A Digital Simulation Method and System for CBRN Environment Based on Task-Oriented Protection Status

Abstract

The present invention discloses a digital simulation method for a CBRN environment based on a task-oriented protection state, including establishing a high-precision multi-scale model through the coupling of simulation models at the micro scale and the macro scale; dynamically adjusting the model complexity according to specific application scenarios and real-time data; acquiring and processing real-time monitoring data for digital simulation of the CBRN environment; constructing a human health effect simulation model based on an individualized health effect model and a long-term effect simulation model; calculating the model based on a distributed computing architecture and edge computing technology to perform digital simulation of the environment; displaying and feeding back the simulation results through an intelligent human-computer interaction interface and a user experience interface; performing emergency response and decision support based on virtual-real combined drills and intelligent decision support; validating the real-time monitoring data through a virtual experiment platform and a data consistency verification mechanism, and calibrating the high-precision model and the human health effect simulation model. A system, an electronic device, and a computer-readable storage medium are also disclosed.

Description

Technical Field

[0001] The present invention relates to the fields of computer simulation and military engineering, mainly to the field of modeling and simulation technology in the military field, and particularly relates to a CBRN environment digital simulation method and system based on mission-oriented protective status. Background Art

[0002] I. MOPP (Mission Oriented Protective Posture) is a standard operating procedure in the military and emergency response fields, aiming to respond to chemical, biological, radiological, and nuclear (CBRN) threats. The MOPP system guides personnel to take appropriate protective measures in the CBRN environment through different protection levels. The CBRN environment simulation technology is designed based on MOPP, aiming to provide a realistic training environment to improve the reaction and operation capabilities of personnel in the real CBRN environment. MOPP (Mission Oriented Protective Posture) is a standard for guiding the operation of the military in the CBRN environment.

[0003] (I) Existing Products and Technologies

[0004] Virtual Reality (VR) and Augmented Reality (AR): These technologies are widely used in simulation training, enabling trainees to experience high-risk scenarios under safe conditions through immersive environments.

[0005] Physical Simulators: Some high-end simulation devices can simulate real CBRN conditions, such as gas diffusion, pollutant propagation, etc.

[0006] Software Simulation Platforms: Combining data analysis and modeling technologies, they provide detailed pollution diffusion models and emergency response plans.

[0007] (II) Development Trends

[0008] Integration and Intelligence: Future CBRN simulation technologies will increasingly integrate multiple technologies, such as big data, artificial intelligence, and the Internet of Things, to provide a more accurate and intelligent simulation environment.

[0009] Enhanced Immersive Experience: With the progress of VR and AR technologies, the simulation environment will be more realistic, providing all-round sensory stimulation and improving the training effect.

[0010] Portability and Operability: Simulation devices and systems will tend to be more portable and easier to operate to adapt to different scenarios and rapid deployment in emergency situations.

[0011] (III) Main Problems

[0012] High cost: The R & D and maintenance costs of high - end simulation equipment and systems are relatively high, which limits their popularization in some units and organizations with limited resources.

[0013] Technical complexity: Complex simulation systems require professional operation and maintenance personnel, increasing the difficulty of use.

[0014] Lack of realism: Although VR and AR technologies have made significant progress, in some cases, the realism of the simulation environment still cannot fully meet the training needs.

[0015] Data and algorithm limitations: The accuracy of simulation systems depends on high - quality data and advanced algorithms. At present, the data collection and processing capabilities in some fields still need to be improved.

[0016] (4) Market demand

[0017] Military training: The military has an urgent need for efficient and realistic CBRN simulation training systems to improve the survival ability and combat efficiency of soldiers in complex battlefield environments.

[0018] Emergency response: Governments and emergency response departments around the world need simulation technology to train personnel and improve their response capabilities in CBRN accidents.

[0019] Industrial safety: Some industrial fields, such as chemical and nuclear industries, also need simulation technology for safety training and accident prevention.

[0020] Education and research: Universities and research institutions have a demand for CBRN simulation technology for teaching and research, promoting the development of this field.

[0021] In summary, the CBRN environmental simulation technology based on MOPP has broad application prospects in multiple fields such as military, emergency response, industrial safety, and education and research. Despite facing some technical and cost challenges, with the continuous progress of technology and the increasing market demand, the development potential of this field is huge.

[0022] 2. CBRN (Chemical, Biological, Radiological, Nuclear) environmental simulation technology is mainly used for training, education and research, helping relevant personnel simulate and respond to real CBRN threats in a safe environment.

[0023] (1) Existing CBRN environmental simulation technologies and systems:

[0024] 1. Joint Conf l ict and Tact ica l Simu l at ion (JCATS)

[0025] Overview: JCATS is a tactical simulation system developed by the US Department of Defense for training and combat exercises. It can simulate complex combat environments, including CBRN events.

[0026] Functions: Simulate various tactical scenarios, including CBRN attacks and protective measures, and support multi-user collaborative operations.

[0027] 2. Virtual Battlespace 3 (VBS3)

[0028] Overview: VBS3 is a virtual battlefield simulation platform developed by Bohemia Interactive Simulations for military training and tactical exercises.

[0029] Functions: Provide highly realistic CBRN environment simulations, support the simulation of various chemical, biological, radiological, and nuclear threats, and are suitable for individual and team training.

[0030] 3. CBRN Defense Virtual Simulation (CBRNDVS)

[0031] Overview: CBRNDVS is a virtual simulation system specifically designed for CBRN protection for training and drills.

[0032] Functions: Simulate the occurrence and handling of CBRN events, including pollutant dispersion, decontamination processes, and the use of personal protective equipment, etc.

[0033] 4. Dugway Proving Ground Simulations

[0034] Overview: Dugway Proving Ground is a test site of the US Army with advanced CBRN simulation and testing facilities.

[0035] Functions: Provide CBRN testing and training in a real environment, including live-fire exercises and simulation training.

[0036] 5. One Semi-Automated Forces (OneSAF)

[0037] Overview: OneSAF is a simulation tool developed by the US Army for tactical training and analysis, supporting the simulation of various combat scenarios.

[0038] Functions: Include the simulation of CBRN threats and can be integrated into broader combat and training systems.

[0039] 6、Chemical Biological Radiological Nuclear Explosive (CBRNE) Simulation Suite

[0040] Overview: The CBRNE Simulation Suite is a comprehensive set of simulation tools dedicated to the training and research of CBRN and explosive threats.

[0041] Functions: Provide detailed pollution dispersion models, decontamination process simulations, emergency response plans, etc.

[0042] 7、Radiation Emergency Assistance Center / Training Site (REAC / TS)

[0043] Overview: REAC / TS is a facility of the US Department of Energy that provides training and research for radioactive accident emergency response.

[0044] Functions: Simulate radioactive contamination and decontamination processes, and provide professional training and technical support.

