Air quality risk early warning and treatment integrated method and system

Abstract

The invention relates to the technical field of air quality detection, in particular to an air quality risk early warning and treatment integrated method and system. The method comprises the following steps: acquiring regional air collection sample data, analyzing the evolution difference of air components among regions, and determining the dynamic response characteristics of air dominant factors; air multi-period pollution superposition conditions are detected according to the dynamic response characteristics of the dominant factors, the air quality pollution risk growth trend is identified, and the ecological influence gradient growth trend is estimated; performing air quality risk assessment on the corrosion and aging trend of the peripheral equipment to obtain air quality abnormal state assessment data; abnormal conditions of air pollution sources are recognized, and pollution source grade classification is formed; formulating air quality treatment strategy data according to the abnormal risk data and the pollution source identification condition, and executing response treatment measures; through air quality risk early warning, air quality treatment is more efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air quality detection, and in particular to an air quality risk early warning and governance integrated method and system. BACKGROUND

[0002] The air quality monitoring method mainly relies on fixed monitoring stations to collect and analyze the concentration of specific pollutants in real time, but this method generally has limited spatial coverage, insufficient dynamic response capability, and ambiguous pollution source identification, making it difficult to cope with increasingly complex regional, multi-time, and component-coupled air pollution situations. Especially in continuous or non-continuous time periods, different pollutants form superimposed pollution phenomena due to insufficient diffusion or residual accumulation, and under the driving of meteorological conditions such as light, humidity, and wind speed, secondary reactions occur, forming complex pollutants such as ozone and fine particulate matter that are difficult to directly trace. Not only does this significantly increase the exposure risk of the population, but it also exacerbates the destruction of the regional ecosystem. Existing technologies in risk early warning are mostly limited to concentration threshold early warning mechanisms, lacking in-depth modeling of pollution dynamic evolution laws, spatiotemporal migration trends, and dominant factor dynamic response mechanisms, making it difficult to effectively predict pollution expansion paths and subsequent ecological risk impacts. However, traditional air quality risk early warning and governance have the problem of inaccurate determination of air quality pollution risk growth trends, and the problem of inaccurate assessment of air quality abnormal state assessment data. SUMMARY

[0003] Therefore, it is necessary to provide an air quality risk early warning and governance integrated method and system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, an air quality risk early warning and governance integrated method includes the following steps: Step S1: Obtain regional air collection sample data; based on the regional air collection sample data, analyze the difference in air composition evolution between regions to obtain air evolution trend difference data; and determine the dynamic response characteristics of air dominant factors based on the air evolution trend difference data; Step S2: Detect air multi-time pollution superposition based on the dynamic response characteristics of air dominant factors; determine the air quality pollution risk growth trend based on the air multi-time pollution superposition; and estimate the ecological impact gradient growth trend based on the air quality pollution risk growth trend; Step S3: Based on the air quality pollution risk growth trend, perform air quality abnormal state assessment to obtain air quality abnormal state assessment data; and based on the air quality abnormal state assessment data and the ecological impact gradient growth trend, determine the air pollution source identification abnormality; Step S4: determining air quality management strategy data according to the air pollution source identification abnormal situation and the air quality abnormal state evaluation data; and performing air quality abnormal adjustment management by using the air quality management strategy data.

[0005] The method can effectively identify the change trend of air composition between different regions by obtaining regional air collection sample data and performing composition evolution difference analysis, thereby accurately extracting the dynamic response characteristics of the pollution evolution dominant factor and enhancing the analysis capability of the pollution driving mechanism. By identifying the dynamic response characteristics of the dominant factor, real-time detection of the superposition and diffusion law of pollutants in continuous or non-continuous time periods can be realized, the pollution retention, complex reaction and structural complex process can be effectively revealed, and the dynamic control capability of the pollution peak evolution trend can be enhanced. On the basis of pollution risk trend identification, the hierarchical and multi-path influence of air quality pollution on the regional ecological system is further evaluated, and the systematic tracking of the gradient change of ecological influence is realized. Based on the pollution risk trend result, the air quality abnormal risk of the high-risk region can be quantitatively evaluated, the early perception capability of potential pollution events can be improved, and the pollution source abnormal evolution characteristics can be identified in combination with the risk characteristics, so as to realize accurate source tracing and hierarchical identification of the pollution source. By fusing the pollution source identification result and the risk evaluation data, a management strategy with response timeliness and structural pertinence can be generated, the management mechanism can be adaptively adjusted, the closed-loop accurate management of air quality abnormality can be realized, and the initiative, cooperation and foresight of urban air management can be improved. Therefore, the present application optimizes the traditional air quality risk early warning and management, solves the problems of inaccurate determination of air quality pollution risk growth trend and inaccurate evaluation of air quality abnormal state evaluation data in the traditional air quality risk early warning and management, and improves the accuracy of determination of air quality pollution risk growth trend and the accuracy of evaluation of air quality abnormal state evaluation data.

[0006] The present application also provides an air quality risk early warning and management integrated system for performing the air quality risk early warning and management integrated method as described above, which comprises: The dominant factor dynamic response module is configured to obtain regional air collection sample data, perform regional air composition evolution difference analysis based on the regional air collection sample data, and obtain air evolution trend difference data; and determine air dominant factor dynamic response characteristics based on the air evolution trend difference data. The ecological influence gradient growth module is configured to detect air multi-period pollution superposition based on the air dominant factor dynamic response characteristics, determine air quality pollution risk growth trend based on the air multi-period pollution superposition, and estimate ecological influence gradient growth trend based on the air quality pollution risk growth trend. The pollution source identification module is used for air quality abnormal state evaluation based on air quality pollution risk growth trend, and air quality abnormal state evaluation data is obtained; and the air pollution source identification abnormal situation is determined based on the air quality abnormal state evaluation data and the ecological influence gradient growth trend; The air quality abnormal adjustment and management module is used for determining air quality management strategy data according to the air pollution source identification abnormal situation and the air quality abnormal state evaluation data; and air quality abnormal adjustment and management is performed by using the air quality management strategy data.

[0007] The air quality risk early warning and management integrated system of the application can realize the air quality risk early warning and management integrated method of the application, is used as a medium for joint operation and signal transmission between modules, and is used for completing the air quality risk early warning and management integrated method. The modules in the system cooperate with each other, and through the construction of the integrated technology chain of air component evolution analysis, pollution superposition detection, risk evaluation and management linkage, the accurate identification of air pollution trend, source tracing and efficient cooperation of closed-loop management are realized. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 It is a step flowchart of an air quality risk early warning and management integrated method; Figure 2 It is a step flowchart of an air quality risk early warning and management integrated method; Figure 1 It is a detailed implementation step flowchart of step S3 in the method; Figure 3 It is a schematic diagram of dominant factor fluctuation amplitude time sequence analysis in the application; The implementation, functional characteristics and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0009] The technical method of the application will be described clearly and completely in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0010] In addition, the accompanying drawings are only schematic diagrams of the application, and are not necessarily drawn to scale. The same reference signs in the drawings represent the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0012] To achieve this, please refer to Figures 1 to 3 , an integrated method for air quality risk warning and control, comprising the following steps: Step S1: Acquire regional air sample data; perform inter-regional air component evolution difference analysis based on the regional air sample data to obtain air evolution trend difference data; determine the dynamic response characteristics of the air dominant factor based on the air evolution trend difference data; In the embodiment of the present invention, multiple air quality sensing nodes are set up, each of which is equipped with a high-precision laser scattering air quality sensor of model DFP-2000, whose detection concentration range is , the sensitivity is , the sampling frequency is The sensor nodes are distributed at 50 sampling points in the urban area and continuously collect air sample data at a frequency of once per second between 0:00 and 24:00 every day. The data includes The real-time concentration values ​​of six types of air pollutants such as air pollutants are collected. The collected data are transmitted to the regional data center via wired communication, and are stored and managed through a relational database based on SQL Server. The ArcGIS system is used to perform spatial interpolation processing on the above raw pollutant concentration data, and the Kriging interpolation algorithm (Kriging) is used to generate a spatial distribution map of pollutants at a 100×100m grid scale. By extracting the average value of the pollutants in each grid over an hourly time period for 7 consecutive days, a time series grid concentration sampling data is formed. Then, based on this data, the NetworkX graph calculation library in Python is used to construct a pollutant evolution trajectory path diagram, and the concentration change sequence of each node on the evolution path is extracted to generate the air component concentration evolution trajectory data. According to the above evolution trajectory data, the first-order difference method is used to calculate The concentration change values ​​of the principal components at consecutive moments are calculated, and the dominant factor is identified using the maximum change rate. Combined with the evolution trajectories of different dominant factors, the dynamic response characteristic data of the air dominant factor is generated by statistically analyzing its fluctuation amplitude, duration, frequency of occurrence and other characteristics. This data is used as the input basis for subsequent pollution superposition trend detection.

[0013] In another embodiment, Time series data of conventional pollutant concentrations; Data sampling frequency: 1 minute / 5 minutes / hour, covering historical pollution data for at least 3 consecutive months.

[0014] Temperature, humidity, air pressure, wind speed, and wind direction. Data source: ground monitoring stations + reanalysis data.

[0015] The models or algorithms used include: Preliminary identification of dominant factors: Use correlation analysis to screen meteorological factors or pollutant factors with a correlation greater than 0.6 with pollution concentration fluctuations; use principal component analysis to reduce the dimensionality of multi-factor data and extract the first few principal components with a cumulative contribution rate greater than 80% as candidate dominant factors.

[0016] Dynamic response feature extraction includes: Using the difference operation ; in, is the factor concentration at time t, is the concentration at the previous moment. It captures the short-term fluctuation of the dominant factor and reflects its change intensity between consecutive monitoring points. For example: In 10 minutes by Sudden increase to , then the difference , indicating that the factor has rapid abnormal fluctuations.

