Chemical industry park emergency response system based on universe and electronic equipment

Abstract

The invention discloses a chemical industry park emergency response system based on meta universe and an electronic device, and the system comprises a data collection module, a data storage and processing module, a meta universe platform module and an intelligent decision module, and the data collection module carries out the real-time monitoring and collection of environment parameters in a chemical industry park. The data storage and processing module is used for storing, analyzing and fusing the collected data and realizing early warning; the meta universe platform module is used for creating a chemical industry park virtual simulation environment and integrating real-time monitoring data and historical data into a virtual model; and the intelligent decision module integrates multi-source data and simulation results, and performs situation awareness and decision support. According to the method, the accuracy of data integration and situation simulation is improved, the emergency response capability and strain level of employees can be remarkably improved, accident loss is effectively reduced, and the safety of a park is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency response, and in particular relates to a chemical park emergency response system and electronic equipment based on metaverse. Background Art

[0002] Chemical parks are the product of modern chemical industry to adapt to resource or raw material conversion, conform to the development trend of large-scale, intensive, optimized, internationalized management and maximized benefits. The area around chemical parks is a highly concentrated area of ​​flammable, explosive and toxic major hazardous sources. Therefore, safety and environmental protection emergency measures in chemical park areas are an indispensable part of the production process;

[0003] The existing emergency response measures in chemical parks mainly include manual inspections, traditional drills, and emergency management systems: manual inspections rely on personnel to regularly inspect equipment and the environment, lack real-time monitoring capabilities, and are unable to quickly identify accidents and locate the source, affecting the timeliness of emergency response. Traditional drill simulation scenarios are complex to organize, consume a lot of manpower and material resources, lack realism and repeatability, and are difficult to fully cover various disaster scenarios. There are also difficulties in coordination and arrangement. The existing emergency management system has a low degree of data integration, and various types of monitoring data are difficult to interconnect. There is a lack of intelligent analysis methods and decision support systems, which affects the accuracy of decision-making. Summary of the invention

[0004] The present invention combines the capabilities of the Internet of Things with Metaverse technology, and proposes a system that fully integrates the capabilities of the Internet of Things and virtual simulation, aiming to achieve panoramic digitization of chemical parks and support a variety of emergency scenarios and training. Specifically, the system consists of four modules: data acquisition module, data storage and processing module, Metaverse platform module and intelligent decision-making module. Each module is designed to meet specific challenges such as monitoring, data integration, simulation and decision-making. Through efficient real-time monitoring, realistic virtual emergency scenarios and intelligent decision-making support, it aims to cope with the complex situations of sudden accidents and improve the emergency response capabilities and drill effects of chemical parks.

[0005] Based on the above problems, the present invention adopts the following technical solutions:

[0006] A chemical park emergency response system based on Metaverse, including: a data acquisition module, a data storage and processing module, a Metaverse platform module and an intelligent decision-making module.

[0007] The data acquisition module includes a variety of sensors and communication units. The various sensors monitor and collect environmental parameters in the chemical park in real time. The communication unit, data storage and processing module are connected to the Metaverse platform module to ensure that the real-time monitoring data is transmitted to the data storage and processing module and the Metaverse platform module respectively;

[0008] The data storage and processing module and the Metaverse platform module are both connected to the intelligent decision-making module, wherein the data storage and processing module is used to store, analyze and integrate the collected data and implement early warning;

[0009] The Metaverse platform module is used to create a virtual simulation environment for chemical parks, integrate real-time monitoring data and historical data into the virtual model, conduct simulation drills in virtual scenes, and practice correct emergency response steps;

[0010] The intelligent decision-making module integrates multi-source data and simulation results to perform situation awareness and decision support.

[0011] Furthermore, the various sensors in the data acquisition module include infrared thermal imaging cameras, gas sensors, lidars and microwave radars, wherein the infrared thermal imaging cameras are used to monitor the temperature distribution of equipment and the environment; the gas sensor network is used to detect the concentration of harmful chemical gases; the lidars and microwave radars are used to provide three-dimensional environmental modeling and environmental scanning data to establish three-dimensional environmental maps.

[0012] Furthermore, the data storage and processing module stores, analyzes and fuses the collected data, including a data collection and confidence assessment unit, a multi-source data fusion and dynamic tracking unit, and an error correction and model optimization unit.