[0045] 8、Chemical Biological Incident Response Force (CBI RF) Training Systems

[0046] Overview: CBI RF is a special force of the US Marine Corps responsible for responding to chemical and biological incidents.

[0047] Functions: Use a series of simulation and training systems to simulate the response and handling processes of chemical and biological threats.

[0048] 9、Hazard Prediction and Assessment Capability (HPAC)

[0049] Overview: HPAC is a prediction and assessment tool developed by the US Department of Defense for simulating the impacts of CBRN incidents.

[0050] Functions: Predict the dispersion and impact ranges of chemical and radioactive substances, and provide emergency response recommendations.

[0051] 10、CBRN Training Simulator (CBRN TS)

[0052] Overview: CBRN TS is a simulator dedicated to CBRN training that provides virtual reality and augmented reality experiences.

[0053] Function: Simulate various CBRN threats and protection measures to provide an immersive training experience.

[0054] (2) Main Functions and Features

[0055] Realism and Immersion: Utilize virtual reality (VR) and augmented reality (AR) technologies to provide a highly realistic training environment.

[0056] Multi-User Collaboration: Support multiple users to train and operate simultaneously, simulating a real team response environment.

[0057] Dynamic Data Integration: Integrate real-time sensor data to provide a dynamically updated simulation environment.

[0058] Intelligent Analysis and Decision Support: Based on big data and AI technologies, provide intelligent analysis and decision support.

[0059] Cross-Platform Compatibility: Support integration with other military and emergency response systems to provide comprehensive training and evaluation solutions.

[0060] (3) Development Trends

[0061] High Precision and High Realism: The simulation system will become more and more realistic, and the simulation accuracy and details will continue to improve.

[0062] Intelligentization and Automation: More intelligent functions, such as automatically generating training scenarios and intelligent response plans, will be introduced.

[0063] Multi-Environment Adaptability: The simulation system will be able to adapt to a variety of complex environments and conditions to provide more comprehensive training support.

[0064] Portability and Mobility: Future simulation devices will be more portable, adapting to rapid deployment in different scenarios and requirements.

[0065] (4) Main Problems and Challenges

[0066] High Cost: The development and maintenance costs of high-end simulation devices and systems are relatively high.

[0067] Technical Complexity: Operation and maintenance require a high level of professional skills.

[0068] Data Quality and Reliability: High-quality data is the basis of the simulation system, but there are still challenges in obtaining and processing high-quality data.

[0069] User Acceptance: Training and promotion are needed to improve users' acceptance and proficiency in new technologies.

[0070] (5) Market Demand

[0071] Military Training: The military has an urgent need for efficient and realistic CBRN simulation training systems.

[0072] Emergency Response: Governments and emergency response departments around the world need simulation technologies to train personnel and improve their response capabilities in CBRN incidents.

[0073] Industrial Safety: The chemical and nuclear industries require simulation technologies for safety training and accident prevention.

[0074] Education and Research: Universities and research institutions have a need for CBRN simulation technologies for teaching and research.

[0075] III. Theoretical Basis

[0076] 1. Physical and Chemical Basic Modeling

[0077] Pollution Diffusion Model: Based on physical and chemical laws, a diffusion model of pollutants is established, including aerodynamics, meteorology, and chemical reaction kinetics models. Common models include Gaussian Plume Model, Computational Fluid Dynamics (CFD), etc.

[0078] Radiation Propagation Model: Using the Monte Carlo method and numerical methods to simulate radiation propagation, considering factors such as absorption, scattering, and attenuation of radiation.

[0079] Biological Diffusion Model: Simulates the spread of biological agents in air, water, and soil, combining knowledge of biology and ecology.

[0080] 2. Virtual Reality (VR) and Augmented Reality (AR) Technologies

[0081] Immersive Simulation: Using VR technology to create an immersive environment, enabling users to experience and respond to CBRN threats in a virtual world.

[0082] Augmented Reality Training: Using AR technology to overlay virtual CBRN threats in the real environment, helping users to train and respond in actual scenarios.

[0083] 3. High-Performance Computing (HPC)

[0084] Parallel Computing and Distributed Computing: Using high-performance computing clusters for large-scale simulation calculations to accelerate the solution speed of models.

[0085] Cloud Computing Platform: Using cloud computing resources for simulation, providing flexible computing and storage capabilities.

[0086] 4. Data Fusion and Sensing Technologies

[0087] Multi-sensor data fusion: Integrate data from different sensors, such as meteorological sensors, pollutant detection sensors, radiation detectors, etc., to improve simulation accuracy.

[0088] Real-time data integration: Integrate real-time monitoring data with the simulation model to dynamically update the simulation scenario.

[0089] 5. Human-computer interaction and visualization

[0090] 3D visualization: Use 3D modeling and visualization tools to display simulation results, enabling users to intuitively understand the diffusion and impact of pollutants.

[0091] Human-computer interaction interface: Develop a user-friendly interface to facilitate users to operate and view simulation results.

[0092] 6. Human health effect simulation

[0093] Toxicology model: Simulate the effects of chemical and biological pollutants on human health, including the effects of acute and chronic exposures.

[0094] Dose-response relationship: Establish a relationship model between pollutant dose and human response for risk assessment and formulation of protective measures.

[0095] 7. Emergency response and decision support system

[0096] Emergency drill simulation: Simulate emergency response scenarios to evaluate the effectiveness of emergency plans and the emergency response capabilities of personnel.

[0097] Decision support tool: Develop a simulation-based decision support system to provide strategies and suggestions for emergency response.

[0098] 8. Experiment and verification

[0099] Experimental verification: Verify the accuracy and reliability of the simulation model through laboratory and field experiments.

[0100] Model calibration: Calibrate the model according to experimental data and actual observation data to improve the accuracy of the simulation.

[0101] IV. Technical challenges and development trends

[0102] 1. Multi-scale modeling: Address simulation problems at different scales from micro (molecular level) to macro (environmental level).

[0103] 2. Uncertainty analysis: Study the impact of uncertainties in model input parameters and initial conditions on simulation results.

[0104] 3. Integrated simulation platform: Develop an integrated simulation platform to integrate different types of models and data sources, improving the overall performance and application value of the simulation system.

[0105] 4. The continuous development of CBRN environmental simulation technology not only improves the ability to respond to CBRN threats, but also provides important tools and methods for research and training in related fields.

[0106] However, there is no CBRN environmental digital simulation technology solution based on task-oriented protection status in the existing technology. Therefore, innovative solutions in computer-generated forces can propose specific improvement and innovation measures for the defects of the current CBRN environmental simulation technology solution. Summary of the Invention

[0107] The object of the present invention is to design a CBRN environmental digital simulation method and system based on task-oriented protection status for the defects of the existing technology. By innovative technical means, the simulation accuracy, real-time performance and user experience are improved, and the emergency response and decision support are enhanced. Through innovative technologies such as multi-scale modeling, real-time data fusion, distributed computing, intelligent sensors, intelligent interaction interfaces, individualized health effect simulation, virtual-real combined emergency drills, and virtual experiment platforms and data consistency verification, the defects of the existing CBRN environmental simulation technology solution are effectively solved. This method improves the accuracy, real-time performance and user experience of CBRN environmental simulation, enhances the emergency response and decision support capabilities, and provides advanced technical means for research and application in related fields.

