Emergency scheduling optimization method and system for chemical production

Abstract

The invention relates to the technical field of emergency scheduling, in particular to an emergency scheduling optimization method and system for chemical production. The method comprises the following steps: acquiring chemical production element data; performing production process monitoring sensor deployment on the chemical production element data to obtain standard production process monitoring sensor deployment data; performing local area network connection on the chemical production process data through standard production process monitoring sensor deployment data to generate a chemical production monitoring network; performing data acquisition on deployment data of the standard production process monitoring sensor based on a chemical production monitoring network to obtain a chemical production monitoring data set; and carrying out production reaction process analysis on the chemical production monitoring data set to generate chemical production reaction process data. According to the invention, through real-time monitoring, dynamic early warning, flexible resource scheduling and a closed-loop feedback mechanism, the accuracy and efficiency of emergency scheduling are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency dispatch, and in particular to an emergency dispatch optimization method and system for chemical production. Background Art

[0002] Early chemical scheduling mainly relied on manual decision-making, and the dispatchers responded to emergencies based on experience and simple rules. However, with the expansion of chemical production scale and the complexity of processes, this traditional method gradually exposed problems such as inefficiency, delayed response and decision-making errors, especially in the face of emergencies, leading to safety hazards and waste of resources. Information technology has been gradually introduced into the chemical industry, and the scheduling system has begun to use computers and preliminary optimization algorithms to improve the speed and accuracy of emergency response. At this stage, mathematical methods such as linear programming and integer programming have become the theoretical basis for scheduling optimization, which can solve simple production scheduling problems to a certain extent. However, in the face of the actual complex chemical production environment, the limitations of traditional algorithms have gradually emerged. Information technology has been gradually introduced into the chemical industry, and the scheduling system has begun to use computers and preliminary optimization algorithms to improve the speed and accuracy of emergency response. At this stage, mathematical methods such as linear programming and integer programming have become the theoretical basis for scheduling optimization, which can solve simple production scheduling problems to a certain extent. However, in the face of the actual complex chemical production environment, the limitations of traditional algorithms have gradually emerged. However, the current traditional dispatching system often lags in responding to emergencies and lacks an effective early warning mechanism. At the same time, it is difficult to achieve fast and flexible resource dispatch in emergency situations, which often leads to waste of resources or failure to arrive in time, resulting in low accuracy and efficiency of emergency dispatch. Summary of the invention

[0003] Based on this, it is necessary to provide an emergency scheduling optimization method and system for chemical production to solve at least one of the above technical problems.

[0004] To achieve the above object, an emergency dispatch optimization method for chemical production is provided, the method comprising the following steps: Step S1: Acquire chemical production factor data; deploy production process monitoring sensors on the chemical production factor data to obtain standard production process monitoring sensor deployment data; connect the chemical production process data to a local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set; Step S2: Perform production reaction process analysis on the chemical production monitoring data set to generate chemical production reaction process data; perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; perform event risk dynamic assessment on the hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; perform response decision classification on the hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; Step S3: Acquire chemical production resource data; perform progressive resource elastic scheduling on chemical production resource data through common event response decision data to generate common event resource scheduling strategy; perform system-wide resource emergency scheduling on chemical production resource data through emergency event response decision data to generate emergency event resource scheduling strategy; perform concurrent execution resource reuse and sharing on common event resource scheduling strategy and emergency event resource scheduling strategy to generate concurrent event resource scheduling strategy; Step S4: construct a feedback closed loop of the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy to generate chemical production real-time response adjustment data; use the chemical production real-time response adjustment data to perform emergency scheduling optimization on the chemical production monitoring data set, generate chemical production emergency scheduling optimization data, and execute chemical production emergency scheduling operations.

[0005] The present invention can collect key data of chemical production in real time and generate a chemical production monitoring data set by deploying standard production process monitoring sensors in a chemical production monitoring network. This monitoring mechanism provides an accurate data basis for subsequent dynamic reaction warning and risk assessment, ensuring comprehensive visualization and accurate control of the production process. Through dynamic analysis of the production reaction process, early warning data of hazardous reactions and non-hazardous reactions are generated in a timely manner. Combined with dynamic assessment of event risks, the system can identify potential risks in advance, improve early warning capabilities, and thus reduce the probability of unexpected events in chemical production. By classifying the decisions of risk assessment results, the early warning data is converted into ordinary event response decisions and emergency event response decisions. This intelligent response decision-making method can automatically adjust the response measures according to different risk levels to ensure efficient scheduling of resources and personnel. The method uses ordinary event resource scheduling strategies and emergency event resource scheduling strategies to perform progressive and emergency resource scheduling, thereby realizing dynamic management of chemical production resources. Through reasonable resource elastic scheduling and emergency scheduling mechanisms, enterprises can ensure that resources are given priority and reasonably allocated in emergencies, avoiding resource waste and scheduling conflicts. When ordinary event and emergency event resource scheduling strategies are executed concurrently, efficient resource utilization is achieved through resource reuse and sharing mechanisms. The concurrent event resource scheduling strategy effectively reduces resource contention conflicts and improves the overall resource utilization efficiency and scheduling flexibility of the system. The feedback closed loop construction of step S4 ensures that the scheduling strategy is adjusted and optimized in real time during the chemical production scheduling process. This mechanism improves the system's response speed to emergencies and ensures the stability and sustainability of scheduling execution. By optimizing emergency scheduling through feedback data, the system can dynamically adjust the emergency operations of chemical production to ensure the smooth progress of production operations under emergencies. The optimized scheduling operation not only improves the efficiency of emergency treatment, but also reduces the impact on the production process after the accident. The method ensures the stability of the chemical production process, reduces the risk of accidents, and greatly improves production safety through an optimization process combining early warning, decision-making, resource scheduling and closed-loop feedback. Therefore, the present invention improves the accuracy and efficiency of emergency scheduling through real-time monitoring, dynamic early warning, flexible resource scheduling and closed-loop feedback mechanism.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire chemical production factor data; Step S12: performing chemical production process analysis on the chemical production factor data to generate chemical production process data; performing production process monitoring sensor deployment based on the chemical production process data to obtain production process monitoring sensor deployment data; Step S13: performing data preprocessing on the production process monitoring sensor deployment data to generate standard production process monitoring sensor deployment data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S14: Connect the chemical production process data to the local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set.

[0007] The present invention can comprehensively capture various data involved in the chemical production process, including each link of the production process, by acquiring chemical production factor data and analyzing the chemical production process, providing a basis for subsequent monitoring and optimization. Based on the reasonable deployment of production process monitoring sensors, effective monitoring of the entire chemical production process is achieved, which helps to improve production efficiency and reduce risks in the production process. By preprocessing sensor data, including data cleaning, missing value filling and standardization, the accuracy and consistency of the collected data are ensured, laying a good foundation for subsequent data analysis. By connecting the chemical production monitoring network through a local area network, it is possible to monitor each link of the production process in real time, and collect production monitoring data in real time, so as to facilitate timely detection of abnormal situations and rapid adjustments. Based on the collected chemical production monitoring data set, data analysis can be further carried out, process flow can be optimized, and the safety and efficiency of production can be ultimately improved.

[0008] Preferably, step S14 comprises the following steps: Step S141: Perform chemical environment building structure analysis on chemical production process data to generate chemical process environment building structure data; perform sensor node connection on standard production process monitoring sensor deployment data according to the chemical process environment building structure data through a wireless sensor network to generate sensor node connection data; Step S142: performing sensor adjacent communication path calculation on the sensor node connection data to obtain sensor adjacent communication path data; constructing a network topology structure for the sensor node connection data based on the sensor adjacent communication path data to generate a chemical production monitoring network; Step S143: Use the chemical production local area network to build a data link for the standard production process monitoring sensor deployment data to generate a chemical production data transmission channel; collect monitoring data from the chemical production local area network according to the chemical production data transmission channel to obtain a chemical production monitoring data set.

[0009] The present invention can help reasonably deploy and optimize the sensor network by analyzing the environmental building structure of the chemical production process, so that the sensor can adapt to different building layouts and environmental conditions, and improve the coverage and accuracy of monitoring. Based on the environmental building structure data of the chemical process, the automatic connection of sensor nodes is realized through the wireless sensor network, the effective communication of each node is ensured, the flexibility and expansibility of the sensor network are improved, and the requirements of different process layouts are adapted. By calculating the adjacent communication paths of the sensor nodes, the optimal communication path data can be obtained, and then a reasonable network topology structure is constructed. This ensures stable and efficient communication between sensor nodes and reduces delays and losses in data transmission. By establishing a chemical production data transmission channel, efficient data transmission is achieved, and the real-time collection and transmission of various monitoring data in the chemical production process are guaranteed. This is of great significance for real-time monitoring of various key points of chemical production and ensuring process safety. Based on the efficient connection and data transmission channel of the sensor network, the chemical production local area network can stably collect monitoring data and generate a complete chemical production monitoring data set, which provides strong data support for subsequent data analysis and production optimization. By collecting and transmitting chemical production monitoring data in real time, abnormal situations in the production process can be quickly identified, which helps to provide early warning and implement corresponding response measures to ensure the safe and reliable operation of the chemical production process.

