Intelligent early warning method for pollution process by fusing atmospheric numerical simulation

Abstract

This invention relates to the field of air pollution early warning technology, and discloses an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation. The method includes: acquiring multi-source monitoring data, meteorological forecast data, and enterprise emission data; constructing a spatial distribution model of pollution concentration, identifying pollution clusters, and establishing their correlation with emission sources; identifying pollution accumulation, transmission and diffusion, and dissipation stages based on the spatiotemporal evolution characteristics of pollution; generating a high-precision initial pollution field by integrating data assimilation technology and spatial interpolation methods; inputting the initial field into a lightweight atmospheric diffusion model and combining it with a deep learning surrogate model to achieve rapid simulation and prediction of the pollution diffusion process; and generating graded and regional early warning information based on the simulation prediction results and pollution evolution stages. This invention improves prediction accuracy and early warning timeliness by integrating data assimilation and numerical simulation, while reducing computational costs, thus achieving intelligent identification and accurate early warning of pollution processes.

Description

Technical Field

[0001] This invention relates to the field of air pollution early warning technology, and more specifically, to an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation. Background Technology

[0002] With the continuous advancement of ecological and environmental governance, air pollution prevention and control has gradually shifted from end-of-pipe treatment to source control and whole-process management. To achieve early detection, early warning, and early intervention of pollution incidents, accurate prediction and intelligent early warning of air pollution processes have become important development directions in the field of environmental monitoring. Currently, the atmospheric environmental monitoring system mainly consists of fixed air quality monitoring stations, mobile monitoring equipment, remote sensing monitoring platforms, and meteorological observation systems. It can achieve real-time monitoring of pollutant concentrations, meteorological parameters, and regional environmental conditions, providing basic data support for environmental management.

[0003] In existing technologies, air pollution early warning mainly relies on historical monitoring data and meteorological forecast information. Statistical analysis models, empirical models, or traditional machine learning algorithms are used to predict future air quality trends, and corresponding levels of early warning information are issued based on the prediction results. However, because the formation and evolution of air pollution are influenced by multiple factors such as pollution emissions, meteorological conditions, regional transport, and topography, they exhibit significant spatiotemporal coupling and nonlinear characteristics. Traditional statistical models struggle to accurately depict the evolution of complex pollution processes, and their prediction accuracy is limited in scenarios involving regionally transported pollution, continuous pollution accumulation, and sudden pollution events, making it difficult to meet the needs of refined environmental management.

[0004] However, to improve predictive capabilities, current research widely employs atmospheric numerical simulation techniques. These techniques involve constructing atmospheric dynamics models, pollutant diffusion models, and chemical reaction models to simulate and analyze the generation, transport, diffusion, and transformation processes of pollutants, thereby predicting the spatiotemporal distribution of future pollutant concentrations. While these methods possess strong capabilities to explain physical mechanisms and reflect the evolution of pollution processes, they suffer from problems such as high computational load, long model runtimes, and sensitivity to input parameters due to the need to solve numerous complex partial differential equations. This results in a low update frequency of prediction results, making it difficult to meet the application requirements of real-time or near-real-time early warning scenarios.

[0005] Meanwhile, with the development of artificial intelligence technology, data-driven models such as deep learning, graph neural networks, and long short-term memory networks are gradually being applied to the field of air quality prediction. These methods can automatically learn pollution change patterns from massive amounts of historical data, demonstrating good predictive ability in short-term prediction tasks. However, due to the lack of constraints on physical mechanisms such as atmospheric diffusion, pollution transport, and chemical reactions, these models are prone to increased prediction bias, decreased stability, and insufficient generalization ability when facing complex meteorological conditions, abnormal pollution events, or scenarios with insufficient training data coverage.

[0006] Furthermore, most existing air pollution early warning systems rely on pollutant concentration predictions for threshold determination and level classification, focusing only on changes in pollution levels and lacking the ability to identify and analyze the evolution stages of pollution processes. They cannot effectively distinguish between different process characteristics such as pollution accumulation, regional transport, peak emission, and pollution dissipation stages. Simultaneously, early warning information is typically released using uniform standards, lacking comprehensive analysis of regional differences, pollution sources, and development trends. This makes it difficult to provide environmental regulatory departments with targeted pollution prevention and control decision-making basis, thus limiting the effectiveness of early warning systems in refined environmental management.

[0007] Therefore, how to construct an atmospheric pollution prediction and early warning method that can integrate atmospheric numerical simulation and artificial intelligence prediction technologies, take into account both physical mechanism constraints and data-driven advantages, and achieve dynamic identification and intelligent early warning of pollution processes has become an urgent technical problem to be solved in this field. Summary of the Invention

[0008] In view of this, the present invention proposes an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation, aiming to solve the problems of existing atmospheric pollution early warning methods lacking the ability to intelligently identify the evolution stages of pollution processes, failing to integrate the advantages of physical mechanism constraints and data-driven approaches, and having insufficient precision in early warning information.

[0009] This invention proposes an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation, comprising: Acquire multi-source monitoring data, meteorological forecast data, and enterprise emission data for the target area; A spatial distribution model of regional pollution concentrations was constructed based on multi-source monitoring data, and the spatial correlation between pollution cluster areas and key emission sources was determined based on the regional pollution concentration spatial distribution model. The corresponding pollution evolution stage type is determined based on multi-source monitoring data, meteorological forecast data, and enterprise emission data; High-precision initial field data is generated by fusing multi-source monitoring data using a combination of data assimilation algorithm and spatial interpolation method. High-precision initial field data are input into a lightweight atmospheric diffusion model to simulate pollution diffusion, and a deep learning proxy model is used to accelerate the simulation calculation process. Based on the results of pollution diffusion simulation and information on the stages of pollution evolution, differentiated early warning information is generated according to different levels and regions.

[0010] Furthermore, multi-source monitoring data includes at least satellite remote sensing inversion data, ground monitoring station data, mobile monitoring data, and lidar detection data; Furthermore, meteorological forecast data should include at least wind field data, temperature data, humidity data, air pressure data, and boundary layer height data; Furthermore, enterprise emission data should include at least emission source location data, emission pollutant type data, emission rate data, and emission height data.

[0011] Furthermore, when constructing a spatial distribution model of regional pollution concentrations based on multi-source monitoring data, the following are included: Atmospheric optical thickness data and aerosol optical thickness data of the target area were obtained based on multi-source satellite remote sensing data, and an inversion relationship between aerosol optical thickness and pollutant concentration was established based on historical monitoring concentration data. Determine the baseline pollution concentration values ​​at each spatial location in the target area at corresponding times based on the inversion relationship; The baseline pollution concentration values ​​were corrected based on the aerosol extinction coefficient to characterize the impact of atmospheric particulate matter on the light propagation process. The pollutant concentration is attenuated and corrected based on the pollutant emission source intensity attenuation factor in order to characterize the concentration change characteristics of pollutants during the propagation and diffusion process. Based on the corrected and attenuated pollution concentration results, a spatial distribution model of pollution concentration in the target area is generated to obtain the distribution of pollutant concentration at each spatial location in the target area at the corresponding time.