[0108] Thus, the main problems in the current CBRN simulation technology are solved. These main problems include: (1) There are many simplifications in the existing models and the behavior of pollutants in complex environments cannot be accurately captured; (2) High-precision models rely on a large amount of data and real-time data integration is difficult; (3) The calculation of high-precision simulation models is complex and the resource requirements are high; (4) The accuracy and reliability of sensor data are insufficient; (5) The interface design and user experience are poor; (6) The existing models cannot comprehensively and accurately simulate human health effects; (7) The authenticity of emergency response simulation is insufficient and the complexity of the decision support system is high; (8) The experimental cost is high and the data is inconsistent.

[0109] The first aspect of the present invention lies in providing a CBRN environmental digital simulation method based on task-oriented protection status, including:

[0110] S1. Based on the simulation model at the micro scale and the simulation model at the macro scale, a high-precision multi-scale model is established by coupling the simulation models at different scales of the micro scale and the macro scale, where the high-precision multi-scale model is used to improve the accuracy of pollutant diffusion and propagation; the model complexity of the high-precision multi-scale model can be dynamically adjusted according to specific application scenarios and real-time data;

[0111] S2. Obtain and process real-time monitoring data for CBRN environmental digital simulation;

[0112] S3. Construct a human health effect simulation model based on the individualized health effect model and the long-term effect simulation model;

[0113] S4. Calculate the high-precision model and the human health effect simulation model based on the distributed computing architecture and edge computing technology, so as to perform digital simulation of the CBRN environment based on the task-oriented protection status.

[0114] Preferably, the S2 includes:

[0115] S21. Obtain and process real-time monitoring data for digital simulation of the CBRN environment, and the real-time monitoring data is multi-source data; the acquisition of the real-time monitoring data is realized through a sensor system, and the sensor system includes a sensor self-calibration module and is configured with redundant sensors; wherein the sensor self-calibration module is built with a sensor self-calibration algorithm to improve the accuracy and reliability of sensor data; the configuration of redundant sensors includes: enhancing the reliability of data through multi-sensor data fusion and correction;

[0116] S22. Process and fuse the multi-source data based on the real-time monitoring data by using machine learning algorithms, and the multi-source data is integrated meteorological data, terrain data and pollutant property data.

[0117] S23. Seamlessly integrate the real-time monitoring data into the high-precision multi-scale model through a standardized real-time data interface to dynamically update and adjust the model complexity of the high-precision multi-scale model;

[0118] Preferably, the individualized health effect model is established based on the consideration of individual differences, and the individualized health effect model is established based on individual physiological data and historical health data; the long-term effect simulation model is established by simulating health impacts at different time scales, and provides a more comprehensive risk assessment as a long-term exposure health effect simulation model.

[0119] Preferably, the distributed computing architecture includes distributing computing tasks to multiple computing nodes; the edge computing technology includes performing data processing and preliminary calculations near the data source.

[0120] Preferably, the method further includes:

[0121] S5. Display and feedback the simulation results of the CBRN environment digital simulation through an intelligent human-computer interaction interface and a user experience interface; the intelligent human-computer interaction interface combines natural language processing and speech recognition technologies to improve the user's operation experience and efficiency; the feedback is based on the user feedback mechanism: establish a user feedback mechanism and continuously optimize the interface design and interaction method according to user feedback.

[0122] Preferably, the method further includes:

[0123] S6. Conducting emergency response and decision support for the digital simulation process based on virtual-real combination drills and intelligent decision support; the virtual-real combination drills include: developing an emergency response drill system combining virtual reality and augmented reality technologies; the intelligent decision support includes: developing an intelligent decision support system using artificial intelligence and big data analysis technologies.

[0124] Preferably, the method further includes:

[0125] S7. Conducting experimental verification on the real-time monitoring data and calibrating the high-precision model and the human health effect simulation model based on a virtual experiment platform and a data consistency verification mechanism; the virtual experiment platform reduces the cost and risk of actual experiments by simulating experimental environments and conditions; the data consistency verification mechanism ensures the consistency of experimental data and simulation data through cross-validation and multi-source data fusion.

[0126] The second aspect of the present invention is to provide a CBRN environment digital simulation system based on a task-oriented protection state, including:

[0127] A high-precision multi-scale modeling module (101), configured to establish a high-precision multi-scale model by coupling the microscopic-scale simulation model and the macroscopic-scale simulation model based on the microscopic-scale simulation model and the macroscopic-scale simulation model, wherein the high-precision multi-scale model is used to improve the accuracy of pollutant diffusion and propagation; the model complexity of the high-precision multi-scale model can be dynamically adjusted according to specific application scenarios and real-time data;

[0128] The high-precision multi-scale modeling module (101) consists of a pollution diffusion model sub-module and a radiation propagation model sub-module; wherein:

[0129] The pollution diffusion model sub-module is used to couple microscopic molecular dynamics and macroscopic fluid mechanics models to accurately simulate the diffusion behavior of chemical and biological pollutants; it has an algorithm for coupling microscopic and macroscopic models and dynamically adjusts the model complexity;

[0130] The radiation propagation model sub-module is used to simulate the propagation of radiation in different media by combining the Monte Carlo method and numerical methods, considering absorption, scattering, and attenuation; an adaptive grid refinement unit is set inside to use adaptive grid refinement in key areas;

[0131] A real-time multi-source data fusion module (102), configured to obtain and process real-time monitoring data for CBRN environment digital simulation;

[0132] In this embodiment, the real-time multi-source data fusion module (102) consists of a data acquisition sub-module, a data preprocessing sub-module, a multi-source data fusion sub-module, and a standardized real-time data interface; where:

[0133] The data acquisition sub-module consists of an intelligent sensor system, which is internally composed of a sensor self-calibration algorithm unit, a redundant sensor configuration unit, a multi-sensor data fusion algorithm unit, and a sensor fault detection and repair unit; the sensor self-calibration algorithm unit internally sets a sensor self-calibration algorithm to improve the accuracy of sensor data, and the redundant sensor configuration unit enhances the reliability of the sensor system through redundant configuration and data fusion; the multi-sensor data fusion algorithm unit fuses the data of multiple sensors based on the Bayesian network algorithm; the sensor fault detection and repair unit internally sets a developed sensor fault detection and self-repair algorithm to ensure the continuous operation of the system;