[0013] Furthermore, the data acquisition and confidence assessment unit integrates data from infrared thermal imaging cameras, gas sensor data, and laser radar and microwave radar data to assess the confidence of the data;

[0014] The multi-source data fusion and dynamic tracking unit combines the estimated values ​​using a weighted fusion method according to the confidence of each sensor to generate an overall target state estimate;

[0015] The error correction and model optimization unit adopts an extended Kalman filter and a dynamic data weight adjustment algorithm to achieve real-time tracking and analysis of the target motion state.

[0016] Furthermore, the multi-source data fusion and dynamic tracking unit includes:

[0017] Convert the data from different sensors into a standard three-dimensional Cartesian coordinate system;

[0018] According to the confidence level of each sensor, the estimated values ​​are combined using a weighted fusion method;

[0019] T(t)=α(t)*T 红外 (t)+β(t)*T 雷达 (t)+γ(t)*T 气体 (t)

[0020] Among them, α(t), β(t), and γ(t) are weight factors related to the covariance of each sensor data, representing the accuracy of different data sources.

[0021] Furthermore, the fusion weight is calculated by the following formula:

[0022] α(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 红外 - 1 (t)

[0023] β(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 雷达 - 1 (t)

[0024] γ(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 气体 - 1 (t)

[0025] Among them, P 红外 (t), P 雷达 (t) and P 气体 (t) are the error covariance matrices of infrared, radar and gas sensor data, respectively.

[0026] Furthermore, the Metaverse platform module includes a virtual environment creation module, a real-time monitoring data integration module, a historical data management module, and a scenario simulation and emergency drill module;

[0027] The virtual environment creation module includes a 3D modeling and environment rendering unit and a physical engine and dynamic model unit. The 3D modeling and environment rendering unit constructs a highly simulated model of a chemical park, wherein the environment includes static and dynamic elements; the physical engine and dynamic model unit is used to simulate flame combustion, chemical reaction and gas diffusion phenomena to ensure realistic simulation effects;

[0028] The real-time monitoring data integration module includes an IoT device access and data transmission unit, which uses MQTT or HTTP protocol to connect various sensor data to the platform through a gateway, supports data stream processing, and updates the status in the virtual environment in real time;

[0029] The historical data management module includes a data warehouse and index management unit and a data analysis and prediction unit. The data warehouse and index management unit is used to build a historical data warehouse, using a distributed database for efficient management and rapid retrieval; the data analysis and prediction unit analyzes historical data through a machine learning model to generate emergency response predictions and trend analysis;

[0030] The scenario simulation and emergency drill module includes a real-time data mapping unit, an emergency scenario setting unit and a virtual training unit. The real-time data mapping unit maps real-time monitoring data into a virtual environment and dynamically updates the environmental status. The emergency scenario setting unit has multiple emergency scenarios built in. The virtual training unit interacts with the scene through a virtual reality device.

[0031] Furthermore, the emergency scenario setting unit specifically implements the following steps:

[0032] Step 1. According to the drill type selected by the user, load the corresponding initialization scene parameters from the database;

[0033] Step 2. According to different drill scenarios, select and load the corresponding physics engine and dynamics model, and load the environmental impact settings;

[0034] Step 3. Collect the real sensor data from the real-time monitoring data integration module and map the data to the virtual scene for dynamic updating of the physical model.

[0035] Furthermore, the situational awareness is based on the fusion results of multi-source data, and conducts accident diffusion trend analysis and equipment status assessment to provide early warning and risk prediction;

[0036] The decision support is to automatically formulate emergency plans based on real-time data and historical drill results.

[0037] On the other hand, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a chemical park emergency response system based on the metaverse when executing the computer program.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention provides an emergency response system that integrates real-time multi-source data collection and processing, immersive virtual drills and intelligent decision-making. The system can not only improve the accuracy of data integration and situation simulation, but also significantly improve the emergency response capabilities and contingency levels of employees, effectively reduce accident losses and ensure the safety of the park.

