AI-based voce governance data processing method and system

Abstract

This invention relates to the field of artificial intelligence technology, specifically to an AI-based VOCs governance data processing method and system, comprising the following steps: quantifying component concentration aggregation based on spatial topology and generating anomaly emission distribution sets; establishing physical transport equations to analyze the permeability of components in adjacent grids to form diffusion cross-boundary tracing maps; combining meteorological vectors and chemical dissipation to perform Gaussian plume reverse tracing evolution to obtain a source matrix; quantifying capacity difference to calculate governance overload risk; and outputting a VOCs targeted emission reduction and governance resource scheduling instruction set. In this invention, by constructing physical equations to accurately characterize cross-boundary flow dynamics to effectively solve the problem of feature omission, and by integrating attenuation dynamics for reverse calculation to significantly improve the accuracy of flow field source strength contribution allocation, the invention deeply analyzes the carrying capacity encroachment mapping relationship and implements spatial pressure clustering dynamic reorganization of constraint boundaries, and comprehensively optimizes the exposure risk management of sensitive areas to achieve full-chain targeted scheduling and efficient overall allocation of governance resources.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an AI-based method and system for processing VOCS governance data. Background Technology

[0002] The field of artificial intelligence (AI) involves using computers to simulate and execute tasks requiring human intelligence, including technologies such as machine learning, natural language processing, and computer vision. Core aspects of this field include intelligent decision support systems, automated reasoning, pattern recognition, data mining and analysis, and deep learning. AI technologies have permeated various industries, such as transportation, finance, and manufacturing, using intelligent algorithms and models to process, analyze, and predict data to improve efficiency, reduce costs, and optimize decision-making. Traditional AI-based VOCs (volatile organic compounds) governance data processing methods address the big data processing challenges in VOCs governance, employing methods such as data collection, preprocessing, and analysis. This method primarily involves collecting data from multiple sources, including environmental monitoring equipment and sensors, cleaning and standardizing the data, and then using machine learning or deep learning algorithms to analyze and predict VOCs trends. The optimized model is then used to adjust governance strategies. However, traditional methods rely on manually set rules and simple data models, lacking adaptability and efficiency.

[0003] Traditional data processing methods rely excessively on manually set rules for operation. Single-dimensional preprocessing methods cannot accurately capture the spatial correlation characteristics within multi-dimensional component concentration fields. When faced with complex and ever-changing cross-boundary transport scenarios, conventional evolutionary analysis is difficult to characterize the evolution of the driving force of material transport. Isolated algorithm models are prone to bias when calculating spatial contribution weights. Fixed processing mechanisms cannot dynamically respond to the coupled changes of meteorological wind fields and chemical dissipation. Static load allocation strategies lead to an imbalance in the allocation of environmental capacity resources, making it difficult to meet the needs of dynamic targeted emission reduction and optimized scheduling. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an AI-based method and system for processing VOCS governance data.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an AI-based VOCs governance data processing method, comprising the following steps: S1: Based on the gridded spatial division topology of the monitoring area, big data processing technology is used to measure the spatial correlation of multidimensional component concentration field data. The Moran index of VOCs components under different spatial slices is calculated to obtain the spatial distribution matrix of component concentration aggregation. The time series evolution deviation of grid concentration is calculated by the local outlier factor algorithm. The deviation exceeding the preset threshold is used as the mutation critical value to generate the spatiotemporal distribution topology set of emission source strength anomalies. S2: Based on the spatiotemporal distribution topology set of the emission source intensity anomalies, establish the physical equation of mass transport at the grid interface, analyze the spatial partial derivative of the concentration gradient and the component permeability between adjacent grids, screen grid cells whose cross-boundary transport exceeds the boundary carrying capacity, and form a cross-boundary tracking map of VOCs diffusion flux at the grid boundary. S3: Based on the cross-boundary tracking map of VOCs diffusion flux at the grid boundary, and combined with the regional meteorological wind field vector and the chemical dissipation rate of VOCs components in the atmosphere, a Gaussian plume reverse tracing evolution model is constructed. The Gaussian plume reverse tracing evolution model is based on the Lagrange particle reverse tracing algorithm. The chemical dissipation attenuation coefficient and the three-dimensional meteorological wind field vector parameters are substituted into the Gaussian plume reverse evolution equation. Reverse iteration is performed with a step size of 60 seconds. The emission contribution weight parameter is determined according to the proportion of the total reduction concentration on the reverse transmission path to the total excess concentration of the receiving grid. The absolute value of the emission source strength is calculated to obtain the responsibility source tracing matrix of wind field and chemical dissipation coupling. S4: Based on the location number of the source tracing matrix of the wind field and chemical dissipation coupling, quantify the capacity difference between the real-time emission carrying capacity and the reduction capacity of the existing treatment facilities for each responsibility grid. Subtract the current available reduction capacity from the real-time emission carrying capacity to obtain the net emission exposure. Divide the net emission exposure by the environmental capacity benchmark to obtain the encroachment ratio. Normalize the encroachment ratio into an overload risk index as the degree of overload risk through a logarithmic risk assessment function. Calculate the degree of overload risk of the regional treatment load and output a gridded treatment load pressure spatial classification table.