[0017] The time-delay cross-correlation function (CCF) was used to measure the correlation between the dominant factor and the pollutant concentration at different lag times and extract the response delay. and Taking the relationship between the two factors as an example, the monitoring time series data are first standardized, and the time delay cross-correlation function (CCF) is calculated within the set lag interval (±24 hours, step length 1 hour). The significant lag effect of the dominant factor on the pollutant is determined by the threshold value of the correlation coefficient greater than 0.6; Use sliding window Fourier transform to characterize the periodic or sudden fluctuation characteristics of factors. For hourly-level concentration time series, first, normalize the data to have a mean of 0 and a standard deviation of 1 to avoid dimension difference affecting the frequency domain features. Select a fixed length of time window, for example, 24 hours (corresponding to the typical period of a day), and set the window sliding step to 1 hour, that is, move forward 1 hour each time to recalculate the frequency spectrum. In each window, use Fast Fourier Transform (FFT) to convert the time series data to the frequency domain to obtain the energy distribution of each frequency component. By calculating the Power Spectral Density (PSD), if the energy is significantly enhanced in the low frequency region (such as 1 / 24h⁻¹), it indicates that the factor has a circadian periodicity. If there is a transient surge of high-frequency energy in a certain window (for example, 1 / 2h⁻¹ or higher frequency components), it is determined that the factor has a burst fluctuation. The periodicity feature is recorded as the periodic energy ratio (the proportion of the energy of the periodic frequency band to the total energy), and the burst feature is recorded as the high-frequency energy peak. The above features can be used as part of the dynamic response quantification index input into the subsequent prediction model.

[0018] Dynamic response quantification index: Dominant influence index : ; wherein is the empirical weight, used to balance the contribution of three factors to the overall influence. (Obtained by regression or machine learning training, the training data used is the historical monitoring data set, including: pollutant concentration data: time series monitoring values; meteorological factor data: temperature, humidity, air pressure, wind speed, wind direction, etc. Use Multiple Linear Regression (MLR) or Random; Forest Regression (RF) to fit the training data and learn the best combination.

[0019] Fluctuation amplitude (reflecting the instantaneous disturbance intensity of the dominant factor): ; wherein, is the maximum difference value, is the concentration mean; Duration : the length of the time interval that continuously exceeds the standard deviation threshold, wherein refers to the monitoring factor (such as ) is the standard deviation of the concentration series over a reference time window (such as the past 24 hours or 7 days).

[0020] Lag time : The best correlation time between factor fluctuation and pollutant concentration peak; Dynamic response implementation methods include: Model training: Use historical pollution data as samples; the candidate factors ( ) and its dynamic characteristic indicators are input into the random forest (RF) / gradient boosting tree (GBDT) / LSTM time series model, and the dynamic response of the pollution leading factor is output. The input of the model includes the time series value of the pollution factor concentration and the dynamic characteristic indicators ( etc.), the output is the dynamic response index of the dominant factor .

[0021] Model and hyperparameter settings: Random Forest (RF): Number of trees ( ), maximum depth ( ),use Exponential as splitting criterion; Gradient Boosting Tree (GBDT): learning rate ( ), the number of weak learners ( ), maximum depth ( ); LSTM: input window length (hourly data is 1 day), the number of hidden layer units is 64, , optimizer , loss function . Hyperparameter selection is based on cross-validation ( ), the prediction accuracy is verified by historical pollution data sets, and the optimal combination is finally selected.

[0022] Step S2: Detecting the overlapping of air pollution over multiple periods based on the dynamic response characteristics of the air dominant factors; determining the growth trend of air quality pollution risk based on the overlapping of air pollution over multiple periods; and estimating the growth trend of the ecological impact gradient based on the growth trend of air quality pollution risk; In the embodiment of the present invention, based on the dynamic response characteristic data of the air dominant factor obtained in step S1, each factor (such as For each grid area, pollutants that meet this condition for three consecutive hours are considered to have a delayed response. Mark the yellow warning area in the grid, and repeat this process in the adjacent grid to obtain the residual trend of adjacent pollutants. Based on the above, the number of grid areas with superposition state is counted, and its time span and number of pollutant types are coded to obtain the mixed growth index. and If the average concentration value shows an increasing state in two consecutive time periods and the superposition state lasts for more than 3 hours, it is considered to be a mixed growth area. Continue to analyze the characteristics of chemical reaction products in the mixed pollution area, such as and The reaction produces ozone ( ) concentrations simultaneously rise to more than 20μg / m³ and appear simultaneously in the region, it is judged that there is a reaction amplification effect. Combining these data, by constructing a pollution transmission-chemical reaction chain logic model, marking the pollution diffusion chain and amplification path, and outputting the air multi-period pollution superposition data. Based on the above pollution superposition data, three types of indicators such as pollution peak growth rate, pollution duration growth rate, and pollutant type expansion rate are used for weighted calculation (weights are 0.5, 0.3, and 0.2 respectively) to obtain air quality pollution risk growth trend data. Based on this, the acid rain frequency data recorded in the local ecological survey database and the regional vegetation type distribution map are superimposed to conduct spatial overlap analysis to identify the overlapping areas of increased pollution risk and acid rain deposition. In this overlapping area, soil Change data (monitoring once every quarter), if the soil If the value decreases by more than 0.5 units, the area is identified as a soil acidification risk zone. Chlorophyll fluorescence images are also used to analyze plant leaf damage, and image recognition algorithms are used to detect areas with structural damage, such as leaf curling and spots. These test results are then correlated with regional vegetation photosynthetic rate monitoring data to calculate the gradient growth trend of ecological impact.

[0023] In another embodiment, regarding the data used: pollutant concentration time series data: The sampling frequency is 1h, and meteorological data (wind speed U, boundary layer height , air pressure ,temperature , relative humidity wait).

[0024] The accumulated pollution at time t is defined as:

[0025] in, :time The cumulative pollution effect of Concentration as an example, suppose at a certain moment The pollution level is , the cumulative effect of which is The hourly concentration is weighted to obtain; : indicates that before time t hourly pollutant concentrations, e.g. Concentration; : represents the weight, which decreases over time, and the pollution concentration at the near time has a greater impact.

[0026] Build diffusion capacity index: ; Where: : represents the wind speed, reflecting the pollution dilution and diffusion capacity; the greater the wind speed, the easier the pollution is to diffuse; : represents the boundary layer height, indicating the atmospheric vertical mixing range, the higher the boundary layer, the easier the pollution is to dilute; : represents the air pressure, the higher the air pressure, the greater the air density, which is not conducive to the diffusion of pollution; The smaller, the less favorable the atmospheric diffusion conditions.

[0027] On the basis of the cumulative index , introduce the meteorological factor correction: ; Where, : temperature, temperature rise will accelerate the generation of secondary pollutants; : relative humidity, humidity rise will promote aerosol generation; : is a prediction function, which can adopt multiple linear regression ( ) and other model forms.

[0028] Regarding parameter acquisition and calibration, the decay coefficient : is fitted through historical pollution process, and least squares method is used to estimate, so that the mean square error (MSE) of the model prediction value and the measured value is minimized; machine learning model hyperparameters: obtained through cross-validation (Cross-validation). Model calibration: retrain with the latest monitoring data, and evaluate the prediction performance through and other indicators.

[0029] It should be noted that by introducing wind speed, boundary layer height, air pressure, temperature, humidity and other meteorological factors on the basis of pollution accumulation, and building diffusion capacity index , the meteorological conditions directly affect the pollution prediction results in the form of correction term, which reflects the coupling relationship between pollutants and meteorological environment.

[0030] Step S3: based on the air quality pollution risk growth trend, evaluate the air quality abnormal state to obtain air quality abnormal state evaluation data; based on the air quality abnormal state evaluation data and the ecological impact gradient growth trend, determine the air pollution source head identification abnormal situation; In this embodiment of the present invention, the "air quality pollution risk growth trend" obtained in the preceding steps is used as a basic data source. Combined with real-time feedback data (corrosion rate in μm / a) from metal corrosion monitoring devices deployed in key areas such as industrial parks, transportation hubs, and densely populated areas, the correlation between air pollution trends and corrosion data is compared and analyzed. If the corrosion rate in a region increases by more than 15% compared to the previous quarter and has a high correlation with the increase in the pollution index (Pearson correlation coefficient r>0.8), it can be inferred that the risk of equipment aging and failure due to pollution has increased. Using the "air quality pollution risk growth trend" obtained in the preceding steps as a basic data source, combined with real-time feedback data (corrosion rate in μm / a) from metal corrosion monitoring devices deployed in key areas such as industrial parks, transportation hubs, and densely populated areas, the correlation between air pollution trends and corrosion data is compared and analyzed. If the corrosion rate in a region increases by more than 15% compared to the previous quarter and has a high correlation with the increase in the pollution index (Pearson correlation coefficient r>0.8), it can be inferred that the risk of equipment aging and failure due to pollution has increased. By comparing risk scores with historical air pollution levels, areas with abnormal air quality are identified and marked as abnormally high-risk. Within these areas, the changing structural characteristics of pollutants are further analyzed, combined with the ecological impact gradient growth trend. Pollutant source tracing technology (based on pollutant fingerprint matching) is used to infer the type of pollution source, such as industrial, transportation, or domestic. If the distribution of identified pollution sources is abnormal (for example, the proportion of industrial sources suddenly increases from 30% to 65%), an abnormal air pollution source identification is considered, and corresponding identification anomaly data is generated, prompting the next stage of remediation.

[0031] Step S4: Determine air quality control strategy data based on abnormal conditions identified at air pollution sources and air quality abnormality status assessment data; and use the air quality control strategy data to perform air quality abnormality adjustment and control.

[0032] In the embodiment of the present application, the pollution source identification abnormal data obtained in step S3 is combined with the air quality abnormal state evaluation data for joint modeling, and a multivariate classification decision tree method is used to identify the pollution control type, distinguish three types of scenes of acute pollution outbreak (short-time concentration sudden increase), chronic pollution accumulation (continuous concentration too high), and composite pollution (multi-source mixing), and match to the control strategy template library respectively. The control strategy template library includes measures for the above three types of pollution problems. For example, for acute outbreak pollution, the emergency emission limit mechanism is triggered first, and the relevant enterprise management task sheet is automatically pushed by the environmental impact assessment system; for chronic accumulation pollution, the periodic micro-spraying system is executed to suppress the accumulation of particulate matter, and the regional green land transpiration ratio model is dynamically adjusted; for composite pollution, the traffic relief system and industrial joint emission control mechanism are cooperatively dispatched. The GIS system is used to superimpose the pollution source spatial distribution map and the urban governance capability resource distribution map to evaluate the defects of the generated governance strategy. Through the response time, control range and execution capability index score between the layout points, the weaknesses in the governance strategy response mechanism are identified, and the air quality governance strategy defect data is generated. According to the above defect data, the governance mechanism optimization processing is performed. For example, if it is found that the execution capability of a certain area is weaker than the response requirement, the management task is adjusted to a stronger regional management center; if a governance strategy has poor inhibition effect on composite pollution sources, a mobile management unit (such as an unmanned vehicle micro-fog spraying system) is introduced for supplementary management. After the above optimization measures are issued to each management system under the dispatching instruction, a complete air quality abnormal adjustment and management process is formed.