[0010] Preferably, step S2 comprises the following steps: Step S21: extracting chemical production raw materials from the chemical production monitoring data set to obtain chemical production raw material data; performing production reaction process analysis on the chemical production raw material data to generate chemical production reaction process data; Step S22: screening the chemical production reaction process data for hazardous materials to obtain hazardous material screening data and non-hazardous material screening data; Step S23: Performing dynamic reaction warning on the hazardous material screening data and the non-hazardous material screening data based on a preset reaction monitoring threshold, and generating hazardous reaction warning data and non-hazardous reaction warning data; Step S24: Perform dynamic event risk assessment on the hazardous reaction warning data and the non-hazardous reaction warning data to generate event risk dynamic assessment data; perform response decision classification on the hazardous reaction warning data and the non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data.

[0011] The present invention ensures accurate identification of raw materials required for production by extracting raw materials from chemical production monitoring data sets, and provides basic data for subsequent safety assessment and optimization through data generated by production reaction process analysis. Hazardous material screening of chemical production reaction process data can effectively identify materials that threaten the environment and human health, and help enterprises take measures in advance to reduce potential risks. Based on the preset reaction monitoring threshold, the system performs dynamic reaction warning on hazardous and non-hazardous materials, and provides warning information in a timely manner to ensure that the production link can quickly respond to abnormal situations and reduce the probability of accidents. Through the dynamic assessment of event risks of hazardous and non-hazardous reaction warning data, different types of events can be comprehensively analyzed to ensure a deeper understanding and cognition of potential risks, and provide support for the risk management of enterprises. According to the dynamic assessment data of event risk, the response decision classification of hazardous and non-hazardous reaction warning data is performed to generate response decision data for common events and emergency events. This classification can help enterprises formulate more targeted response strategies and improve the efficiency and effectiveness of emergency response. Through a series of screening, early warning and evaluation, the system's safety management level in the chemical production process has been significantly improved, which can better prevent and control accident risks, ensure production safety and environmental protection. The generated event risk dynamic assessment data and response decision data provide an important basis for the continuous improvement of chemical companies, helping them to continuously optimize production processes and management strategies and achieve a balance between safety and efficiency.

[0012] Preferably, step S24 includes the following steps: Step S241: extracting event risk features from the hazardous reaction warning data and the non-hazardous reaction warning data to obtain event risk feature data, wherein the event risk feature extraction includes reaction material hazard level extraction, temperature and pressure dynamic extraction, gas concentration extraction, and reaction equipment status extraction; Step S242: Divide the event risk feature data into data sets to generate a model training set and a model test set; perform model training on the model training set using a long short-term memory network algorithm to generate an event risk dynamic prediction pre-model; perform model iterative optimization on the event risk dynamic prediction pre-model using the model test set, thereby generating an event risk dynamic prediction model; Step S243: importing the hazardous reaction warning data and the non-hazardous reaction warning data into the event risk dynamic prediction model to perform event risk dynamic assessment and generate event risk dynamic assessment data; Step S244: Based on the event risk dynamic assessment data, the response priorities of the hazardous reaction warning data and the non-hazardous reaction warning data are divided to generate event response priority data; according to the event response priority data, the response decisions of the hazardous reaction warning data and the non-hazardous reaction warning data are classified to generate ordinary event response decisions and emergency event response decisions.

[0013] The present invention can fully understand the characteristics of event risk through dynamic extraction of the dangerous level of reactive substances, temperature and pressure, gas concentration and reaction equipment status. This provides an important basis for subsequent risk assessment, making risk management more scientific and effective. The event risk feature data is divided into a model training set and a test set, and training is performed through a long short-term memory network algorithm to achieve iterative optimization of the event risk dynamic prediction model. This data-driven modeling method can improve the accuracy of risk prediction and capture potential risk changes in a timely manner. The hazardous and non-hazardous reaction warning data are input into the event risk dynamic prediction model to achieve a dynamic assessment of event risk. This enables enterprises to monitor and evaluate risk conditions in real time during the production process, thereby improving response speed and response capabilities. Based on the event risk dynamic assessment data, the response priority of the hazardous and non-hazardous reaction warning data is divided, ensuring that when a risk event occurs, the most urgent and important situations can be quickly identified and handled, and resource allocation is optimized. By classifying the event response priority data, response decisions for ordinary events and emergency events are generated. This mechanism ensures that when facing different types of events, corresponding countermeasures can be taken to improve the pertinence and effectiveness of emergency management. Overall, the implementation of step S24 enables the enterprise to establish a dynamic, safe and efficient risk management system to ensure effective response to various potential risks in the chemical production process and ensure production safety and environmental protection. Through the application of dynamic prediction models, enterprises can continuously track and learn the characteristic changes of event risks, continuously optimize their own production processes and safety management strategies, and achieve continuous improvement.

[0014] Preferably, step S3 comprises the following steps: Step S31: Acquire chemical production resource data; Step S32: performing common event classification processing on chemical production resource data through common event response decision data to generate common event classification data; performing progressive resource elastic scheduling on chemical production resource data according to the common event classification data to generate a common event resource scheduling strategy; Step S33: using the emergency response decision data to classify the chemical production resource data into rapid event hazard levels, generating emergency event hazard level classification data; performing system-wide resource emergency dispatch on the chemical production resource data according to the emergency event hazard level classification data, generating an emergency event resource dispatch strategy; Step S34: Perform concurrent execution detection on the common event resource scheduling strategy and the emergency event resource scheduling strategy. When it is found that the common event resource scheduling strategy and the emergency event resource scheduling strategy are executed concurrently, the chemical production resource data is reused and shared based on the common event resource scheduling strategy and the emergency event resource scheduling strategy to generate a concurrent event resource scheduling strategy.

[0015] The present invention provides basic data support for subsequent resource scheduling and decision-making by acquiring chemical production resource data, ensuring the timely update and accuracy of all resource information. The resources are graded using common event response decision data, so that enterprises can effectively manage resources according to the nature and impact of the event. This graded processing provides a basis for the orderly scheduling of resources and improves the flexibility and response speed of the production process. Progressive resource elastic scheduling is achieved through common event classification data, ensuring that when common events occur, enterprises can gradually adjust resource allocation, reduce resource waste, improve resource utilization efficiency, and reduce interference with the production process. The use of emergency event response decision data allows chemical production resources to quickly divide hazard levels, take targeted emergency scheduling measures, and ensure that in critical moments, resources can be quickly and effectively deployed to where they are needed, reducing accident risks. The full-system resource emergency scheduling strategy based on emergency event hazard level classification data can ensure that all resources can be quickly assembled and mobilized in emergencies to minimize the impact of events on production and improve emergency response capabilities. By detecting the concurrent execution of common event resource scheduling strategies and emergency event resource scheduling strategies, resource conflicts can be effectively identified and handled, and resource reuse and sharing can be achieved. This mechanism avoids repeated investment of resources, improves resource utilization efficiency, and reduces operating costs. The generated concurrent event resource scheduling strategy provides enterprises with a more flexible and efficient resource scheduling solution, ensuring that when facing complex events, resource utility can be maximized and overall production efficiency and safety can be improved. Through systematic event response and resource scheduling strategies, decision-making efficiency is improved, allowing enterprises to respond quickly to various events and ensure production continuity and safety.

[0016] Preferably, performing emergency dispatch of chemical production resource data for the entire system according to the emergency event hazard level classification data includes: Perform resource scheduling demand analysis on chemical production resource data based on emergency event hazard level classification data to generate emergency resource demand data; perform resource availability scan on chemical production resource data based on emergency resource demand data to generate resource availability data; Based on the emergency resource demand data and resource availability data, the resource scheduling path of the chemical production monitoring network is planned to generate resource scheduling path data; dynamic resource scheduling response feedback is performed on the resource scheduling path data to generate resource scheduling feedback data; The resource scheduling feedback data is synchronized with the entire system resources to generate system resource synchronization data; emergency resources are prioritized for chemical production resource data through system resource synchronization data to generate emergency event resource scheduling strategies.

[0017] The present invention can accurately identify and evaluate the demand for emergency resources by analyzing the emergency event hazard level classification data. This precise analysis provides a clear basis for resource allocation and ensures timely response in crisis situations. By using the emergency resource demand data to scan the availability of chemical production resource data, the status and availability of existing resources can be quickly identified. This helps enterprises to clearly understand the situation of deployable resources and avoid unnecessary waste of resources. Based on the emergency resource demand data and resource availability data, the resource scheduling path planning of the chemical production monitoring network can optimize the resource allocation process, ensure that resources can quickly reach the required location in an emergency, and improve the efficiency of emergency response. Dynamic resource scheduling response feedback is performed on the resource scheduling path data, and the effect and progress of resource scheduling can be monitored in real time. Through timely feedback, enterprises can quickly adjust resource scheduling strategies to ensure the flexibility and effectiveness of emergency response. By synchronizing the resource scheduling feedback data of the entire system, it is ensured that all related systems and resource states are consistent. This synchronization mechanism enhances the coordination between departments and helps to form a joint force to deal with emergencies. Using system resource synchronization data to prioritize emergency resource allocation for chemical production resources ensures that the most important resources can be quickly deployed in place at critical moments. This priority management effectively improves the effectiveness of emergency response and minimizes the impact of accidents. Through the implementation of the above steps, enterprises can quickly and effectively mobilize resources in the face of emergencies, thereby reducing the risk of accidents and ensuring employee and production safety. The systematic resource scheduling and feedback mechanism enhances the company's ability to respond to emergencies, enabling it to quickly and effectively allocate resources in crises and ensure the continuity and safety of production.