[0006] As a further aspect of the present invention, the spatiotemporal distribution topology set of emission source intensity anomalies includes a concentration aggregation spatial coordinate matrix, abrupt change critical threshold nodes, a grid topology connection sequence, and local outlier calibration parameters; the grid boundary VOCs diffusion flux cross-border tracking map includes a material transport driving force vector table, an interface permeability distribution block, and a cross-border transport dynamic flow chain; the source tracing matrix of wind field and chemical dissipation coupling includes a chemical dissipation attenuation coefficient set, reverse transport path weight coordinates, and multi-source contribution quantification duty cycle; and the gridded governance load pressure spatial classification table includes a net emission exposure difference table, an environmental capacity encroachment index, and cluster classification boundary labels.

[0007] As a further aspect of the present invention, the step of obtaining the spatiotemporal distribution topology set of emission source intensity anomalies specifically includes: S111: Based on the gridded spatial division topology of the monitoring area, the big data processing engine is used to read the multidimensional concentration field data of VOCs components in the grid unit, calculate the Moran index of each VOCs component under the differentiated spatial slice and measure the spatial correlation to obtain the spatial distribution matrix of component concentration aggregation. S112: Based on the spatial distribution matrix of component concentration aggregation, the local outlier factor algorithm is introduced to calculate the time series evolution deviation of grid concentration, capture the spatial coordinate nodes where the aggregation index rises sharply and the deviation exceeds the limit, and obtain the spatial extreme value cluster of concentration change. S113: For the spatial extreme value clusters of abrupt concentration changes, map them to the geographic coordinate system of the monitoring grid, analyze the topological connection relationship between the extreme value clusters and the grid numbers, and generate a spatiotemporal distribution topology set of emission source intensity anomalies.

[0008] As a further aspect of the present invention, the steps for obtaining the cross-boundary tracing map of VOCS diffusion flux at the grid boundary are as follows: S211: Based on the spatiotemporal distribution topology set of the emission source intensity anomaly, extract the interface coordinates between the anomaly grid and its adjacent grids, substitute them into the physical equation of mass transport to solve the spatial partial derivative of the concentration gradient on the interface, and obtain the distribution map of the mass transport driving force between adjacent grids. S212: Based on the distribution map of mass transport driving force between adjacent grids, the permeability of VOCs components at the interface is calculated by combining the atmospheric turbulence diffusion coefficient, the absolute amount of cross-boundary transport within a unit period is calculated, and a dynamic flow direction sequence of cross-boundary transport flux between grids is generated. S213: Call the dynamic flow sequence of inter-grid cross-boundary transport flux, compare it with the preset grid boundary bearing capacity threshold, filter and define the boundary areas where the transport flux is in an overloaded state and the associated grid numbers, and form a grid boundary VOCS diffusion flux cross-boundary tracking map.

[0009] As a further aspect of the present invention, the steps for obtaining the source tracing matrix of the wind field and chemical dissipation coupling are specifically as follows: S311: Based on the cross-boundary tracking map of VOCs diffusion flux at the grid boundary, extract the three-dimensional meteorological wind field vector parameters of the overload transport associated grid, and combine the photochemical reaction consumption rate of key VOCs components with the secondary organic aerosol conversion rate to obtain the chemical dissipation attenuation coefficient. S312: Based on the chemical dissipation attenuation coefficient and the three-dimensional meteorological wind field vector parameters, substitute them into the Gaussian plume reverse evolution equation to perform multi-source reverse tracing evolution calculation, deduce the spatial transport trajectory of VOCs pollutants in the original time period, and obtain the reverse transport path attenuation layer. S313: Call the reverse transmission path attenuation layer, calculate the emission contribution weight parameter of each transport source grid to the high concentration accumulation area, aggregate the absolute value of emission source strength of high weight grids, and obtain the responsibility source tracing matrix of wind field and chemical dissipation coupling.

[0010] As a further aspect of the present invention, the steps for obtaining the gridded management load pressure spatial classification scale are as follows: S411: Based on the source tracing matrix of the wind field and chemical dissipation coupling, extract the actual emission source strength equivalent in the high-weight responsibility grid, compare the design reduction capacity of the current end-of-pipe treatment facilities in the grid with the current operating loss, and calculate the net emission exposure of a single grid dimension. S412: Based on the net emission exposure of the single grid dimension, introduce the environmental capacity benchmark of the surrounding area, calculate the proportion of net exposure to the environmental capacity benchmark, and quantify the governance load overload risk index of each responsible grid. S413: Based on the governance load overload risk index, the responsibility grid is spatially clustered using the natural breakpoint grading method. Corresponding pressure rating labels are assigned to the differentiated clustering intervals, and a gridded governance load pressure spatial grading scale is output.

[0011] As a further aspect of the present invention, the method further includes step S5: S5: Call the gridded governance load pressure spatial classification table, compare the load pressure level of the differentiated grid with the spatial proximity of the environmentally sensitive target, deconstruct the targeted emission reduction constraints, reorganize the load allocation weight of the governance equipment, and output the VOCs targeted emission reduction and governance resource scheduling instruction set; The VOCs-targeted emission reduction and governance resource scheduling instruction set includes environmental exposure sensitivity coefficient values, spatial coordinated emission reduction quotas, equipment power adjustment limit codes, and operating condition load reduction constraint ratios.