[0033] Preferably, step S1 comprises the following steps: Step S11: Obtain regional air collection sample data, and set the detection range of the air quality sensor to 0-1000 μg / m³, the detection sensitivity to ±1 μg / m³, and the gas sampling frequency to 1 Hz; In the embodiment of the present application, during the construction of the urban air quality monitoring system, 120 air quality sampling nodes are deployed in the target area, and a laser particle counting type air quality sensor (model: AM510, manufacturer: TSI) with real-time detection function is selected. The detection range of the device is limited to 0-1000 μg / m³, the detection sensitivity is ±1 μg / m³, and the gas sampling frequency is set to 1 Hz. The installation height of each sensor is uniformly 3 meters above the ground, and they are all arranged southward to avoid building obstruction and heat source interference. The data collection module uses an industrial-grade GPRS communication module to transmit the sampling data to the central data processing platform in real time, and the data is reported in JSON structure format. A data integrity monitoring program is simultaneously deployed to trigger an alarm prompt and mark invalid samples for nodes with continuous missing sampling data within every 5 minutes. Through the above configuration, complete regional air collection sample data is obtained.

[0034] Step S12: using an air quality sensor to detect regional air composition data on regional air sample data to obtain regional air composition data; In this embodiment of the present invention, deployed air quality sensors are used to extract concentration values ​​for six pollutants: PM2.5, PM10, NO2, SO2, CO, and O3, from air samples collected at each sampling node. All raw data is aggregated into a central database, and a data preprocessing procedure is performed. The processing flow includes: removing outliers using the 3σ principle; smoothing data based on a 30-second sliding average; and segmented aggregation of hourly data to calculate three types of statistics: average concentration, maximum concentration, and minimum concentration. The processed data is labeled as regional air composition data, using the sampling node number and timestamp as the primary key to form a complete data table structure, supporting subsequent spatial analysis.

[0035] Step S13: determining the spatial distribution of regional air components based on the regional air composition data; In this embodiment of the present invention, based on the "regional air composition data" obtained in step S12, a spatial distribution model is established using the geographic information system tool ArcGIS to rasterize the regional space. The monitoring area is divided into 100m×100m grid cells, and the spatial coordinates of each sampling node are determined. Kriging interpolation is then used to fit the spatial distribution of six types of pollutants, outputting a pollutant distribution raster map. Each cell in the raster map contains the estimated concentration value of each pollutant. This result is summarized as the "regional air composition spatial distribution." Statistical characteristics such as the maximum, minimum, and average values ​​of each pollutant across the entire region, as well as the coordinates of the distribution hotspot centers, are recorded to provide a basis for subsequent trend analysis.

[0036] Step S14: performing an analysis of the difference in air composition evolution between regions based on the spatial distribution of regional air composition to obtain air evolution trend difference data; In this embodiment of the present invention, based on the "spatial distribution of regional air components," a time-series distribution layer set is constructed on an hourly basis. A 7-day x 24-hour sequence of distribution maps is constructed for each pollutant and organized into a three-dimensional matrix in chronological order. Each hourly layer consists of a 1024 x 1024 spatial grid, recording the trajectory of pollutant concentration changes. Differential analysis is used to calculate the change values ​​between time slices and identify the growth or decay paths of pollutant concentrations. Based on this, the KL divergence analysis method is used to calculate the differences in concentration evolution between regions, generating "air evolution trend difference data." This data records the distance measurement values ​​of the pollution evolution paths in pairs of regions, providing input for the identification of dominant factors.

[0037] Step S15: determining air composition evolution dominant factor data according to air evolution trend difference data; In the embodiment of the present application, the regions with significant differences in the air evolution trend difference data (KL divergence greater than 0.5) are analyzed in depth, and the principal component analysis (PCA) method is used to identify the dominant factors affecting the evolution. The pollutants represented by the first two principal components with a cumulative variance contribution rate of more than 85% are selected as the evolution dominant factors of the region. The dominant factors and their time-varying trends in different regions are counted to generate the air composition evolution dominant factor data. This data is indexed by region number and contains dominant factor categories, change direction (up / down), change speed, mutation time points, etc. It provides the basis for the construction of dynamic response characteristics.

[0038] Step S16: determining air dominant factor dynamic response characteristics based on air composition evolution dominant factor data.

[0039] In the embodiment of the present application, for the concentration time series of each dominant factor in the air composition evolution dominant factor data, the first-order difference sequence is calculated, and the fluctuation amplitude ΔC and the change duration ΔT are extracted. According to the set rules, ΔC greater than 20 μg / m³ and ΔT less than 2 h are identified as sudden increase type, ΔC less than 5 μg / m³ and ΔT greater than 6 h are identified as slow change type, and the rest are divided into periodic, sustained increase or sustained decrease. To further quantify the influence of the dominant factor, the integral function of the fluctuation type occurrence frequency, change intensity and time length in the whole region is calculated, and the air dominant factor dynamic response characteristics are obtained according to this. The feature structure contains dominant factor number, response category, response intensity level and spatial and temporal distribution region, which provides parameter support for pollution superposition identification.

[0040] Preferably, step S14 comprises the following steps: Step S141: performing 100x100m spatial distribution grid processing on the spatial distribution of regional air composition to obtain air composition spatial distribution grid data; In the embodiment of the present application, for the "regional air composition spatial distribution situation" generated in step S13, the measured data coverage range in the research area is selected, and the entire spatial area is divided into equilateral square grid units, the unit size is set to 100m*100m, the division method is based on the WGS-84 coordinate system and the UTM projection system, and the spatial gridding operation is performed through the geographic information processing platform ArcGIS. In the process of grid division, it is ensured that each sensor position point is uniquely attributed to a single grid unit, and the actual position coordinates of the sensor are used as the attribution basis. For the grid units on the boundary of the area that cannot be covered, a data default identifier is set, and the grid is marked as empty. In each grid unit, all air composition measured values falling into the unit are collected to form air composition spatial distribution grid data, and the data structure is a three-dimensional array with dimensions of pollutant type, grid number and measurement time. The gridding result provides a structured spatial distribution basis for subsequent calculation.

[0041] Step S142: According to the air composition spatial distribution grid data, the air composition grid mean distribution situation is calculated; In the embodiment of the present application, based on the air composition spatial distribution grid data obtained in step S141, the statistical calculation is performed for each type of pollutant in each grid unit. The statistical method uses the arithmetic mean formula to average the pollutant concentration values at multiple sampling time points in each grid unit. The specific operation is as follows: the time period is divided into every hour, the sum of all sampling values in the time period is divided by the sample number to obtain the hourly grid concentration mean value. The hourly mean values of all grids are uniformly generated into a two-dimensional matrix, the matrix rows and columns correspond to the geographical coordinate grid point numbers, and the matrix values are the air composition grid mean distribution values of the corresponding grid units to form the "air composition grid mean distribution situation". This data is used to depict the smooth characteristics of air quality in the spatial dimension, and provides input for subsequent time series evolution modeling.

[0042] Step S143: The air composition grid mean distribution situation is continuously sampled in different time segments to obtain time series grid concentration sampling data; In the embodiment of the present application, the "air composition grid mean distribution situation" obtained in step S142 is continuously sampled according to fixed time segments. The time segment division adopts a sliding time window method, the sliding window size is set to 1 hour, the sliding step is 30 minutes, and all time segments covering 7 consecutive days are covered. The corresponding grid mean data is extracted in each time window, organized in time sequence, and formed into "time series grid concentration sampling data". The data structure is a four-dimensional matrix with dimensions of pollutant type, time segment number, grid number and grid mean concentration value. All data are archived in a structured database, indexed by pollutant type and time, and an index field is established to facilitate subsequent path extraction and trend analysis processing.

[0043] Step S144: Intra-regional multi-time period concentration evolution path extraction is performed on the time sequence grid concentration sampling data to obtain air component concentration evolution trajectory data. In the embodiment of the present application, the intra-regional air pollutant multi-time period concentration evolution path is extracted from the "time sequence grid concentration sampling data" generated in step S143. The concentration gradient vector analysis method is used to calculate the direction and amplitude change of the concentration change of the grid unit in the continuous time segment. The specific operation includes: for any two adjacent time periods, the concentration difference of each grid is calculated, and the local concentration migration vector field is constructed in combination with the geographical adjacent grid direction. Further, a directed graph structure composed of grid connection relationships is formed within the entire region, the nodes are grid numbers, and the edge weights are concentration difference and direction information. By performing a breadth-first traversal algorithm on the graph structure, the continuous concentration change path and its time label are recorded to obtain "air component concentration evolution trajectory data", which contains the migration trend of each pollutant in the spatial dimension and the change trajectory in the time dimension.

[0044] Step S145: Air component evolution trend feature analysis is performed on the air component concentration evolution trajectory data to obtain air component evolution trend feature data. In the embodiment of the present application, the "air component concentration evolution trajectory data" output in step S144 is subjected to trend feature extraction, and a multi-dimensional concentration change clustering method (such as DBSCAN) is used to identify the concentration evolution mode. The concentration change rate, change direction, and consistency degree in the concentration evolution path are extracted as features, and the path is divided into different evolution types according to the evolution features, including continuous increase type, periodic fluctuation type, and sharp mutation type. For each path type, the average growth rate, stability coefficient, average duration, and spatial diffusion range are extracted. The trend feature parameters of all paths are integrated to generate "air component evolution trend feature data". The data takes path number as index and records evolution type label, trend intensity value, and spatial coverage range, etc. to provide a quantitative index basis for inter-regional difference calculation.