[0134] The data preprocessing sub-module is used to preprocess the sensor data to remove noise and outliers;

[0135] The multi-source data fusion sub-module is used to process and fuse the multi-source data based on the real-time monitoring data using machine learning algorithms, and the machine learning algorithms include methods such as Kalman filtering and particle filtering. Fusing the multi-source data includes fusing meteorological data, terrain data, and pollutant property data of the multi-source data fusion;

[0136] The standardized real-time data interface is used to seamlessly integrate the real-time monitoring data into the high-precision multi-scale model through the standardized real-time data interface, and to dynamically update and adjust the model complexity of the high-precision multi-scale model;

[0137] The individualized health effect simulation module (103) is used to construct a human health effect simulation model based on an individualized health effect model and a long-term effect simulation model;

[0138] The individualized health effect simulation module (103) consists of an individualized model sub-module and a long-term effect simulation sub-module; where,

[0139] The individualized model sub-module is used to build the first part of the human health effect simulation model based on the health effect model of individual physiological difference data, combining individual physiological data and historical health data; where the individual physiological difference data is determined by collecting individual physiological data through channels such as wearable devices;

[0140] The long-term effect simulation sub-module is used to establish a long-term exposure health effect simulation model as the second part of the human health effect simulation model;

[0141] The health effect evaluation algorithms are set inside both the individualized model sub-module and the long-term effect simulation sub-module, and the health effect evaluation algorithms are health effect evaluation algorithms based on big data analysis and machine learning;

[0142] The distributed and edge computing architecture module (104) is used to calculate the high-precision model and the human health effect simulation model based on the distributed computing architecture and edge computing technology, so as to perform digital simulation of the CBRN environment based on the task-oriented protection status;

[0143] The distributed and edge computing architecture module (104) consists of a distributed computing sub-module and an edge computing sub-module; among them:

[0144] The distributed computing sub-module is used to distribute computing tasks to multiple computing nodes, communicate using MPI (Message Passing Interface), and the distributed computing sub-module is built-in with a task scheduling algorithm to optimize computing resource allocation;

[0145] The edge computing sub-module deploys edge nodes at key positions, uses local computing capabilities to perform real-time data processing, and performs preliminary computing and data processing near the data source;

[0146] The intelligent human-computer interaction interface module (105) is used to display and feedback the simulation results of the CBRN environment digital simulation through the intelligent human-computer interaction interface and the user experience interface;

[0147] The intelligent human-computer interaction interface module (105) consists of a natural language processing sub-module and a 3D visualization sub-module; among them,

[0148] The natural language processing sub-module includes an intelligent interaction system based on natural language processing, supporting voice and text input;

[0149] The 3D visualization sub-module is used to display pollutant diffusion and impacts using 3D modeling and visualization technologies; the 3D visualization sub-module includes an intelligent interaction algorithm unit and a user interface optimization unit; among them, the intelligent interaction algorithm unit is built-in with an intelligent interaction algorithm based on machine learning and natural language processing technologies, and the user interface optimization unit is used to continuously optimize the interface design according to user feedback to improve the user experience;

[0150] The virtual-real combination emergency response drill module (106) is used to perform emergency response and decision support for the digital simulation process based on virtual-real combination drills and intelligent decision support;

[0151] The virtual-reality combined emergency response drill module (106) consists of a virtual reality (VR) sub-module, an augmented reality (AR) sub-module, a virtual-reality combined simulation platform, and an emergency plan optimization sub-module; specifically:

[0152] The virtual reality (VR) sub-module is used to create an immersive emergency response drill environment;

[0153] The augmented reality (AR) sub-module is used to superimpose virtual CBRN threats on the actual environment to enhance the practicality of the drill;

[0154] The virtual-reality combined simulation platform is a simulation platform integrating the virtual reality (VR) sub-module and the augmented reality (AR) sub-module, and is used to implement virtual-reality combined emergency drills;

[0155] The emergency plan optimization sub-module is used to optimize the emergency plan through simulation and drills to improve the emergency response ability;

[0156] The virtual experiment platform and data consistency verification module (107) is used to conduct experimental verification on the real-time monitoring data based on the virtual experiment platform and the data consistency verification mechanism, and calibrate the high-precision model and the human health effect simulation model;

[0157] The virtual experiment platform and data consistency verification module (107) consists of a virtual experiment creation sub-module, a data consistency verification sub-module, a virtual experiment algorithm sub-module, and a data cross-validation sub-module; specifically:

[0158] The virtual experiment creation sub-module is used to create a virtual experiment environment, simulate actual experimental conditions, and reduce the cost and risk of actual experiments;

[0159] The data consistency verification sub-module is used to ensure the consistency of experimental data and simulation data based on the data consistency verification mechanism;

[0160] The virtual experiment algorithm sub-module is used to set the simulation algorithm for virtual experiments and simulate laboratory and on-site experimental conditions;

[0161] The data cross-validation sub-module verifies and corrects experimental data and simulation data based on cross-validation technology.

[0162] A third aspect of the present invention provides an electronic device, including a processor and a memory, where the memory stores multiple instructions, and the processor is used to read the instructions and execute the method described in the first aspect.

[0163] A fourth aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores multiple instructions, and the multiple instructions can be read and executed by the processor to execute the method described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0164] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the related art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0165] Figure 1 FIG. is a flowchart of a CBRN environment digital simulation method based on a task-oriented protection state according to an embodiment of the present invention;

[0166] Figure 2 FIG. is an architecture diagram of a CBRN environment digital simulation system based on a task-oriented protection state according to an embodiment of the present invention;

[0167] Figure 3 FIG. is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0168] The following will clearly and completely describe the technical solutions of the present invention with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0169] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and should not be construed as indicating or implying relative importance.

[0170] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0171] Embodiment 1

[0172] See Figure 1 , this embodiment provides a digital simulation method for CBRN environment based on task-oriented protection status, including:

[0173] S1. Based on the simulation model at the micro scale and the simulation model at the macro scale, a high-precision multi-scale model is established by coupling the simulation models at different scales of the micro scale and the macro scale, where the high-precision multi-scale model is used to improve the accuracy of pollutant diffusion and propagation; the model complexity of the high-precision multi-scale model can be dynamically adjusted according to specific application scenarios and real-time data, so as to balance computing resources and simulation accuracy;

[0174] This step S1 is used to implement model simplification and accuracy improvement, so as to solve the technical problem that existing models are simplified too much and cannot accurately capture the behavior of pollutants in complex environments.

[0175] S2. Obtain and process real-time monitoring data for digital simulation of CBRN environment;

[0176] As a preferred implementation manner, the S2 includes:

[0177] S21. Obtain and process real-time monitoring data for digital simulation of CBRN environment, and the real-time monitoring data is multi-source data;

[0178] In this embodiment, the acquisition of real-time monitoring data is realized through a sensor system. However, the accuracy and reliability of sensor data in the prior art are insufficient. Therefore, the sensor system includes a sensor self-calibration module and is configured with redundant sensors; where the sensor self-calibration module has a built-in sensor self-calibration algorithm to improve the accuracy and reliability of sensor data; the configuration of redundant sensors includes: enhancing the reliability of data through multi-sensor data fusion and correction.