[0012] Furthermore, based on the combined data assimilation algorithm and spatial interpolation method, multi-source monitoring data are fused to generate high-precision initial field data, including: Error analysis of the observation data at each monitoring point is performed based on the Kalman filter algorithm to estimate the corresponding observation error covariance matrix in order to determine the credibility weight of different monitoring data sources. By spatially interpolating and fusing satellite remote sensing data with ground monitoring station data, monitoring blind spots and data gaps can be filled in to obtain a continuous spatially distributed pollution concentration field. By combining meteorological forecast data, the fused pollution concentration field is corrected for temporal consistency in order to eliminate the deviation between data at different time scales and generate high-precision initial field data with spatiotemporal consistency. Quality control is performed on the high-precision initial field data by comparing the fused concentration value at each spatial location with the average concentration of its spatially adjacent nodes, where: When the difference between the fusion concentration value and the average concentration value is less than the preset outlier threshold, the fusion concentration value is determined as the final initial field concentration value after quality control. When the difference between the fused concentration value and the average concentration value is greater than or equal to the preset outlier threshold, the fused concentration value is determined to be an outlier, and it is replaced and corrected based on the average concentration of adjacent nodes in space to obtain the final initial field concentration value after quality control.

[0013] Furthermore, when high-precision initial field data is input into a lightweight atmospheric diffusion model for pollution diffusion simulation, and the simulation calculation process is accelerated using a deep learning proxy model, the following steps are taken: The traditional atmospheric diffusion model is structurally simplified and its parameters are reduced in dimensionality, while retaining the core diffusion and transport modules. A deep learning proxy model is trained based on historical simulation data and actual observation data to establish a nonlinear mapping relationship between input parameters and diffusion results. During the operation of the lightweight atmospheric diffusion model, the computationally complex convection and diffusion terms are replaced with the prediction results of the deep learning proxy model. Select the appropriate diffusion parameters based on the type of pollution. For particulate matter diffusion, use the gravity settling correction coefficient, and for gaseous pollutants such as ozone, use the photochemical attenuation coefficient.

[0014] Furthermore, when determining the corresponding pollution evolution stage type based on multi-source monitoring data, meteorological forecast data, and enterprise emission data, the following are included: Atmospheric diffusion conditions are determined based on wind field information and boundary layer height changes in meteorological forecast data; the trend of emission source intensity changes is determined based on the time-series changes in enterprise emission data. Determine the stage of the pollution process based on the spatiotemporal evolution characteristics of pollution concentration; Based on the pollution process stage identification model, the corresponding pollution evolution stage is determined by combining diffusion conditions, changes in emission intensity, and concentration evolution characteristics. Among them, the pollution accumulation stage corresponds to the characteristic of continuous increase in concentration under calm and stable weather conditions, the regional transmission stage corresponds to the characteristic of high-value areas moving along the wind direction under the condition of prevailing wind shear, and the pollution dissipation stage corresponds to the characteristic of gradual decrease in concentration under the condition of improved diffusion conditions.

[0015] Furthermore, when generating differentiated early warning information by level and region based on pollution diffusion simulation results and pollution evolution stage information, the following are included: The warning level is determined based on the predicted pollution concentration threshold, where: A yellow alert is issued when the hourly concentration exceeds 150 μg / m³. 3 ; An orange alert corresponds to an hourly concentration exceeding 250 μg / m³. 3 ; A red alert is issued when the hourly concentration exceeds 350 μg / m³. 3 ; The early warning issuance strategy is adjusted according to the type of pollution evolution stage, including: Strengthen regional joint prevention and control measures during the accumulation phase, enhance upstream and downstream collaborative early warning during the transmission phase, and gradually lift control measures during the dissipation phase. Based on the results of pollution source analysis, key control areas are identified, and early warning information is issued to the corresponding areas.

[0016] Furthermore, when selecting the appropriate diffusion parameter configuration based on the type of pollution, the following should be included: Obtain pollutant type information and classify pollutants into particulate pollutants and gaseous pollutants; When the pollutant is particulate matter, the corresponding gravity settling correction coefficient is calculated based on particulate matter density, particulate matter size, gravitational acceleration, Cunningham correction factor and aerodynamic viscosity to characterize the settling and diffusion characteristics of particulate matter under gravity. The gravity settling correction coefficient is positively correlated with particulate density, particulate size square, gravitational acceleration and Cunningham correction coefficient, and negatively correlated with aerodynamic viscosity. The particulate matter diffusion process is corrected based on the gravity settling correction coefficient to obtain particulate matter pollution diffusion parameters; When the pollutant is a gaseous pollutant, the photochemical attenuation coefficient is determined based on the solar radiation intensity, nitrogen oxide concentration, and volatile organic compound concentration. The photochemical attenuation coefficient is used to characterize the attenuation features of gaseous pollutants during the photochemical reaction process, and corresponding gaseous pollutant diffusion parameters are generated. Construct a corresponding pollution diffusion parameter configuration scheme based on the particulate matter pollution diffusion parameters or gaseous pollution diffusion parameters.

[0017] Furthermore, this also includes forming a closed-loop optimization, specifically: Real-time acquisition of multi-source monitoring data and meteorological forecast data; updating of initial field data. The updated initial field data is input into the lightweight atmospheric diffusion model for rolling prediction; Obtain the deviation data between the actual observation data and the prediction results, and feed it back to the data assimilation module to correct the assimilation parameters; To obtain data on the effectiveness of environmental management after the issuance of early warnings, assess the effectiveness of early warnings, and optimize the parameters of the early warning decision-making model; The environmental management effectiveness data includes the implementation of control measures, response to changes in pollution concentration, and public health impact assessment data.

[0018] Furthermore, it also includes verifying the effectiveness of early warning data, including: If the deviation between the predicted concentration and the actual observed concentration exceeds a preset threshold, the observation weight of the data assimilation module will be increased. If the accuracy of identifying pollution evolution stages decreases, more feature parameters should be introduced into the stage identification model. If the warning level does not match the actual pollution level, the warning threshold will be adjusted based on historical statistical deviations.