[0045] Step S146: Inter-regional air component evolution difference analysis is performed according to the air component evolution trend feature data to obtain air evolution trend difference data. In the embodiment of the present application, based on the "air composition evolution trend feature data" formed in step S145, the spatially adjacent regions are taken as the basic analysis unit to construct the inter-regional evolution feature comparison matrix. The specific operation is as follows: any two adjacent regions are selected, and the evolution trend feature vector groups of all pollutants in the regions are extracted respectively, and the Mahalanobis distance or KL divergence is calculated as the measurement index of the inter-regional air composition evolution difference. The above operation is performed on all region pairs to construct a complete inter-regional evolution trend difference matrix, and each element in the matrix represents the pollution trend difference intensity between the region pairs. The air evolution trend difference data is output, which includes region pair number, pollutant type, trend difference measurement value and difference level label (low difference, medium difference and high difference), and provides accurate input basis for subsequent dominant factor identification and dynamic response feature construction.

[0046] Preferably, step S16 comprises the following steps: Step S161: performing difference operation on the air composition evolution dominant factor data to obtain air dominant factor time series difference features; In the embodiment of the present application, the air composition evolution dominant factor data obtained in step S15 is subjected to difference operation. The specific operation of difference operation is as follows: the numerical difference of the dominant factor at two consecutive time points is calculated in sequence to obtain a group of difference value data. The difference value data can reflect the change amplitude of the dominant factor concentration over time, remove the influence of the overall trend, and thus highlight the characteristics of short-term fluctuations. The difference process is completed by using the difference function in the data processing software, the input is the time series data of the dominant factor concentration, and the output is the air dominant factor time series difference feature sequence. The obtained difference data will be used as the basis data for subsequent fluctuation amplitude analysis.

[0047] Step S162: identifying the dominant factor fluctuation amplitude change according to the air dominant factor time series difference features; In the embodiment of the present application, based on the air dominant factor time series difference feature sequence obtained in step S161, the difference amplitude of each time point is classified and determined. The specific operation is as follows: according to the historical air quality monitoring data in the region, different fluctuation amplitude thresholds are preset to distinguish low fluctuation, medium fluctuation and high fluctuation levels. The absolute values of the time series difference data are compared point by point, and the corresponding fluctuation level labels are marked respectively. The processing is realized by conditional judgment and data traversal to form a fluctuation amplitude change label sequence, which reflects the fluctuation intensity of the dominant factor at each time point. The label sequence provides a basis for subsequent continuous duration marking and fluctuation type judgment.

[0048] Step S163: Labeling the change duration of the dominant factor fluctuation amplitude variation, and obtaining dominant factor fluctuation change duration data; In the embodiment of the present application, the fluctuation amplitude variation label sequence obtained in step S162 is subjected to continuous fluctuation section recognition and duration labeling. The specific method is as follows: the fluctuation amplitude label sequence is traversed, and the time interval of each continuous section maintaining the same fluctuation level is recognized and recorded. The time length of each continuous section is calculated, which is usually obtained by multiplying the time interval of continuous sampling by the number of time points. Each continuous fluctuation duration is labeled, and a data structure is formed to record the fluctuation level, start time, end time and duration information. The labeling result provides a basis for subsequent fluctuation type analysis and influence calculation.

[0049] Step S164: Labeling the dominant factor fluctuation type of the dominant factor fluctuation amplitude variation, and obtaining dominant factor fluctuation type data; In the embodiment of the present application, based on the fluctuation amplitude variation and duration data obtained in steps S162 and S163, the fluctuation type of the air dominant factor is labeled and classified. The specific operation is as follows: combined with the continuous fluctuation duration and fluctuation mode, the classification is performed according to the pre-defined categories, mainly including five types of sudden increase fluctuation (sharp rise in a short time), slow change fluctuation (slow change process), periodic fluctuation (regular periodic cycle exists), continuous rise fluctuation (long-term gradual upward trend) and continuous decline fluctuation (long-term gradual downward trend). By analyzing the change trend and time length of the time series difference data, the belonging fluctuation type is judged and labeled. The fluctuation type data is arranged into a label sequence for subsequent dominant influence calculation.

[0050] Step S165: Calculating the dominant influence according to the dominant factor fluctuation type data and the dominant factor fluctuation change duration data, and obtaining dominant factor influence data; In the embodiment of the present application, the fluctuation type data obtained in step S164 and the fluctuation change duration labeled in step S163 are combined to calculate the influence of each dominant factor. The specific method is as follows: according to the characteristics and duration of different fluctuation types, weight values are assigned to calculate the comprehensive influence score. The calculation process is based on a rule base, and the weight can be set according to the empirical data of the influence of the dominant factor on air quality. The calculation formula is executed by a data processing program, the input is the fluctuation type and the corresponding duration, and the output is the influence data of each dominant factor, which forms quantitative data reflecting the comprehensive influence of the dominant factor, and is used for dynamic response characteristic judgment.

[0051] Step S166: Determining the air dominant factor dynamic response characteristic based on the dominant factor influence data and the dominant factor fluctuation amplitude variation; In the embodiment of the present application, based on the dominant factor influence data obtained in step S165 and the fluctuation amplitude change obtained in step S162, the dynamic response characteristics of the air dominant factor are determined. The specific operation is: comprehensive analysis of the influence value and the fluctuation amplitude distribution, combined with the change trend in the time dimension, to generate the dominant factor dynamic response characteristic description data. The characteristic data covers the fluctuation intensity, change type, duration and influence level of the dominant factor, etc. to form a standardized feature set. Through database storage and calling of the data, key input information is provided for subsequent air quality risk assessment and early warning, and the accurate description of dynamic response capability is realized.

[0052] Preferably, the air multi-period pollution superposition situation is detected according to the air dominant factor dynamic response characteristics in step S2, including: According to the air dominant factor dynamic response characteristics, the insufficient pollution concentration decay condition is estimated; In the embodiment of the present application, according to the obtained air dominant factor dynamic response characteristics, the fluctuation amplitude and duration of the dominant factor described by the characteristics are used to analyze the decay trend of the pollution concentration over time, extract the time series data of the air dominant factor concentration change over time, and calculate the decay ratio of the pollution concentration at each time point relative to the peak concentration. The sliding time window technology is adopted to statistically analyze the pollution concentration decay rate in the continuous time period, and to determine whether the pollution concentration decay reaches the preset decay threshold. If the concentration decay rate is lower than the threshold, the pollution concentration decay is recorded as insufficient. The result is stored in the form of decay rate curve and insufficient decay time period data to provide a basis for subsequent analysis.

[0053] According to the insufficient pollution concentration decay condition, the pollution factor lag response situation is determined; In the embodiment of the present application, the pollution concentration decay insufficient time period is used to analyze the lag response of the pollution factor in the region. The specific operation is: cross-time series correlation analysis is performed on the time series of different dominant factor concentrations, and the time lag difference between the concentration curves of each factor is calculated. By comparing the peak time of the dominant factor concentration with the subsequent concentration change time, the lag response time length of the pollution factor is determined. The lag response information is output in the form of time lag value and corresponding pollution factor identifier to provide a basis for judging the pollution residue trend.

[0054] Based on the pollution factor lag response situation, the non-continuous pollution residue trend of the region is statistically analyzed; In the embodiment of the present application, according to the pollution factor lag response time, combined with the air quality data of each monitoring point, the residual characteristics of pollutants in the region in the non-continuous time period are counted, the lag response period is divided into several non-continuous time intervals, the concentration of pollutants in each time interval is statistically analyzed, and the concentration average, peak value and fluctuation range are calculated. Map these statistical data to the spatial coordinate system to generate a non-continuous pollutant residual trend chart. The trend chart takes the spatial distribution and time period of pollutant residue as the core data to support the subsequent judgment of multi-period pollution superposition.

[0055] Statistical adjacent area pollutant residual trend based on insufficient pollutant concentration decay condition; In the embodiment of the present application, combined with the determination result of insufficient pollutant concentration decay, the pollutant residual situation in the geographical area adjacent to the target area is counted. The specific operation includes collecting air quality monitoring data in the adjacent area, using the same time window to analyze the time sequence of the pollutant concentration, calculating the residual rate and change trend of the pollutant in the adjacent area. Through the spatial interpolation method, the pollutant residual trend distribution map of the adjacent area is formed. The graphic data is compared and analyzed with the residual trend data of the target area to provide data support for judging the cross-regional diffusion and mixed growth of pollutants.

[0056] Estimate the mixed growth condition of air pollutants based on the non-continuous pollutant residual trend of the region and the pollutant residual trend of the adjacent area; In the embodiment of the present application, the non-continuous pollutant residual trend in the region and the pollutant residual trend in the adjacent area are integrated, and a multi-dimensional time-space data fusion technology is used to collect non-continuous pollutant residual data in the target area and pollutant residual data in the adjacent area. These data include the dynamic change of pollutant concentration with time and the distribution information in space. Then, the two types of data are uniformly time calibrated and spatially positioned to ensure that the time points and spatial coordinates of different data sources are consistent, realizing the comparability and fusion basis of the data. Then, using the multi-dimensional data fusion method, the time sequence and spatial coordinate information are combined to build a fusion data set containing time and space double dimensions, and the superposition analysis of the pollutant concentration change trend is carried out, focusing on identifying the superposition peak value of the pollutant concentration in different time periods and different geographical positions, and clearly identifying the cumulative effect of the pollutant concentration in space and the peak value intensity. By analyzing the number, duration and concentration amplitude of the pollutant superposition peak value, a series of indicators are formed to characterize the mixed growth state of the pollutant, which can quantify the mixed growth characteristics of the pollutant in the target area, including peak value frequency, peak value intensity and peak value duration, etc. The above statistical results are arranged into mixed growth indicators and output in the form of specific numerical values, which are used for subsequent judgment and risk assessment of multi-period air pollution superposition, to ensure accurate identification and quantitative description of the mixed growth state of the pollutant.