[0012] As a further aspect of the present invention, the step of obtaining the VOCs-targeted emission reduction and governance resource scheduling instruction set specifically includes: S511: Call the gridded governance load pressure spatial classification table, extract the grid number corresponding to the high pressure classification label, perform spatial overlay analysis on the geographical boundary of the grid in the specified area and the protection distance limit spatial layer of the urban environmental sensitive target, and deconstruct to obtain the environmental exposure sensitivity coefficient. S512: Based on the environmental exposure sensitivity coefficient, combined with the source strength category and the adjustable load margin of the treatment equipment in the high-pressure grid, construct a multi-objective function for targeted emission reduction, calculate the optimal load reduction ratio under the overall compliance condition of the region, and obtain the spatial coordinated emission reduction quota benchmark. S513: Based on the spatially coordinated emission reduction quota benchmark, reorganize the load allocation weight of cross-grid treatment equipment, identify the operating condition restriction instructions for key abnormal pollution sources and the power adjustment parameters of end-of-pipe treatment facilities, and output a set of VOCs targeted emission reduction and treatment resource scheduling instructions.

[0013] The AI-based VOCs governance data processing system is used to execute the aforementioned AI-based VOCs governance data processing method, and the system includes: The spatial correlation analysis module, based on the gridded spatial division topology of the monitoring area, uses a big data processing engine to measure the spatial correlation of component concentration fields. It obtains the spatial distribution matrix of component concentration aggregation by calculating the Moran index of VOCs components under different spatial slices, and calculates the time series evolution deviation of grid concentrations through the local outlier factor algorithm. The deviation exceeding the preset threshold is used as the mutation critical value to generate a spatiotemporal distribution topology set of emission source strength anomalies. The transboundary flux tracking module, based on the spatiotemporal distribution topology set of the emission source intensity anomaly, analyzes the spatial partial derivatives of the concentration gradient and component permeability between adjacent grids, filters grid cells whose transboundary transport exceeds the boundary carrying capacity, and establishes a transboundary tracking map of VOCs diffusion flux at the grid boundary. The responsible source reverse inference module, based on the cross-boundary tracing map of VOCs diffusion flux at the grid boundary, integrates wind field vectors and chemical dissipation attenuation coefficients to perform Gaussian reverse tracing evolution. The Gaussian plume reverse tracing evolution model is based on the Lagrange particle reverse tracing algorithm. It substitutes the chemical dissipation attenuation coefficients and three-dimensional meteorological wind field vector parameters into the Gaussian plume reverse evolution equation, performs reverse iteration with a step size of 60 seconds, determines the emission contribution weight parameter according to the proportion of the total reduction concentration on the reverse transmission path to the total excess concentration of the receiving grid, calculates the absolute value of emission source strength, and outputs the responsible source tracing matrix coupled with wind field and chemical dissipation. The governance load classification module, based on the source tracing matrix of the wind field and chemical dissipation coupling, subtracts the current available reduction capacity from the real-time emission carrying capacity to obtain the net emission exposure, divides the net emission exposure by the environmental capacity benchmark to obtain the encroachment ratio, normalizes the encroachment ratio into an overload risk index as the degree of overload risk through a logarithmic risk assessment function, and performs pressure clustering based on the breakpoint grading method to generate a gridded governance load pressure spatial grading table; The targeted scheduling and control module, based on the gridded governance load pressure spatial classification scale, analyzes the environmental exposure sensitivity limit, solves the collaborative emission reduction quota and reorganizes the equipment load allocation weight, and outputs a VOCs targeted emission reduction and governance resource scheduling instruction set.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention quantifies the concentration aggregation of components based on spatial topology and constructs physical transport equations to accurately characterize the cross-boundary flow dynamics of materials, effectively solving the problem of missing spatial features caused by single processing methods. It integrates meteorological wind fields and photochemical attenuation dynamics for inverse deduction and calculation, significantly improving the accuracy and robustness of source strength contribution allocation under complex flow field environments, strengthening the ability to trace the source of abnormal fluctuations, deeply analyzing the encroachment mapping relationship between emission exposure and environmental carrying capacity benchmarks and implementing spatial clustering, dynamically reorganizing equipment working constraint boundaries, comprehensively optimizing the pollution exposure risk management path in environmentally sensitive areas, and ultimately achieving full-chain targeted scheduling and efficient allocation of governance resources from macro-capacity assessment to micro-load reduction. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the spatiotemporal distribution topology set of emission source intensity anomalies in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the cross-boundary tracing map of VOCS diffusion flux at the grid boundary in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the source tracing matrix for the coupling of wind field and chemical dissipation in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the spatial classification scale of grid-based load pressure in this invention. Figure 6 This is a flowchart illustrating the process of obtaining the VOCs targeted emission reduction and governance resource scheduling instruction set in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Example 1, please refer to Figure 1This invention provides a technical solution, an AI-based method for processing VOCs governance data, comprising the following steps: S1: Based on the gridded spatial division topology of the monitoring area, big data processing technology is used to measure the spatial correlation of multidimensional component concentration field data. The Moran index of VOCs components under different spatial slices is calculated to obtain the spatial distribution matrix of component concentration aggregation. The time series evolution deviation of grid concentration is calculated by the local outlier factor algorithm. The deviation exceeding the preset threshold is used as the mutation critical value to generate the spatiotemporal distribution topology set of emission source strength anomalies. S2: Based on the spatiotemporal distribution topology set of emission source intensity anomalies, establish the physical equation of mass transport at the grid interface, analyze the spatial partial derivative of the concentration gradient and the component permeability between adjacent grids, screen grid cells whose cross-boundary transport exceeds the boundary carrying capacity, and form a cross-boundary tracking map of VOCs diffusion flux at the grid boundary. S3: Based on the cross-boundary tracking map of VOCs diffusion flux at the grid boundary, and combined with the regional meteorological wind field vector and the chemical dissipation rate of VOCs components in the atmosphere, a Gaussian plume reverse tracing evolution model is constructed. The Gaussian plume reverse tracing evolution model is based on the Lagrange particle reverse tracing algorithm. The chemical dissipation attenuation coefficient and the three-dimensional meteorological wind field vector parameters are substituted into the Gaussian plume reverse evolution equation. Reverse iteration is performed with a step size of 60 seconds. The emission contribution weight parameter is determined according to the proportion of the total reduction concentration on the reverse transmission path to the total excess concentration of the receiving grid. The absolute value of the emission source strength is calculated to obtain the responsibility source tracing matrix of wind field and chemical dissipation coupling. S4: Based on the location number of the source tracing matrix of the responsibility source coupled with wind field and chemical dissipation, quantify the capacity difference between the real-time emission carrying capacity and the reduction capacity of the existing treatment facilities for each responsibility grid. Subtract the current available reduction capacity from the real-time emission carrying capacity to obtain the net emission exposure. Divide the net emission exposure by the environmental capacity benchmark to obtain the encroachment ratio. Normalize the encroachment ratio into an overload risk index as the degree of overload risk through a logarithmic risk assessment function. Calculate the degree of overload risk of the regional treatment load and output a gridded treatment load pressure spatial classification table. S5: Call the gridded governance load pressure spatial classification table, compare the load pressure level of the differentiated grid with the spatial proximity of environmentally sensitive targets, deconstruct the targeted emission reduction constraints, reorganize the load allocation weight of the governance equipment, and output the VOCs targeted emission reduction and governance resource scheduling instruction set.