[0018] Preferably, step S34 includes the following steps: Step S341: Perform concurrent execution detection on the common event resource scheduling strategy and the emergency event resource scheduling strategy. When it is found that the common event resource scheduling strategy and the emergency event resource scheduling strategy are executed concurrently, classify the resource conflicts of chemical production resource data based on the common event resource scheduling strategy and the emergency event resource scheduling strategy to generate hard resource conflict data and soft resource conflict data; Step S342: using the hard resource conflict data to perform emergency event resource preemption on the chemical production resource data, and generating hard resource preemption strategy data; using the soft resource conflict data to perform common event resource scheduling delay on the chemical production resource data, and generating soft resource delay strategy data; Step S343: performing resource sharing on chemical production resource data based on the hard resource preemption strategy data and the soft resource delay strategy data to generate resource sharing status data; performing resource reuse on the resource sharing status data to generate resource reuse data; Step S344: performing conflict concurrent execution feedback on the common event resource scheduling strategy and the emergency event resource scheduling strategy according to the resource sharing state data and the resource reuse data, and generating a concurrent event resource scheduling strategy.

[0019] The present invention can timely identify resource conflicts by concurrently executing detection of common event resource scheduling strategies and emergency event resource scheduling strategies, thereby performing reasonable resource allocation. This efficient resource use method maximizes the utilization rate of available resources and reduces idleness and waste. The conflicts found in the concurrent execution process are divided into hard resource conflicts and soft resource conflicts, which helps enterprises to clarify the use priority of different types of resources. Such classification makes the scheduling strategy more refined and can take effective measures for different situations. By preempting emergency event resources through hard resource conflict data, it can be ensured that the most important resources at critical moments meet emergency needs first. This mechanism reduces the emergency response delay caused by insufficient resources and improves the response speed and efficiency. By delaying the scheduling of common event resources through soft resource conflict data, enterprises can adjust the resource use of common events in a timely manner without affecting the overall production process. This flexibility ensures that the impact of common events is minimized in emergency situations, while maximizing the emergency needs. Based on hard resource preemption strategy data and soft resource delay strategy data, the resource sharing state data and its reuse formed can further optimize resource allocation and avoid unnecessary repeated investment and resource waste. Resource reuse not only saves costs, but also improves the overall liquidity of resources. By generating concurrent event resource scheduling strategies through conflict feedback on resource sharing status data and resource reuse data, resource scheduling strategies can be adjusted in real time, enhancing the company's resilience. The dynamic feedback mechanism makes resource scheduling more efficient and intelligent, and can adapt to changing production environments and needs. The implementation of this series of measures has improved the scheduling capabilities of enterprises in the face of complex production environments, enabling them to flexibly respond to various emergencies and ensure the continuity and stability of production. Through reasonable resource conflict management and emergency scheduling strategies, the risk of accidents caused by improper resource scheduling is reduced, ensuring the safety of employees and production processes.

[0020] Preferably, step S4 comprises the following steps: Step S41: executing the chemical production monitoring strategy on the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy, and generating chemical production scheduling execution data; Step S42: constructing a feedback closed loop for chemical production scheduling execution data based on cloud platform technology to generate real-time response adjustment data for chemical production; Step S43: Utilize the chemical production real-time response adjustment data to perform emergency dispatch optimization on the chemical production monitoring data set, generate chemical production emergency dispatch optimization data, and execute chemical production emergency dispatch operations.

[0021] The present invention realizes comprehensive monitoring of the chemical production monitoring network by combining common event resource scheduling strategies, emergency event resource scheduling strategies and concurrent event resource scheduling strategies. The coordinated application of different strategies ensures the visual management of the chemical production process and reduces the possibility of errors in production. The feedback closed-loop mechanism built based on cloud platform technology can provide real-time feedback and adjustment to the chemical production scheduling execution data. In this way, the enterprise can quickly respond to any problems that arise in the production process, ensure that the scheduling process is always in the optimal state, and improve the flexibility and adaptability of the scheduling system. By optimizing the emergency scheduling of the real-time response adjustment data, chemical production can quickly adjust the resource scheduling strategy and generate optimized emergency scheduling data when an emergency occurs. This not only improves production safety, but also reduces the probability of accidents and shortens the emergency response time. The generation and feedback closed-loop construction of chemical production scheduling execution data make the entire scheduling system more efficient. Whether it is the processing of common events or the response to emergency events, it can be dynamically adjusted through an automated feedback mechanism, thereby improving the overall production efficiency. Through the application of emergency scheduling optimization data, enterprises can perform emergency scheduling operations more intelligently. When faced with a complex production environment, the system can autonomously perform optimal emergency resource allocation and scheduling to ensure stable chemical production. The introduction of real-time response adjustment data enables enterprises to promptly identify and respond to potential risks in the production process. By optimizing emergency scheduling operations, enterprises can respond quickly in emergencies, ensure employee safety and reduce the risk of material and equipment damage. The execution of chemical production monitoring strategies and real-time feedback mechanisms enable the system to dynamically adjust according to changes in production conditions. This flexibility greatly improves the company's ability to adapt to emergencies and ensures production continuity. Through cloud platform technology, enterprises can process large amounts of chemical production scheduling data and provide timely feedback and optimization. The efficient processing capabilities of the cloud platform ensure real-time performance and avoid scheduling problems caused by data lags.

[0022] In this specification, an emergency dispatch optimization system for chemical production is provided, which is used to execute the above-mentioned emergency dispatch optimization method for chemical production. The emergency dispatch optimization system for chemical production includes: The monitoring network module is used to obtain chemical production factor data; deploy production process monitoring sensors on chemical production factor data to obtain standard production process monitoring sensor deployment data; connect chemical production process data to the local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set; The dynamic risk assessment module is used to analyze the production reaction process of the chemical production monitoring data set to generate chemical production reaction process data; to perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; to perform event risk dynamic assessment on the hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; to perform response decision classification on the hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; The resource scheduling module is used to obtain chemical production resource data; perform progressive resource elastic scheduling on chemical production resource data through common event response decision data to generate common event resource scheduling strategies; perform system-wide resource emergency scheduling on chemical production resource data through emergency event response decision data to generate emergency event resource scheduling strategies; perform concurrent execution resource reuse and sharing on common event resource scheduling strategies and emergency event resource scheduling strategies to generate concurrent event resource scheduling strategies; The closed-loop feedback control module is used to construct a feedback closed-loop for the chemical production monitoring network through common event resource scheduling strategies, emergency event resource scheduling strategies and concurrent event resource scheduling strategies, and generate real-time response adjustment data for chemical production; use the real-time response adjustment data for chemical production to perform emergency scheduling optimization on the chemical production monitoring data set, and generate chemical production emergency scheduling optimization data to execute chemical production emergency scheduling operations.

[0023] The beneficial effect of the present invention is that by acquiring chemical production factor data and deploying production process monitoring sensors, a chemical production monitoring network is generated, which realizes real-time monitoring and data collection of the entire chemical production process, ensures the comprehensiveness and timeliness of production data, and solves the problem of incomplete or inaccurate data acquisition in traditional monitoring methods. By analyzing the reaction process and dynamic early warning of the chemical production monitoring data set, identifying harmful and non-harmful reactions in advance, and performing dynamic event risk assessment and response decision classification, the prediction ability and response speed of emergency events are greatly improved, and the possibility and impact of accidents are reduced. According to the ordinary event response decision and the emergency event response decision, progressive resource scheduling and system-wide resource emergency scheduling are carried out, combined with the resource reuse sharing strategy, to ensure the flexible allocation of resources in different events, improve resource utilization efficiency, and avoid resource waste or scheduling delay. Through the feedback closed-loop mechanism, the resource scheduling strategy is combined with the monitoring data to build a real-time response adjustment system, so that the optimization process of emergency scheduling is more intelligent and dynamic, ensuring real-time adjustment and continuous optimization in the production process, and effectively improving the overall efficiency and emergency handling capability of the scheduling system. Therefore, the present invention improves the accuracy and efficiency of emergency dispatch through real-time monitoring, dynamic early warning, flexible resource dispatch and closed-loop feedback mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of an emergency dispatch optimization method for chemical production; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , an emergency dispatch optimization method for chemical production, the method comprising the following steps: Step S1: Acquire chemical production factor data; deploy production process monitoring sensors on the chemical production factor data to obtain standard production process monitoring sensor deployment data; connect the chemical production process data to a local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set; Step S2: Perform production reaction process analysis on the chemical production monitoring data set to generate chemical production reaction process data; perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; perform event risk dynamic assessment on the hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; perform response decision classification on the hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; Step S3: Acquire chemical production resource data; perform progressive resource elastic scheduling on chemical production resource data through common event response decision data to generate common event resource scheduling strategy; perform system-wide resource emergency scheduling on chemical production resource data through emergency event response decision data to generate emergency event resource scheduling strategy; perform concurrent execution resource reuse and sharing on common event resource scheduling strategy and emergency event resource scheduling strategy to generate concurrent event resource scheduling strategy; Step S4: construct a feedback closed loop of the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy to generate chemical production real-time response adjustment data; use the chemical production real-time response adjustment data to perform emergency scheduling optimization on the chemical production monitoring data set, generate chemical production emergency scheduling optimization data, and execute chemical production emergency scheduling operations.