[0057] Estimate air pollutant chemical reaction growth condition according to air pollutant mixed growth condition; In the embodiment of the present application, based on the obtained mixed growth index, the chemical reaction growth of the pollutant is estimated in combination with the chemical properties and reaction mechanism of the air pollutant. The specific operation is: using historical environmental chemical data and reaction rate parameters, in combination with the peak value and duration of the mixed concentration of the pollutant, the chemical reaction generation rate of the pollutant and the concentration growth trend of the product are estimated. The estimation process relies on air chemical kinetics knowledge, and infers the time period and degree of chemical reaction intensification through concentration time series analysis. The estimation result includes the chemical reaction growth curve of the pollutant and the key time point data, which supports the monitoring of the reaction product of the pollutant.

[0058] Detect air pollutant reaction product amplification effect according to air pollutant chemical reaction growth condition; In the embodiment of the present application, in combination with the chemical reaction growth condition of the air pollutant, the amplification effect of the air pollutant reaction product is analyzed, and high-precision air quality monitoring equipment is used to continuously and dynamically monitor the concentration of multiple reaction products in the target area. The monitoring frequency is set to seconds or minutes to ensure that the subtle changes in the concentration of the reaction product are captured. The monitoring data includes the real-time concentration values of various reaction products and their time stamps, which are uniformly summarized and pretreated by the data acquisition system to eliminate abnormal values and noise data. Subsequently, using the ratio analysis method, the monitored concentration changes of the reaction product are compared with the concentration changes of the precursor pollutants (such as volatile organic compounds, nitrogen oxides, etc.) in the corresponding time period. The specific operation is to calculate the ratio change curve of the reaction product concentration relative to the precursor pollutant concentration, identify the amplification multiple of the product concentration relative to the precursor concentration, and the multiple reflects the increase and reaction activity level of the pollutant generated product in the chemical reaction process. Then, the ratio analysis result is statistically analyzed in the time dimension to calculate the change trend of the amplification multiple in different time periods, and the duration and intensity change of the amplification effect are determined, and the spatial coordinates of the monitoring point are combined to generate the spatial distribution map of the amplification effect using spatial interpolation and heat map drawing technology, revealing the distribution characteristics and change rules of the reaction product concentration amplification in different regions. The obtained reaction product concentration amplification multiple and its corresponding time and spatial distribution data are arranged into structured data, which are used as key input indicators for air multi-period pollution superposition judgment, ensuring accurate identification of pollution superposition effect and providing data basis for subsequent risk assessment.

[0059] Detect air multi-period pollution superposition according to air pollutant reaction product amplification effect and air pollutant mixed growth condition.

[0060] In the embodiment of the present application, the mixed growth index is combined with the reaction product amplification effect data to comprehensively evaluate the multi-period superposition phenomenon of air pollution. The specific operation includes time series superposition analysis and spatial distribution overlap detection to determine the cumulative effect and interaction degree of pollutants and their products in multiple time periods. Based on the threshold determination rule, it is determined whether the pollution superposition degree reaches the pollution risk level. The step outputs air multi-period pollution superposition data, including pollution superposition time interval, spatial range and intensity level, which provides data support for subsequent pollution risk growth trend judgment.

[0061] Preferably, determining the air quality pollution risk growth trend according to the air multi-period pollution superposition situation in step S2 comprises: determining the air pollution structure complication degree according to the air multi-period pollution superposition situation; In the embodiment of the present application, the air quality pollution risk growth trend is determined according to the air multi-period pollution superposition situation, which requires systematic analysis of the concentration data of each pollutant in the air multi-period pollution superposition situation to determine the complication degree of the air pollution structure. High-precision air quality monitoring equipment is used to continuously collect concentration data of various pollutants (such as PM2.5, PM10, NOx, SO2, O3, etc.) in multiple periods, and combined with spatial distribution information, a multi-dimensional structure model of air pollution components is constructed. Through statistical analysis method, the number of pollutant types, concentration distribution difference, coexistence ratio and other indicators are quantified to form quantitative data of pollution structure complication degree. This data reflects the diversity of pollutant types and the complexity of their spatial and temporal distribution, providing a basis for subsequent abnormal analysis of pollutant migration path.

[0062] estimating the abnormal situation of pollutant migration path according to the air pollution structure complication degree; In the embodiment of the present application, based on the above-mentioned air pollution structure complication degree, the abnormal situation of pollutant migration path is estimated. This process uses pollutant diffusion tracking technology to identify the deviation between the actual migration path of pollutants and the conventional path by spatial backtracking and path fitting of the concentration change trajectory of pollutants in the monitoring data. Using GIS geographic information system technology, the data of concentration change of pollutants with time is superimposed and analyzed with topography, meteorological conditions and emission source location to determine the abnormal area and degree of pollutant migration path, forming quantitative indicators of the abnormal situation of pollutant migration path.

[0063] monitoring the local thermal disturbance situation of air pollutants based on the abnormal situation of pollutant migration path; In the embodiment of the present application, based on the abnormal condition of the pollutant migration path, the local thermal disturbance of air pollutants is monitored. High-resolution thermal infrared remote sensing technology and ground temperature sensors are used to continuously collect ground and air temperature data of the target area, and combined with the spatial position of the abnormal area of the pollutant migration path, the temperature abnormal fluctuation of the area is monitored. Through the analysis of time series temperature data, it is determined whether there is thermal disturbance in the abnormal area of pollutant migration, including temperature peak, gradient change and heat vortex formation and other characteristics, to form a local thermal disturbance data set.

[0064] According to the local thermal disturbance of air pollutants, the trend of air secondary meteorological disturbance is predicted, and the trend of air secondary meteorological disturbance is obtained. In the embodiment of the present application, according to the local thermal disturbance of air pollutants, the trend of air secondary meteorological disturbance is predicted. Statistical analysis method based on historical meteorological data and on-site thermal disturbance data is used to predict the time series of the change trend of local thermal disturbance. The probability, intensity and duration of secondary meteorological disturbance in the future time period are predicted, and the trend data of air secondary meteorological disturbance is output as an important input for subsequent pollution-meteorological coupling analysis.

[0065] According to the abnormal condition of the pollutant migration path, the growth condition of the pollutant peak frequency is determined. In the embodiment of the present application, continuous time series data of pollutant concentration is obtained, which is collected by multiple monitoring stations in the region at fixed time intervals. For this time series data, a peak detection algorithm is used to identify the peak value events of the pollutant concentration, and the peak value is defined as the local maximum value and meets the preset minimum peak height and peak width threshold, so as to exclude the influence of noise and small fluctuations. By counting the number of peak value events identified by each monitoring point in the monitoring period, the number of peak values appearing in a unit time is calculated to form peak frequency data. Secondly, the peak frequency data is analyzed in space with the abnormal area of the pollutant migration path to confirm which peak value events occur in the abnormal area of the migration path. The number of peak value events and their time interval in the area are further analyzed to count the frequency change trend of the peak value events, including the shortening of the average time interval between peak values and the increase of the number of peak values. Using time series analysis method, the peak frequency is dynamically monitored to generate time series data of the growth of peak frequency. The time series data reflects the change trend of the concentration peak value of the pollutant in the abnormal area of the migration path, and can reveal the phenomenon that the abnormal migration path causes the frequent occurrence of the concentration peak value of the pollutant. Through the above processing, the specific growth condition data of the peak frequency of the pollutant is obtained, which provides a quantitative basis for the subsequent judgment of the growth trend of the air quality pollution risk. In the above steps, the algorithm parameters of peak detection and frequency statistics are strictly set according to the pre-defined standard to ensure the accuracy and consistency of data processing, and the data interface is used to realize the close connection between the steps to ensure the coherence of data flow.

[0066] Predicting pollution-weather coupling enhancement trend based on pollutant peak frequency growth condition and air secondary weather disturbance trend; In the embodiment of the present application, based on the pollutant peak frequency growth condition and the air secondary weather disturbance trend, the pollution-weather coupling enhancement trend is predicted. A data fusion method is used to jointly analyze the peak frequency time series and the secondary weather disturbance trend data to identify the mutual enhancement relationship between the pollutant concentration peak value and the weather disturbance. By statistical correlation and trend superposition analysis, the enhancement degree of the pollution-weather coupling phenomenon is determined to form the pollution-weather coupling enhancement trend data.

[0067] Monitoring air high pollution self-enhancement feedback chain according to pollution-weather coupling enhancement trend; In the embodiment of the present application, based on the previously obtained pollution-meteorological coupling enhancement trend data, combined with the time, space and concentration distribution characteristics of historical high pollution events, a comprehensive database containing pollutant concentration, meteorological parameters and pollution event evolution process is established. Through the database, the system dynamic analysis technology is used to deeply mine the time sequence characteristics and spatial distribution pattern of high pollution events, and to identify the continuous high pollution events triggered in the coupling enhancement process. The system dynamic analysis technology covers dynamic causal relationship identification, feedback link monitoring and time delay effect evaluation, through analyzing the mutual influence of pollutant diffusion, chemical transformation and meteorological condition change in the air, the internal mechanism and feedback chain of high pollution event formation are revealed. For the high pollution event triggered by pollution-meteorological coupling, a high pollution self-enhancement feedback chain state model containing factors such as pollutant emission intensity, meteorological disturbance intensity, pollutant chemical reaction rate and diffusion characteristics is constructed, and the interaction and duration of each link in the feedback chain are simulated and quantified by using the model, and the key nodes and key processes are identified. The output of the state model includes dynamic indicators of feedback chain operation, such as pollutant concentration increase rate, meteorological disturbance change rate and their interaction strength, so as to form the operation state data of high pollution self-enhancement feedback chain. The operation state data provides quantitative basis for subsequent air quality risk assessment and early warning, and realizes real-time identification and dynamic monitoring of continuous high pollution events, ensuring the continuity and accuracy of the feedback chain monitoring process. In the above process, the pollution-meteorological coupling enhancement trend data and the high pollution self-enhancement feedback chain state model are closely related, and the information is seamlessly transferred through the data input and output interface, forming a closed-loop monitoring system.

[0068] Determining the air quality pollution risk growth trend based on the air high pollution self-enhancement feedback chain; In the embodiment of the present application, based on the state data of the air high pollution self-enhancement feedback chain, the air quality pollution risk growth trend is determined. The running state of the feedback chain is combined with the concentration, distribution and meteorological conditions of the pollutants to comprehensively evaluate the growth trend of the pollution risk. The air quality pollution risk growth trend data is output as an important basis for overall air quality risk early warning and governance decision-making. The data of each step is transmitted through the interface to ensure the close connection and information continuity of the front and back processing links, forming a complete air quality pollution risk growth trend evaluation chain.

