Chemical production safety emergency dispatching optimization method and system

Abstract

The application relates to the technical field of chemical safety, and discloses a chemical safety production emergency dispatching optimization method, and operation steps are as follows: step S1: real-time collection of multi-dimensional monitoring data of a chemical production site, noise reduction, normalization and space-time fusion processing of the data and the like. The chemical safety production emergency dispatching optimization method and system, through multi-source data real-time collection and space-time fusion processing, comprehensively integrate on-site environment, equipment, personnel and video monitoring information, completely solve the data island problem of the traditional emergency mode, provide accurate data support for risk research and judgment, adopt a deep learning model for accident identification and risk grading, replace artificial experience judgment, significantly improve the risk judgment speed and accuracy, realize millisecond-level early warning and rapid response, the system can automatically match optimal emergency resources and disposal plans, greatly shorten the emergency preparation time, effectively curb the spread of accidents, and provide solid intelligent protection for chemical safety production.

Description

Technical Field

[0001] This invention relates to the field of chemical safety technology, specifically to a method and system for optimizing emergency dispatching in chemical safety production. Background Technology

[0002] As a pillar industry of the national economy, the chemical industry involves high temperature, high pressure, flammable and explosive, and toxic and harmful media in its production processes. It has long process chains, high equipment density, and is difficult to manage safety risks. Currently, emergency dispatch in chemical safety production relies heavily on manual experience and instruction transmission, resulting in problems such as scattered data collection, delayed information transmission, and a lack of overall coordination in resource allocation. When an accident occurs, it is difficult to quickly and accurately identify the risk level and scope of impact. Emergency supplies, rescue teams, and disposal equipment are often improperly configured and dispatched untimely. Rescue route planning is also not dynamically optimized based on real-time road conditions and the on-site danger situation. Traditional models have low response efficiency and poor coordination, easily leading to the escalation of accidents, causing casualties, property damage, and environmental pollution, and failing to meet the actual needs of modern chemical enterprises for safe production and efficient emergency response.

[0003] With the large-scale and intelligent development of chemical industrial parks, the shortcomings of the existing emergency dispatch system have become increasingly prominent. Most enterprises' emergency systems have not achieved multi-source data fusion and intelligent analysis. Information from sensors, videos, and equipment status is independent, forming data silos, making it difficult to build a comprehensive safety situation awareness capability. Emergency dispatch lacks standardized processes and intelligent optimization algorithms, relying on manual instructions, which is prone to misjudgment, omissions, and dispatch delays. During the rescue process, it is impossible to adjust the plan in real time according to the development of the accident. The efficiency of multi-department and multi-team collaborative handling is low, and the resource utilization rate and emergency response success rate are difficult to guarantee. Developing emergency dispatch methods and systems for chemical safety production with intelligent perception, rapid assessment, and dynamic optimization has become an urgent need for the industry's safe development. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing emergency dispatch in chemical safety production. It has advantages such as full-domain data fusion, intelligent risk assessment, precise resource matching, dual-objective path optimization, and dynamic closed-loop dispatch. It solves the problems of data silos, delayed response, unreasonable resource allocation, suboptimal rescue paths, low coordination efficiency, and inability to dynamically adjust traditional chemical emergency dispatch methods.

[0006] (II) Technical Solution

[0007] To achieve the aforementioned goals of comprehensive data fusion, intelligent risk assessment, precise resource matching, dual-objective path optimization, and dynamic closed-loop scheduling, this invention provides the following technical solution: a method for optimizing emergency scheduling in chemical safety production, with the following steps:

[0008] Step S1: Collect multi-dimensional monitoring data from the chemical production site in real time, perform noise reduction, normalization and spatiotemporal fusion processing on the data, and construct a full-domain safety situation awareness dataset;

[0009] Step S2: Based on the fused dataset, a deep learning model is used to identify accident types and intelligently assess risk levels, outputting accident risk assessment results;

[0010] Step S3: Based on the risk assessment results, and combined with emergency resource inventory, location, and capability parameters, perform intelligent matching and scheduling of emergency resources and allocate tasks.