[0029] The present invention can collect key data of chemical production in real time and generate a chemical production monitoring data set by deploying standard production process monitoring sensors in a chemical production monitoring network. This monitoring mechanism provides an accurate data basis for subsequent dynamic reaction warning and risk assessment, ensuring comprehensive visualization and accurate control of the production process. Through dynamic analysis of the production reaction process, early warning data of hazardous reactions and non-hazardous reactions are generated in a timely manner. Combined with dynamic assessment of event risks, the system can identify potential risks in advance, improve early warning capabilities, and thus reduce the probability of unexpected events in chemical production. By classifying the decisions of risk assessment results, the early warning data is converted into ordinary event response decisions and emergency event response decisions. This intelligent response decision-making method can automatically adjust the response measures according to different risk levels to ensure efficient scheduling of resources and personnel. The method uses ordinary event resource scheduling strategies and emergency event resource scheduling strategies to perform progressive and emergency resource scheduling, thereby realizing dynamic management of chemical production resources. Through reasonable resource elastic scheduling and emergency scheduling mechanisms, enterprises can ensure that resources are given priority and reasonably allocated in emergencies, avoiding resource waste and scheduling conflicts. When ordinary event and emergency event resource scheduling strategies are executed concurrently, efficient resource utilization is achieved through resource reuse and sharing mechanisms. The concurrent event resource scheduling strategy effectively reduces resource contention conflicts and improves the overall resource utilization efficiency and scheduling flexibility of the system. The feedback closed loop construction of step S4 ensures that the scheduling strategy is adjusted and optimized in real time during the chemical production scheduling process. This mechanism improves the system's response speed to emergencies and ensures the stability and sustainability of scheduling execution. By optimizing emergency scheduling through feedback data, the system can dynamically adjust the emergency operations of chemical production to ensure the smooth progress of production operations under emergencies. The optimized scheduling operation not only improves the efficiency of emergency treatment, but also reduces the impact on the production process after the accident. The method ensures the stability of the chemical production process, reduces the risk of accidents, and greatly improves production safety through an optimization process combining early warning, decision-making, resource scheduling and closed-loop feedback. Therefore, the present invention improves the accuracy and efficiency of emergency scheduling through real-time monitoring, dynamic early warning, flexible resource scheduling and closed-loop feedback mechanism.

[0030] In the embodiment of the present invention, reference Figure 1The above is a schematic flow chart of the steps of an emergency dispatch optimization method for chemical production of the present invention. In this example, the emergency dispatch optimization method for chemical production includes the following steps: Step S1: Acquire chemical production factor data; deploy production process monitoring sensors on the chemical production factor data to obtain standard production process monitoring sensor deployment data; connect the chemical production process data to a local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set; In the embodiment of the present invention, chemical production factors are defined, including equipment operating parameters (such as temperature, pressure, flow rate), raw material information, chemical reaction characteristics in the production process, energy consumption data, etc. In addition, the geographical distribution of production facilities, environmental conditions and process data of each production stage are also included. Through the existing production monitoring system, historical data and production operation management system (such as ERP or SCADA system), the relevant factor data of chemical production are collected. These data will serve as the basis for the subsequent monitoring sensor deployment. Based on the chemical production factor data, determine the links that need to be monitored for key process parameters, such as temperature monitoring points and pressure monitoring points of key equipment. Evaluate the sensor requirements in each production stage and select specific sensor types (such as temperature sensors, flow sensors, pressure sensors, etc.). In the chemical production process, standardized sensor deployment is carried out according to process requirements to ensure that each sensor can accurately cover the target parameters, generate standard production process monitoring sensor deployment data, and record the type of sensor, deployment location, monitoring parameters and connection configuration with the monitoring system. In order to ensure that each sensor can upload data in time, plan the local area network connection plan for the chemical production site. The topological structure of the local area network should cover every corner of the production facility to ensure stable data transmission of the sensor. Connect the deployed sensors to the local area network of chemical production via wired or wireless means. Ensure that each sensor can upload monitoring data to the monitoring center in real time through the local area network. Generate a chemical production monitoring network through the local area network connection, which is the basis for real-time data collection in the entire chemical production process. Based on the chemical production monitoring network, set the time interval, frequency and automation rules for data collection. The sensors begin to collect real-time data from production equipment and processes, and transmit them to the central data processing unit through the local area network. The data processing unit receives the data uploaded by the sensor, performs preliminary cleaning and storage, and generates a chemical production monitoring data set, which contains the real-time production data collected by each sensor as the core input data for subsequent scheduling and analysis.

[0031] Step S2: Perform production reaction process analysis on the chemical production monitoring data set to generate chemical production reaction process data; perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; perform event risk dynamic assessment on the hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; perform response decision classification on the hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; In the embodiment of the present invention, by extracting the data of key variables in the production reaction process, such as temperature, pressure, reactant concentration, flow rate, etc., from the chemical production monitoring data set generated in step S1. The monitoring data is preprocessed, including data cleaning, outlier processing and noise filtering, to ensure that the input data is accurate and reliable. The production reaction process analysis model is adopted to simulate the physical and chemical changes in the chemical reaction. A kinetic model, a thermodynamic model or a machine learning model can be used, and the specific model selection depends on the complexity and requirements of the chemical process. The temperature, pressure and other parameters of the reaction process are analyzed over time to identify potential abnormal reactions or deviations from the normal process. Through the reaction process analysis, chemical production reaction process data is generated, which records the dynamic changes of the reaction process and reflects the reaction status in the production process. According to the process requirements and safety standards, the warning thresholds of each key production reaction parameter are set. For example, when the temperature or pressure exceeds a certain safety range, the warning is triggered. The chemical production reaction process data is monitored and compared in real time to identify potential abnormal situations. Once it is detected that the parameter exceeds the set threshold, the warning mechanism is triggered to generate corresponding warning data. According to the severity of the warning, the warning is divided into two categories: Hazardous reaction warning data: indicates reactions that cause safety accidents or major process deviations, and immediate action is required. Non-hazardous reaction warning data: indicates minor abnormalities that affect production efficiency or quality but do not pose a direct safety threat. Based on the process characteristics and previous data of chemical production, an event risk dynamic assessment model is established. The model may include multi-factor analysis (for example, temperature rise rate, reaction time, raw material properties) and machine learning prediction algorithms. Event risk assessment is performed on each warning data, including predicted risk level, impact range and time window. Based on the results of the risk assessment model, event risk dynamic assessment data is generated. This data reflects the risk level of various warnings in the current production process and provides an in-depth understanding of potential risks. According to the event risk dynamic assessment data, decision classification criteria are set. For hazardous reactions, the priority is high and emergency response needs to be taken immediately. For non-hazardous reactions, ordinary event response plans can be adopted. Based on the results of the risk dynamic assessment, two types of response decision data are generated: Ordinary event response decision data: Applicable to non-hazardous minor abnormalities, and normal production status can be restored by adjusting production process parameters or equipment operation. Emergency response decision data: Applicable to hazardous reactions, through the activation of emergency plans, rapid deployment of resources, suspension of production or evacuation of personnel and other measures to prevent the occurrence of safety accidents.

[0032] Step S3: Acquire chemical production resource data; perform progressive resource elastic scheduling on chemical production resource data through common event response decision data to generate common event resource scheduling strategy; perform system-wide resource emergency scheduling on chemical production resource data through emergency event response decision data to generate emergency event resource scheduling strategy; perform concurrent execution resource reuse and sharing on common event resource scheduling strategy and emergency event resource scheduling strategy to generate concurrent event resource scheduling strategy; In the embodiment of the present invention, the chemical production resource data includes human resources (such as operators), material resources (such as raw materials and chemical reagents), equipment resources (such as reactors, pumps, conveyor belts, etc.) and environmental resources (such as energy consumption, production space, etc.). Resource data is obtained from the chemical production management system, inventory management system and equipment monitoring system. Through real-time monitoring means, the availability and status information of current resources are collected, including resource quantity, operating status, maintenance records, etc. The ordinary event response decision data generated in step S2 is analyzed to understand the impact and demand of the current ordinary event on resource scheduling. According to the demand and production plan of ordinary events, progressive resource elastic scheduling is implemented. Flexible scheduling rules are set, such as prioritizing available resources and dynamically adjusting according to the current production load to ensure efficient use of resources. Develop and record ordinary event resource scheduling strategies, and clarify the deployment plans and usage plans of various types of resources in ordinary event responses. The emergency event response decision data generated in step S2 is analyzed to determine the resource requirements and priorities of emergency events. When an emergency event occurs, an emergency resource scheduling strategy for the entire system is adopted. According to the priority of the emergency response, quickly mobilize necessary equipment, personnel and material resources, including adjusting production plans, temporarily transferring resources or sending additional staff. Develop and record emergency event resource scheduling strategies to ensure efficient and rapid response in emergency situations. Analyze the current resource scheduling strategies for ordinary events and emergency events, and identify the overlapping and reusable parts of resources between the two. Ensure that ordinary events and emergency events can work together in resource scheduling to avoid resource conflicts. Integrate the resource scheduling of ordinary events and emergency events, and develop concurrent event resource scheduling strategies to ensure the optimal allocation of resources when two events occur at the same time. Clarify the resource sharing and reuse mechanism to ensure that normal production is not affected while meeting emergency needs.