[0019] The spatiotemporal distribution topology set of emission source strength anomalies includes a concentration aggregation spatial coordinate matrix, steep change critical threshold nodes, grid topology connection sequence, and local outlier calibration parameters. The cross-border tracking map of VOCs diffusion flux at the grid boundary includes a material transport driving force vector table, interface permeability distribution block, and cross-border transport dynamic flow chain. The source tracing matrix of wind field and chemical dissipation coupling includes a set of chemical dissipation attenuation coefficients, reverse transport path weight coordinates, and multi-source contribution quantification duty cycle. The gridded governance load pressure spatial classification table includes a net emission exposure difference table, environmental capacity encroachment index, and cluster classification boundary labels. The VOCs targeted emission reduction and governance resource scheduling instruction set includes environmental exposure sensitivity coefficient values, spatial collaborative emission reduction quotas, equipment power adjustment limit codes, and operating condition load reduction constraint ratios.

[0020] Please see Figure 2 The specific steps for obtaining the spatiotemporal distribution topology set of emission source intensity anomalies are as follows: S111: Based on the gridded spatial division topology of the monitoring area, the big data processing engine is used to read the multidimensional concentration field data of VOCs components in the grid unit, calculate the Moran index of each VOCs component under the differentiated spatial slice and measure the spatial correlation to obtain the spatial distribution matrix of component concentration aggregation. A 1000m x 1000m two-dimensional geospatial grid system covering the target monitoring area was constructed. The 120 grid cells were geocoded using a latitude and longitude coordinate system and assigned spatial identifiers. Using photoionization detectors deployed at the grid nodes and a gas chromatography-mass spectrometry (GC-MS) system, raw sequence data of multidimensional concentration fields of volatile organic compounds (VOCs) within the grid cells were simultaneously acquired at 15-minute intervals. The acquired data covered the concentration values ​​of 15 species, including benzene, toluene, and xylene. Preprocessing and filtering were performed on the raw time-series data. The Laida criterion was used to remove noise points deviating from the mean by more than three standard deviations. Cubic spline interpolation was used to repair missing data due to transmission packet loss. The maximum-minimum normalization method was used to map the component concentration parameters to a dimensionless interval of 0 to 1, eliminating the interference of characteristic dimension differences on subsequent spatial measurements. For the preprocessed multidimensional concentration field data, the toluene component concentration values ​​of all 120 grid cells in a single time slice were extracted, and a global spatial weight matrix was constructed. The weight matrix elements are based on the inverse of the geometric distance between grid center points; the closer the grids are, the larger the spatial weight coefficient. The deviation between the global average concentration parameter and the grid cell concentration is calculated. The spatial weight matrix is ​​cross-multiplied with the concentration deviation vector, and divided by the sum of the grid concentration variances to derive the global Moran's index for volatile organic compound components. When the calculated global Moran's index is greater than 0.3, the component is determined to exhibit positive spatial autocorrelation characteristics within the monitoring area. Based on this, the local Moran's index is further calculated by multiplying the concentration observation value of the grid itself with the local spatial cross-multiplication of the concentration observation values ​​of adjacent grids to measure the spatial correlation strength within a single grid domain environment. Based on the local Moran's index calculation results, continuous spatial grid coordinates with a local spatial correlation strength greater than 0.5 are selected. The grid number and concentration values ​​are then concatenated in an upgraded dimension to generate a spatial distribution matrix of component concentration clusters.