[0011] Step S4: Based on real-time traffic and on-site obstacle information, a dual-objective optimization algorithm is used to generate the shortest time and lowest risk path for emergency rescue;

[0012] Step S5: Issue dispatch instructions and continuously receive on-site feedback, dynamically iterate and optimize the dispatch plan until the emergency response is completed.

[0013] Preferably, the multi-dimensional monitoring data in step S1 includes toxic and harmful gas concentration, temperature, pressure, liquid level, equipment vibration, video images, and personnel and vehicle positioning data.

[0014] Preferably, the risk level intelligent assessment in step S2 divides the accident into four levels: general, major, serious, and extremely serious, and automatically matches the corresponding emergency response plan.

[0015] Preferably, the emergency resources in step S3 include fire-fighting equipment, leak-sealing materials, protective materials, decontamination devices, emergency teams, and transport vehicles, and their allocation follows the principles of proximity priority, capability matching, and redundancy backup.

[0016] Preferably, the dynamic iterative optimization in step S5 automatically triggers secondary scheduling when the accident situation escalates or resources are insufficient, and synchronously updates the rescue path and disposal instructions.

[0017] A chemical safety production emergency dispatch optimization system includes a data acquisition module, a risk assessment module, a resource scheduling module, a path planning module, and a collaborative control module.

[0018] Preferably, the data acquisition module consists of a sensor unit, a positioning terminal, a video monitoring unit, and an edge computing gateway.

[0019] Preferably, the risk assessment module has a built-in accident identification model and risk classification algorithm, which can achieve millisecond-level risk judgment and early warning.

[0020] Preferably, the collaborative control module supports the synchronous issuance of instructions from multiple departments, real-time status feedback, and visualized monitoring of the entire scheduling process.

[0021] (III) Beneficial Effects

[0022] Compared with existing technologies, this invention provides a method and system for optimizing emergency dispatching in chemical safety production, which has the following beneficial effects:

[0023] 1. This chemical safety production emergency dispatch optimization method and system comprehensively integrates on-site environment, equipment, personnel, and video monitoring information through real-time acquisition and spatiotemporal fusion processing of multi-source data. It completely solves the data silo problem of traditional emergency models, provides accurate data support for risk assessment, and adopts a deep learning model for accident identification and risk classification, replacing manual experience judgment. This significantly improves the speed and accuracy of risk assessment, achieves millisecond-level early warning and rapid response, and the system can automatically match the optimal emergency resources and response plans, greatly shortening emergency preparation time, effectively curbing the spread of accidents, and providing solid intelligent protection for chemical safety production.

[0024] 2. This chemical safety production emergency dispatch optimization method and system generates the shortest time and lowest risk rescue path through a dual-objective optimization algorithm, and supports dynamic iterative adjustment of the dispatch plan. It can optimize resource allocation and rescue routes in real time according to changes in the accident site situation. Relying on the collaborative control module, it realizes the synchronous issuance of instructions from multiple departments, real-time feedback of the handling status, and full-process visual supervision, which greatly improves the efficiency of cross-departmental collaboration and resource utilization. This invention can effectively improve the success rate of emergency rescue, reduce casualties, property losses and environmental pollution, and comprehensively improve the emergency management level and inherent safety capabilities of chemical enterprises and industrial parks. Attached Figure Description

[0025] Figure 1 This is the overall flowchart of the chemical safety production emergency dispatch optimization method of the present invention;

[0026] Figure 2 This is a diagram illustrating the multi-dimensional data acquisition and spatiotemporal fusion processing of this invention.