[0033] Step S4: construct a feedback closed loop of the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy to generate chemical production real-time response adjustment data; use the chemical production real-time response adjustment data to perform emergency scheduling optimization on the chemical production monitoring data set, generate chemical production emergency scheduling optimization data, and execute chemical production emergency scheduling operations.

[0034] In an embodiment of the present invention, a unified scheduling framework is established by integrating the resource scheduling strategy for common events, the resource scheduling strategy for emergency events, and the resource scheduling strategy for concurrent events. Ensure the relevance and interoperability between various scheduling strategies so that they can provide mutual feedback and support in actual operations. Establish a feedback closed loop in the chemical production monitoring network to ensure that each scheduling decision can be fed back to the monitoring system. The feedback includes resource utilization, production efficiency, abnormal events, etc. Real-time data collection and analysis are achieved to monitor the execution effect of the current scheduling strategy and its impact on the production process. By analyzing the feedback information, chemical production real-time response adjustment data is generated, which includes real-time adjustment requirements, scheduling effect evaluation, and next adjustment suggestions. In-depth analysis is performed on the real-time response adjustment data for chemical production to identify the changing trends and abnormal situations in the current monitoring data set. Special attention is paid to factors that affect the efficiency of emergency response, such as resource shortages, equipment failures, or insufficient personnel. Based on the real-time response adjustment data, the effectiveness of the current emergency scheduling plan is evaluated, and optimization suggestions are proposed. Considering the availability, priority, and historical scheduling performance of resources, the allocation and scheduling strategy of emergency resources are adjusted. According to the optimized scheduling plan, generate chemical production emergency scheduling optimization data, which provides a basis for emergency scheduling and ensures that scheduling operations can be implemented quickly and efficiently when emergencies occur. Formulate a specific implementation plan for emergency scheduling operations, including task allocation, resource allocation, time scheduling, etc. Ensure that all relevant personnel and equipment can be put into emergency scheduling in the shortest time to improve the emergency response speed. During the execution of emergency scheduling, continuously monitor the scheduling effect and obtain feedback information in real time. If new abnormal situations are found, adjust the scheduling strategy in time to adapt to the changes. Maintain connection with the monitoring network to ensure transparency and traceability during the scheduling execution process. After the emergency scheduling operation is completed, evaluate and summarize the entire scheduling process to identify successful experiences and shortcomings. Feedback the evaluation results to the feedback closed-loop system to optimize future scheduling strategies and emergency plans.

[0035] Preferably, step S1 comprises the following steps: Step S11: Acquire chemical production factor data; Step S12: performing chemical production process analysis on the chemical production factor data to generate chemical production process data; performing production process monitoring sensor deployment based on the chemical production process data to obtain production process monitoring sensor deployment data; Step S13: performing data preprocessing on the production process monitoring sensor deployment data to generate standard production process monitoring sensor deployment data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S14: Connect the chemical production process data to the local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set.

[0036] In an embodiment of the present invention, data is collected from multiple sources such as chemical production equipment, management systems, and production records, including information such as temperature, pressure, flow, and chemical composition. Ensure that the data is stored in a unified format (such as CSV, JSON) to facilitate subsequent processing. Store the acquired data in a secure database to ensure data integrity and accessibility. Use data mining technology to analyze chemical production factor data to identify key links and influencing factors in the production process. Use process modeling tools (such as BPMN) to visualize the production process to help identify monitoring points. According to the analysis results, determine the key parameters that need to be monitored and their corresponding sensor types (such as temperature sensors, pressure sensors, etc.). Develop a layout plan for sensors to ensure that all key process nodes are covered and generate sensor deployment data. Process the noise and outliers in the acquired sensor deployment data to ensure data accuracy. Use interpolation, mean method, and other techniques to fill in missing data to ensure data integrity. Convert data from different sources into a unified standard format (such as converting all temperature units to degrees Celsius) for subsequent analysis. Connect standard production process monitoring sensors to the local area network through network protocols (such as MQTT, HTTP) to form a chemical production monitoring network. Use network management tools to monitor the connection status and ensure the stability of the connection between the sensor and the data center. Develop a data acquisition program to regularly collect data from sensors in the monitoring network and generate a chemical production monitoring data set. Store the collected data in a database in real time for subsequent analysis and decision support.

[0037] Preferably, step S14 comprises the following steps: Step S141: Perform chemical environment building structure analysis on chemical production process data to generate chemical process environment building structure data; perform sensor node connection on standard production process monitoring sensor deployment data according to the chemical process environment building structure data through a wireless sensor network to generate sensor node connection data; Step S142: performing sensor adjacent communication path calculation on the sensor node connection data to obtain sensor adjacent communication path data; constructing a network topology structure for the sensor node connection data based on the sensor adjacent communication path data to generate a chemical production monitoring network; Step S143: Use the chemical production local area network to build a data link for the standard production process monitoring sensor deployment data to generate a chemical production data transmission channel; collect monitoring data from the chemical production local area network according to the chemical production data transmission channel to obtain a chemical production monitoring data set.

[0038] In an embodiment of the present invention, by collecting and analyzing the building structure data of the chemical production area, including the location, shape, height and other physical characteristics of the building. Building information modeling (BIM) technology is used to generate chemical process environment building structure data to ensure the accuracy and integrity of the data. According to the building structure data, the wireless sensor network (WSN) technology is used to combine the standard production process monitoring sensor deployment data with the building structure data, determine the optimal connection mode of the sensor node, generate sensor node connection data, and record the connection relationship and communication capability between each sensor node to facilitate subsequent network construction. The adjacent communication path between each sensor node is calculated using a graph theory algorithm (such as the Dijkstra algorithm) to ensure the efficiency and reliability of data transmission, generate sensor adjacent communication path data, and record the communication path and signal strength between each sensor node in detail. Based on the sensor adjacent communication path data, the topological structure of the chemical production monitoring network is constructed to determine the core nodes and edge nodes of the network. The constructed network topology is verified using network simulation tools (such as NS2, OMNeT++) to ensure its stability and adaptability. In the chemical production local area network, based on the sensor node connection data and network topology, a data link for standard production process monitoring sensors is constructed to ensure that data can be efficiently and accurately transmitted from the sensor to the data center. Ensure that the data link uses reliable protocols (such as TCP / IP, UDP) for communication to improve the security and stability of data transmission. According to the constructed chemical production data transmission channel, a data acquisition program is designed to collect data from monitoring sensors at regular intervals. The collected data is stored in real time in the central database for subsequent data analysis, monitoring and decision support, and a chemical production monitoring data set is generated.

[0039] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: extracting chemical production raw materials from the chemical production monitoring data set to obtain chemical production raw material data; performing production reaction process analysis on the chemical production raw material data to generate chemical production reaction process data; Step S22: screening the chemical production reaction process data for hazardous materials to obtain hazardous material screening data and non-hazardous material screening data; Step S23: Performing dynamic reaction warning on the hazardous material screening data and the non-hazardous material screening data based on a preset reaction monitoring threshold, and generating hazardous reaction warning data and non-hazardous reaction warning data; Step S24: Perform dynamic event risk assessment on the hazardous reaction warning data and the non-hazardous reaction warning data to generate event risk dynamic assessment data; perform response decision classification on the hazardous reaction warning data and the non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data.

[0040] In the embodiment of the present invention, relevant raw material information is extracted from the chemical production monitoring data set, mainly including the name, property, quantity and usage of various chemical raw materials. Data mining technology (such as association rule mining) is used to identify the raw material usage pattern and trend, and generate chemical production raw material data. A chemical reaction kinetics model is used to analyze the extracted chemical production raw material data, establish a reaction equation and a rate equation, generate chemical production reaction process data, and record in detail the conversion rate of each raw material in the reaction process, product generation and other information. According to the existing chemical safety data (such as MSDS material safety data sheet) and chemical industry standards, hazardous chemical raw materials are screened out, and hazardous material screening data is generated, including hazard types (such as toxicity, corrosiveness, flammability, etc.). Chemical raw materials that are not identified as hazardous materials are confirmed, and non-hazardous material screening data is generated to ensure the safety of all raw materials. According to historical data and expert experience, the reaction monitoring threshold of hazardous materials is set to determine when an early warning needs to be issued. Based on the hazardous material screening data and non-hazardous material screening data, the reaction process data is monitored in real time. When it is detected that the concentration, temperature or pressure of hazardous materials exceeds the preset threshold, hazardous reaction warning data is generated; if it does not exceed the threshold, non-hazardous reaction warning data is generated. Quantitative risk assessment methods (such as fault tree analysis and event tree analysis) are used to dynamically evaluate hazardous reaction warning data and non-hazardous reaction warning data, generate event risk dynamic assessment data, and record the probability of occurrence, consequences and severity of potential risks. According to the event risk dynamic assessment data, the hazardous reaction warning data and non-hazardous reaction warning data are classified, and corresponding response decisions are made to generate ordinary event response decision data (for routine events) and emergency event response decision data (for critical events), including response measures, responsible persons, timetables and other information.