[0069] Preferably, the step S2 of estimating the ecological impact gradient growth trend according to the air quality pollution risk growth trend comprises: Detecting the acid accumulation status of air pollutants according to the air quality pollution risk growth trend; In the embodiment of the present application, according to the growth trend of air quality pollution risk, the detection of the acid accumulation condition of pollutants in the air is carried out. The operation is based on the continuous monitoring of air pollutant data, and special attention is paid to the accumulation of acid gases such as sulfur oxides (SOx) and nitrogen oxides (NOx). By time series analysis of the monitoring data, the cumulative concentration value of acid pollutants is calculated to form an acid accumulation concentration curve. The content of acid substances in the air sample is quantitatively determined by using high-precision chemical analysis instruments to ensure the accuracy of the data. The acid accumulation concentration is used as input data for the next estimation stage.

[0070] Estimating the aggravation degree of air acid rain deposition probability based on the acid accumulation condition of air pollutants; In the embodiment of the present application, based on the acid accumulation concentration data, the aggravation degree of air acid rain deposition probability is estimated. This process combines environmental meteorological data with chemical analysis methods, and comprehensively considers parameters such as precipitation, precipitation pH value and pollutant concentration to calculate the probability and intensity trend of acid rain formation. The specific method includes statistical analysis of the precipitation pH value collected by regional meteorological stations, combined with pollutant emissions and atmospheric diffusion conditions, to calculate the spatial distribution and time frequency of acid rain occurrence. The result forms an acid rain deposition probability aggravation index to quantify the potential growth trend of acid rain events in the region.

[0071] Determining the degree of abnormal soil acidification based on the aggravation degree of air acid rain deposition probability; In the embodiment of the present application, according to the aggravation degree of acid rain deposition probability, the degree of abnormal soil acidification in the region is determined. This step collects soil samples in the region, measures the change of soil pH value using soil pH detector, and focuses on analyzing the soil acidification level in the acid rain affected area. By comparing the historical soil pH data with the current measurement results, the aggravation speed and range of soil acidification are calculated to obtain the soil abnormal acidification degree index. In addition, combined with the soil heavy metal solubility data, the influence of acidification on soil structure and nutrient status is further evaluated.

[0072] Determining the damaged condition of plant leaf structure in the region according to the aggravation degree of air acid rain deposition probability; In the embodiment of the present application, based on the determination of the aggravation degree of acid rain deposition probability, the monitoring of the damaged condition of plant leaf structure in the region is carried out. This step combines remote sensing technology and field sampling, uses high-resolution multispectral imaging equipment to collect vegetation leaf image data, and uses image processing technology to identify the corrosion, spot and discoloration area on the leaf surface. In cooperation with the optical microscope, the leaf cell structure is observed, the damaged area and degree of the leaf are quantitatively analyzed, and the plant leaf structure damage index is formed. The index reflects the direct influence of air pollution on the physiological health of vegetation.

[0073] According to the degree of abnormal acidification of regional soil and the damaged condition of leaf structure of plants, the attenuation of the photosynthetic efficiency of regional vegetation is detected; In the embodiment of the present application, the attenuation of the photosynthetic efficiency of regional vegetation is detected according to the degree of abnormal acidification of regional soil and the damaged condition of leaf structure of plants. Real-time photosynthetic parameter measurement of vegetation leaves is performed by a photosynthetic rate measuring instrument, including net photosynthetic rate, stomatal conductance and chlorophyll content. Fixed-point monitoring and dynamic sampling are combined to construct a time series curve of the photosynthetic efficiency of vegetation, and the decline amplitude of the photosynthetic efficiency is calculated. The data are correlated with the damaged index of leaves and the degree of soil acidification to obtain the attenuation condition of the overall photosynthetic function of vegetation in the region.

[0074] The abnormal attenuation condition of plant root structure is determined based on the attenuation of the photosynthetic efficiency of regional vegetation; In the embodiment of the present application, the abnormal attenuation condition of plant root structure is determined based on the attenuation data of the photosynthetic efficiency of vegetation. Detailed information of root cell wall damage, root hair shedding and microbial flora change is obtained through underground root sampling and high-precision scanning electron microscope analysis. Root respiration rate and absorption capacity are detected by a root activity detector to quantitatively reflect the attenuation level of root function. The abnormal attenuation data of root structure and the aforementioned photosynthetic efficiency data are causally correlated to construct a comprehensive index system.

[0075] The aggravation of soil and water loss in the region is determined based on the abnormal attenuation condition of plant root structure; In the embodiment of the present application, the aggravation of soil and water loss in the region is determined based on the abnormal attenuation condition of plant root structure. Soil erosion monitoring equipment and soil and water conservation investigation methods are used in combination with remote sensing image analysis to quantitatively evaluate the soil erosion rate and loss range in the region. The aggravation degree of soil and water loss in the region with reduced vegetation coverage is monitored to form a soil and water loss aggravation index. The index reflects the soil structure damage and the decline of soil and water conservation capacity caused by abnormal plant root function.

[0076] The growth trend of the ecological impact gradient is estimated according to the aggravation of soil and water loss in the region and the abnormal attenuation condition of plant root structure.

[0077] In the embodiment of the present application, the growth trend of the ecological impact gradient is estimated according to the aggravation of soil and water loss in the region and the abnormal attenuation data of plant root structure. Comprehensive evaluation methods are used to weight and aggregate the aforementioned index data according to the weight to generate an index of the growth trend of the ecological impact gradient. The index reflects the dynamic change of the multi-level influence of air quality pollution on the ecological system, and provides a scientific basis for subsequent risk early warning and management. The steps are closely connected through data interfaces to realize continuous data flow and information integration, ensuring the integrity of the process and the accuracy of the data.

[0078] In another embodiment, the reduction of vegetation yield or health degree is calculated for atmospheric pollutants (such as O3, SO2) using existing dose-response models (such as AOT40 model); Evaluation method: Substitute the pollution level into the above model to obtain the corresponding ecological response value (such as vegetation damage rate, eutrophication risk index, etc.), and classify it as mild, moderate and severe to form a "gradient" of ecological impact. Calculate the results at different stages over time, i.e. the trend of ecological impact.

[0079] Please refer to Figure 3 , which is a schematic diagram of the time series analysis of the fluctuation amplitude of the dominant factor in the present application; Preferably, step S3 comprises the following steps: Step S31: Detecting the corrosion intensification of the surrounding equipment based on the growth trend of air quality pollution risk; In the embodiment of the present application, based on the growth trend of air quality pollution risk, the corrosion intensification of the surrounding equipment is detected. The operation uses metal corrosion monitoring sensors to monitor the key equipment in the region in real time, including electrochemical corrosion probes, resistance type corrosion rate measuring instruments, etc., which can accurately measure the corrosion rate and corrosion morphology of the equipment surface. Combined with the concentration of acidic pollutants in the air and humidity data, the corrosion rate calculation formula is used to quantitatively analyze the corrosion intensification degree. Key parameters such as corrosion current density and corrosion potential are collected during the monitoring process to form a corrosion intensification trend curve. This data is used as input for the subsequent steps to determine the corrosion pressure faced by the equipment.

[0080] Step S32: Determine the aging failure trend of the surrounding equipment according to the corrosion intensification of the surrounding equipment; In the embodiment of the present application, the aging failure trend of the surrounding equipment is determined according to the corrosion intensification of the surrounding equipment. Based on the corrosion monitoring data, combined with the historical maintenance records and service life data of the equipment, the statistical analysis method is used to calculate the aging process of the equipment. The specific operation is as follows: compare the corrosion rate of the equipment material with the material failure standard to evaluate the remaining life; secondly, combined with the degree of mechanical strength weakening caused by corrosion, an equipment aging model is established to infer the future failure probability. By analyzing the changes of the equipment aging indicators in time series, the aging failure trend data is generated. This trend data reflects the potential failure risk of the equipment caused by corrosion.

[0081] Step S33: Perform air quality abnormal state evaluation according to the aging failure trend of the surrounding equipment and the growth trend of the ecological impact gradient to obtain air quality abnormal state evaluation data; In the embodiment of the present application, according to the aging failure trend of the peripheral equipment and the gradient growth trend of the ecological influence, air quality risk assessment is carried out to obtain air quality abnormal state evaluation data. This step combines equipment aging risk and ecological influence data through multi-index fusion analysis to form an air quality risk comprehensive evaluation index. The operation process includes: standardizing various risk index data to ensure consistent data dimensions; secondly, using the weight assignment method to weight and sum the equipment aging risk and ecological influence data to calculate the comprehensive risk index; finally, setting a threshold according to the comprehensive risk index to judge the air quality abnormal risk level. This process realizes data storage and calculation through a database management system, outputs air quality abnormal state evaluation data for subsequent anomaly identification.

[0082] Step S34: determining air pollution source identification abnormality based on air quality abnormal state evaluation data; In the embodiment of the present application, based on the air quality abnormal state evaluation data, the air pollution source identification abnormality is determined. Using abnormal risk data, combined with the regional pollution source database, traceability analysis is carried out. In the operation, data comparison and spatial positioning technology are used to match high-risk areas and pollution source distribution. The specific steps include cross analysis of high-risk areas of abnormal risk to identify corresponding industrial emissions, traffic pollution and pollution sources. Through GIS (Geographic Information System) technology, the abnormal area and the pollution source are spatially superimposed to determine the abnormal pollution source. Output air pollution source identification abnormality data to provide accurate basis for subsequent management strategy. The data is transmitted between each step in sequence to ensure the coherence of the process and the accuracy of the information.

[0083] Especially important is that step S32 includes the following steps: Step S321: detecting the equipment metal surface material loss according to the peripheral equipment corrosion intensification; In the embodiment of the present application, the equipment metal surface material loss is detected according to the peripheral equipment corrosion intensification. High-precision metal surface detection instruments such as laser scanning thickness gauge or ultrasonic thickness gauge are used to regularly scan and measure the external metal surface of the equipment to obtain metal thickness and surface integrity data. During the measurement process, a multi-point scanning method is used to cover the key corrosion areas of the equipment surface to ensure the spatial representativeness of the data. The collected metal thickness data is compared and analyzed with the historical initial thickness data to calculate the material loss and then reflect the corrosion degree. Qualitative analysis of the morphological characteristics of the metal surface microcracks and corrosion pits is carried out in combination with surface micro-image acquisition equipment (such as an electron microscope). Through the above measurement and analysis, quantitative description data of the equipment metal surface material loss is obtained to provide an accurate basis for subsequent structure strength evaluation.