[0019] Compared with existing technologies, the beneficial effects of this invention are as follows: By assimilating and fusing multi-source monitoring data with meteorological forecast data, high-precision construction of the initial pollution field is achieved, providing accurate boundary conditions for model prediction; the fusion architecture of a lightweight atmospheric diffusion model and a deep learning proxy model significantly reduces computational complexity while ensuring physical mechanism constraints, enabling high-frequency operation; the pollution evolution stage identification method enables intelligent discrimination of different processes such as pollution accumulation, regional transmission, and gradual dissipation, providing stage-specific characteristics for refined early warning; the graded and regional differentiated early warning generation mechanism releases targeted early warning information based on the pollution characteristics and evolution stages of different regions, improving the practical value of early warning information; the closed-loop optimization mechanism of "data acquisition—assimilation and fusion—model prediction—stage identification—early warning release—effect evaluation" continuously improves the accuracy and reliability of the prediction and early warning system; and by integrating the advantages of both physical mechanisms and data-driven approaches, a technical system with both interpretability and predictive capabilities is formed, providing a scientific basis for environmental management decisions. Through the above processing, an integrated technical system can be realized, from satellite identification of pollution clusters to multi-source monitoring and location of polluting enterprises, and then to numerical simulation prediction of pollution evolution. This will promote the transformation of air pollution early warning from "post-event response" to "pre-event warning", and significantly improve the foresight and initiative of environmental management. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation, provided as an embodiment of the present invention; Figure 2 This is a flowchart illustrating an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation, as provided in an embodiment of the present invention. Detailed Implementation

[0021] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] In some embodiments of the present invention, see Figure 1-2 As shown, this invention proposes an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation, comprising: S100: Acquire multi-source monitoring data, meteorological forecast data, and enterprise emission data for the target area.

[0023] S200: Construct a spatial distribution model of regional pollution concentration based on multi-source monitoring data, and determine the spatial correlation between pollution cluster areas and key emission sources.

[0024] S300: Determine the corresponding pollution evolution stage type based on multi-source monitoring data, meteorological forecast data, and enterprise emission data.

[0025] S400: Based on the combination of data assimilation algorithm and spatial interpolation method, multi-source monitoring data are fused and processed to generate high-precision initial field data.

[0026] S500: Inputs high-precision initial field data into a lightweight atmospheric diffusion model to simulate pollution diffusion, and accelerates the simulation calculation process through a deep learning proxy model.

[0027] S600: Generate differentiated early warning information by level and region based on pollution diffusion simulation results and pollution evolution stage information.

[0028] Specifically, this embodiment provides an intelligent early warning method for pollution processes that integrates atmospheric numerical simulation, applicable to pollution process prediction and intelligent early warning when there is an atmospheric pollution risk in the target area. This invention executes the method of this embodiment through data interaction between multi-source monitoring equipment, a meteorological forecasting system, an enterprise emission monitoring platform, and a data processing center. The observation data from the multi-source monitoring equipment is obtained through a data acquisition interface, a signal transmission channel, a data verification module, and a quality control module. It should be noted that the improvement of this invention does not focus on the hardware structure of the multi-source monitoring equipment, the meteorological forecasting system, or the enterprise emission monitoring platform itself, but rather on the intelligent fusion of pollution data, the dynamic identification of process evolution, and the accurate generation of early warning information.

[0029] Before issuing a pollution warning, multi-source monitoring data, meteorological forecast data, and enterprise emission data for the target area are acquired. Multi-source monitoring data includes satellite remote sensing inversion data, ground monitoring station data, mobile monitoring data, and lidar detection data. Satellite remote sensing inversion data may include atmospheric optical thickness data, aerosol optical thickness data, pollutant optical characteristic data, and cloud mask data. Aerosol optical thickness data can be used to characterize the aerosol load of the entire atmosphere; a higher aerosol optical thickness value indicates a higher aerosol concentration. Ground monitoring station data may include PM2.5 concentration data, PM10 concentration data, SO2 concentration data, NO2 concentration data, CO concentration data, and O3 concentration data. PM2.5 concentration data can be used to characterize the degree of fine particulate matter pollution, and PM10 concentration data can be used to characterize the degree of inhalable particulate matter pollution. Mobile monitoring data includes monitoring vehicle travel path data, real-time pollutant concentration data, and GPS positioning data. Lidar detection data includes atmospheric boundary layer height data, aerosol vertical distribution data, and extinction coefficient profile data. Meteorological forecast data includes wind field data, temperature data, humidity data, air pressure data, and boundary layer height data. Wind field data may include wind speed, wind direction, and turbulence intensity data. Temperature data may include surface temperature, vertical temperature gradient, and temperature advection data. Humidity data may include relative humidity, absolute humidity, and dew point temperature data. Air pressure data may include sea level pressure, geopotential height, and pressure gradient data. Enterprise emission data includes emission source location data, pollutant type data, emission rate data, and emission height data. Emission source location data includes enterprise geographic coordinates, emission outlet coordinates, and plant boundary data. Pollutant type data includes particulate matter emission type data, gaseous pollutant emission type data, and volatile organic compound (VOC) emission type data. Emission rate data includes hourly emissions, daily emissions, and annual average emissions. Emission height data includes chimney height, emission lift height, and effective emission height data.

[0030] When constructing a spatial distribution model of regional pollution concentration based on multi-source monitoring data, it includes two steps: establishing the inversion relationship between pollutant concentration and optical depth (AOD) and constructing the spatial distribution model. First, atmospheric optical depth and aerosol optical depth (AOD) of the target area are obtained based on multi-source satellite remote sensing data, and an inversion relationship between pollutant concentration and AOD is established. Satellite remote sensing technology can acquire large-scale aerosol optical depth (AOD) information. AOD is a physical quantity characterizing the ability of aerosol particles to extinct solar radiation and is positively correlated with aerosol concentration. This invention achieves the inversion of pollutant concentration through a pre-established AOD-PM2.5 calibration curve. The calibration curve is established based on historical synchronous observation data and fitted using the least squares method or machine learning method. In some embodiments, the regional pollution concentration spatial distribution model can be expressed as: ; in, Indicates position At the moment The concentration of pollutants, in units of ; Indicates position At the moment The aerosol optical thickness is dimensionless. This represents the aerosol extinction coefficient, with units of . This reflects the extinction ability of aerosols per unit concentration to light; This represents the attenuation factor of pollutant emission source intensity, is dimensionless, and is used to correct for the effect of changes in emission source intensity on concentration. Aerosol extinction coefficient. The calibration data can be determined by regression analysis using pre-collected calibration data, which includes synchronous satellite remote sensing data and ground monitoring station data under different meteorological conditions, seasons, and pollution levels.

[0031] Spatial correlation analysis can be used to determine the spatial correlation between pollution clusters and key emission sources. Spatial correlation reflects the degree of spatial coupling between high-pollution areas and emission sources. The spatial correlation established in this invention can be expressed as follows: ; in, Indicates the first The first polluted grid and the first Spatial correlation coefficients among emission sources; Indicates the first Concentration time series of each pollution grid; Indicates the first Time series of emission intensity from individual emission sources; Represents covariance operation; and These represent the corresponding standard deviations. The closer the spatial correlation coefficient is to 1, the greater the contribution of the emission source to the pollution grid. In some embodiments, the spatial correlation can be dynamically corrected in conjunction with meteorological conditions. When the prevailing wind direction is from the emission source to the pollution grid, the weight of the correlation coefficient is increased; when the prevailing wind direction is in the opposite direction, the weight of the correlation coefficient is decreased.