[0040] 2. The present invention uses the Metaverse platform to simulate complex emergency scenarios (such as fire, gas leakage, explosion and other chemical accidents) in a safe environment through a virtual environment, and combines VR technology for interactive training. Through AR and VR and other technical means to build highly realistic simulation scenes in a virtual environment, provide low-cost and highly realistic simulation training for emergency personnel, not only can they practice repeatedly in a safe virtual environment to improve the timeliness and efficiency of the drill, but also can accurately simulate disaster scenes, conduct pre-emptive protection and rehearsal, thereby improving the intelligent management level and emergency response efficiency of the chemical park.

[0041] 3. The present invention optimizes data fusion and analysis algorithms, supports real-time emergency response decisions, and improves response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the system composition of the present invention. DETAILED DESCRIPTION

[0043] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and the present embodiment. It should be understood that the implementation examples described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0044] Example 1

[0045] like Figure 1 The embodiment shown provides a chemical park emergency response system based on Metaverse, including: a data acquisition module, a data storage and processing module, a Metaverse platform module and an intelligent decision-making module.

[0046] The data acquisition module includes a variety of sensors and communication units. The various sensors monitor and collect environmental parameters in the chemical park in real time. The communication unit is connected to the data storage and processing module and the Metaverse platform module to ensure that the real-time monitoring data is transmitted to the data storage and processing module and the Metaverse platform module respectively;

[0047] The data storage and processing module and the Metaverse platform module are both connected to the intelligent decision-making module, wherein the data storage and processing module is used to store, analyze and integrate the collected data and implement early warning;

[0048] The Metaverse platform module is used to create a virtual simulation environment for chemical parks, integrate real-time monitoring data and historical data into the virtual model, conduct simulation drills in virtual scenes, and practice correct emergency response steps;

[0049] The intelligent decision-making module integrates multi-source data and simulation results to perform situation awareness and decision support.

[0050] In this embodiment, the data acquisition module is used to monitor the environmental parameters in the chemical park in real time. Sensor types include environmental monitoring sensors (such as temperature sensors, pressure sensors, and gas sensors) for monitoring temperature, pressure, and concentration of harmful gases (such as carbon monoxide, hydrogen sulfide, and volatile organic compounds); equipment monitoring sensors such as liquid level sensors and vibration sensors are used to monitor the liquid level of hazardous chemicals and the status of equipment to ensure that early warnings are triggered in the event of leakage or abnormal vibration. Video monitoring and personnel tracking systems are distributed in key locations in the park to achieve environmental video and personnel location tracking.

[0051] The specific embodiments of this application are as follows: To achieve accurate and reliable target detection and status assessment, and to meet the needs of chemical parks for early warning of hazardous substance leakage and fire, personnel positioning and equipment status monitoring in emergency situations. The system is composed of multiple sensor integration and communication units, including infrared thermal imaging cameras, gas sensor networks, laser radars and microwave radars, and is achieved by combining multi-source data fusion and dynamic tracking algorithms.

[0052] Among them, infrared thermal imaging cameras are used to monitor the temperature distribution of equipment and the environment to detect potential fire sources or overheating areas of equipment. The device can provide effective monitoring even in a smoky environment. The gas sensor network is used to be deployed in key areas of the park to detect the concentration of harmful chemical gases (such as carbon monoxide, hydrogen sulfide, volatile organic compounds, etc.), and to provide leak warnings in combination with gas diffusion models. LiDAR and microwave radar are used to provide high-precision three-dimensional environmental modeling and environmental scanning data to establish three-dimensional environmental maps, support emergency evacuation route planning and dynamic obstacle detection, especially in severe weather conditions, microwave radar can still provide stable and reliable data. The communication unit uses the Internet of Things technology to achieve efficient transmission of collected data, and seamlessly connects with the data storage and processing module to ensure the stable transmission of real-time monitoring data.

[0053] The data storage and processing module stores, analyzes and fuses the collected data to improve monitoring accuracy and reduce false alarm rate. It includes data collection and confidence assessment unit, multi-source data fusion and dynamic tracking unit, error correction and model optimization unit.

[0054] The data collection and confidence assessment integrates data from infrared thermal imaging cameras, gas sensor networks and radar data to evaluate the confidence of the data.

[0055] The data collected by the infrared thermal imaging camera is processed to generate an environmental heat map, which is analyzed by algorithms to detect and assess the risk of fire or equipment failure. The system can also dynamically adjust camera parameters according to environmental conditions (such as smoke concentration) to optimize data collection accuracy. The data collected by the gas sensor is combined with the diffusion assessment model to calculate the real-time concentration distribution of harmful gases, and the false alarm probability model is combined to reduce false alarms and ensure the reliability of data analysis. Radar data is filtered and error corrected to generate high-precision three-dimensional models and dynamic target information.