[0179] S22. Based on the real-time monitoring data, use machine learning algorithms to process and fuse the multi-source data, so as to improve the accuracy and real-time performance of the data. The multi-source data is integrated meteorological data, terrain data, and pollutant property data.

[0180] S23. Seamlessly integrate the real-time monitoring data into the high-precision multi-scale model through a standardized real-time data interface to realize dynamic update and adjustment of the model complexity of the high-precision multi-scale model;

[0181] This step S2 solves the technical problem of difficult real-time data integration for high-precision models that rely on a large amount of data through the method of data dependence and real-time data integration.

[0182] S3. Build a human health effect simulation model based on the individualized health effect model and the long-term effect simulation model, thereby solving the technical problem that the existing models cannot comprehensively and accurately simulate human health effects.

[0183] As a preferred implementation, the individualized health effect model is established based on the consideration of individual differences. The individualized health effect model is established based on individual physiological data and historical health data, thereby improving the accuracy of the model; the long-term effect simulation model is established by simulating health impacts at different time scales, providing a more comprehensive risk assessment as a long-term exposure health effect simulation model.

[0184] S4. Calculate the high-precision model and the human health effect simulation model based on the distributed computing architecture and edge computing technology, thereby performing digital simulation of the CBRN environment based on the task-oriented protection status;

[0185] Step S4 solves the technical problems of complex calculation and high resource requirements of the high-precision simulation model.

[0186] As a preferred implementation, the distributed computing architecture includes distributing computing tasks to multiple computing nodes to improve computing efficiency; the edge computing technology includes performing data processing and preliminary calculation near the data source to reduce the load and latency of the central computing node.

[0187] As a preferred implementation, the method further includes:

[0188] S5. Display and feedback the simulation results of the CBRN environment digital simulation through the intelligent human-computer interaction interface and the user experience interface;

[0189] Step S5 is used to solve the technical problems of poor interface design and user experience.

[0190] As a preferred implementation: the intelligent human-computer interaction interface combines natural language processing and speech recognition technologies to improve the user's operation experience and efficiency; the feedback is based on the user feedback mechanism: establish a user feedback mechanism and continuously optimize the interface design and interaction method according to user feedback.

[0191] As a preferred implementation, the method further includes:

[0192] S6. Perform emergency response and decision support for the digital simulation process based on virtual-real combined drills and intelligent decision support;

[0193] Step S6 is used to solve the technical problems of insufficient authenticity of emergency response simulation and high complexity of the decision support system.

[0194] As a preferred embodiment, the virtual-real combination drill includes: developing an emergency response drill system that combines virtual reality and augmented reality technologies to improve the authenticity and practicality of simulation; the intelligent decision support includes: using artificial intelligence and big data analysis technologies to develop an intelligent decision support system to provide more accurate and efficient emergency response strategies.

[0195] As a preferred embodiment, the method further includes:

[0196] S7. Based on the virtual experiment platform and the data consistency verification mechanism, conduct experimental verification on the real-time monitoring data, and calibrate the high-precision model and the human health effect simulation model.

[0197] Step S7 is used to solve the technical problems of high experimental costs and inconsistent data.

[0198] As a preferred embodiment, the virtual experiment platform reduces the costs and risks of actual experiments by simulating experimental environments and conditions; the data consistency verification mechanism ensures the consistency of experimental data and simulation data through cross-validation and multi-source data fusion.

[0199] Embodiment 2

[0200] As Figure 2 shown, this embodiment provides a CBRN environment digital simulation system based on task-oriented protection status, including:

[0201] A high-precision multi-scale modeling module 101, which is used to establish a high-precision multi-scale model by coupling the microscopic-scale simulation model and the macroscopic-scale simulation model based on the microscopic-scale simulation model and the macroscopic-scale simulation model, wherein the high-precision multi-scale model is used to improve the accuracy of pollutant diffusion and propagation; the model complexity of the high-precision multi-scale model can be dynamically adjusted according to specific application scenarios and real-time data, so as to balance computing resources and simulation accuracy.

[0202] In this embodiment, the high-precision multi-scale modeling module 101 is composed of a pollution diffusion model sub-module and a radiation propagation model sub-module; among them:

[0203] The pollution diffusion model sub-module is used to couple the microscopic molecular dynamics and the macroscopic hydrodynamics model to accurately simulate the diffusion behavior of chemical and biological pollutants; it has an algorithm for coupling microscopic and macroscopic models and dynamically adjusts the model complexity.

[0204] The radiation propagation model sub-module is used to simulate the propagation of radiation in different media by combining the Monte Carlo method and numerical methods, considering absorption, scattering, and attenuation; an adaptive grid refinement unit is set inside to use adaptive grid refinement in key areas to improve the calculation accuracy.

[0205] The real-time multi-source data fusion module 102 is used to obtain and process real-time monitoring data for digital simulation of the CBRN environment;

[0206] In this embodiment, the real-time multi-source data fusion module 102 is composed of a data acquisition sub-module, a data preprocessing sub-module, a multi-source data fusion sub-module, and a standardized real-time data interface; among them:

[0207] The data acquisition sub-module is composed of an intelligent sensor system, and is internally composed of a sensor self-calibration algorithm unit, a redundant sensor configuration unit, a multi-sensor data fusion algorithm unit, and a sensor fault detection and repair unit; a sensor self-calibration algorithm is set inside the sensor self-calibration algorithm unit to improve the accuracy of sensor data, and the redundant sensor configuration unit enhances the reliability of the sensor system through redundant configuration and data fusion; the multi-sensor data fusion algorithm unit fuses the data of multiple sensors based on algorithms such as Bayesian networks to improve data reliability; a developed sensor fault detection and self-repair algorithm is set inside the sensor fault detection and repair unit to ensure the continuous operation of the system.

[0208] The data preprocessing sub-module is used to preprocess the sensor data to remove noise and outliers;

[0209] The multi-source data fusion sub-module is used to process and fuse the multi-source data based on the real-time monitoring data using machine learning algorithms, and the machine learning algorithms include methods such as Kalman filtering and particle filtering. Fusing the multi-source data includes fusing the multi-source data fusion meteorological data, terrain data, and pollutant property data;

[0210] The standardized real-time data interface is used to seamlessly integrate the real-time monitoring data into the high-precision multi-scale model through the standardized real-time data interface, and to dynamically update and adjust the model complexity of the high-precision multi-scale model.