[0032] Determining the corresponding pollution evolution stage type based on multi-source monitoring data, meteorological forecast data, and enterprise emission data involves four steps: diffusion condition assessment, emission intensity change analysis, concentration evolution characteristic extraction, and comprehensive stage type determination. First, atmospheric diffusion conditions are assessed based on wind field information and boundary layer height changes in meteorological forecast data. Wind field information includes wind speed, wind direction, and turbulence intensity. When wind speed is lower than... The wind conditions were calm, and the atmospheric diffusion capacity was weak; when the wind speed was... to The conditions between these points represent moderate diffusion; when the wind speed is higher than [a certain value], the diffusion conditions are moderate. This is under conditions of strong diffusion. Boundary layer height data reflects the thickness of the atmospheric boundary layer; the boundary layer height is lower than... During a stable stratification, the vertical diffusion of pollutants is suppressed; the boundary layer height is... to The boundary layer is in a moderately stable state between these points; the boundary layer height is higher than [the specified value]. The current unstable stratification facilitates the vertical diffusion of pollutants. Secondly, the temporal changes in enterprise emission data are used to determine the trend of emission source intensity. Emission source intensity trends can be categorized as increasing, stable, and decreasing. When the hourly emission rate increases by more than [a certain percentage] compared to the historical average, [the following trend is observed]. The trend is strengthening, and the reduction exceeds [a certain percentage]. If the trend is weakening, it indicates a decreasing trend; otherwise, it indicates a stable trend. Next, the stage of the pollution process is determined based on the spatiotemporal evolution characteristics of pollution concentration. These characteristics include concentration level, spatial distribution pattern, and rate of change. Finally, based on the pollution process stage identification model, combined with diffusion conditions, changes in emission intensity, and concentration evolution characteristics, the corresponding pollution evolution stage is determined. In some embodiments, the rules for determining the pollution evolution stage type are shown in the table below: Through the above multi-dimensional analysis, the present invention can dynamically determine the pollution evolution stage type based on real-time monitoring data and forecast data of the target area, realize intelligent identification of pollution processes, and provide stage feature support for refined early warning.

[0033] The process of fusing multi-source monitoring data using a combination of data assimilation algorithms and spatial interpolation methods to generate high-precision initial field data involves four stages: observation error covariance estimation, spatial interpolation fusion, temporal consistency correction, and quality control. First, the observation error covariance matrix for each monitoring point is estimated using a Kalman filter algorithm. Different monitoring devices exhibit varying observation accuracies; satellite remote sensing data has high spatial resolution but suffers from inversion errors, ground monitoring station data has high accuracy but limited spatial coverage, and mobile monitoring data offers flexibility but limited representativeness. This invention employs an adaptive Kalman filter method to estimate the observation error variance for each monitoring point using historical observation data. The observation error covariance matrix can be expressed as: in, Indicates time The observation error covariance matrix; Indicates time The Kalman gain matrix; Represents the observation matrix; Indicates time The predicted covariance matrix; This represents the process noise covariance matrix. In some embodiments, the Kalman gain matrix is ​​used. It is possible Calculation, where To observe the noise covariance matrix.

[0034] Satellite remote sensing data and ground monitoring station data are spatially interpolated and fused to fill in data gaps. The spatial interpolation method used is Kriging interpolation, which automatically determines interpolation weights based on the known spatial correlation of monitoring points, thus achieving optimal unbiased estimation. The Kriging interpolation formula can be expressed as: in, Indicates position Interpolation concentration at the location; Indicates the first The observed concentration at each monitoring point; Indicates the first Kriging weights for each monitoring point; This indicates the number of monitoring points. The Kriging weights are obtained by solving the Kriging equations, which take into account the spatial correlation between monitoring points and observation errors.

[0035] The fusion results are corrected for temporal consistency based on meteorological forecast data to generate high-precision initial field data with spatiotemporal consistency. The temporal consistency correction considers the temporal continuity of pollution concentration, ensuring that concentration values ​​between adjacent times should not exhibit abrupt changes. This invention employs a temporal smoothing filter to correct the fusion results; the smoothing formula can be expressed as: ; in, Indicates position At the moment The final initial field concentration value; This represents the concentration value after spatial interpolation and fusion. The time consistency weight is typically set to 1. to ; This represents the interpolated concentration value from the previous time step. Quality control of the high-precision initial field data is performed according to the formula to filter out outliers: ; in, Indicates position At the moment Final initial field concentration value after quality control; This represents the average concentration of adjacent nodes in space; This represents the threshold for outlier detection, typically set to [value]. or the average value of adjacent nodes The larger of the two values ​​is taken. This process effectively fills in the data gaps and improves the spatial integrity and temporal consistency of the initial field data.

[0036] When high-precision initial field data is input into a lightweight atmospheric diffusion model for pollution diffusion simulation, and the simulation calculation process is accelerated using a deep learning surrogate model, the process includes four steps: lightweight model construction, surrogate model training, accelerated computation implementation, and parameter configuration optimization. First, the traditional atmospheric diffusion model is simplified in structure and its parameters are reduced in dimensionality, retaining the core diffusion and transport modules. Traditional atmospheric diffusion models are based on Euler or Lagrange equations and contain complex chemical reaction modules, gas-phase dynamics modules, and aerosol microphysics modules, resulting in long computation times. This invention retains the core convective diffusion and transport modules, simplifies or omits secondary chemical transformation modules, forming a lightweight atmospheric diffusion model. The governing equations of the lightweight model can be expressed as: ; in, Indicates pollutant concentration; Indicates time; Indicates wind speed at Components in three directions; Indicates the turbulent diffusion coefficient; This represents the source term. In some embodiments, the turbulent diffusion coefficient can be dynamically calculated based on the boundary layer height and the Richardson number, where the Richardson number is... The calculation formula is ,in It is the acceleration due to gravity. For potential temperature, This represents the potential temperature gradient.

[0037] A deep learning proxy model is trained based on historical simulation data and actual observation data to establish a nonlinear mapping relationship between input parameters and diffusion results. The deep learning proxy model employs a convolutional neural network or long short-term memory network architecture. Input parameters include wind field data, temperature field data, humidity field data, initial concentration field data, and boundary layer height data; the output is the predicted concentration field data for future times. In some embodiments, the network structure of the proxy model may include… Convolutional layers Fully connected layers and Output layer, convolutional layer uses The convolution kernel and activation function used are ReLU functions.

[0038] In the operation of the lightweight atmospheric diffusion model, the computationally complex convection and diffusion terms are replaced with the prediction results of a deep learning surrogate model. Specifically, after each time step iteration, the lightweight model inputs the current wind field, temperature field, and concentration field data into the deep learning surrogate model. The surrogate model directly outputs the predicted concentration field value for the next time step, thus skipping the complex numerical solution process and accelerating computation. In some embodiments, the prediction results of the deep learning surrogate model and the calculation results of the lightweight model are weighted and fused. The fusion formula can be expressed as: ; in, This indicates the output concentration of the mixture model; This represents the predicted concentration of the deep learning agent model; This represents the calculated concentration from the lightweight atmospheric diffusion model; The fusion coefficient is usually set to 1. to The value is determined based on the principle of minimizing the prediction error of historical verification data.