[0056] The system adopts a weighted fusion method to unify the data of various sensors in a coordinate system for fusion calculation. The fusion weight is dynamically adjusted according to the data covariance matrix to ensure the reliability of the data integration results.

[0057] Among them, multi-source data fusion and dynamic tracking adopt steps such as coordinate system conversion, multi-source data weighted fusion and weight calculation: the coordinate system conversion converts the data of different sensors (such as the Cartesian coordinates of infrared thermal imaging, the polar coordinates of radar and the spatial distribution data of gas sensors) into a standard three-dimensional Cartesian coordinate system for easy fusion processing.

[0058] Furthermore, multi-source data weighted fusion includes fusion algorithm and weight calculation:

[0059] The fusion algorithm combines the estimated values ​​using a weighted fusion method based on the confidence of each sensor to generate an overall target state estimate:

[0060] T(t)=α(t)*T 红外 (t)+β(t)*T 雷达 (t)+γ(t)*T 气体 (t)

[0061] Among them, α(t), β(t), and γ(t) are weight factors related to the covariance of each sensor data, representing the accuracy of different data sources.

[0062] The weight calculation is to calculate the fusion weight through the following formula to achieve uncertainty processing of multi-source data:

[0063] α(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 红外 -1 (t)

[0064] β(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 雷达 - 1 (t)

[0065] γ(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 气体 - 1 (t)

[0066] Among them, P 红外 (t), P 雷达 (t) and P 气体 (t) are the error covariance matrices of infrared, radar and gas sensor data, respectively.

[0067] The error correction and model optimization adopt the extended Kalman filter (EKF) and dynamic data weight adjustment algorithm to achieve real-time tracking and analysis of the target motion state.

[0068] Furthermore, the extended Kalman filter (EKF) is used to track the accident source or moving target, combined with the nonlinear observation model for precise positioning and diffusion velocity estimation.

[0069] This specific embodiment integrates multiple advanced sensor technologies and optimizes data fusion and tracking algorithms to respond to emergencies in chemical parks in real time, such as gas leaks, fires, or equipment failures, significantly improving the accuracy and speed of emergency response. In addition, the reasonable layout of sensors and efficient data processing methods also ensure the accuracy of monitoring.

[0070] In this embodiment, the Metaverse platform module is used to create a highly simulated virtual simulation environment, reproduce the chemical park in a virtual environment through AR, VR and digital twin technology, and integrate real-time monitoring data and historical data into the virtual model. Emergency responders can conduct immersive drills in virtual scenes, simulate emergencies such as fire, toxic gas leaks or explosions, experience realistic disaster scenes, and practice correct emergency response steps. The platform consists of a multi-layer structure, including a virtual environment creation module, a real-time monitoring data integration module, a historical data management module, and a scene simulation and emergency drill module. The platform adopts a cloud-based and edge computing combined architecture to support large-scale real-time computing and data transmission.

[0071] The virtual environment creation module includes a 3D modeling and environment rendering unit and a physical engine and dynamics model unit.

[0072] The 3D modeling and environment rendering unit constructs a high-simulation model of the chemical park by using Unity or Unreal Engine development engine, wherein the environment includes static and dynamic elements (such as flames, smoke, gas leaks, etc., which are simulated by particle systems).

[0073] The physical engine and dynamic model unit are used to simulate phenomena such as flame combustion, chemical reaction and gas diffusion to ensure realistic simulation effects.

[0074] Furthermore, the real-time monitoring data integration module includes an Internet of Things device access and data transmission unit.

[0075] Among them, the IoT device access and data transmission unit uses MQTT or HTTP protocol to connect various sensor data to the platform through the gateway. It supports data stream processing and real-time updates of the status in the virtual environment.

[0076] Furthermore, the historical data management module includes a data warehouse and index management unit and a data analysis and prediction unit.

[0077] The data warehouse and index management unit are used to construct a historical data warehouse, and a distributed database is used for efficient management and rapid retrieval.

[0078] Among them, the data analysis and prediction unit analyzes historical data through machine learning models to generate emergency response forecasts and trend analysis.