[0021] S112: Based on the spatial distribution matrix of component concentration clusters, the local outlier factor algorithm is introduced to calculate the time series evolution deviation of grid concentration, capture the spatial coordinate nodes where the clustering index rises sharply and the deviation exceeds the limit, and obtain the spatial extreme value cluster of concentration change. For the spatial distribution matrix of component concentration aggregation, 48 consecutive observation periods were segmented according to the time series dimension. The absolute concentration values ​​of each grid in each period were extracted to form a one-dimensional time series vector. A local outlier factor algorithm was introduced to calculate and analyze the evolutionary deviation of the time series vector. The nearest neighbor range hyperparameter k of the algorithm was set to 15. The Euclidean distance from the target grid time series data point to the 15th nearest neighbor data point was calculated and defined as the 15th distance. For any reference point in the neighborhood of the target point, the maximum value between the 15th distance of the reference point and the true Euclidean distance between the two points was taken as the reachability distance of the target point relative to the reference point. The reachability distances of all 15 reference points in the neighborhood of the target point were summarized and the reciprocal of the average value was calculated to obtain the local reachability density of the target data point. The ratio of the local reachability density of the 15 nearest neighbors to the local reachability density of the target point was calculated, and the arithmetic mean of all ratios was taken as the local outlier factor score. A deviation threshold of 1.8 was set for detection. This threshold was calculated based on the statistical distribution of 10,000 sets of historical fluctuation data, with the average local outlier score of the historical data concentrated between 0.95 and 1.10. The scores of each grid node were checked individually, capturing spatial coordinate nodes where the local outlier score suddenly increased to above 1.8 and the aggregation index increased by more than 30% within 15 minutes. The coordinates of the nodes meeting the criteria were extracted and combined, isolated single points were removed, and the set of extreme points with spatial connectivity greater than or equal to two adjacent grids was retained, resulting in the spatial extreme value cluster of abrupt concentration changes. Empirical comparison with test data from a chemical industrial park showed that setting the local outlier threshold to 1.8 achieved an accuracy rate of 94% in detecting abrupt changes, an 18% improvement in accuracy compared to the fixed concentration threshold method.

[0022] The aggregation index is a spatial autocorrelation statistic that uses the global Moran index as the overall measure and the local Moran index as the grid-level quantitative indicator. Through the coupling calculation of spatial weight matrix and concentration deviation, it realizes multi-scale quantitative characterization of the spatial aggregation characteristics of VOCs emission sources from macro to micro, providing basic data support for subsequent emission anomaly identification and source tracing analysis.

[0023] The global Moran index is used to measure the overall spatial aggregation of VOCs components within a monitoring area. Its calculation formula is: I = (n / So)·[ΣᵢΣⱼwᵢⱼ(xᵢ-x)(xⱼ-x)] / [Σᵢ(xᵢ-x)²]; Where n is the total number of grid cells, xᵢ and xⱼ are the VOCs component concentration values ​​of grid i and grid j respectively, x is the global average concentration, wᵢⱼ is the spatial weight matrix (constructed based on the reciprocal of the geometric distance between grid center points), and S0 is the sum of all spatial weights. The global Moran's index ranges from -1 to 1: a positive value indicates positive spatial autocorrelation (high concentrations clustered adjacent to each other), a negative value indicates negative autocorrelation (alternating high and low concentrations), and a value close to 0 indicates spatial random distribution. For example, if the calculated global Moran's index is greater than 0.3, it is determined that the VOCs components exhibit significant positive spatial clustering characteristics within the monitoring area.

[0024] Based on the global clustering determination, the local Moran index is further calculated to identify the clustering intensity of specific grid cells within their local neighborhood. The calculation method is as follows: Iᵢ = (xᵢ-x) / mo·Σⱼwᵢⱼ(xⱼ-x); Where mo is the concentration variance normalization factor, and the local Moran index reflects the local spatial interaction strength between the concentration observation of a single grid and the concentration observation of its neighboring grids.

[0025] Filter the coordinates of continuous spatial grids with local Moran index greater than 0.5, and concatenate their grid numbers with concentration values ​​in an up-dimensional manner to generate a spatial distribution matrix of component concentration aggregation. Construct the spatial distribution matrix of component concentration aggregation, and the local Moran index of each grid in this matrix is ​​the specific value of its "aggregation index".