[0027] Figure 3 This is the accident type identification and risk level intelligent judgment diagram of the present invention;

[0028] Figure 4 This is a diagram of the dynamic iterative optimization and secondary scheduling mechanism of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see Figure 1-4 An optimization method for emergency dispatching in chemical safety production, with the following steps:

[0031] Step S1: Collect multi-dimensional monitoring data from the chemical production site in real time, perform noise reduction, normalization and spatiotemporal fusion processing on the data, and construct a full-domain safety situation awareness dataset;

[0032] Step S2: Based on the fused dataset, a deep learning model is used to identify accident types and intelligently assess risk levels, outputting accident risk assessment results;

[0033] Step S3: Based on the risk assessment results, and combined with emergency resource inventory, location, and capability parameters, perform intelligent matching and scheduling of emergency resources and allocate tasks.

[0034] Step S4: Based on real-time traffic and on-site obstacle information, a dual-objective optimization algorithm is used to generate the shortest time and lowest risk path for emergency rescue;

[0035] Step S5: Issue dispatch instructions and continuously receive on-site feedback, dynamically iterate and optimize the dispatch plan until the emergency response is completed.

[0036] In the implementation of the case, the multi-dimensional monitoring data in step S1 includes the concentration of toxic and harmful gases, temperature, pressure, liquid level, equipment vibration, video images, and personnel and vehicle positioning data.

[0037] Among them, the sensor unit is responsible for continuously collecting physical quantity data such as gas, temperature and pressure, liquid level, and equipment vibration; the video monitoring unit transmits the scene back in real time; the positioning terminal tracks the location information of personnel and vehicles in real time; and the edge computing gateway completes the preliminary cleaning and transmission of data nearby, comprehensively covering the full-dimensional status information of the chemical production site environment, equipment, personnel, and vehicles, with no monitoring blind spots or data omissions.

[0038] By denoising and filtering the raw data to remove interference signals, normalizing the data to unify the data dimensions, and spatiotemporally fusing and integrating information from different time and space nodes, data fragmentation and inconsistency are eliminated, forming a complete, accurate, and usable all-domain security situation awareness dataset, providing a high-quality data foundation for subsequent risk assessment.

[0039] In the implementation of the case, the intelligent risk level assessment in step S2 classifies the accident into four levels: general, major, serious, and extremely serious, and automatically matches the corresponding emergency response plan.

[0040] Among them, the deep learning model is trained and optimized based on massive historical accident data, which can quickly identify typical accident types such as leakage, fire and explosion. The risk classification algorithm combines key indicators such as the scope of accident impact, degree of harm and spread speed to automatically classify accidents into four levels: general, major, serious and extremely serious. The classification results are objective and accurate, avoiding the subjectivity and error of human judgment.

[0041] By intelligently identifying accident types and automatically classifying risk levels, the system can quickly retrieve standardized emergency response plans for the corresponding levels, clarify the handling procedures, division of responsibilities, protection requirements, and handling measures, and achieve automated and integrated risk assessment and plan matching, significantly shortening the emergency response activation time.

[0042] In the implementation of the case, the emergency resources in step S3 include fire-fighting equipment, leak-stopping materials, protective materials, decontamination devices, emergency teams and transport vehicles, and the allocation follows the principles of proximity priority, capacity matching, and redundancy backup.

[0043] The system includes a built-in emergency resource database that updates in real time the inventory quantity, storage location, expiration date, and performance parameters of various materials, as well as information such as the personnel composition, professional capabilities, and attendance status of emergency teams, and the carrying capacity and driving status of transport vehicles, forming a dynamic resource ledger.

[0044] By combining risk assessment results with real-time resource status, and adhering to the principles of prioritizing nearby resources to shorten rescue arrival time, matching capabilities to accident handling needs, and providing redundancy for emergencies, the system achieves precise matching of resources and tasks, automatically generates the optimal scheduling and allocation plan, and avoids resource waste and allocation errors.