[0079] Furthermore, the scenario simulation and emergency drill module includes a real-time data mapping unit, an emergency scenario setting unit and a virtual training unit.

[0080] Among them, the real-time data mapping unit maps the real-time monitoring data into the virtual environment, dynamically updates the environmental status, such as temperature, pressure, gas concentration, etc., to reflect the actual situation of the park.

[0081] Among them, the emergency scenario setting unit has built-in multiple emergency scenarios, including fire, chemical leakage, explosion, toxic gas leakage, etc., and supports customization of scene parameters such as fire spread path, harmful gas diffusion model, etc.

[0082] The specific implementation steps are as follows:

[0083] 1. Scene initialization

[0084] The first step is parameter setting. According to the drill type selected by the user, the system loads the corresponding initialization scene parameters from the database. These parameters include environmental conditions (such as temperature, humidity, wind speed, wind direction), location of hazard sources, physical characteristics of the scene, etc.

[0085] Preferably, the user can customize or adjust these parameters through a graphical interface to meet specific exercise requirements.

[0086] The three-dimensional environment in the initialization scene is generated by loading the virtual environment creation module. The highly simulated three-dimensional model of the chemical park includes buildings, equipment, pipelines, storage tanks, etc., and the basic particle system of dynamic effects (flame, smoke, explosion, etc.) is pre-loaded.

[0087] 2. Physical and dynamic model loading

[0088] According to different drill scenarios, select and load the corresponding physics engine and dynamics model. These models are based on particle systems or fluid dynamics and are responsible for simulating phenomena such as fire spread, chemical flow and diffusion, and toxic gas diffusion. The physics engine calculates physical interaction processes, including gravity, fluid mechanics, heat conduction, explosion shock waves, etc. At the same time, load environmental impact settings, such as external environmental factors (wind speed, wind direction, temperature changes, etc.), which will affect the propagation path and speed of flame spread, smoke diffusion, and gas leakage.

[0089] The particle system is a computer graphics technology used to simulate and render a large number of tiny particles to create complex, natural dynamic effects. This technology is often used to express natural phenomena such as fire, smoke, dust, explosions, rain, snow, and flowing water, especially in 3D simulation and virtual reality environments. The particle system uses a large number of simple particles combined with dynamic calculations to show realistic visual effects.

[0090] 3. Dynamic simulation and real-time update

[0091] According to the real sensor data collected by the real-time monitoring data integration module, such as ambient temperature, gas concentration, wind speed, etc., and mapped to the virtual scene, the real-time data is used to dynamically update the physical model to keep the exercise process consistent with the real environment. The particle system is used uniformly to simulate various physical phenomena. According to the selected scene type, the behavior and characteristics of the particles are adjusted.

[0092] Specific embodiments, such as

[0093] Fire scene: Use particle system to simulate the propagation path and speed of flame burning, smoke diffusion and gas leakage.

[0094] Chemical leakage scenario: Liquid leakage is simulated using fluid simulation, chemical analysis and diffusion are represented by a particle system, and the diffusion rate and concentration distribution are adjusted according to environmental parameters.

[0095] Among them, fluid mechanics or heat conduction numerical models are run in the background to calculate the movement trajectory and diffusion path of the substance. These numerical models include flow-diffusion equations, Navier-Stokes equations, etc., which are used to accurately predict the diffusion behavior of hazardous substances. Among them, the virtual training unit interacts with the scene through virtual reality (VR) equipment to simulate the triggering of alarms, emergency evacuation, use of fire-fighting equipment (such as the use of fire extinguishers), and rescue of the wounded. An interactive experience and real-time feedback mechanism is used to prompt users with correct or incorrect operation methods, and the drill results are recorded for subsequent analysis and optimization of emergency strategies.

[0096] Specifically, employees can perform emergency operations in the virtual space, such as using fire extinguishers and triggering alarms, so that they can become familiar with and practice emergency response strategies (such as water spraying, chemical blocking, personnel evacuation, etc.) in a safe virtual space. For example, when simulating a fire scene, the system can set up the effects of smoke diffusion, flame spread, and temperature rise, and provide clear directional sound prompts with the support of audio space technology, such as flame bursts and evacuation instructions.