[0084] Step S322: estimating the equipment structure strength decline degree based on the equipment metal surface material loss; In the embodiment of the present application, the structural strength reduction degree of the equipment is estimated based on the material loss of the metal surface of the equipment. The structural mechanics analysis method is adopted, the measured metal material loss thickness data is input into the equipment structure model, and the current structure stress state is simulated. The mechanical properties of the key stress parts of the equipment are calculated by using the finite element analysis technology, and the stress concentration and strength reduction degree caused by the material loss are evaluated. According to the calculation results, the decline amplitude of the overall and local structural strength of the equipment is quantified, and the change of the carrying capacity of the equipment is evaluated in combination with the material mechanical property parameters (such as yield strength and fatigue limit). The equipment structural strength reduction degree data is output, and the current structure safety boundary of the equipment is determined.

[0085] Step S323: Estimate the equipment external structure leakage aggravation condition according to the equipment structural strength reduction degree; In the embodiment of the present application, the equipment external structure leakage aggravation condition is estimated according to the equipment structural strength reduction degree. The stress distribution change of the sealing part and the interface area of the equipment is analyzed by using the structural strength reduction data. The micro-cracks and leakage risks of the weak structure parts are judged in combination with the equipment design drawings and the sealing material performance parameters. The leakage detection is performed on the key parts by using the permeation test method (such as pressure difference test and gas tracer detection), and the leakage range and degree are confirmed. The leakage aggravation model is established by correlating the structural strength reduction degree with the leakage detection data, the equipment external structure leakage aggravation condition is quantitatively evaluated, and the leakage risk data output is formed.

[0086] Step S324: Estimate the equipment performance attenuation degree according to the equipment external structure leakage aggravation condition; In the embodiment of the present application, the equipment performance attenuation degree is estimated according to the equipment external structure leakage aggravation condition. Based on the leakage risk data, the influence of the leakage on the internal working condition of the equipment is analyzed, such as fluid pressure drop, medium loss and heat exchange efficiency reduction. In combination with the equipment operation parameter acquisition system, the change trend of the key performance indicators of the equipment, such as pressure, temperature and flow, is monitored. The performance attenuation amplitude is quantified by comparing the data of the normal working condition and the current working condition of the equipment. In combination with the leakage aggravation model, the overall performance attenuation degree of the equipment caused by the leakage is inferred, the performance attenuation data is formed, and the basis for equipment maintenance and repair is provided.

[0087] Step S325: Determine the peripheral equipment aging failure trend according to the equipment performance attenuation degree and the equipment structural strength reduction degree; In the embodiment of the present application, the aging failure trend of the peripheral equipment is determined according to the equipment performance attenuation degree and the equipment structure strength decline degree, the equipment structure strength decline data and the performance attenuation data are integrated, the time series analysis method is used to analyze the change trend and the correlation of the two types of indexes. By setting the failure threshold and the critical index, the current aging stage and the failure risk level of the equipment are judged. According to the trend analysis result, the aging failure trend data of the peripheral equipment is formed, the probability and the time window of the equipment failure are determined, and a quantitative basis is provided for risk warning and maintenance plan. This step realizes the failure trend judgment from physical wear to functional attenuation through comprehensive analysis of data, and ensures the accurate reflection of the influence of equipment aging in air quality risk assessment.

[0088] Preferably, step S4 comprises the following steps: Step S41: determining air quality management strategy data according to air pollution source identification abnormal situation and air quality abnormal state evaluation data; In the embodiment of the present application, the air quality management strategy data is determined according to the air pollution source identification abnormal situation and the air quality abnormal state evaluation data. The operation takes the pollution source identification abnormal situation data obtained in the foregoing step S34 and the air quality abnormal state evaluation data obtained in step S33 as input, uses a multi-factor analysis method to comprehensively evaluate the regional pollution characteristics, the pollutant types and the concentration levels, classifies and manages the abnormal pollution sources, labels different types such as industrial emissions, motor vehicle exhaust, dust and biological source pollution; secondly, according to the spatial distribution of pollution sources and the concentration distribution characteristics of pollutants, specific management measures are developed, such as setting up emission restriction schemes in industrial areas, implementing vehicle restriction measures in traffic-intensive areas, etc.; thirdly, the pollution degree and the influence range are analyzed in combination with the abnormal risk data, and a set of management strategy schemes are formed. The strategy data is saved in a structured data format, including management measure category, implementation area, management strength and time node, which serves as a basis for subsequent management execution.

[0089] Step S42: air quality management strategy defect evaluation based on air quality management strategy data, to obtain air quality management strategy defect data; In the embodiment of the present application, based on the air quality management strategy data, air quality management strategy defect evaluation is performed to obtain air quality management strategy defect data. In implementation, the actual emission reduction efficiency and coverage of the management measures are calculated by comparing and analyzing the historical data of the implementation effect of the management strategy, using the field monitoring data and the air quality change data before and after management. The specific operation includes: constructing a management effect evaluation index system, such as emission reduction rate, concentration drop amplitude and management coverage; applying statistical methods to evaluate the compliance and deficiencies of each index; identifying technical bottlenecks, inadequate implementation or inaccurate strategy matching defects in the management process. Through defect positioning and classification, management strategy defect data is formed, and specific problems existing in each management measure are clarified to provide data support for response mechanism optimization.

[0090] Step S43: performing air management response mechanism optimization processing according to the air quality management strategy defect data to obtain air management response mechanism optimization data; In the embodiment of the present application, according to the management strategy defect data, air management response mechanism optimization processing is performed to obtain air management response mechanism optimization data. This step identifies the weak links in the management scheme by analyzing the defect data, and adjusts the management parameters and response processes using an iterative optimization method. The specific operation is: adjusting the timeliness and pertinence of pollution control measures, optimizing pollution source control priority and resource allocation; introducing a real-time feedback mechanism to dynamically incorporate field monitoring results into management adjustment, improving response flexibility; combining management strategy defects and environmental change trends to adjust warning thresholds and early warning response levels in the response mechanism. The optimization results are embodied in the form of a data model, including the optimized management measure adjustment scheme, response process correction and implementation schedule, which serve as input data for the next step of management execution.

[0091] Step S44: performing air quality abnormality adjustment management using the air management response mechanism optimization data; In the embodiment of the present application, the air management response mechanism optimization data is used to perform air quality abnormality adjustment management. This operation specifically implements the optimized response mechanism scheme into management actions, including starting pollution source emission control, adjusting traffic control schemes, enhancing dust management and promoting clean energy replacement measures. During the execution process, the environmental monitoring system collects air quality parameters in real time to monitor the implementation effect of the management measures and the change of air quality, ensuring that the management measures accurately cover high-risk areas and key pollution periods. The management execution data includes measure start time, regional coverage, pollutant concentration change and equipment operation status, forming a closed-loop feedback, and the feedback data is fed back to the management strategy database to provide a basis for continuous improvement.

[0092] Especially important is that step S41 includes the following steps: Step S411: determining the air pollution source distribution according to the air pollution source identification abnormal situation; In the embodiment of the present application, the air pollution source distribution is determined according to the air pollution source identification abnormal situation. The spatial position of the target area is mapped by using the air pollution source identification abnormal data. The spatial coordinates and distribution range of each abnormal pollution source are accurately positioned by using the geographic information system (GIS) technology to superimpose the abnormal identification data and the regional map. The pollution source distribution is finely divided by using the spatial interpolation algorithm in combination with the spatial layout of the environmental monitoring station and the environmental factors such as wind direction and terrain. The pollution source distribution map is generated. The map directly reflects the spatial concentration and distribution density of the pollution source, and provides basic spatial data for subsequent emission substance parameter analysis. In the data processing process, the pollution source distribution data is ensured to correspond to the abnormal identification data one by one, and the pollution source distribution data is output as the input of the subsequent step.

[0093] Step S412: determining the pollution source emission substance parameter based on the air pollution source identification abnormal situation; In the embodiment of the present application, the pollution source emission substance parameter is determined based on the air pollution source identification abnormal situation. The emission substance data of the corresponding emission point is collected according to the identified abnormal pollution source. The high-precision gas analyzer and particulate matter monitoring equipment are used to continuously monitor the main pollutants such as SO2, NOx, PM2.5 and VOC, so as to obtain the emission concentration, emission rate and composition ratio. The emission substance data is sampled in real time, and the emission intensity and total amount of the emission substance are calculated in combination with the emission port type, equipment working condition and process parameter. The emission substance parameter data is cleaned and corrected to exclude abnormal values and sampling errors, so as to form an accurate pollutant emission parameter data set, which provides a quantitative basis for the pollution source abnormality level determination.

[0094] Step S413: determining the pollution source abnormality level data based on the pollution source emission substance parameter and the air pollution source distribution; In the embodiment of the present application, the pollution source abnormality level data is determined based on the pollution source emission substance parameter and the air pollution source distribution. The emission substance parameter data and the pollution source distribution data are combined, and the abnormality degree of each pollution source is determined by using the grading evaluation method. A multi-index comprehensive evaluation system is used, which includes the emission substance concentration, emission intensity, distribution density and regional environmental sensitivity index. A weight coefficient is assigned to each pollution source, and the comprehensive abnormality score is calculated. According to the score range, the pollution sources are divided into different abnormality levels, such as mild abnormality, moderate abnormality and severe abnormality. The abnormality level data is output in the form of numerical value and level label, which accurately reflects the environmental risk status of the pollution source and provides a grading reference for the development of treatment strategies.

