assessment results of the chemical production environment by deeply mining the inherent correlation of the data, and dynamically updates the model parameters according to the real-time data. The contingency plan optimization module includes a contingency plan optimization strategy knowledge base. Based on the real-time risk quantification assessment results, the contingency plan optimization module dynamically simulates the response process, resource allocation, and rescue path of the emergency plan to predict the implementation effect of the plan. When the simulation results show that there are potential implementation problems in the plan, the module retrieves suitable optimization strategies from the contingency plan optimization strategy knowledge base based on the potential implementation problems and generates an adjusted optimized emergency plan. The system integration module establishes a two-way data interaction channel with the existing monitoring system of the chemical enterprise through intermediate software, obtains real-time monitoring data, and feeds back the execution instructions of the optimized emergency plan to the relevant implementing departments.

[0007] Furthermore, the data acquisition and storage module follows a dedicated protocol for data transmission and uses a dedicated format for data storage.

[0008] Furthermore, the dynamic risk assessment model module is seamlessly integrated with the data acquisition and storage module.

[0009] Furthermore, the contingency plan optimization strategy knowledge base stores optimization strategies for different accident scenario parameter combinations. The contingency plan optimization module simulates virtual accident scenarios with multiple parameter combinations, records and verifies effective optimization schemes, and automatically updates them to the knowledge base.

[0010] Furthermore, the intermediate software of the system integration module has data format conversion and instruction distribution functions.

[0011] This invention also includes the following technical solutions: A method for dynamic simulation and intelligent optimization of emergency response plans for chemical enterprises using the above system includes the following steps: S1. Collect historical accident data and real-time production environment data such as equipment operating parameters, meteorological conditions, and personnel distribution of chemical enterprises through the data acquisition and storage module; S2. Analyze real-time production environment data using the dynamic risk assessment model module and output the real-time risk level; S3. Based on the real-time risk level, the emergency response plan, resource allocation, and rescue route are dynamically simulated using the plan optimization module to simulate the execution process of the plan. S4. Determine if there are potential execution problems in the simulation results; if so, identify the problem type and retrieve the corresponding optimization strategy from the contingency plan optimization strategy knowledge base to generate an optimized contingency plan; if not, maintain the original contingency plan. S5. The finalized emergency response plan instructions are sent to the implementing department through the system integration module.

[0012] Furthermore, in S4, potential implementation problems identified include evacuation routes being blocked due to the spread of fire, insufficient manpower in the rescue team, and the time required to deploy rescue supplies exceeding predetermined thresholds.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a dynamic simulation and intelligent optimization system for emergency response plans in chemical enterprises. Through the collaborative operation of a data acquisition and storage module, a dynamic risk assessment model module, a plan optimization module, and an integration module with the enterprise's existing systems, it utilizes supervised learning training of a machine learning model based on historical accident data with clearly labeled risk outcomes. Combined with multi-source real-time data, it achieves precise quantitative risk assessment of the chemical production environment. The system can dynamically simulate all elements of the emergency response plan's response process, resource allocation, and rescue routes. It quickly retrieves suitable solutions and generates optimized emergency response plans through a built-in optimization strategy knowledge base. Simultaneously, it leverages dedicated middleware software to achieve bidirectional data interaction and closed-loop operation with the enterprise's existing monitoring system. This effectively solves the problems of existing technologies where emergency response plans lack comprehensive simulation of the entire process and are difficult to dynamically adapt to instantaneous changes at the accident site. It significantly improves the scientific rigor, relevance, and real-time adaptability of emergency response plans, ensuring smooth emergency response processes, precise and efficient resource allocation, and safe and rapid rescue operations. Ultimately, it comprehensively enhances the chemical enterprise's ability to handle sudden accidents, maximizing the protection of personnel safety, minimizing property damage, and reducing environmental impact. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention is further described in detail below through specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0015] A dynamic simulation and intelligent optimization system for emergency response plans in chemical enterprises includes a data acquisition and storage module, a dynamic risk assessment module, a plan optimization module, and a system integration module; The data acquisition and storage module collects equipment operating parameters, meteorological conditions, and personnel distribution information in real time through high-precision sensors, meteorological monitoring stations, and personnel positioning systems deployed within the chemical enterprise. It also retrieves historical accident data from the enterprise database through a secure connection channel and aggregates the above data to a data storage center using distributed storage technology. The dynamic risk assessment model module adopts a machine learning model with a deep learning neural network or a hybrid model architecture. It performs supervised learning training based on historical accident data, receives real-time data transmitted by the data acquisition and storage module, outputs real-time risk quantitative assessment results of the chemical production environment by deeply mining the inherent correlation of the data, and dynamically updates the model parameters according to the real-time data. The contingency plan optimization module includes a contingency plan optimization strategy knowledge base. Based on the real-time risk quantification assessment results, the contingency plan optimization module dynamically simulates the response process, resource allocation, and rescue path of the emergency plan to predict the implementation effect of the plan. When the simulation results show that there are potential implementation problems in the plan, the module retrieves suitable optimization strategies from the contingency plan optimization strategy knowledge base based on the potential implementation problems and generates an adjusted optimized emergency plan. The system integration module establishes a two-way data interaction channel with the existing monitoring system of the chemical enterprise through intermediate software, obtains real-time monitoring data, and feeds back the execution instructions of the optimized emergency plan to the relevant implementing departments.