[0097] Specific implementation examples

[0098] - Fire environment emergency escape design: Based on the characteristics of fire, scenarios such as smoke spread and temperature changes are simulated, escape routes and refuge points are designed, and users are guided to master escape skills through immersive drills.

[0099] -Virtual simulation application process: covers the "training-planning-coordination-testing" links, and formulates corresponding simulation exercise tasks for emergency personnel at different levels.

[0100] - Virtual environment construction method: clarify learning objectives, select the appropriate VR platform (such as Unity or UnrealEngine), develop interactive functions and test and optimize them, and finally deploy them for employee training.

[0101] Intelligent decision-making module: Integrates multi-source data and simulation results to provide scientific, fast and accurate emergency decision support. The module functions include situation awareness and decision support.

[0102] The situational awareness is based on the results of multi-source data fusion, conducts accident diffusion trend analysis and equipment status assessment, and provides early warning and risk prediction.

[0103] The decision support is to automatically formulate emergency plans based on real-time data and historical drill results, covering material deployment, personnel scheduling and evacuation route planning. The system can also automatically generate emergency drill reports and continuously improve emergency plans through data analysis to ensure that drill results and emergency response capabilities continue to improve.

[0104] Specific implementation steps:

[0105] 1) Data collection and integration: Use sensors and IoT devices to monitor environmental data in the chemical park (such as temperature, gas concentration, equipment status, etc.) in real time and process them together with historical data.

[0106] 2) Construction of digital twin model: Establish a virtual model of the park, map the monitoring data into the virtual scene, and realize real-time dynamic update and visual display.

[0107] 3) Create virtual scenarios: Design realistic emergency drill scenarios, such as chemical plant fires or chemical leaks, and construct various realistic environmental elements (smoke, flames, chemical diffusion, etc.).

[0108] 4) Development of interactive functions: Achieve interactivity, such as using fire extinguishers, triggering alarms, guiding escape routes, etc., so that employees can participate in and operate various emergency procedures.

[0109] 5) Immersive experience: Combined with 3D audio spatialization technology, the realism of the simulated environment is enhanced to ensure that users can feel environmental prompt sounds from all directions, such as flames, evacuation instructions, etc.

[0110] The three-dimensional audio spatialization technology is as follows:

[0111] -Function description: Improve the immersion and authenticity of emergency drills and provide multi-dimensional sound effects through audio spatialization technology.

[0112] - Implementation: Use 3D audio processing technologies, such as binaural audio and real-time head tracking, to simulate the dynamic changes of sound at a fire scene, helping users perceive the direction and distance of sound in a virtual environment and improve their ability to react to escape.

[0113] 6) Analysis and prediction: Conduct emergency situation analysis and event prediction based on virtualized data to provide data support for emergency response.

[0114] Example 2

[0115] This embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a chemical park emergency response system based on the metaverse when executing the computer program.

[0116] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

[0117] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0118] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of patent protection of the present invention. Under the enlightenment of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested for the present invention shall be based on the attached claims.

Claims

1. A chemical park emergency response system based on Metaverse, characterized in that: include: Data acquisition module, data storage and processing module, metaverse platform module and intelligent decision-making module, The data acquisition module includes a variety of sensors and communication units. The various sensors monitor and collect environmental parameters in the chemical park in real time. The communication unit, data storage and processing module are connected to the Metaverse platform module to ensure that the real-time monitoring data is transmitted to the data storage and processing module and the Metaverse platform module respectively; The data storage and processing module and the Metaverse platform module are both connected to the intelligent decision-making module, wherein the data storage and processing module is used to store, analyze and integrate the collected data and implement early warning; The Metaverse platform module is used to create a virtual simulation environment for chemical parks, integrate real-time monitoring data and historical data into the virtual model, conduct simulation drills in virtual scenes, and practice correct emergency response steps; The intelligent decision-making module integrates multi-source data and simulation results to perform situation awareness and decision support.

2. A chemical park emergency response system based on metaverse according to claim 1, characterized in that: The various sensors in the data acquisition module include infrared thermal imaging cameras, gas sensors, laser radars and microwave radars, among which the infrared thermal imaging cameras are used to monitor the temperature distribution of equipment and the environment; the gas sensor network is used to detect the concentration of harmful chemical gases; the laser radars and microwave radars are used to provide three-dimensional environmental modeling and environmental scanning data to establish three-dimensional environmental maps.