[0026] S113: For spatial extreme value clusters of abrupt concentration changes, map them to the geographic coordinate system of the monitoring grid, analyze the topological connection relationship between the extreme value clusters and the grid numbers, and generate a topological set of spatiotemporal distribution of emission source intensity anomalies; The coordinate parameters of each extreme node within the spatial extreme value cluster of abrupt concentration changes are extracted and projected onto the geographic coordinate system of the monitoring grid, which includes road network and industrial zoning layers, using an affine transformation matrix. A polygon intersection test algorithm is used to analyze the relative position of the geometric center of the extreme node to the boundary of the underlying grid, determining the grid number to which the extreme point belongs. Connectivity data of the vertices of the outer contour of the extreme value cluster are retrieved to construct a topological graph structure with grid numbers as nodes and adjacency relationships and extreme value propagation directions as directed edges. The diffusion area growth rate of the extreme value cluster on the time axis is calculated. When the diffusion area growth rate exceeds 15% for three consecutive observation periods, the grid number covered by the extreme value cluster and its topological connecting edges are marked as a strong emission source. The grid numbers, geographic coordinates, extreme value occurrence timestamps, and topological connectivity data with strong emission labels are structured and packaged to generate a spatiotemporal distribution topology set of strong emission source anomalies.

[0027] Table 1: Analysis of Concentration Aggregation Characteristics of Volatile Organic Compounds

[0028] As shown in Table 1, data features of three concentration clusters within the monitoring area were extracted. The results in the table show that grids with local outlier scores exceeding the set threshold of 1.8 exhibited out-of-range average concentration observations. The algorithm outputs highly sensitive results in capturing concentration extreme clusters.

[0029] Please see Figure 3 The specific steps for obtaining the cross-boundary tracing map of VOCs diffusion flux at the grid boundary are as follows: S211: Based on the spatiotemporal distribution topology set of emission source intensity anomalies, extract the interface coordinates between the anomaly grid and its adjacent grids, substitute them into the physical equation of mass transport to solve the spatial partial derivative of the concentration gradient at the interface, and obtain the distribution map of the driving force of mass transport between adjacent grids. The data dictionary of the spatiotemporal distribution topology set of strong emission source anomalies is invoked to retrieve the anomaly grid numbers marked as strong emission sources. The latitude and longitude coordinates of the four vertices of the anomaly grid are matched according to the geocoding library. The geometric line segment coordinates of the common interface between the anomaly grid and adjacent grids in the east, south, west, and north directions are calculated and extracted. An orthogonal two-dimensional Cartesian coordinate system is established, and the interface coordinates are substituted into the Euler flow field mass transport physical equations. The spatial partial derivatives of the concentration gradient at the center point of the interface are solved using the central difference numerical approximation method along the x and y axes. The difference between the concentration value at the center point of the anomaly grid and the concentration values ​​at the center points of adjacent grids is obtained, divided by the straight-line physical distance of 1000 meters between the two center points, to calculate the discretized first-order spatial partial derivatives. The partial derivative results at the interface are collected and multiplied by the apparent density of the air medium to obtain a concentration gradient vector array reflecting the trend of mass transfer from high to low concentration, i.e., a distribution map of the driving force of mass transport between adjacent grids.

[0030] S212: Based on the distribution map of mass transport driving force between adjacent grids, combined with the atmospheric turbulence diffusion coefficient, the permeability of VOCs components at the interface is calculated, the absolute amount of cross-boundary transport within a unit period is calculated, and a dynamic flow direction sequence of cross-boundary transport flux between grids is generated. By integrating three-dimensional wind speed and direction data and atmospheric temperature vertical lapse rate data collected from micro-meteorological stations, and based on the Pasquale atmospheric stability classification standard, when the wind speed is 3.5 m / s and the solar radiation is of moderate intensity, the atmospheric turbulent stability level is determined to be D-level neutral. Substituting the Taylor turbulent diffusion parameter empirical constant corresponding to the D-level stability, the atmospheric turbulent diffusion coefficient in the normal direction of the interface is calculated to be 45 m² / s. The concentration gradient value in the mass transport driving force distribution map between adjacent grids is multiplied by the calculated atmospheric turbulent diffusion coefficient to obtain the permeability of volatile organic compound components at the interface. The duration of a single time period of 900 seconds is extracted, and the permeability parameter, the physical effective cross-sectional area parameter of the interface, and the duration parameter are multiplied to calculate the absolute amount of cross-border transport through a specific interface within a unit period of 900 seconds. The absolute amount of cross-boundary transport at each interface is marked with a positive or negative sign according to the direction from the source grid to the target grid. Positive values ​​represent net outflow and negative values ​​represent net inflow. The data are stored in series in chronological order to generate a dynamic flow sequence of cross-boundary transport flux between grids.