[0016] Furthermore, the data acquisition and storage module follows a dedicated protocol for data transmission and uses a dedicated format for data storage.

[0017] Furthermore, the dynamic risk assessment model module is seamlessly integrated with the data acquisition and storage module.

[0018] Furthermore, the contingency plan optimization strategy knowledge base stores optimization strategies for different accident scenario parameter combinations. The contingency plan optimization module simulates virtual accident scenarios with multiple parameter combinations, records and verifies effective optimization schemes, and automatically updates them to the knowledge base.

[0019] Furthermore, the intermediate software of the system integration module has data format conversion and instruction distribution functions.

[0020] A method for dynamic simulation and intelligent optimization of emergency response plans for chemical enterprises using the above system includes the following steps: S1. Collect historical accident data and real-time production environment data such as equipment operating parameters, meteorological conditions, and personnel distribution of chemical enterprises through the data acquisition and storage module; S2. Analyze real-time production environment data using the dynamic risk assessment model module and output the real-time risk level; S3. Based on the real-time risk level, the emergency response plan, resource allocation, and rescue route are dynamically simulated using the plan optimization module to simulate the execution process of the plan. S4. Determine if there are potential execution problems in the simulation results; if so, identify the problem type and retrieve the corresponding optimization strategy from the contingency plan optimization strategy knowledge base to generate an optimized contingency plan; if not, maintain the original contingency plan. S5. The finalized emergency response plan instructions are sent to the implementing department through the system integration module.

[0021] Furthermore, in S4, potential implementation problems identified include evacuation routes being blocked due to the spread of fire, insufficient manpower in the rescue team, and the time required to deploy rescue supplies exceeding predetermined thresholds.

[0022] This invention first collects historical accident data, equipment operating parameters, meteorological conditions, and personnel distribution information from a wide range of data collection channels. Historical accident data includes detailed records of the causes, development processes, losses, and countermeasures taken for various past accidents, providing valuable experience for subsequent risk assessments. Equipment operating parameters reflect the real-time operating status of chemical production units, such as key indicators like temperature, pressure, flow rate, and rotational speed; any abnormal fluctuations could be potential triggers for accidents. Meteorological conditions include temperature, humidity, wind speed and direction, and precipitation in and around the plant area; severe weather often exacerbates the severity of accidents or hinders rescue operations. Personnel distribution information is precise, down to the number and location of employees in different workshops, floors, and areas at various times, ensuring accurate evacuation and rescue in emergency situations. Based on this rich data foundation, a dynamic risk assessment model is constructed. This model uses advanced machine learning algorithms and data analysis techniques to deeply mine the inherent correlations between data and quantitatively assess the real-time risk status of the chemical enterprise's production environment.

[0023] Next, combining real-time data obtained from the company's existing real-time monitoring system, such as real-time feedback on equipment malfunctions from sensors, real-time weather updates from weather stations, and the latest personnel movement data obtained through personnel positioning systems, a pre-constructed dynamic risk assessment model is used to dynamically simulate key aspects of the emergency plan, including response procedures, resource allocation, and rescue routes. Taking the response procedure as an example, the model can quickly determine which level of emergency response should be initiated based on the current accident risk level, notify the relevant responsible personnel, and estimate the execution time for each step. Regarding resource allocation, it accurately calculates the required quantities of fire-fighting equipment, protective gear, and rescue equipment, as well as the optimal allocation route from the warehouse to the accident site. For rescue routes, considering the distribution of obstacles at the accident site, real-time road conditions, and changes in dangerous areas, it dynamically plans the safest and fastest rescue channels. Through this dynamic simulation, the effectiveness of the plan's implementation under different scenarios is predicted in advance, and potential problems and weaknesses are simulated.