[0211] The individualized health effect simulation module 103 is used to construct a human health effect simulation model based on the individualized health effect model and the long-term effect simulation model;

[0212] In this embodiment, the individualized health effect simulation module 103 is composed of an individualized model sub-module and a long-term effect simulation sub-module; among them,

[0213] The individualized model sub-module is used to construct the first part of the human health effect simulation model based on the health effect model of individual physiological difference data, in combination with individual physiological data and historical health data; wherein the individual physiological difference data is determined by collecting individual physiological data through channels such as wearable devices;

[0214] The long-term effect simulation sub-module is used to establish a long-term exposure health effect simulation model as the second part of the human health effect simulation model;

[0215] Health effect evaluation algorithms are set inside both the individualized model sub-module and the long-term effect simulation sub-module, and the health effect evaluation algorithm is a health effect evaluation algorithm based on big data analysis and machine learning.

[0216] The distributed and edge computing architecture module 104 is used to calculate the high-precision model and the human health effect simulation model based on the distributed computing architecture and edge computing technology, so as to perform digital simulation of the CBRN environment based on the task-oriented protection status;

[0217] In this embodiment, the distributed and edge computing architecture module 104 is composed of a distributed computing sub-module and an edge computing sub-module; where:

[0218] The distributed computing sub-module is used to distribute computing tasks to multiple computing nodes, communicate using MPI (Message Passing Interface), and the distributed computing sub-module is built-in with a task scheduling algorithm to optimize computing resource allocation;

[0219] The edge computing sub-module deploys edge nodes at key positions, uses local computing capabilities to perform real-time data processing, and performs preliminary calculations and data processing near the data source to reduce the load on the central node.

[0220] The intelligent human-computer interaction interface module 105 is used to display and feedback the simulation results of the CBRN environment digital simulation through the intelligent human-computer interaction interface to the user experience interface;

[0221] In this embodiment, the intelligent human-computer interaction interface module 105 is composed of a natural language processing sub-module and a 3D visualization sub-module; where,

[0222] The natural language processing sub-module includes an intelligent interaction system based on natural language processing, supporting voice and text input;

[0223] The 3D visualization sub-module is used to display the pollutant diffusion and impact using 3D modeling and visualization technologies; the 3D visualization sub-module includes an intelligent interaction algorithm unit and a user interface optimization unit; wherein the intelligent interaction algorithm unit is built-in with an intelligent interaction algorithm based on machine learning and natural language processing technologies, and the user interface optimization unit is used to continuously optimize the interface design according to user feedback to improve the user experience.

[0224] The virtual-reality combined emergency response drill module 106 is used to perform emergency response and decision support for the digital simulation process based on virtual-reality combined drills and intelligent decision support;

[0225] In this embodiment, the virtual-reality combined emergency response drill module 106 is composed of a virtual reality (VR) sub-module, an augmented reality (AR) sub-module, a virtual-reality combined simulation platform, and an emergency plan optimization sub-module; wherein:

[0226] The virtual reality (VR) sub-module is used to create an immersive emergency response drill environment;

[0227] The augmented reality (AR) sub-module is used to superimpose virtual CBRN threats in the actual environment to improve the practicality of the drill;

[0228] The virtual-reality combined simulation platform is a simulation platform integrating the virtual reality (VR) sub-module and the augmented reality (AR) sub-module, and is used to implement virtual-reality combined emergency drills;

[0229] The emergency plan optimization sub-module is used to optimize the emergency plan through simulation and drills to improve the emergency response ability.

[0230] The virtual experiment platform and data consistency verification module 107 is used to perform experimental verification on the real-time monitoring data and calibrate the high-precision model and the human health effect simulation model based on the virtual experiment platform and the data consistency verification mechanism;

[0231] In this embodiment, the virtual experiment platform and data consistency verification module 107 is composed of a virtual experiment creation sub-module, a data consistency verification sub-module, a virtual experiment algorithm sub-module, and a data cross-verification sub-module; wherein:

[0232] The virtual experiment creation sub-module is used to create a virtual experiment environment, simulate actual experimental conditions, and reduce the cost and risk of actual experiments;

[0233] The data consistency verification sub-module is used to ensure the consistency of experimental data and simulation data based on the data consistency verification mechanism;

[0234] The virtual experiment algorithm sub-module is used to set the simulation algorithm of the virtual experiment and simulate the laboratory and field experimental conditions;

[0235] The data cross-validation sub-module checks and corrects experimental data and simulation data based on cross-validation technology.

[0236] Specific implementation technical details include:

[0237] 1. High-precision multi-scale modeling

[0238] Multi-scale pollution diffusion model: Combining models at the microscopic (molecular dynamics) and macroscopic (hydrodynamics) scales to achieve high-precision pollution diffusion simulation.

[0239] Adaptive grid refinement technology: Using adaptive grid refinement in areas with drastic changes in pollution concentration to improve simulation accuracy.

[0240] 2. Real-time multi-source data fusion

[0241] Multi-source data preprocessing and fusion: Using advanced signal processing and machine learning algorithms to preprocess and fuse multi-source data, improving data accuracy and real-time performance.

[0242] Standardized data interface: Developing a standardized data interface to achieve seamless integration of different data sources and support real-time dynamic updates.

[0243] 3. Distributed and edge computing architecture

[0244] Distributed computing architecture: Adopting a distributed computing architecture to distribute computing tasks to multiple nodes and using an efficient task scheduling algorithm to optimize computing resources.

[0245] Edge computing nodes: Deploying edge computing nodes to perform preliminary data processing and computing near the data source, reducing the load and latency of the central node.

[0246] 4. Intelligent sensor system

[0247] Sensor self-calibration algorithm: Developing a sensor self-calibration algorithm to improve the accuracy and reliability of sensor data.

[0248] Redundant sensor configuration and data fusion: Enhancing system reliability and data accuracy through a redundant sensor system and multi-sensor data fusion.

[0249] 5. Intelligent human-computer interaction interface

[0250] Intelligent interaction interface: Combining natural language processing and speech recognition technologies to develop an intelligent human-computer interaction interface, improving the user's operation experience and efficiency.

[0251] 3D visualization technology: Using 3D modeling and visualization technologies to intuitively display pollutant diffusion and impacts, improving the accuracy of user understanding and decision-making.

[0252] 6. Individualized Health Effect Simulation

[0253] Individualized Health Effect Model: Develop a health effect model that takes into account individual physiological differences, combines individual physiological data and historical health data to improve the accuracy of the model.

[0254] Long-term Effect Simulation: Establish a health effect simulation model for long-term exposure, and provide a more comprehensive risk assessment by simulating health impacts at different time scales.

[0255] 7. Virtual-Reality and Reality Combined Emergency Response Drill

[0256] Virtual-Reality and Reality Combined Simulation Platform: Combine virtual reality (VR) and augmented reality (AR) technologies to develop a virtual-reality and reality combined emergency response drill platform, improving the practicality and authenticity of the drill.