3. A chemical park emergency response system based on metaverse according to claim 1, characterized in that: The data storage and processing module stores, analyzes and fuses the collected data, and includes a data collection and confidence assessment unit, a multi-source data fusion and dynamic tracking unit, and an error correction and model optimization unit.

4. A chemical park emergency response system based on metaverse according to claim 3, characterized in that: The data acquisition and confidence assessment unit integrates data from infrared thermal imaging cameras, gas sensor data, and laser radar and microwave radar data to assess the confidence of the data; The multi-source data fusion and dynamic tracking unit combines the estimated values ​​using a weighted fusion method according to the confidence of each sensor to generate an overall target state estimate; The error correction and model optimization unit adopts an extended Kalman filter and a dynamic data weight adjustment algorithm to achieve real-time tracking and analysis of the target motion state.

5. A chemical park emergency response system based on metaverse according to claim 4, characterized in that: The multi-source data fusion and dynamic tracking unit includes: Convert the data from different sensors into a standard three-dimensional Cartesian coordinate system; According to the confidence level of each sensor, the estimated values ​​are combined using a weighted fusion method; T(t)=α(t)*T 红外 (t)+β(t)*T 雷达 (t)+γ(t)*T 气体 (t) Among them, α(t), β(t), and γ(t) are weight factors related to the covariance of each sensor data, representing the accuracy of different data sources.

6. A chemical park emergency response system based on metaverse according to claim 5, characterized in that: The fusion weight is calculated by the following formula: α(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 红外 - 1 (t) β(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 雷达 - 1 (t) γ(t)=[P 红外 -1 (t)+P 雷达 - 1 (t)+P 气体 -1 (t)] -1 P 气体 - 1 (t) Among them, P 红外 (t), P 雷达 (t) and P 气体 (t) are the error covariance matrices of infrared, radar and gas sensor data, respectively.

7. A chemical park emergency response system based on metaverse according to claim 1, characterized in that: The Metaverse platform module includes a virtual environment creation module, a real-time monitoring data integration module, a historical data management module, and a scenario simulation and emergency drill module; The virtual environment creation module includes a 3D modeling and environment rendering unit and a physical engine and dynamic model unit. The 3D modeling and environment rendering unit constructs a highly simulated model of a chemical park, wherein the environment includes static and dynamic elements; the physical engine and dynamic model unit is used to simulate flame combustion, chemical reaction and gas diffusion phenomena to ensure realistic simulation effects; The real-time monitoring data integration module includes an IoT device access and data transmission unit, which uses MQTT or HTTP protocol to connect various sensor data to the platform through a gateway, supports data stream processing, and updates the status in the virtual environment in real time; The historical data management module includes a data warehouse and index management unit and a data analysis and prediction unit. The data warehouse and index management unit is used to build a historical data warehouse, using a distributed database for efficient management and rapid retrieval; the data analysis and prediction unit analyzes historical data through a machine learning model to generate emergency response predictions and trend analysis; The scenario simulation and emergency drill module includes a real-time data mapping unit, an emergency scenario setting unit and a virtual training unit. The real-time data mapping unit maps real-time monitoring data into a virtual environment and dynamically updates the environmental status. The emergency scenario setting unit has multiple emergency scenarios built in. The virtual training unit interacts with the scene through a virtual reality device.

8. A chemical park emergency response system based on metaverse according to claim 7, characterized in that: The specific implementation steps of the emergency scenario setting unit are as follows: Step 1. According to the drill type selected by the user, load the corresponding initialization scene parameters from the database; Step 2. According to different drill scenarios, select and load the corresponding physics engine and dynamics model, and load the environmental impact settings; Step 3. Collect the real sensor data from the real-time monitoring data integration module and map the data to the virtual scene for dynamic updating of the physical model.

9. A chemical park emergency response system based on metaverse according to claim 1, characterized in that: The situation awareness is based on the fusion results of multi-source data, and conducts accident diffusion trend analysis and equipment status assessment to provide early warning and risk prediction; The decision support is to automatically formulate emergency plans based on real-time data and historical drill results.

10. An electronic device, characterized in that: It includes: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the chemical park emergency response system based on the metaverse as described in any one of claims 1 to 9 when executing the computer program.

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