[0024] If the system identifies potential problems during simulations, such as an evacuation route being deemed unsafe due to fire spread, or a rescue team being unable to effectively control the accident due to insufficient manpower as originally planned, the system will automatically adjust the emergency plan using built-in optimization algorithms. This includes replanning evacuation routes to avoid high-risk areas, using path planning algorithms combined with real-time risk maps to find alternative safe evacuation routes, dynamically adjusting rescue force deployment based on accident development, and reallocating tasks, personnel numbers, and equipment configurations for each rescue team based on the real-time situation assessment model. This ensures efficient and orderly rescue operations, ultimately achieving an intelligent upgrade of the emergency plan, enabling it to adapt to the complex and ever-changing production environment of chemical enterprises.

[0025] This invention first requires the establishment of a big data acquisition and storage architecture to ensure the system can acquire and process massive amounts of multi-source data, providing a solid foundation for subsequent risk assessment and contingency plan optimization. Next, advanced machine learning algorithms are used to construct a dynamic risk assessment model, trained with historical data to improve the accuracy of risk assessment. This model is deployed on a high-performance computing server, receiving and processing the latest production environment data in real time and quickly outputting risk assessment results. Simultaneously, combining industry expert experience and extensive simulation results, a contingency plan optimization strategy knowledge base is constructed, forming a rich reserve of optimization strategies. When the system detects a problem, the optimization algorithm can quickly retrieve suitable optimization strategies from the knowledge base and adjust parameters and customize solutions according to the actual situation. Furthermore, by developing specialized middleware software, seamless integration of this invention's system with the enterprise's existing monitoring system is achieved, ensuring timely two-way data interaction and instruction feedback, guaranteeing the closed-loop operation of the entire emergency management process, thereby effectively improving the chemical enterprise's ability to respond to emergencies.

[0026] The following is a detailed description of the specific implementation steps: 1. Big Data Acquisition and Storage Architecture Construction: High-precision sensors, meteorological monitoring stations, and personnel positioning systems are deployed within the chemical enterprise to collect data on equipment operating parameters, meteorological conditions, and personnel distribution. This massive amount of data is then aggregated in real-time to the data storage center using dedicated data transmission protocols and storage formats. Distributed storage technology ensures data reliability, scalability, and fast read / write performance.

[0027] 2. Training and Deployment of the Dynamic Risk Assessment Model: Select a suitable machine learning algorithm (such as a deep learning neural network or a hybrid model), use historical data for supervised learning training, and continuously adjust the model parameters to improve the accuracy of risk assessment. Deploy the trained model on a high-performance computing server, seamlessly integrate with the real-time monitoring data access module, and ensure that the latest production environment data can be received and processed in real time, quickly outputting risk assessment results.

[0028] 3. Construction of a knowledge base for emergency response plan optimization strategies: Senior experts in the field of chemical safety production were invited to summarize and categorize emergency response plan optimization strategies for various typical accident scenarios, forming a preliminary knowledge framework. Utilizing the system's dynamic simulation function, thousands of simulations were conducted on virtual accident scenarios under different parameter combinations. The implementation effects of the plans before and after optimization were recorded and analyzed. Successful optimization solutions were incorporated into the knowledge base, enriching the reserve of optimization strategies.

[0029] 4. Integration with Existing Enterprise Monitoring Systems: Gain a thorough understanding of the data interface specifications and communication protocols of various monitoring platforms, including existing equipment management systems, safety production monitoring systems, and environmental monitoring systems. Develop dedicated middleware software to enable bidirectional data interaction between this invention's system and these existing systems. This ensures real-time access to the latest information from the enterprise's production line and timely feedback of optimized emergency response plan instructions to relevant implementing departments, guaranteeing the closed-loop operation of the entire emergency management process.

[0030] This invention provides a dynamic simulation and intelligent optimization system for emergency response plans in chemical enterprises. Through the collaborative operation of a data acquisition and storage module, a dynamic risk assessment model module, a plan optimization module, and an integration module with the enterprise's existing systems, it utilizes supervised learning training of a machine learning model based on historical accident data with clearly labeled risk outcomes. Combined with multi-source real-time data, it achieves precise quantitative risk assessment of the chemical production environment. The system can dynamically simulate all elements of the emergency response plan's response process, resource allocation, and rescue routes. It quickly retrieves suitable solutions and generates optimized emergency response plans through a built-in optimization strategy knowledge base. Simultaneously, it leverages dedicated middleware software to achieve bidirectional data interaction and closed-loop operation with the enterprise's existing monitoring system. This effectively solves the problems of existing technologies where emergency response plans lack comprehensive simulation of the entire process and are difficult to dynamically adapt to instantaneous changes at the accident site. It significantly improves the scientific rigor, relevance, and real-time adaptability of emergency response plans, ensuring smooth emergency response processes, precise and efficient resource allocation, and safe and rapid rescue operations. Ultimately, it comprehensively enhances the chemical enterprise's ability to handle sudden accidents, maximizing the protection of personnel safety, minimizing property damage, and reducing environmental impact.