[0257] Emergency Plan Optimization: Continuously optimize the emergency plan through simulation and drill to improve the emergency response ability.

[0258] 8. Virtual Experiment Platform and Data Consistency Verification

[0259] Virtual Experiment Platform: Develop a virtual experiment platform to simulate actual experimental conditions, reducing the cost and risk of actual experiments.

[0260] Data Consistency Verification Mechanism: Establish a data consistency verification mechanism to ensure the consistency of experimental data and simulation data through cross-validation and multi-source data fusion.

[0261] The algorithm model and architecture system used in this embodiment

[0262] 1. Multi-scale Pollution Diffusion and Radiation Propagation Model

[0263] Model Coupling Algorithm: The coupling algorithm of the multi-scale pollution diffusion model and the radiation propagation model can dynamically adjust the complexity of the model at different scales.

[0264] Adaptive Grid Refinement Technology: The algorithm and implementation method of using adaptive grid refinement in key areas to improve the simulation accuracy.

[0265] 2. Real-time Multi-source Data Fusion System

[0266] Multi-source Data Preprocessing Algorithm: Signal processing and machine learning algorithms for multi-source data preprocessing.

[0267] Standardized Data Interface Design: Standardized data interfaces and protocols for real-time dynamic data integration.

[0268] 3. Distributed and Edge Computing Architecture

[0269] Distributed computing task scheduling algorithm: An efficient task scheduling algorithm for optimizing computing resource allocation.

[0270] Edge computing node configuration and management: Configuration, management methods, and preliminary data processing algorithms for edge computing nodes.

[0271] 4. Self-calibration and redundant configuration of intelligent sensors

[0272] Sensor self-calibration algorithm: A self-calibration algorithm for improving the accuracy and reliability of sensor data.

[0273] Multi-sensor data fusion algorithm: A data fusion algorithm for redundant sensor systems to enhance system reliability.

[0274] 5. Intelligent human-computer interaction and 3D visualization

[0275] Intelligent human-computer interaction interface design: A design method for intelligent human-computer interaction interfaces combining natural language processing and speech recognition.

[0276] 3D visualization technology: 3D modeling and visualization technology for demonstrating pollutant diffusion and impacts.

[0277] 6. Individualized health effect simulation

[0278] Individualized health effect model: Design and implementation methods for health effect models considering individual physiological differences.

[0279] Long-term effect simulation algorithm: Algorithms and models for simulating long-term exposure health effects.

[0280] 7. Virtual-reality combined emergency response drill system

[0281] Virtual-reality combined simulation platform: Design and implementation methods for virtual-reality combined emergency response drill systems integrating VR and AR technologies.

[0282] Emergency plan optimization algorithm: An emergency plan optimization algorithm based on simulation and drills.

[0283] 8. Virtual experiment platform and data consistency verification

[0284] Virtual experiment simulation algorithm: Simulation algorithms and platform designs for virtual experiments.

[0285] Data consistency verification mechanism: A cross-verification mechanism for verifying and correcting the consistency of experimental data and simulation data.

[0286] The present invention also provides a memory storing multiple instructions for implementing the method as in Embodiment 1.

[0287] As Figure 3As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301. The memory 302 stores multiple instructions that can be loaded and executed by the processor, enabling the processor to execute the method as in Embodiment 1.