[0041] Preferably, step S24 includes the following steps: Step S241: extracting event risk features from the hazardous reaction warning data and the non-hazardous reaction warning data to obtain event risk feature data, wherein the event risk feature extraction includes reaction material hazard level extraction, temperature and pressure dynamic extraction, gas concentration extraction, and reaction equipment status extraction; Step S242: Divide the event risk feature data into data sets to generate a model training set and a model test set; perform model training on the model training set using a long short-term memory network algorithm to generate an event risk dynamic prediction pre-model; perform model iterative optimization on the event risk dynamic prediction pre-model using the model test set, thereby generating an event risk dynamic prediction model; Step S243: importing the hazardous reaction warning data and the non-hazardous reaction warning data into the event risk dynamic prediction model to perform event risk dynamic assessment and generate event risk dynamic assessment data; Step S244: Based on the event risk dynamic assessment data, the response priorities of the hazardous reaction warning data and the non-hazardous reaction warning data are divided to generate event response priority data; according to the event response priority data, the response decisions of the hazardous reaction warning data and the non-hazardous reaction warning data are classified to generate ordinary event response decisions and emergency event response decisions.

[0042] In an embodiment of the present invention, the hazard level information of chemical reaction substances is obtained by querying a chemical database (such as GHS, MSDS) to generate hazard level data of the reaction substances. The temperature and pressure changes during the reaction process are monitored in real time by sensors, and the data are organized into a temperature and pressure dynamic data set to reflect the actual state of the reaction. The gas concentration during the reaction process is monitored by a gas sensor, especially the state of the reaction equipment is monitored by harmful gases, including the operating state, fault state, maintenance records and other information of the equipment, and the equipment state data is generated. The above extracted data are integrated to generate a complete event risk feature data set to provide a basis for subsequent dynamic evaluation. The event risk feature data set is divided into a model training set and a model test set according to a certain ratio (such as 70% training and 30% testing) to ensure the effectiveness and generalization ability of the model. The model training set is trained using a long short-term memory network (LSTM) algorithm, and its advantages in time series data processing are used to generate a dynamic prediction pre-model for event risk. Appropriate hyperparameters (such as learning rate, batch size, number of iterations, etc.) are set, and cross-validation is used to improve the reliability of the model. The event risk dynamic prediction pre-model is iteratively optimized using the model test set, and the prediction accuracy of the model is improved by continuously adjusting the model parameters (such as hidden layer nodes, activation functions, etc.). Finally, the optimized event risk dynamic prediction model is generated. The hazardous reaction warning data and non-hazardous reaction warning data are imported into the event risk dynamic prediction model to conduct real-time event risk dynamic assessment, generate event risk dynamic assessment data, and provide information such as risk status and potential risk level in the current response process. Based on the event risk dynamic assessment data, the response priority of the hazardous reaction warning data and the non-hazardous reaction warning data is divided. By setting the risk score threshold, the data is classified into high priority and low priority events for subsequent response. According to the event response priority data, the response decision classification of the hazardous reaction warning data and the non-hazardous reaction warning data is generated to generate ordinary event response decisions (for low priority events) and emergency event response decisions (for high priority events), including specific emergency measures, responsible teams and response time frames.

[0043] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Acquire chemical production resource data; Step S32: performing common event classification processing on chemical production resource data through common event response decision data to generate common event classification data; performing progressive resource elastic scheduling on chemical production resource data according to the common event classification data to generate a common event resource scheduling strategy; Step S33: using the emergency response decision data to classify the chemical production resource data into rapid event hazard levels, generating emergency event hazard level classification data; performing system-wide resource emergency dispatch on the chemical production resource data according to the emergency event hazard level classification data, generating an emergency event resource dispatch strategy; Step S34: Perform concurrent execution detection on the common event resource scheduling strategy and the emergency event resource scheduling strategy. When it is found that the common event resource scheduling strategy and the emergency event resource scheduling strategy are executed concurrently, the chemical production resource data is reused and shared based on the common event resource scheduling strategy and the emergency event resource scheduling strategy to generate a concurrent event resource scheduling strategy.

[0044] In an embodiment of the present invention, resource data related to chemical production, including raw material inventory, equipment status, staffing, and production progress, are collected from various sensors, monitoring systems, and enterprise resource planning (ERP) systems. The acquired resource data is integrated to ensure a unified data format, and a data center or database is established for subsequent access and analysis. According to the ordinary event response decision data, a classification standard for ordinary events is formulated, for example, classification is performed according to factors such as the scope of impact of the event and potential risks (such as level 1, level 2, etc.). The chemical production resource data is subjected to ordinary event classification processing to generate ordinary event classification data. The priority of resource use is marked through data analysis tools. According to the ordinary event classification data, a progressive resource elastic scheduling model is constructed to ensure that resources are flexibly allocated without affecting production, generate an ordinary event resource scheduling strategy, and clarify the priority of resource allocation, scheduling method, and responsibilities of scheduling personnel. According to the emergency event response decision data, a hazard level classification standard for emergency events is formulated, such as classification according to factors such as the danger of the reaction material and the failure state of the equipment. The chemical production resource data is subjected to rapid event hazard level classification to generate emergency event hazard level classification data. According to the emergency event hazard level classification data, design a system-wide emergency resource dispatch plan to ensure that when an emergency occurs, the required resources can be dispatched quickly and effectively, generate an emergency event resource dispatch strategy, and ensure that the resources of each department can quickly respond to the needs of the emergency. Develop a concurrent execution detection system to monitor the execution status of common event resource dispatch strategies and emergency event resource dispatch strategies in real time. Set the conditions for concurrent execution. For example, when two strategies dispatch the same resources at the same time, the system will trigger an alarm and record relevant information. When it is detected that the common event resource dispatch strategy and the emergency event resource dispatch strategy are executed concurrently, start the resource reuse and sharing mechanism to ensure that resources can be used efficiently and reduce waste. Generate a concurrent event resource dispatch strategy, clarify the reused resources, dispatch priorities and responsible teams, and ensure that the resource dispatch of the two events can coexist harmoniously.

[0045] Preferably, performing emergency dispatch of chemical production resource data for the entire system according to the emergency event hazard level classification data includes: Perform resource scheduling demand analysis on chemical production resource data based on emergency event hazard level classification data to generate emergency resource demand data; perform resource availability scan on chemical production resource data based on emergency resource demand data to generate resource availability data; Based on the emergency resource demand data and resource availability data, the resource scheduling path of the chemical production monitoring network is planned to generate resource scheduling path data; dynamic resource scheduling response feedback is performed on the resource scheduling path data to generate resource scheduling feedback data; The resource scheduling feedback data is synchronized with the entire system resources to generate system resource synchronization data; emergency resources are prioritized for chemical production resource data through system resource synchronization data to generate emergency event resource scheduling strategies.

[0046] In the embodiment of the present invention, the use records and inventory of various resources (such as raw materials, equipment, and personnel) in the chemical production process are collected. The emergency event hazard level classification data is sorted out to determine the specific types and quantities of resources required for different events. A resource demand analysis model is established based on the chemical production process and historical event data. The impact of different reaction conditions (temperature, pressure, reactant concentration, etc.) on resource demand is considered to quantify the demand. The model is used for simulation to output resource demand data under different hazard levels to form a clear resource demand list, including: the types and quantities of various raw materials, the required equipment and its technical parameters, and the skill requirements and quantity of personnel. Evaluation criteria are defined, including inventory quantity, equipment availability (maintenance status, failure rate), personnel deployment, etc. The enterprise resource management system (ERP) and warehouse management system (WMS) are used to query and update the available status of various resources in real time. An availability score (such as 0-100 points) is generated for each resource to indicate its current availability. The scanning results are summarized to generate resource availability data, including the status of each type of resource (available, occupied, under maintenance), and a visual report is formed. Select a specific path planning algorithm (such as Dijkstra or genetic algorithm) to ensure that the optimal path planning can be performed considering resource demand and availability. Input emergency resource demand data and resource availability data into the path planning model. Calculate the optimal dispatch path from the resource supply point to the demand point, considering road conditions, transportation time and cost. Output dispatch path data, including: the best path for resources from the supply point to the demand point, the estimated transportation time for each path, the consumed resources and staffing. During the resource scheduling process, establish a real-time feedback mechanism to collect data from the scheduling execution process through sensors and monitoring systems. Collect various feedback data during scheduling execution, including transportation status, resource usage, equipment status and personnel feedback. Perform data analysis to identify potential problems and scheduling bottlenecks. Integrate feedback information and generate resource scheduling feedback data. The report content includes: actual and planned deviation analysis, resource utilization efficiency and response time, difficulties encountered in the scheduling process and solutions. Design and implement a resource synchronization mechanism to ensure that various departments can update and share resource usage information in a timely manner. Use information technology systems (such as MES, SCADA) to achieve real-time data updates and synchronization, ensure that each department can obtain the latest resource usage status, and generate system resource synchronization data, including: resource usage of each department, resource remaining status, and future availability forecast. Formulate priority rules for resource allocation based on the hazard level of the incident, resource availability, and urgency of the incident. Priority rules should consider the following factors: the severity of the hazard incident, the urgency and importance of the resources, and the actual availability of the resources. Based on the resource synchronization data, implement emergency resource priority allocation to ensure that the most urgent resource needs are met first.Ultimately, an emergency resource dispatch strategy is formed, including: priority resource allocation plans, teams responsible for allocating various types of resources and contact information, specific allocation schedules and implementation steps.