[0031] The foregoing descriptions have outlined some exemplary embodiments of the present invention. It is understood that these embodiments are merely illustrative and do not constitute a limitation on the scope of protection of the present invention. Features in these embodiments can be rearranged in suitable ways, and the resulting solutions remain within the scope of protection claimed by the present invention. All other embodiments obtained by those skilled in the art based on the foregoing embodiments without inventive effort, i.e., all modifications, equivalent substitutions, and improvements made within the spirit and principles of this application, fall within the scope of protection claimed by the present invention.

Claims

1. A dynamic simulation and intelligent optimization system for emergency response plans in chemical enterprises, characterized in that, It includes a data acquisition and storage module, a dynamic risk assessment module, a contingency plan optimization module, and a system integration module; The data acquisition and storage module collects equipment operating parameters, meteorological conditions, and personnel distribution information in real time through high-precision sensors, meteorological monitoring stations, and personnel positioning systems deployed within the chemical enterprise. It also retrieves historical accident data from the enterprise database through a secure connection channel and aggregates the above data to a data storage center using distributed storage technology. The dynamic risk assessment model module adopts a machine learning model with a deep learning neural network or a hybrid model architecture. It performs supervised learning training based on historical accident data, receives real-time data transmitted by the data acquisition and storage module, outputs real-time risk quantitative assessment results of the chemical production environment by deeply mining the inherent correlation of the data, and dynamically updates the model parameters according to the real-time data. The contingency plan optimization module includes a contingency plan optimization strategy knowledge base. Based on the real-time risk quantification assessment results, the contingency plan optimization module dynamically simulates the response process, resource allocation, and rescue path of the emergency plan to predict the implementation effect of the plan. When the simulation results show that there are potential implementation problems in the plan, the module retrieves suitable optimization strategies from the contingency plan optimization strategy knowledge base based on the potential implementation problems and generates an adjusted optimized emergency plan. The system integration module establishes a two-way data interaction channel with the existing monitoring system of the chemical enterprise through intermediate software, obtains real-time monitoring data, and feeds back the execution instructions of the optimized emergency plan to the relevant implementing departments.

2. The dynamic simulation and intelligent optimization system for emergency response plans of chemical enterprises according to claim 1, characterized in that, The data acquisition and storage module follows a proprietary protocol for data transmission and uses a proprietary format for data storage.

3. The dynamic simulation and intelligent optimization system for emergency response plans of chemical enterprises according to claim 1, characterized in that, The dynamic risk assessment model module is seamlessly integrated with the data acquisition and storage module.

4. The dynamic simulation and intelligent optimization system for emergency response plans of chemical enterprises according to claim 1, characterized in that, The contingency plan optimization strategy knowledge base stores optimization strategies for different accident scenario parameter combinations. The contingency plan optimization module simulates virtual accident scenarios with multiple parameter combinations, records and verifies effective optimization schemes, and automatically updates them to the knowledge base.

5. The dynamic simulation and intelligent optimization system for emergency response plans of chemical enterprises according to claim 1, characterized in that, The intermediate software of the system integration module has data format conversion and instruction distribution functions.

6. A method for dynamic simulation and intelligent optimization of emergency response plans for chemical enterprises using the system described in any one of claims 1-5, characterized in that, Includes the following steps: S1. Collect historical accident data, equipment operating parameters, meteorological conditions, and real-time production environment data of chemical enterprises through the data acquisition and storage module; S2. Analyze real-time production environment data using the dynamic risk assessment model module and output the real-time risk level; S3. Based on the real-time risk level, the emergency response plan, resource allocation, and rescue route are dynamically simulated using the plan optimization module to simulate the execution process of the plan. S4. Determine if there are potential execution problems in the simulation results; if so, identify the problem type and retrieve the corresponding optimization strategy from the contingency plan optimization strategy knowledge base to generate an optimized contingency plan; if not, maintain the original contingency plan. S5. The finalized emergency response plan instructions are sent to the implementing department through the system integration module.

7. The method for dynamic simulation and intelligent optimization of emergency response plans for chemical enterprises according to claim 6, characterized in that, In S4, potential execution problems identified include evacuation routes being blocked due to the spread of fire, insufficient manpower in the rescue team, and the time required to deploy rescue supplies exceeding the predetermined threshold.