[0288] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A CBRN environment digital simulation system based on task-oriented protection state, used to implement a CBRN environment digital simulation method based on task-oriented protection state, characterized in that: include: S1, based on a micro-scale simulation model and a macro-scale simulation model, a high-precision multi-scale model is established by coupling the micro-scale simulation model and the macro-scale simulation model of different scales, wherein the high-precision multi-scale model is used to improve the accuracy of pollutant diffusion and propagation; the model complexity of the high-precision multi-scale model can be dynamically adjusted according to specific application scenarios and real-time data; S2, acquires and processes real-time monitoring data for digital simulation of CBRN environment; S3, construct a human health effect simulation model based on the individual health effect model and the long-term effect simulation model; S4, calculating the high-precision multi-scale model and the human health effect simulation model based on a distributed computing architecture and edge computing technology to perform a digital simulation of the CBRN environment based on a task-oriented protection state; The CBRN environment digital simulation system based on task-oriented protection status includes: A high-precision multi-scale modeling module (101) is used to establish a high-precision multi-scale model based on a micro-scale simulation model and a macro-scale simulation model by coupling the micro-scale simulation model and the macro-scale simulation model of different scales, wherein the high-precision multi-scale model is used to improve the accuracy of pollutant diffusion and propagation; the model complexity of the high-precision multi-scale model can be dynamically adjusted according to specific application scenarios and real-time data; The high-precision multi-scale modeling module (101) is composed of a pollution diffusion model submodule and a radiation propagation model submodule; wherein: The pollution diffusion model submodule is used to couple the microscopic molecular dynamics and macroscopic fluid mechanics models to simulate the diffusion behavior of chemical and biological pollutants with high precision; it has an algorithm for coupling the microscopic and macroscopic models to dynamically adjust the complexity of the model; The radiation propagation model submodule is used to combine the Monte Carlo method and the numerical method to simulate the propagation of radiation in different media, taking into account absorption, scattering and attenuation; an adaptive grid refinement unit is set internally to use adaptive grid refinement in key areas; A real-time multi-source data fusion module (102), used for acquiring and processing real-time monitoring data for digital simulation of CBRN environment; The real-time multi-source data fusion module (102) is composed of a data acquisition submodule, a data preprocessing submodule, a multi-source data fusion submodule and a standardized real-time data interface; wherein: The data acquisition submodule is composed of an intelligent sensor system, which is internally provided with a sensor self-calibration algorithm unit, a redundant sensor configuration unit, a multi-sensor data fusion algorithm unit and a sensor fault detection and repair unit; the sensor self-calibration algorithm unit is internally provided with a sensor self-calibration algorithm to improve the accuracy of sensor data, and the redundant sensor configuration unit enhances the reliability of the sensor system through redundant configuration and data fusion; the multi-sensor data fusion algorithm unit fuses data of multiple sensors based on a Bayesian network algorithm; the sensor fault detection and repair unit is internally provided with a developed sensor fault detection and self-repair algorithm to ensure the continuous operation of the system; The data preprocessing submodule is used to preprocess the sensor data to remove noise and outliers; The multi-source data fusion submodule is used to process and fuse the multi-source data based on the real-time monitoring data using a machine learning algorithm, wherein the machine learning algorithm includes a Kalman filter and a particle filter, and the fusion of the multi-source data includes fusing the multi-source data with meteorological data, terrain data, and pollutant property data; The standardized real-time data interface is used to seamlessly integrate the real-time monitoring data into the high-precision multi-scale model through the standardized real-time data interface, so as to dynamically update and adjust the model complexity of the high-precision multi-scale model; An individualized health effect simulation module (103), used to construct a human health effect simulation model based on the individualized health effect model and the long-term effect simulation model; The individualized health effect simulation module (103) is composed of an individualized model submodule and a long-term effect simulation submodule; wherein, The individualized model submodule is used to construct a health effect model based on individual physiological difference data, combining individual physiological data and historical health data to construct the first part of a human health effect simulation model; wherein the individual physiological difference data is determined by collecting individual physiological data through wearable devices and other channels; The long-term effect simulation submodule is used to establish a long-term exposure health effect simulation model as the second part of the human health effect simulation model; A health effect assessment algorithm is set inside the individualized model submodule and the long-term effect simulation submodule, and the health effect assessment algorithm is a health effect assessment algorithm based on big data analysis and machine learning; A distributed and edge computing architecture module (104), used for calculating the high-precision multi-scale model and the human health effect simulation model based on a distributed computing architecture and edge computing technology, thereby performing a digital simulation of a CBRN environment based on a task-oriented protection state; The distributed and edge computing architecture module (104) is composed of a distributed computing submodule and an edge computing submodule; wherein: The distributed computing submodule is used to distribute computing tasks to multiple computing nodes and use MPI for communication. The distributed computing submodule has a built-in task scheduling algorithm to optimize computing resource allocation; The edge computing submodule deploys edge nodes at key locations, uses local computing power to perform real-time data processing, and performs preliminary calculations and data processing close to the data source; An intelligent human-machine interaction interface module (105) is used to display and provide feedback on the simulation results of the CBRN environment digital simulation through an intelligent human-machine interaction interface and a user experience interface; The intelligent human-machine interaction interface module (105) is composed of a natural language processing submodule and a 3D visualization submodule; wherein, The natural language processing submodule includes an intelligent interaction system based on natural language processing, supporting voice and text input; The 3D visualization submodule is used to display the diffusion and impact of pollutants using 3D modeling and visualization technology; the 3D visualization submodule includes an intelligent interaction algorithm unit and a user interface optimization unit; wherein the intelligent interaction algorithm unit has a built-in intelligent interaction algorithm based on machine learning and natural language processing technology, and the user interface optimization unit is used to continuously optimize the interface design according to user feedback to improve user experience; A virtual-reality combined emergency response drill module (106), used for emergency response and decision support for a digital simulation process based on virtual-reality combined drill and intelligent decision support; The virtual-reality combined emergency response drill module (106) is composed of a virtual reality submodule, an augmented reality submodule, a virtual-reality combined simulation platform and an emergency plan optimization submodule; wherein: The virtual reality submodule is used to create an immersive emergency response drill environment; The augmented reality submodule is used to superimpose virtual CBRN threats in the actual environment to improve the practicality of the exercise; The virtual-reality combined simulation platform is a simulation platform integrating the virtual reality submodule and the augmented reality submodule, and is used to implement virtual-reality combined emergency drills; The emergency plan optimization submodule is used to optimize the emergency plan and improve the emergency response capability through simulation and drills; A virtual experiment platform and data consistency verification module (107), used to perform experimental verification on the real-time monitoring data and perform model calibration on the high-precision multi-scale model and the human health effect simulation model based on the virtual experiment platform and the data consistency verification mechanism; The virtual experiment platform and data consistency verification module (107) is composed of a virtual experiment creation submodule, a data consistency verification submodule, a virtual experiment algorithm submodule and a data cross-validation submodule; wherein: The virtual experiment creation submodule is used to create a virtual experiment environment, simulate actual experiment conditions, and reduce the cost and risk of actual experiments; The data consistency check submodule is used to ensure the consistency of experimental data and simulation data based on the data consistency check mechanism; The virtual experiment algorithm submodule is used to set the simulation algorithm of the virtual experiment and simulate laboratory and field experiment conditions; The data cross-validation submodule verifies and corrects experimental data and simulation data based on cross-validation technology.

2. A CBRN environment digital simulation system based on task-oriented protection status according to claim 1, characterized in that: The S2 includes: S21, acquiring and processing real-time monitoring data for digital simulation of CBRN environment, wherein the real-time monitoring data is multi-source data; the acquisition of the real-time monitoring data is realized by a sensor system, wherein the sensor system includes a sensor self-calibration module and is configured with redundant sensors; wherein the sensor self-calibration module has a built-in sensor self-calibration algorithm to improve the accuracy and reliability of sensor data; and the configuration of redundant sensors includes: enhancing data reliability through multi-sensor data fusion and correction; S22, processing and fusing the multi-source data using a machine learning algorithm based on the real-time monitoring data, the multi-source data being integrated meteorological data, topographic data, and pollutant property data; S23, seamlessly integrating the real-time monitoring data into the high-precision multi-scale model through a standardized real-time data interface, so as to dynamically update and adjust the model complexity of the high-precision multi-scale model.

3. A CBRN environment digital simulation system based on task-oriented protection status according to claim 2, characterized in that: The individualized health effect model is established based on individual differences and is based on individual physiological data and historical health data; the long-term effect simulation model is established by simulating health effects at different time scales, and provides a more comprehensive risk assessment as a long-term exposure health effect simulation model.

4. A CBRN environment digital simulation system based on task-oriented protection status according to claim 3, characterized in that: The distributed computing architecture includes allocating computing tasks to multiple computing nodes; the edge computing technology includes performing data processing and preliminary calculations close to the data source.

5. A CBRN environment digital simulation system based on task-oriented protection status according to claim 4, characterized in that: The method further comprises: S5, displaying and feeding back the simulation results of the digital simulation of the CBRN environment through an intelligent human-computer interaction interface and a user experience interface; the intelligent human-computer interaction interface combines natural language processing and speech recognition technology to improve the user's operating experience and efficiency; the feedback is based on a user feedback mechanism: establish a user feedback mechanism, and continuously optimize the interface design and interaction method according to user feedback.

6. A CBRN environment digital simulation system based on task-oriented protection status according to claim 5, characterized in that: The method further comprises: S6, emergency response and decision support for the digital simulation process based on virtual-reality combined drills and intelligent decision support; the virtual-reality combined drills include: developing a virtual-reality combined emergency response drill system based on virtual reality and augmented reality technology; the intelligent decision support includes: developing an intelligent decision support system using artificial intelligence and big data analysis technology.

7. A CBRN environment digital simulation system based on task-oriented protection status according to claim 6, characterized in that: The method further comprises: S7, based on the virtual experiment platform and the data consistency verification mechanism, the real-time monitoring data is experimentally verified, and the high-precision multi-scale model and the human health effect simulation model are calibrated; the virtual experiment platform reduces the cost and risk of actual experiments by simulating the experimental environment and conditions; the data consistency verification mechanism ensures the consistency of experimental data and simulation data through cross-validation and multi-source data fusion.

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