[0045] In the implementation of the case, the dynamic iterative optimization in step S5 automatically triggers secondary scheduling when the accident situation escalates or resources are insufficient, and simultaneously updates the rescue path and disposal instructions.

[0046] The system continuously receives feedback information from the scene, such as the development of the accident, resource consumption, rescue progress, and environmental changes. It compares the preset targets with the actual handling results in real time. When it detects that the scope of the accident has expanded, the risk level has increased, or the dispatched resources cannot meet the handling needs, it immediately activates the dynamic adjustment mechanism.

[0047] By recalculating resource needs, planning dispatch schemes, and optimizing rescue routes based on the latest on-site information, and simultaneously issuing updated disposal instructions, a closed-loop management system for the entire emergency dispatch process is achieved, ensuring that rescue operations always align with the actual on-site situation and maximizing the effectiveness of emergency response.

[0048] An emergency dispatch optimization system for chemical safety production includes a data acquisition module, a risk assessment module, a resource scheduling module, a path planning module, and a collaborative control module.

[0049] Among them, the five modules are independent yet coordinated: the data acquisition module is responsible for front-end information acquisition; the risk assessment module completes intelligent analysis and judgment; the resource scheduling module executes the allocation of materials and teams; the route planning module generates the optimal rescue route; and the collaborative control module coordinates the issuance of instructions and progress monitoring, together forming a complete emergency dispatch optimization system.

[0050] By enabling data exchange and functional collaboration between modules, the problem of fragmentation in traditional emergency systems is broken down, achieving intelligent and integrated operation of the entire process from data perception to command issuance, and from resource allocation to on-site handling.

[0051] In the case implementation, the data acquisition module consists of a sensor unit, a positioning terminal, a video surveillance unit, and an edge computing gateway.

[0052] Among them, the sensor unit covers the detection of toxic, harmful, flammable and explosive gases and the monitoring of temperature, pressure, liquid level and equipment vibration; the positioning terminal adopts high-precision positioning technology to realize real-time tracking of personnel and vehicles; the video monitoring unit is equipped with high-definition cameras and intelligent analysis functions; and the edge computing gateway realizes local data preprocessing and low-latency transmission.

[0053] By enabling multi-device collaboration and rapid edge processing, the system ensures the comprehensiveness, real-time nature, and accuracy of data collection, providing stable and reliable front-end data support for the entire system.

[0054] In the case implementation, the risk assessment module has a built-in accident identification model and risk classification algorithm, which can achieve millisecond-level risk judgment and early warning.

[0055] Among them, the accident identification model is built on a deep learning framework and has been trained and iteratively optimized with a large number of chemical accident samples. It can quickly match on-site data and accident characteristics. The risk classification algorithm sets judgment rules according to the national chemical safety accident classification standard and calculates risk scores by combining multi-dimensional data.

[0056] Through the efficient collaborative operation of models and algorithms, the accident type and risk level can be quickly determined in milliseconds, and risk warnings can be issued in advance, saving valuable time for emergency response and enhancing the initiative in risk prevention and control.

[0057] In the implementation of the case, the collaborative control module supports the synchronous issuance of instructions from multiple departments, real-time status feedback, and visualized supervision of the entire scheduling process.

[0058] Among them, the module builds a unified communication and supervision platform, which connects with external cooperation units such as enterprise safety management departments, emergency rescue teams, fire protection, and environmental protection, and has functions such as batch issuance of instructions, real-time data upload, synchronized display of images, and progress tracking and recording.

[0059] By enabling cross-departmental and cross-team information sharing and command coordination through a unified platform, managers can intuitively grasp the overall situation of the rescue, monitor the progress of the response and the execution of instructions in a timely manner, and significantly improve the coordination efficiency and control of emergency dispatch.