[0039] The appropriate diffusion parameters are configured according to the type of pollution. For particulate matter diffusion, a gravity settling correction factor is used, while for gaseous pollutants such as ozone, a photochemical attenuation factor is used. The diffusion behavior of particulate matter in the atmosphere is significantly influenced by gravity settling; therefore, a gravity settling correction factor is used. The calculation formula is: ; in, Indicates particulate matter density; Indicates the diameter of the particles; Represents gravitational acceleration; This represents the Cunningham slip correction factor; Indicates aerodynamic viscosity. For diameters smaller than... For particulate matter with a diameter greater than [missing information], gravitational settling is relatively weak; For particulate matter, gravity deposition is significantly enhanced, necessitating the inclusion of a gravity deposition correction term in the model. The atmospheric lifetime of gaseous pollutants such as ozone is affected by photochemical decay, and the photochemical decay coefficient... It is related to solar radiation intensity, nitrogen oxide concentration, and volatile organic compound concentration, and can be calculated using empirical formulas or photochemical mechanism models.

[0040] Generating differentiated early warning information by level and region based on pollution diffusion simulation results and pollution evolution stages involves four steps: determining the warning level, adjusting the warning strategy, identifying key areas, and disseminating information through multiple channels. First, the warning level is determined based on the predicted pollution concentration threshold. The warning levels are divided into four levels: blue, yellow, orange, and red. A blue warning corresponds to an hourly concentration exceeding [a certain threshold]. A yellow alert corresponds to an hourly concentration exceeding [a certain value]. An orange alert corresponds to an hourly concentration exceeding [a certain threshold]. A red alert corresponds to an hourly concentration exceeding [a certain threshold]. In different embodiments, the specific value of the warning threshold can be appropriately adjusted based on the environmental quality standards, historical pollution levels, and health risk assessment results of the target area.

[0041] The early warning issuance strategy is adjusted according to the type of pollution evolution stage. When pollution is in the accumulation stage, regional joint prevention and control measures are strengthened, and control recommendations for key emission sources are added to the early warning information, requiring relevant enterprises to take emission reduction measures, and regional emergency response is activated if necessary. When pollution is in the transmission stage, upstream and downstream coordinated early warning is strengthened, pollution transmission early warning information is notified to upstream areas, downstream areas are advised to prepare for prevention, and pollution transmission paths are tracked to identify key transmission channels. When pollution is in the dissipation stage, control measures are gradually lifted, the early warning information indicates that the pollution situation is improving, the assessment is made on whether the emergency response status can be lifted, and production and daily life are guided to gradually return to normal.

[0042] Based on the pollution source apportionment results, key control areas are identified, and early warning information is issued to the corresponding areas. Pollution source apportionment employs source apportionment models or source contribution analysis methods to identify key emission sources and key industries that significantly contribute to pollution in the target area. In some embodiments, the pollution source apportionment results can be represented as a source contribution matrix, with matrix elements... Indicates the first The emission source for the first The pollution contribution ratio of each monitoring point or grid. The rule for determining key control areas is: the source contribution ratio exceeds... The regions where the emission sources are located, and where the cumulative contribution exceeds [a certain percentage]. The regions where the top few emission sources are located. When issuing targeted early warning information to the corresponding regions, the early warning information is sent to the management departments and relevant enterprises in the key control areas through mobile push, SMS notification or broadcast.

[0043] Early warning information will be disseminated through multiple channels, including sending detailed information to management departments and issuing health protection tips to the public. Detailed information includes the spatiotemporal distribution map of predicted pollution concentrations, the basis for judging the stages of pollution evolution, a list of key control areas, and recommended control measures, for management departments' decision-making reference. Health protection tips include public education information on the health impacts of pollution, recommendations for outdoor activity protection, health protection measures for sensitive groups, and recommendations for stockpiling protective equipment, for the public's self-protection reference. In some embodiments, health protection tips may be released in a tiered manner, referring to the table below: The closed-loop optimization process includes four stages: real-time data update, rolling forecast, deviation feedback, and effect evaluation. In the S1 real-time data update stage, multi-source monitoring data and meteorological forecast data are collected to update the initial field data. The data update frequency is determined based on the monitoring equipment capabilities; ground monitoring station data is typically updated hourly, satellite remote sensing data is typically updated daily, and meteorological forecast data is typically updated weekly. Hour or per Updated hourly. During the S2 rolling forecast phase, the updated initial field data is input into the lightweight atmospheric diffusion model for rolling forecasts. The time window for rolling forecasts is typically [time window missing]. to The rolling forecast interval is typically one hour. In the S3 bias feedback phase, the bias data between the actual observed data and the forecast results is acquired and fed back to the data assimilation module to correct the assimilation parameters. The bias data is used to calculate the forecast error, and the formula for calculating the forecast error is... ; in This represents the actual observed concentration. To predict concentrations for the model. In the S4 effectiveness evaluation phase, environmental management effectiveness data after the issuance of the warning is acquired to assess the effectiveness of the warning and optimize the parameters of the warning decision-making model. Environmental management effectiveness data includes the implementation status of control measures, response to changes in pollution concentration, and public health impact assessment data. The implementation status of control measures includes indicators such as the implementation rate of enterprise emission reduction measures, the implementation rate of vehicle restriction measures, and the shutdown rate of construction sites; the response to changes in pollution concentration includes indicators such as the magnitude of concentration reduction and the time to achieve compliance after the issuance of the warning; and the public health impact assessment data includes indicators such as changes in the number of people seeking medical treatment for respiratory diseases and changes in the sales volume of protective equipment in pharmacies.

[0044] When verifying the effectiveness of early warning data, if the deviation between the predicted concentration and the actual observed concentration exceeds a preset threshold, the observation weight of the data assimilation module will be increased. The preset threshold can be set to... That is, if the deviation between the predicted concentration and the actual observed concentration exceeds [a certain value], If the accuracy of pollution evolution stage identification decreases, more feature parameters are introduced into the stage identification model. The stage identification accuracy is evaluated using a confusion matrix method; if the accuracy is lower than [a certain value], [further details are needed]. If the warning level does not match the actual pollution level, the warning threshold is adjusted based on historical statistical bias. The assessment of the warning level matching rate uses a level correspondence analysis method; if the matching rate is lower than [a certain value], [further details are needed]. If the warning threshold is adjusted based on historical warning deviation data, the adjustment direction is the opposite of the deviation, and the adjustment range is 0.2 times the average historical deviation.

[0045] Specifically, in one implementation, the target area is a provincial capital city with an urban area of ​​approximately [area missing]. Subordinate The city comprises one administrative district. It is a heavy industrial city with numerous steel, chemical, and building materials enterprises within its jurisdiction, facing significant pressure in air pollution control. Multi-source monitoring data acquired by the system indicates that the aerosol optical thickness retrieved by satellite remote sensing is 0.2, and the hourly PM2.5 concentration at ground monitoring stations is... PM10 hourly concentration was Ozone hourly concentration is Weather forecast data indicates that the surface wind speed is... The wind direction is southeast-east, and the boundary layer height is... The relative humidity is air pressure is Enterprise emission data shows that key emission sources total Homes, including steel companies Home, chemical enterprise Home, building materials companies The daily average PM2.5 emissions from this household are approximately .