[0031] S213: Call the dynamic flow sequence of cross-boundary transport flux between grids, compare it with the preset grid boundary bearing capacity threshold, filter and define the boundary areas where the transport is in an overload state and the associated grid numbers, and form a cross-boundary tracking map of VOCs diffusion flux at the grid boundary. The system loads a database of grid boundary carrying capacity thresholds calculated based on regional historical meteorological conditions and environmental carrying capacity. For the industrial core area grid, a net boundary carrying capacity flux threshold of 15,000 mg per cycle is set. The system retrieves the dynamic flow direction sequence of inter-grid cross-boundary transport flux and extracts the absolute amount of cross-boundary transport at each interface. The absolute amount of cross-boundary transport is subtracted from the 15,000 mg carrying capacity threshold for each value. If the calculated difference is greater than 0, the volatile organic compound (VOC) transport at the physical interface is considered overloaded. The flow direction sequence is traversed to select the spatial line segment coordinates of the boundary areas with a difference greater than 0, along with the source and receiving grid numbers directly connected to the line segments. The overloaded boundary areas and associated grid numbers are highlighted and rendered on the basic geographic layer. Directed arrows are used to indicate the direction and magnitude of the overloaded mass flow, forming a cross-boundary VOC diffusion flux tracking map of the grid boundary. This map quantifies the load of cross-regional pollution transport, improving the spatial resolution of cross-boundary pollution liability determination to the 1000-meter level.

[0032] Please see Figure 4 The specific steps for obtaining the source tracing matrix of the coupling between wind field and chemical dissipation are as follows: S311: Based on the cross-boundary tracking map of VOCs diffusion flux at the grid boundary, the three-dimensional meteorological wind field vector parameters of the overload transport associated grid are extracted, and the chemical dissipation attenuation coefficient is obtained by combining the photochemical reaction consumption rate of key VOCs components and the conversion rate of secondary organic aerosols. The cross-boundary tracing map of volatile organic compound diffusion flux at the grid boundary was analyzed to identify the overload transport receiving grid number highlighted in the background. Meteorological Doppler radar data stream was accessed to extract 3D meteorological wind field vector parameters matching the receiving grid, including a horizontal zonal wind speed of 2.5 m / s, a horizontal meridional wind speed of 1.8 m / s, and a vertical convective wind speed of 0.2 m / s. The built-in photochemical reaction mechanism database was retrieved to obtain component reaction parameters under light intensity and temperature conditions. For the characteristic pollutant toluene, its consumption rate constant for photochemical reaction with atmospheric hydroxyl radicals was retrieved as 0.00005 per second. Based on historical calibration data from the smog chamber experiment, the secondary organic aerosol conversion rate of toluene at 70% humidity was extracted to be 12%. Since the photochemical reaction consumption rate constant and the secondary organic aerosol conversion rate have different dimensions, they cannot be directly added together. To accurately calculate the mass attenuation ratio of pollutants during transport, both should be converted to a unified dimension before calculation. The calculated comprehensive chemical dissipation attenuation coefficient of the components should be based on a correction calculation of the photochemical reaction rate and the secondary organic aerosol conversion rate, rather than a simple addition, to avoid errors.

[0033] S312: Based on the chemical dissipation attenuation coefficient and the three-dimensional meteorological wind field vector parameters, the multi-source reverse tracing evolution calculation is performed by substituting them into the Gaussian plume reverse evolution equation to deduce the spatial transport trajectory of VOCs pollutants in the original time period and obtain the reverse transport path attenuation layer. A Lagrange particle inverse tracing algorithm framework was used to numerically solve the Gaussian plume inverse evolution equation, setting the high-concentration exceedance point of the receiving grid as the origin of the inverse evolution coordinates. The calculated chemical dissipation attenuation coefficient of 0.00017 m / s and the three-dimensional meteorological wind field vector parameters (2.5 m / s zonal, 1.8 m / s meridional, and 0.2 m / s vertical) were substituted into the evolution equation. A time step of 60 seconds was used, with the time variable negative, to drive the model to perform multi-source inverse tracing evolution calculations along the opposite direction of the wind field vector. In each iteration, the exponential inverse function of the dissipation attenuation coefficient was used to compensate for the concentration amplification of particles along the inverse path, simulating the original mass state of pollutants before consumption. 120 consecutive iterations were executed to reconstruct the spatial transport trajectory of volatile organic compound pollutants over the past 7200 seconds. The grid coordinates and corresponding compensated concentration values ​​of the reverse trajectory path were recorded, interpolated and smoothed, and then superimposed to generate an inverse transport path attenuation layer.

[0034] S313: Call the reverse transmission path attenuation layer, calculate the emission contribution weight parameter of each transport source grid to the high concentration accumulation area, aggregate the absolute value of emission source strength of high weight grids, and obtain the responsibility source tracing matrix of wind field and chemical dissipation coupling. The raster image metadata in the attenuation layer of the reverse transmission path is read, and the trajectory residence time and corresponding total reduction concentration covered within the pollution source grid are extracted. The total excess concentration monitored within the receiving grid is set as the weight denominator. The total accumulated reduction concentration within each transport source grid is divided by the weight denominator to calculate the emission contribution weight parameter of the source grid to the high-concentration accumulation area. All source grids are traversed, and high-weight grid nodes with emission contribution weight parameters greater than 15% are selected. For high-weight grids, the continuous emission monitoring equipment interface of key polluting enterprises within them is called, aggregating the real-time collected flue gas flow parameters and exhaust stack outlet concentration parameters, and multiplying them to obtain the absolute value of the emission source strength. The absolute value of the emission source strength of each high-weight grid is multiplied by the corresponding emission contribution weight parameter to construct a responsibility source tracing matrix coupling wind field and chemical dissipation. Experimental data shows that, combined with reverse tracing calculation using 12% secondary conversion rate attenuation compensation, the accuracy of pollution source strength location reaches 91%, which is 22% higher than the physical wind field tracing model without chemical dissipation coupling.