[0060] In summary, this chemical safety production emergency dispatch optimization method and system comprehensively integrates on-site environment, equipment, personnel, and video monitoring information through real-time acquisition and spatiotemporal fusion processing of multi-source data. This completely solves the data silo problem of traditional emergency models, providing precise data support for risk assessment. The system employs deep learning models for accident identification and risk classification, replacing manual experience-based judgment, significantly improving the speed and accuracy of risk assessment, achieving millisecond-level early warning and rapid response. The system can automatically match optimal emergency resources and response plans, greatly shortening emergency preparation time, effectively curbing the spread of accidents, and providing solid intelligent protection for chemical safety production.

[0061] Furthermore, by generating the shortest time and lowest risk rescue path through a dual-objective optimization algorithm, and supporting dynamic iterative adjustment of the scheduling scheme, the system can optimize resource allocation and rescue routes in real time according to changes in the situation at the accident site. Relying on the collaborative control module, it enables simultaneous issuance of instructions from multiple departments, real-time feedback of the handling status, and full-process visual supervision, significantly improving cross-departmental collaboration efficiency and resource utilization. This invention can effectively improve the success rate of emergency rescue, reduce casualties, property losses, and environmental pollution, and comprehensively enhance the emergency management level and inherent safety capabilities of chemical enterprises and industrial parks.

[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An optimization method for emergency dispatching in chemical safety production, characterized in that: The operation steps are as follows: Step S1: Collect multi-dimensional monitoring data from the chemical production site in real time, perform noise reduction, normalization and spatiotemporal fusion processing on the data, and construct a full-domain safety situation awareness dataset; Step S2: Based on the fused dataset, a deep learning model is used to identify accident types and intelligently assess risk levels, outputting accident risk assessment results; Step S3: Based on the risk assessment results, and combined with emergency resource inventory, location, and capability parameters, perform intelligent matching and scheduling of emergency resources and allocate tasks. Step S4: Based on real-time traffic and on-site obstacle information, a dual-objective optimization algorithm is used to generate the shortest time and lowest risk path for emergency rescue; Step S5: Issue dispatch instructions and continuously receive on-site feedback, dynamically iterate and optimize the dispatch plan until the emergency response is completed.

2. The chemical safety production emergency dispatch optimization method according to claim 1, characterized in that: The multi-dimensional monitoring data in step S1 includes toxic and harmful gas concentration, temperature, pressure, liquid level, equipment vibration, video images, and personnel and vehicle positioning data.

3. The method for optimizing emergency dispatch in chemical safety production according to claim 1, characterized in that: The intelligent risk level assessment in step S2 classifies accidents into four levels: general, major, serious, and extremely serious, and automatically matches the corresponding emergency response plan.

4. The method for optimizing emergency dispatch in chemical safety production according to claim 1, characterized in that: The emergency resources in step S3 include fire-fighting equipment, leak-sealing materials, protective materials, decontamination devices, emergency teams, and transport vehicles. The allocation follows the principles of proximity priority, capacity matching, and redundancy backup.

5. The method for optimizing emergency dispatch in chemical safety production according to claim 1, characterized in that: The dynamic iterative optimization in step S5 automatically triggers secondary scheduling when the accident situation escalates or resources become insufficient, and simultaneously updates the rescue path and disposal instructions.

6. A chemical safety production emergency dispatch optimization system, characterized in that: The system includes a data acquisition module, a risk assessment module, a resource scheduling module, a path planning module, and a collaborative control module.

7. The chemical safety production emergency dispatch optimization system according to claim 6, characterized in that: The data acquisition module consists of a sensor unit, a positioning terminal, a video monitoring unit, and an edge computing gateway.

8. The chemical safety production emergency dispatch optimization system according to claim 6, characterized in that: The risk assessment module has a built-in accident identification model and risk classification algorithm, which can achieve millisecond-level risk judgment and early warning.

9. The chemical safety production emergency dispatch optimization system according to claim 6, characterized in that: The collaborative control module supports the synchronous issuance of instructions from multiple departments, real-time status feedback, and visualized monitoring of the entire scheduling process.

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