[0047] Preferably, step S34 includes the following steps: Step S341: Perform concurrent execution detection on the common event resource scheduling strategy and the emergency event resource scheduling strategy. When it is found that the common event resource scheduling strategy and the emergency event resource scheduling strategy are executed concurrently, classify the resource conflicts of chemical production resource data based on the common event resource scheduling strategy and the emergency event resource scheduling strategy to generate hard resource conflict data and soft resource conflict data; Step S342: using the hard resource conflict data to perform emergency event resource preemption on the chemical production resource data, and generating hard resource preemption strategy data; using the soft resource conflict data to perform common event resource scheduling delay on the chemical production resource data, and generating soft resource delay strategy data; Step S343: performing resource sharing on chemical production resource data based on the hard resource preemption strategy data and the soft resource delay strategy data to generate resource sharing status data; performing resource reuse on the resource sharing status data to generate resource reuse data; Step S344: performing conflict concurrent execution feedback on the common event resource scheduling strategy and the emergency event resource scheduling strategy according to the resource sharing state data and the resource reuse data, and generating a concurrent event resource scheduling strategy.

[0048] In the embodiment of the present invention, chemical production resource data is collected, including the real-time status of common event resource scheduling strategies and emergency event resource scheduling strategies. A concurrent detection algorithm is used to analyze the collected data to identify whether there is overlap and concurrent execution of common event resource scheduling strategies and emergency event resource scheduling strategies. When a conflict occurs in physical access to a resource, it is marked as a hard resource conflict. The conflict usually involves hardware resources such as equipment and machines. When a conflict occurs in the use time or allocation of a resource, it is marked as a soft resource conflict. Such conflicts usually involve time periods, task priorities, etc. Based on the detected hard resource conflict data, a preemption strategy for emergency event resources is implemented to prioritize the resource requirements of emergency events, and hard resource preemption strategy data is generated to guide the reallocation of resources. For soft resource conflicts, a delay in common event resource scheduling is implemented to postpone the execution of low-priority tasks, and soft resource delay strategy data is generated as a basis for scheduling decisions. Based on the hard resource preemption strategy data and the soft resource delay strategy data, the chemical production resource data is analyzed to determine the resources that can be shared, and resource sharing status data is generated to reflect the current resource usage and sharing status. Based on the generated resource sharing status data, perform resource reuse analysis, determine the resource reuse method, and generate resource reuse data to optimize the overall resource utilization. According to the resource sharing status data and resource reuse data, evaluate the conflict between the resource scheduling strategy for common events and the resource scheduling strategy for emergency events. Feedback the conflict information and adjust the concurrent execution strategy. Combined with the above feedback, form the final concurrent event resource scheduling strategy to ensure that the resource requirements of common events and emergency events are handled more effectively in future resource scheduling.

[0049] Preferably, step S4 comprises the following steps: Step S41: executing the chemical production monitoring strategy on the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy, and generating chemical production scheduling execution data; Step S42: constructing a feedback closed loop for chemical production scheduling execution data based on cloud platform technology to generate real-time response adjustment data for chemical production; Step S43: Utilize the chemical production real-time response adjustment data to perform emergency dispatch optimization on the chemical production monitoring data set, generate chemical production emergency dispatch optimization data, and execute chemical production emergency dispatch operations.

[0050] In the embodiment of the present invention, the normal operation of chemical production is ensured by scheduling routine tasks and resource allocation in daily production. In an emergency, resources are allocated preferentially to deal with emergencies or abnormal situations. When ordinary events and emergency events occur at the same time, a concurrent scheduling strategy is adopted to coordinate resource use to avoid conflicts and bottlenecks. In the chemical production monitoring network, key production indicators such as equipment operation status, raw material flow, temperature, and pressure are monitored in real time. The above three scheduling strategies are implemented to ensure that production activities are synchronized with the monitoring system and generate chemical production scheduling execution data. This data contains information such as resource usage, task completion progress, and equipment status. The cloud platform collects, stores, and processes chemical production scheduling execution data in real time, and uses distributed computing and big data analysis to comprehensively analyze resource scheduling, equipment status, and task execution in the production process. Based on the analysis results, a feedback closed loop is constructed to adjust the scheduling strategy in real time. The role of the feedback closed loop is to respond to anomalies in the monitoring network and production process through automated means, ensure that the scheduling strategy can flexibly respond to changes, and generate chemical production real-time response adjustment data, which is used to update and optimize the current scheduling plan to adapt to changes or emergencies at the production site. Use the generated real-time response adjustment data for chemical production to identify potential emergency situations or resource bottlenecks in the current monitoring data set. Further analyze and adjust the chemical production monitoring data set, implement emergency dispatch optimization, and generate chemical production emergency dispatch optimization data, which records the reallocation of emergency resources, priority adjustments, and task optimization suggestions during the production process. According to the emergency dispatch optimization data, start the corresponding emergency dispatch operation to respond to emergencies at the production site in a timely manner. Ensure that resource allocation, task scheduling, and equipment operation are in the optimal state when performing emergency dispatch operations, and minimize the impact of emergencies on production efficiency and safety.

[0051] In this specification, an emergency dispatch optimization system for chemical production is provided, which is used to execute the above-mentioned emergency dispatch optimization method for chemical production. The emergency dispatch optimization system for chemical production includes: The monitoring network module is used to obtain chemical production factor data; deploy production process monitoring sensors on chemical production factor data to obtain standard production process monitoring sensor deployment data; connect chemical production process data to the local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set; The dynamic risk assessment module is used to analyze the production reaction process of the chemical production monitoring data set to generate chemical production reaction process data; to perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; to perform event risk dynamic assessment on the hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; to perform response decision classification on the hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; The resource scheduling module is used to obtain chemical production resource data; perform progressive resource elastic scheduling on chemical production resource data through common event response decision data to generate common event resource scheduling strategies; perform system-wide resource emergency scheduling on chemical production resource data through emergency event response decision data to generate emergency event resource scheduling strategies; perform concurrent execution resource reuse and sharing on common event resource scheduling strategies and emergency event resource scheduling strategies to generate concurrent event resource scheduling strategies; The closed-loop feedback control module is used to construct a feedback closed-loop for the chemical production monitoring network through common event resource scheduling strategies, emergency event resource scheduling strategies and concurrent event resource scheduling strategies, and generate real-time response adjustment data for chemical production; use the real-time response adjustment data for chemical production to perform emergency scheduling optimization on the chemical production monitoring data set, and generate chemical production emergency scheduling optimization data to execute chemical production emergency scheduling operations.

[0052] The beneficial effect of the present invention is that by acquiring chemical production factor data and deploying production process monitoring sensors, a chemical production monitoring network is generated, which realizes real-time monitoring and data collection of the entire chemical production process, ensures the comprehensiveness and timeliness of production data, and solves the problem of incomplete or inaccurate data acquisition in traditional monitoring methods. By analyzing the reaction process and dynamic early warning of the chemical production monitoring data set, identifying harmful and non-harmful reactions in advance, and performing dynamic event risk assessment and response decision classification, the prediction ability and response speed of emergency events are greatly improved, and the possibility and impact of accidents are reduced. According to the ordinary event response decision and the emergency event response decision, progressive resource scheduling and system-wide resource emergency scheduling are carried out, combined with the resource reuse sharing strategy, to ensure the flexible allocation of resources in different events, improve resource utilization efficiency, and avoid resource waste or scheduling delay. Through the feedback closed-loop mechanism, the resource scheduling strategy is combined with the monitoring data to build a real-time response adjustment system, so that the optimization process of emergency scheduling is more intelligent and dynamic, ensuring real-time adjustment and continuous optimization in the production process, and effectively improving the overall efficiency and emergency handling capability of the scheduling system. Therefore, the present invention improves the accuracy and efficiency of emergency dispatch through real-time monitoring, dynamic early warning, flexible resource dispatch and closed-loop feedback mechanism.

[0053] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0054] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An emergency dispatch optimization method for chemical production, characterized in that: The following steps are involved: Step S1: Acquire chemical production factor data; perform production process monitoring sensor deployment on the chemical production factor data to obtain standard production process monitoring sensor deployment data; Through the deployment data of standard production process monitoring sensors, the chemical production process data is connected to the local area network to generate a chemical production monitoring network; Based on the chemical production monitoring network, data is collected from the deployment data of standard production process monitoring sensors to obtain a chemical production monitoring data set; Step S2: Perform production reaction process analysis on the chemical production monitoring data set to generate chemical production reaction process data; perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; Performing dynamic event risk assessment on hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; classifying response decisions on hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; Step S3: Acquire chemical production resource data; perform progressive resource elastic scheduling on the chemical production resource data through common event response decision data to generate a common event resource scheduling strategy; perform system-wide resource emergency scheduling on the chemical production resource data through emergency event response decision data to generate an emergency event resource scheduling strategy; Concurrently execute resource reuse and sharing for common event resource scheduling strategies and emergency event resource scheduling strategies to generate concurrent event resource scheduling strategies; Step S4: construct a feedback closed loop of the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy to generate chemical production real-time response adjustment data; use the chemical production real-time response adjustment data to perform emergency scheduling optimization on the chemical production monitoring data set, generate chemical production emergency scheduling optimization data, and execute chemical production emergency scheduling operations.