[0095] Step S414: determining air quality management strategy data according to pollution source abnormality level data and air quality abnormality state evaluation data; In the embodiment of the present application, the pollution source abnormality level data and the air quality abnormality state evaluation data are associated and analyzed to form a decision support basis for the development of management strategies. According to the spatial distribution characteristics of the abnormality level and risk data, a strategy development framework is used to clearly identify the key areas and key pollution sources for management. Different management targets and technical measures are set for different abnormality levels, such as strengthening emission control, adjusting industrial production plans, or optimizing traffic management. The management strategy data includes management area division, management measure type, execution time node, and expected management effect index. The management strategy data is verified by an expert system or a rule base to ensure the pertinence and scientificity of the strategy. The structured management strategy data is output to guide subsequent management response and adjustment.

[0096] The present application also provides an air quality risk early warning and management integrated system for executing the air quality risk early warning and management integrated method as described above, which comprises: A dominant factor dynamic response module is configured to obtain regional air collection sample data, perform inter-regional air composition evolution difference analysis based on the regional air collection sample data, and obtain air evolution trend difference data, and determine air dominant factor dynamic response characteristics based on the air evolution trend difference data. An ecological impact gradient growth module is configured to detect air multi-period pollution superposition based on the air dominant factor dynamic response characteristics, determine an air quality pollution risk growth trend based on the air multi-period pollution superposition, and estimate an ecological impact gradient growth trend based on the air quality pollution risk growth trend. A pollution source identification module is configured to perform air quality abnormality state evaluation based on the air quality pollution risk growth trend to obtain air quality abnormality state evaluation data, and determine air pollution source identification abnormality based on the air quality abnormality state evaluation data and the ecological impact gradient growth trend. An air quality abnormality adjustment management module is configured to determine air quality management strategy data based on the air pollution source identification abnormality and the air quality abnormality state evaluation data, and execute air quality abnormality adjustment management using the air quality management strategy data.

[0097] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. An integrated method for air quality risk warning and control, characterized in that: The following steps are involved: Step S1: Acquire regional air sample data; Based on regional air sample data, the air composition evolution differences between regions are analyzed to obtain air evolution trend difference data; Determine the dynamic response characteristics of the air dominant factor based on the difference data of air evolution trend; Step S2: Detecting the overlapping of air pollution over multiple periods based on the dynamic response characteristics of the air dominant factors; determining the growth trend of air quality pollution risk based on the overlapping of air pollution over multiple periods; Estimate the ecological impact gradient growth trend based on the air quality pollution risk growth trend; Step S3: performing an air quality abnormality state assessment based on the air quality pollution risk growth trend to obtain air quality abnormality state assessment data; Identify abnormal air pollution sources based on abnormal air quality status assessment data and ecological impact gradient growth trends; Step S4: Determine air quality control strategy data based on abnormal conditions identified at air pollution sources and air quality abnormality status assessment data; and use the air quality control strategy data to perform air quality abnormality adjustment and control.

2. The integrated air quality risk warning and control method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire regional air sample data and set the air quality sensor's detection range to 0-1000 μg / m³, the detection sensitivity to ±1 μg / m³, and the gas sampling frequency to 1 Hz; Step S12: using an air quality sensor to detect regional air composition data on regional air sample data to obtain regional air composition data; Step S13: determining the spatial distribution of regional air components based on the regional air composition data; Step S14: performing an analysis of the difference in air composition evolution between regions based on the spatial distribution of regional air composition to obtain air evolution trend difference data; Step S15: determining the dominant factor data of air component evolution based on the air evolution trend difference data; Step S16: Determine the dynamic response characteristics of the air dominant factor based on the air composition evolution dominant factor data.

3. The integrated air quality risk warning and control method according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: performing 100×100m spatial distribution grid processing on the spatial distribution of regional air components to obtain air component spatial distribution grid data; Step S142: Counting the grid mean distribution of air components based on the air component spatial distribution grid data; Step S143: performing continuous sampling processing on the grid mean distribution of air components in different time segments to obtain time series grid concentration sampling data; Step S144: extracting the concentration evolution path of multiple time periods within the region from the time series grid concentration sampling data to obtain air component concentration evolution trajectory data; Step S145: performing air component evolution trend characteristic analysis on the air component concentration evolution trajectory data to obtain air component evolution trend characteristic data; Step S146: performing an analysis of the difference in air composition evolution among regions based on the air composition evolution trend characteristic data to obtain air composition evolution trend difference data.

4. The integrated air quality risk warning and control method according to claim 2 is characterized in that: Step S16 includes the following steps: Step S161: performing a differential operation on the data of the dominant factor of air component evolution to obtain a time series differential feature of the dominant factor of air; Step S162: Identify the fluctuation amplitude change of the dominant factor based on the time series difference characteristics of the air dominant factor; Step S163: Mark the duration of the change in the dominant factor fluctuation amplitude to obtain the duration data of the dominant factor fluctuation change; Step S164: performing dominant factor fluctuation type label processing on the dominant factor fluctuation amplitude change to obtain dominant factor fluctuation type data; Step S165: Calculate the dominant influence based on the dominant factor fluctuation type data and the dominant factor fluctuation change duration data to obtain the dominant factor influence data; Step S166: Determine the dynamic response characteristics of the air dominant factor based on the dominant factor influence data and the change in the dominant factor fluctuation amplitude.

5. The integrated air quality risk warning and control method according to claim 1 is characterized in that: In step S2, detecting the superposition of air pollution in multiple time periods according to the dynamic response characteristics of the air dominant factor includes: Estimate the insufficient attenuation of pollutant concentrations based on the dynamic response characteristics of the air dominant factors; Determine the delayed response of pollution factors based on the insufficient decay of pollutant concentrations; Statistical analysis of regional non-continuous pollutant residual trends based on the lagged response of pollution factors; Calculate the pollutant residual trend in adjacent areas based on the insufficient decay of pollutant concentration; Estimate the mixed growth of air pollutants based on the residual trends of non-continuous pollutants in the region and the residual trends of pollutants in adjacent regions; Estimate the growth status of chemical reactions of air pollutants based on the growth status of air pollutants mixture; Detect the amplification effect of air pollutant reaction products based on the growth status of air pollutant chemical reactions; The superposition of air pollution in multiple periods is detected based on the amplification effect of air pollutant reaction products and the mixed growth status of air pollutants.

6. The integrated air quality risk warning and control method according to claim 1 is characterized in that: In step S2, determining the growth trend of air quality pollution risk based on the superposition of air pollution in multiple time periods includes: Determine the complexity of the air pollution structure based on the superposition of air pollution in multiple time periods; Estimate abnormal conditions in pollutant migration paths based on the complexity of air pollution structures; Monitor local thermal disturbances of air pollutants based on abnormal conditions in pollutant migration paths; According to the local thermal disturbance of air pollutants, the trend of secondary air meteorological disturbance is predicted to obtain the trend of secondary air meteorological disturbance; Determine the pollutant peak frequency growth status based on the abnormal conditions of the pollutant migration path; Based on the growth of pollutant peak frequency and the trend of secondary air meteorological disturbance, the strengthening trend of pollution-meteorological coupling is predicted; Monitor the self-reinforcing feedback chain of high air pollution based on the increasing trend of pollution-meteorological coupling; Determine the growth trend of air quality pollution risk based on the self-reinforcing feedback chain of high air pollution.

7. The integrated air quality risk warning and control method according to claim 1 is characterized in that: In step S2, estimating the ecological impact gradient growth trend based on the air quality pollution risk growth trend includes: Detect the acid accumulation of air pollutants based on the growth trend of air quality pollution risks; Estimate the probability of increased acid rain deposition based on the acid accumulation of air pollutants; Determine the abnormal acidification degree of regional soil based on the probability of increased acid rain deposition in the air; Determine the damage to the plant leaf structure in the region based on the probability of increased acid rain deposition in the air; Detect the attenuation of regional vegetation photosynthetic efficiency based on the abnormal acidification of regional soil and the damage to regional plant leaf structures; Determine the abnormal attenuation of plant root structure based on the attenuation of regional vegetation photosynthetic efficiency; Determine the severity of regional soil erosion based on the abnormal attenuation of plant root structures; The ecological impact gradient growth trend is estimated based on the intensified soil and water loss in the region and the abnormal attenuation of plant root structure.

8. The integrated air quality risk warning and control method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Detecting the aggravation of corrosion of peripheral equipment based on the growth trend of air quality pollution risk; Step S32: determining the aging failure trend of the peripheral equipment according to the aggravated corrosion of the peripheral equipment; Step S33: evaluating the abnormal state of air quality based on the aging failure trend of peripheral equipment and the gradient growth trend of ecological impact, and obtaining abnormal state of air quality evaluation data; Step S34: determining an abnormal situation of the air pollution source identification based on the air quality abnormal state assessment data.

9. The integrated air quality risk warning and control method according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: determining air quality control strategy data based on air pollution source identification abnormality and air quality abnormality state assessment data; Step S42: performing an air quality control strategy deficiency assessment based on the air quality control strategy data to obtain air quality control strategy deficiency data; Step S43: Optimizing the air quality control response mechanism based on the air quality control strategy defect data to obtain air quality control response mechanism optimization data; Step S44: Utilize the air quality control response mechanism to optimize data and perform air quality abnormality adjustment and control.

10. An integrated air quality risk warning and management system, characterized by: For executing the air quality risk early warning and governance integrated method according to claim 1, the air quality risk early warning and governance integrated system comprises: The dominant factor dynamic response module is used to obtain regional air sample data; based on the regional air sample data, the air composition evolution difference analysis between regions is performed to obtain air evolution trend difference data; based on the air evolution trend difference data, the dynamic response characteristics of the air dominant factor are determined; The ecological impact gradient growth module is used to detect the superposition of air pollution over multiple periods based on the dynamic response characteristics of air dominant factors; determine the growth trend of air quality pollution risk based on the superposition of air pollution over multiple periods; and estimate the ecological impact gradient growth trend based on the growth trend of air quality pollution risk; The pollution source identification module is used to evaluate the abnormal air quality status based on the growth trend of air quality pollution risk and obtain abnormal air quality status assessment data; based on the abnormal air quality status assessment data and the ecological impact gradient growth trend, it determines the abnormal situation of air pollution source identification; The air quality anomaly adjustment and management module is used to determine air quality management strategy data based on abnormal conditions identified by air pollution sources and air quality anomaly status assessment data; and to perform air quality anomaly adjustment and management using air quality management strategy data.

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