[0046] The system constructs a spatial distribution model of regional pollution concentration based on multi-source monitoring data, and calculates it using Formula 1. The parameter values ​​are: aerosol extinction coefficient. Emission source intensity attenuation factor The calculated average PM2.5 concentration in the target area is: The deviation from the data from the ground monitoring station is Within acceptable limits. The system identifies [the following] through spatial correlation analysis. Several high-pollution clusters are located in the southeast, northeast, southwest, west, and central urban areas of the city, respectively. These high-pollution areas are... The spatial correlation coefficient of key emission sources in the country exceeds .

[0047] The system determines from meteorological forecast data that the current atmospheric diffusion conditions are calm and stable (wind speed...). ), boundary layer height The system establishes a stable stratification. Based on the temporal changes in enterprise emission data, the emission source intensity is determined to be on a stable trend. The system identifies the pollution process as being in a pollution accumulation stage (concentration continuously rising and spatially concentrated) based on the spatiotemporal evolution characteristics of pollution concentration. The system then determines the current pollution evolution stage as a pollution accumulation stage according to the pollution process stage identification rules.

[0048] The system generates high-precision initial field data based on data assimilation algorithms and spatial interpolation methods. The observation error covariance matrix of the Kalman filter is estimated based on historical data, and the observation error variance of the satellite remote sensing data is set as follows: The observation error variance of the ground monitoring station data is set as Spatial interpolation uses the Kriging method, with the interpolation grid resolution set to [value missing]. Time consistency weight Set as Outlier detection thresholds in quality control Set as The generated high-precision initial field data has complete spatial coverage and good temporal consistency, which can meet the boundary condition requirements of the model prediction.

[0049] The system inputs high-precision initial field data into a lightweight atmospheric diffusion model to simulate pollution diffusion, while accelerating the simulation process using a deep learning surrogate model. The lightweight model retains the core convection-diffusion transport module, and the computational grid resolution is set to [specified value]. The time step is set to Deep learning agent models are based on the past. The training on historical simulation data from [year] has been completed, and the network structure is as follows: Layered convolutional networks. Fusion coefficients of hybrid models. Set as Select the appropriate diffusion parameters based on the pollution type. For PM2.5, use the gravity settling correction factor, set to [value missing]. The system is in Completed within minutes Hourly pollution diffusion simulation, compared to traditional atmospheric diffusion models to The computation time is 1 hour, and the speedup is approximately 100%. to times.

[0050] The system generates differentiated early warning information by level and region based on pollution diffusion simulation results and pollution evolution stage information. (Predicted future...) The hourly PM2.5 concentration range is as follows: to The highest concentration appeared in the future. Hours. The system determines the warning level as orange alert based on the concentration prediction threshold. The early warning issuance strategy should be adjusted according to the stage of pollution evolution. Currently, we are in the pollution accumulation stage, and the early warning information should strengthen regional joint prevention and control measures. Key emission sources should implement temporary emission reduction measures, with the reduction rate not less than [percentage missing]. Determined based on pollution source analysis results The key control areas are the southeastern industrial park, the western building materials industrial park, and the northeastern chemical industrial park. The system disseminates early warning information through multiple channels, pushing detailed information to the Municipal Ecology and Environment Bureau, including a forecast of the spatiotemporal distribution of concentrations, a list of key control areas, and recommendations for control measures; it also issues health protection tips to the public, advising sensitive groups to reduce outdoor activities and wear masks when going out.

[0051] After the warning is issued, the system enters the closed-loop optimization phase. Multi-source monitoring data and meteorological forecast data are collected in real time, and the initial field data is updated hourly. The updated initial field data is then input into the lightweight atmospheric diffusion model for rolling predictions, with a rolling prediction interval of [missing information]. Hour. Obtain the deviation data between the actual observation data and the predicted results. The deviation data shows the deviation data at the [number]th [hour]. Hourly predicted concentration The actual observed concentration was The deviation is It did not exceed the preset threshold. The observation weights of the data assimilation module remain unchanged. Data on the effectiveness of environmental management and the implementation of control measures are obtained. Among the key emission sources in the country The country implemented emission reduction measures, with an implementation rate of [percentage missing]. The average emission reduction rate is The response to changes in pollution concentration indicates that after the warning was issued... PM2.5 concentration within hours from Descending to The decrease was The rate of descent is Through the aforementioned closed-loop optimization mechanism, the system can continuously optimize model parameters, improving the accuracy and reliability of prediction and early warning.

[0052] Understandably, this embodiment achieves high-precision construction of the initial pollution field through the assimilation and fusion of multi-source monitoring data and meteorological forecast data, providing accurate boundary conditions for model prediction; through the fusion architecture of a lightweight atmospheric diffusion model and a deep learning proxy model, the computational complexity is significantly reduced while ensuring physical mechanism constraints, making high-frequency operation feasible; through the pollution evolution stage identification method, intelligent discrimination of different processes such as pollution accumulation, regional transmission, and gradual dissipation is achieved, providing stage feature support for refined early warning; through a graded and regional differentiated early warning generation mechanism, targeted early warning information is issued according to the pollution characteristics and evolution stages of different regions, improving the practical value of early warning information; through a closed-loop optimization mechanism of "data acquisition—assimilation and fusion—model prediction—stage identification—early warning issuance—effect evaluation," the accuracy and reliability of the prediction and early warning system are continuously improved; by integrating the advantages of both physical mechanisms and data-driven approaches, a technical system with both interpretability and predictive capabilities is formed, providing a scientific basis for environmental management decisions.

[0053] In one data-driven implementation, the target area is an industrial park with an area of ​​approximately [area missing]. Approximately 100 companies have moved in. The system covers industries such as chemical, pharmaceutical, printing, and surface treatment. Multi-source monitoring data acquired by the system indicates that the aerosol optical thickness retrieved by satellite remote sensing is... The hourly PM2.5 concentration at the ground monitoring station was PM10 hourly concentration was Ozone hourly concentration is VOCs hourly concentration is LiDAR detection data indicates that the boundary layer height is... The vertical distribution of aerosol extinction coefficients shows a characteristic of weak extinction in the upper layer and strong extinction in the lower layer. Meteorological forecast data indicates that the surface wind speed is... The wind direction is southwest, and the relative humidity is air pressure is Boundary layer height predicts future The hour will rise to Enterprise emission data shows that key emission sources total Homes, including chemical companies Home, pharmaceutical companies Home, printing company Home, surface treatment company The daily average VOC emissions from this household are approximately .

[0054] The system constructs a spatial distribution model of regional pollution concentration based on multi-source monitoring data, and calculates it using Formula 1. The parameter values ​​are: aerosol extinction coefficient. Emission source intensity attenuation factor The calculated average VOCs concentration in the target area is: The deviation from the data from the ground monitoring station is Within acceptable limits. The system identifies [the following] through spatial correlation analysis. There are three high-pollution clusters located in the central, southern, and northern parts of the park, respectively. These high-pollution areas are... The spatial correlation coefficient of key emission sources in the country exceeds .