2. The emergency dispatch optimization method for chemical production according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire chemical production factor data; Step S12: performing chemical production process analysis on the chemical production factor data to generate chemical production process data; performing production process monitoring sensor deployment based on the chemical production process data to obtain production process monitoring sensor deployment data; Step S13: performing data preprocessing on the production process monitoring sensor deployment data to generate standard production process monitoring sensor deployment data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S14: Connect the chemical production process data to the local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set.

3. The emergency dispatch optimization method for chemical production according to claim 2, characterized in that: Step S14 includes the following steps: Step S141: Perform chemical environment building structure analysis on chemical production process data to generate chemical process environment building structure data; perform sensor node connection on standard production process monitoring sensor deployment data according to the chemical process environment building structure data through a wireless sensor network to generate sensor node connection data; Step S142: performing sensor adjacent communication path calculation on the sensor node connection data to obtain sensor adjacent communication path data; constructing a network topology structure for the sensor node connection data based on the sensor adjacent communication path data to generate a chemical production monitoring network; Step S143: Use the chemical production local area network to build a data link for the standard production process monitoring sensor deployment data to generate a chemical production data transmission channel; collect monitoring data from the chemical production local area network according to the chemical production data transmission channel to obtain a chemical production monitoring data set.

4. The emergency dispatch optimization method for chemical production according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting chemical production raw materials from the chemical production monitoring data set to obtain chemical production raw material data; performing production reaction process analysis on the chemical production raw material data to generate chemical production reaction process data; Step S22: screening the chemical production reaction process data for hazardous materials to obtain hazardous material screening data and non-hazardous material screening data; Step S23: Performing dynamic reaction warning on the hazardous material screening data and the non-hazardous material screening data based on a preset reaction monitoring threshold, and generating hazardous reaction warning data and non-hazardous reaction warning data; Step S24: Perform dynamic event risk assessment on the hazardous reaction warning data and the non-hazardous reaction warning data to generate event risk dynamic assessment data; perform response decision classification on the hazardous reaction warning data and the non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data.

5. The emergency dispatch optimization method for chemical production according to claim 4, characterized in that: Step S24 includes the following steps: Step S241: extracting event risk features from the hazardous reaction warning data and the non-hazardous reaction warning data to obtain event risk feature data, wherein the event risk feature extraction includes reaction material hazard level extraction, temperature and pressure dynamic extraction, gas concentration extraction, and reaction equipment status extraction; Step S242: Divide the event risk feature data into data sets to generate a model training set and a model test set; perform model training on the model training set using a long short-term memory network algorithm to generate an event risk dynamic prediction pre-model; perform model iterative optimization on the event risk dynamic prediction pre-model using the model test set, thereby generating an event risk dynamic prediction model; Step S243: importing the hazardous reaction warning data and the non-hazardous reaction warning data into the event risk dynamic prediction model to perform event risk dynamic assessment and generate event risk dynamic assessment data; Step S244: Based on the event risk dynamic assessment data, the response priorities of the hazardous reaction warning data and the non-hazardous reaction warning data are divided to generate event response priority data; according to the event response priority data, the response decisions of the hazardous reaction warning data and the non-hazardous reaction warning data are classified to generate ordinary event response decisions and emergency event response decisions.

6. The emergency dispatch optimization method for chemical production according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Acquire chemical production resource data; Step S32: performing common event classification processing on chemical production resource data through common event response decision data to generate common event classification data; performing progressive resource elastic scheduling on chemical production resource data according to the common event classification data to generate a common event resource scheduling strategy; Step S33: using the emergency response decision data to classify the chemical production resource data into rapid event hazard levels, generating emergency event hazard level classification data; performing system-wide resource emergency dispatch on the chemical production resource data according to the emergency event hazard level classification data, generating an emergency event resource dispatch strategy; Step S34: Perform concurrent execution detection on the common event resource scheduling strategy and the emergency event resource scheduling strategy. When it is found that the common event resource scheduling strategy and the emergency event resource scheduling strategy are executed concurrently, the chemical production resource data is reused and shared based on the common event resource scheduling strategy and the emergency event resource scheduling strategy to generate a concurrent event resource scheduling strategy.

7. The emergency dispatch optimization method for chemical production according to claim 6, characterized in that: According to the emergency event hazard level classification data, the chemical production resource data is used for emergency dispatch of the whole system resources, including: Perform resource scheduling demand analysis on chemical production resource data based on emergency event hazard level classification data to generate emergency resource demand data; perform resource availability scan on chemical production resource data based on emergency resource demand data to generate resource availability data; Based on the emergency resource demand data and resource availability data, the resource scheduling path of the chemical production monitoring network is planned to generate resource scheduling path data; dynamic resource scheduling response feedback is performed on the resource scheduling path data to generate resource scheduling feedback data; The resource scheduling feedback data is synchronized with the entire system resources to generate system resource synchronization data; emergency resources are prioritized for chemical production resource data through system resource synchronization data to generate emergency event resource scheduling strategies.

8. The emergency dispatch optimization method for chemical production according to claim 6, characterized in that: Step S34 includes the following steps: Step S341: Perform concurrent execution detection on the common event resource scheduling strategy and the emergency event resource scheduling strategy. When it is found that the common event resource scheduling strategy and the emergency event resource scheduling strategy are executed concurrently, classify the resource conflicts of chemical production resource data based on the common event resource scheduling strategy and the emergency event resource scheduling strategy to generate hard resource conflict data and soft resource conflict data; Step S342: using the hard resource conflict data to perform emergency event resource preemption on the chemical production resource data, and generating hard resource preemption strategy data; using the soft resource conflict data to perform common event resource scheduling delay on the chemical production resource data, and generating soft resource delay strategy data; Step S343: performing resource sharing on chemical production resource data based on the hard resource preemption strategy data and the soft resource delay strategy data to generate resource sharing status data; performing resource reuse on the resource sharing status data to generate resource reuse data; Step S344: performing conflict concurrent execution feedback on the common event resource scheduling strategy and the emergency event resource scheduling strategy according to the resource sharing state data and the resource reuse data, and generating a concurrent event resource scheduling strategy.

9. The emergency dispatch optimization method for chemical production according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: executing the chemical production monitoring strategy on the chemical production monitoring network through the common event resource scheduling strategy, the emergency event resource scheduling strategy and the concurrent event resource scheduling strategy, and generating chemical production scheduling execution data; Step S42: constructing a feedback closed loop for chemical production scheduling execution data based on cloud platform technology to generate real-time response adjustment data for chemical production; Step S43: Utilize the chemical production real-time response adjustment data to perform emergency dispatch optimization on the chemical production monitoring data set, generate chemical production emergency dispatch optimization data, and execute chemical production emergency dispatch operations.

10. An emergency dispatch optimization system for chemical production, characterized in that: Used to execute the emergency dispatch optimization method for chemical production according to claim 1, the emergency dispatch optimization system for chemical production comprises: The monitoring network module is used to obtain chemical production factor data; deploy production process monitoring sensors on chemical production factor data to obtain standard production process monitoring sensor deployment data; connect chemical production process data to the local area network through the standard production process monitoring sensor deployment data to generate a chemical production monitoring network; collect data on the standard production process monitoring sensor deployment data based on the chemical production monitoring network to obtain a chemical production monitoring data set; The dynamic risk assessment module is used to analyze the production reaction process of the chemical production monitoring data set to generate chemical production reaction process data; to perform dynamic reaction warning on the chemical production reaction process data to generate hazardous reaction warning data and non-hazardous reaction warning data; to perform event risk dynamic assessment on the hazardous reaction warning data and non-hazardous reaction warning data to generate event risk dynamic assessment data; to perform response decision classification on the hazardous reaction warning data and non-hazardous reaction warning data according to the event risk dynamic assessment data to generate common event response decision data and emergency event response decision data; The resource scheduling module is used to obtain chemical production resource data; perform progressive resource elastic scheduling on chemical production resource data through common event response decision data to generate common event resource scheduling strategies; perform system-wide resource emergency scheduling on chemical production resource data through emergency event response decision data to generate emergency event resource scheduling strategies; perform concurrent execution resource reuse and sharing on common event resource scheduling strategies and emergency event resource scheduling strategies to generate concurrent event resource scheduling strategies; The closed-loop feedback control module is used to construct a feedback closed-loop for the chemical production monitoring network through common event resource scheduling strategies, emergency event resource scheduling strategies, and concurrent event resource scheduling strategies, and generate real-time response adjustment data for chemical production; use the real-time response adjustment data for chemical production to perform emergency scheduling optimization on the chemical production monitoring data set, and generate chemical production emergency scheduling optimization data to execute chemical production emergency scheduling operations.

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