[0055] The system determines from meteorological forecast data that the current atmospheric diffusion conditions are moderate (wind speed...). ), boundary layer height The situation is moderately stable. Based on the time-series changes in enterprise emission data, the emission source intensity is showing an increasing trend (increasing compared to the historical average). Based on the spatiotemporal evolution characteristics of pollution concentration, the system determines that the pollution process is in a regional transport stage (high-value areas move along the prevailing wind direction). The system also determines the current pollution evolution stage type as a regional transport stage based on pollution process stage identification rules.

[0056] The system generates high-precision initial field data based on data assimilation algorithms and spatial interpolation methods. The observation error covariance matrix of the Kalman filter is estimated based on historical data, and the observation error variance of the satellite remote sensing data is set as follows: The observation error variance of the ground monitoring station data is set as The observation error variance of the lidar detection data is set as Spatial interpolation uses the Kriging method, with the interpolation grid resolution set to [value missing]. Time consistency weight Set as Outlier detection thresholds in quality control Set as The generated high-precision initial field data has complete spatial coverage and good temporal consistency.

[0057] The system inputs high-precision initial field data into a lightweight atmospheric diffusion model to simulate pollution diffusion, while accelerating the simulation process using a deep learning surrogate model. The lightweight model retains the core convection-diffusion transport module, and the computational grid resolution is set to [specified value]. The time step is set to Deep learning agent models are based on the past. The training on historical simulation data from [year] has been completed, and the network structure is as follows: Layered convolutional networks. Fusion coefficients of hybrid models. Set as Select the appropriate diffusion parameters based on the type of pollution. For VOCs, use the photochemical attenuation coefficient, set to [value missing]. Considering the influence of solar radiation intensity, the daytime attenuation coefficient is set to... The nighttime attenuation coefficient is set to The system is in Completed within minutes Simulation of pollution diffusion over hours.

[0058] The system generates differentiated early warning information by level and region based on pollution diffusion simulation results and pollution evolution stage information. (Predicted future...) The hourly concentration range of VOCs is as follows: to PM2.5 hourly concentration range is to The system determines the warning level to be a yellow alert based on the predicted VOCs concentration threshold. Based on the predicted PM2.5 concentration threshold, the warning level is determined to be a yellow warning. (Corresponding to a yellow alert). The overall alert level is determined to be a yellow alert. The alert issuance strategy is adjusted according to the pollution evolution stage. Currently, it is in the regional transmission stage; the alert information strengthens upstream and downstream coordination and reminds the southern and northern areas of the industrial park to take precautions against pollution transmission. This is determined based on the pollution source apportionment results. The key control areas are the central chemical industrial zone and the southern pharmaceutical industrial zone. The system disseminates early warning information through multiple channels.

[0059] After the warning is issued, the system enters the closed-loop optimization phase. Multi-source monitoring data and meteorological forecast data are collected in real time, and the initial field data is updated hourly. The updated initial field data is then input into the lightweight atmospheric diffusion model for rolling prediction. Deviation data between actual observation data and prediction results is obtained; the deviation data shows that the predicted VOCs concentration for the 6th hour is... The actual observed concentration was The deviation is It did not exceed the preset threshold. Obtain data on the effectiveness of environmental management and the implementation status of control measures. Among the key emission sources in the country The country implemented emission reduction measures, with an implementation rate of [percentage missing]. The average emission reduction rate is The effectiveness of the closed-loop optimization mechanism of the present invention was verified through the parameter adjustment process of the above-described data-driven embodiments.

[0060] The above-described data-driven embodiments illustrate the complete processing steps of constructing a spatial distribution model of regional pollution concentration, identifying pollution evolution stages, fusing data assimilation and spatial interpolation, accelerating the process by combining a lightweight model with a deep learning proxy model, and generating differentiated early warnings. The aerosol extinction coefficient, emission source intensity attenuation factor, fusion coefficient, numerical values, and threshold values ​​mentioned above are all example values. In actual implementation, they can be calibrated or adjusted according to the topographic features, pollution source distribution characteristics, meteorological conditions, and monitoring capabilities of the target area, and do not constitute a limitation on the scope of protection of this invention.

[0061] In the above embodiments, the assimilation and fusion of multi-source monitoring data and meteorological forecast data achieves high-precision construction of the initial pollution field, providing accurate boundary conditions for model prediction; the fusion architecture of a lightweight atmospheric diffusion model and a deep learning proxy model significantly reduces computational complexity while ensuring physical mechanism constraints, enabling high-frequency operation; the pollution evolution stage identification method enables intelligent discrimination of different processes such as pollution accumulation, regional transmission, and gradual dissipation, providing stage feature support for refined early warning; the hierarchical and regional differentiated early warning generation mechanism releases targeted early warning information based on the pollution characteristics and evolution stages of different regions, improving the practical value of early warning information; the closed-loop optimization mechanism of "data acquisition—assimilation and fusion—model prediction—stage identification—early warning release—effect evaluation" continuously improves the accuracy and reliability of the prediction and early warning system; and by integrating the advantages of both physical mechanisms and data-driven approaches, a technical system with both interpretability and predictive capabilities is formed, providing a scientific basis for environmental management decisions. Through the above processing, an integrated technical system can be realized, from satellite identification of pollution clusters to multi-source monitoring and location of polluting enterprises, and then to numerical simulation prediction of pollution evolution. This will promote the transformation of air pollution early warning from "post-event response" to "pre-event warning", and significantly improve the foresight and initiative of environmental management.

[0062] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent early warning of pollution processes integrating atmospheric numerical simulation, characterized in that, include: Acquire multi-source monitoring data, meteorological forecast data, and enterprise emission data for the target area; A spatial distribution model of regional pollution concentrations was constructed based on multi-source monitoring data, and the spatial correlation between pollution cluster areas and key emission sources was determined based on the regional pollution concentration spatial distribution model. The corresponding pollution evolution stage type is determined based on multi-source monitoring data, meteorological forecast data, and enterprise emission data; High-precision initial field data is generated by fusing multi-source monitoring data using a combination of data assimilation algorithm and spatial interpolation method. High-precision initial field data are input into a lightweight atmospheric diffusion model to simulate pollution diffusion, and a deep learning proxy model is used to accelerate the simulation calculation process. Based on the results of pollution diffusion simulation and information on the stages of pollution evolution, differentiated early warning information is generated according to different levels and regions.

2. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 1, characterized in that, Multi-source monitoring data includes at least satellite remote sensing inversion data, ground monitoring station data, mobile monitoring data, and lidar detection data; Meteorological forecast data should include at least wind field data, temperature data, humidity data, air pressure data, and boundary layer height data. Enterprise emission data should include at least the emission source location data, emission pollutant type data, emission rate data, and emission height data.

3. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 2, characterized in that, When constructing a spatial distribution model of regional pollution concentration based on multi-source monitoring data, the following should be included: Atmospheric optical thickness data and aerosol optical thickness data of the target area were obtained based on multi-source satellite remote sensing data, and an inversion relationship between aerosol optical thickness and pollutant concentration was established based on historical monitoring concentration data. Determine the baseline pollution concentration values ​​at each spatial location in the target area at corresponding times based on the inversion relationship; The baseline pollution concentration values ​​were corrected based on the aerosol extinction coefficient to characterize the impact of atmospheric particulate matter on the light propagation process. The pollutant concentration is attenuated and corrected based on the pollutant emission source intensity attenuation factor in order to characterize the concentration change characteristics of pollutants during the propagation and diffusion process. Based on the corrected and attenuated pollution concentration results, a spatial distribution model of pollution concentration in the target area is generated to obtain the distribution of pollutant concentration at each spatial location in the target area at the corresponding time.

4. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 3, characterized in that, High-precision initial field data is generated by fusing multi-source monitoring data using a combination of data assimilation algorithms and spatial interpolation methods, including: Error analysis of the observation data at each monitoring point is performed based on the Kalman filter algorithm to estimate the corresponding observation error covariance matrix in order to determine the credibility weight of different monitoring data sources. By spatially interpolating and fusing satellite remote sensing data with ground monitoring station data, monitoring blind spots and data gaps can be filled in to obtain a continuous spatially distributed pollution concentration field. By combining meteorological forecast data, the fused pollution concentration field is corrected for temporal consistency in order to eliminate the bias between data at different time scales and generate high-precision initial field data with spatiotemporal consistency. Quality control is performed on the high-precision initial field data by comparing the fused concentration value at each spatial location with the average concentration of its spatially adjacent nodes, where: When the difference between the fusion concentration value and the average concentration value is less than the preset outlier threshold, the fusion concentration value is determined as the final initial field concentration value after quality control. When the difference between the fused concentration value and the average concentration value is greater than or equal to the preset outlier threshold, the fused concentration value is determined to be an outlier, and it is replaced and corrected based on the average concentration of adjacent nodes in space to obtain the final initial field concentration value after quality control.

5. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 4, characterized in that, When high-precision initial field data is input into a lightweight atmospheric diffusion model for pollution diffusion simulation, and the simulation calculation process is accelerated using a deep learning surrogate model, the following steps are included: The traditional atmospheric diffusion model is structurally simplified and its parameters are reduced in dimensionality, while retaining the core diffusion and transport modules. A deep learning proxy model is trained based on historical simulation data and actual observation data to establish a nonlinear mapping relationship between input parameters and diffusion results. During the operation of the lightweight atmospheric diffusion model, the computationally complex convection and diffusion terms are replaced with the prediction results of the deep learning proxy model. Select the appropriate diffusion parameters based on the type of pollution. For particulate matter diffusion, use the gravity settling correction coefficient, and for gaseous pollutants such as ozone, use the photochemical attenuation coefficient.

6. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 2, characterized in that, When determining the corresponding pollution evolution stage type based on multi-source monitoring data, meteorological forecast data, and enterprise emission data, the following are included: Atmospheric diffusion conditions are determined based on wind field information and boundary layer height changes in meteorological forecast data; the trend of emission source intensity changes is determined based on the time-series changes in enterprise emission data. Determine the stage of the pollution process based on the spatiotemporal evolution characteristics of pollution concentration; Based on the pollution process stage identification model, the corresponding pollution evolution stage is determined by combining diffusion conditions, changes in emission intensity, and concentration evolution characteristics. Among them, the pollution accumulation stage corresponds to the characteristic of continuous increase in concentration under calm and stable weather conditions, the regional transmission stage corresponds to the characteristic of high-value areas moving along the wind direction under the condition of prevailing wind shear, and the pollution dissipation stage corresponds to the characteristic of gradual decrease in concentration under the condition of improved diffusion conditions.

7. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 1, characterized in that, When generating differentiated early warning information by level and region based on pollution diffusion simulation results and pollution evolution stage information, the following are included: The warning level is determined based on the predicted pollution concentration threshold, where: Yellow warning corresponds to hourly concentrations exceeding 150 μg / m 3 ; Orange warning corresponds to hourly concentrations exceeding 250 μg / m 3 ; Red alert corresponds to hourly concentrations exceeding 350 μg / m 3 ; The early warning issuance strategy is adjusted according to the type of pollution evolution stage, including: Strengthen regional joint prevention and control measures during the accumulation phase, enhance upstream and downstream collaborative early warning during the transmission phase, and gradually lift control measures during the dissipation phase. Based on the results of pollution source analysis, key control areas are identified, and early warning information is issued to the corresponding areas.

8. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 5, characterized in that, When selecting the appropriate diffusion parameter configuration based on the type of pollution, the following are included: Obtain pollutant type information and classify pollutants into particulate pollutants and gaseous pollutants; When the pollutant is particulate matter, the corresponding gravity settling correction coefficient is calculated based on particulate matter density, particulate matter size, gravitational acceleration, Cunningham correction factor and aerodynamic viscosity to characterize the settling and diffusion characteristics of particulate matter under gravity. The gravity settling correction coefficient is positively correlated with particulate density, particulate size square, gravitational acceleration and Cunningham correction coefficient, and negatively correlated with aerodynamic viscosity. The particulate matter diffusion process is corrected based on the gravity settling correction coefficient to obtain particulate matter pollution diffusion parameters; When the pollutant is a gaseous pollutant, the photochemical attenuation coefficient is determined based on the solar radiation intensity, nitrogen oxide concentration, and volatile organic compound concentration. The photochemical attenuation coefficient is used to characterize the attenuation features of gaseous pollutants during the photochemical reaction process, and corresponding gaseous pollutant diffusion parameters are generated. Construct a corresponding pollution diffusion parameter configuration scheme based on the particulate matter pollution diffusion parameters or gaseous pollution diffusion parameters.

9. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 1, characterized in that, This also includes forming a closed-loop optimization, specifically: Real-time acquisition of multi-source monitoring data and meteorological forecast data; updating of initial field data. The updated initial field data is input into the lightweight atmospheric diffusion model for rolling prediction; Obtain the deviation data between the actual observation data and the prediction results, and feed it back to the data assimilation module to correct the assimilation parameters; To obtain data on the effectiveness of environmental management after the issuance of early warnings, assess the effectiveness of early warnings, and optimize the parameters of the early warning decision-making model; The environmental management effectiveness data includes the implementation of control measures, response to changes in pollution concentration, and public health impact assessment data.

10. The intelligent early warning method for pollution processes integrating atmospheric numerical simulation as described in claim 9, characterized in that, It also includes verifying the effectiveness of the early warning data, including: If the deviation between the predicted concentration and the actual observed concentration exceeds a preset threshold, the observation weight of the data assimilation module will be increased. If the accuracy of identifying pollution evolution stages decreases, more feature parameters should be introduced into the stage identification model. If the warning level does not match the actual pollution level, the warning threshold will be adjusted based on historical statistical deviations.

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