[0035] Table 2: Source Grid Contribution Weighting Analysis Table:

[0036] As shown in Table 2, the contribution weight parameters of the three source grids are listed through reverse deduction calculation. The table results show that the emission contribution weight of grid 58 reaches 41%, which is the core responsible source for the receiving grid exceeding the standard. In the subsequent matrix aggregation and governance scheduling, it should be listed as the highest priority control object, directly providing a quantitative target for targeted emission reduction.

[0037] Please see Figure 5 The specific steps for obtaining the spatial classification scale of load pressure in grid-based governance are as follows: S411: Based on the source tracing matrix of the coupled wind field and chemical dissipation, the actual emission source strength equivalent in the high-weight responsibility grid is extracted, and the design reduction capacity of the current end-of-pipe treatment facilities and the current operating loss are compared in parallel to calculate the net emission exposure of a single grid dimension. The source-tracing matrix of wind field and chemical dissipation coupling is read to locate the high-weight responsibility grids with the highest weights. The emission source strength equivalent values ​​for each industrial node within the grid are extracted from the underlying equipment operation database. For example, the current hourly volatile organic compound emission source strength of a chemical plant node is extracted as 450 kg. The hardware ledger of the end-of-pipe treatment facilities deployed at the node is queried synchronously, and their rated design reduction capacity is extracted as 500 kg per hour. The furnace temperature of the oxidizer combustion chamber is read as 780 degrees Celsius through the IoT data gateway, which deviates from the standard operating condition of 850 degrees Celsius. Combined with the combustion efficiency performance degradation curve, the operating loss rate is calculated to be 12%. Multiplying the rated design reduction capacity of 500 kg by the operating efficiency of 88% after deducting losses, the current available reduction capacity is calculated to be 440 kg per hour. A parallel difference comparison is performed between the source strength equivalent of 450 kg and the available reduction capacity of 440 kg, and it is calculated that there is a net emission exposure of 10 kg per hour in a single grid dimension, which is an overflow pollution load exceeding the end-of-pipe treatment capacity.

[0038] S412: Based on the net emission exposure of a single grid, introduce the environmental capacity benchmark of the surrounding area, calculate the proportion of net exposure to the environmental capacity benchmark, and quantify the governance load overload risk index of each responsible grid. The environmental capacity encroachment index refers to the proportion of net emission exposure within a single grid dimension that encroaches on the environmental capacity benchmark of the surrounding area.

[0039] The regional ecological environment quality planning database is accessed, and atmospheric environmental capacity benchmark values ​​for the surrounding area are introduced based on the annual diffusion meteorological conditions and land functional zoning attributes of the geographical location. The benchmark value defines the maximum net emissions allowed per hour for a single grid without affecting regional air quality standards; here, the environmental capacity benchmark is set at 5 kg per hour. The calculated net emission exposure of 10 kg per grid dimension is divided by the environmental capacity benchmark of 5 kg to calculate the environmental capacity encroachment index, which represents a 200% encroachment ratio of the net exposure to the environmental capacity benchmark. Based on the environmental capacity encroachment index, a logarithmic risk assessment function is constructed and normalized to quantify the governance load overload risk index of the responsible grid, which is 8.5 (out of 10), reflecting the degree of pressure exerted by the grid's current emission status on the surrounding ecological environment.

[0040] S413: Based on the governance load overload risk index, the responsibility grid is spatially clustered using the natural breakpoint grading method. The differential clustering intervals are assigned corresponding pressure rating labels, and a gridded governance load pressure spatial grading scale is output. The responsibility grid and its corresponding governance load overload risk index are imported into a one-dimensional data array. A natural breakpoint grading algorithm is used to perform spatial pressure clustering on the data array. The algorithm calculates the global mean and overall variance of the risk index within the data array. Multiple sets of splitting nodes are iteratively set within the effective interval of the data sequence, dividing the data into four independent intervals. For each set of partitioning schemes, the sum of the local variances of the risk index within each sub-interval is calculated. Through iterative comparison, the set of splitting nodes that minimizes the data differences within each sub-interval while maximizing the data differences between different intervals is identified as the clustering breakpoint. Based on the determined breakpoints, the clustering intervals are assigned four pressure grading labels in ascending order of risk: "Low Pressure Safety," "Medium Pressure Warning," "High Pressure Overload," and "Extreme Pressure Runaway." The labeled grid numbers and their corresponding spatial coordinate data are combined and encapsulated to output a gridded spatial grading scale for governance load pressure. This scale provides a data index for environmental regulatory departments to implement tiered response control.

[0041] Please see Figure 6 The specific steps for obtaining the instruction set for VOCs targeted emission reduction and governance resource scheduling are as follows: S511: Call the grid-based governance load pressure spatial classification scale, extract the grid number corresponding to the high pressure classification label, perform spatial overlay analysis on the geographical boundary of the grid in the specified area and the protection distance limit spatial layer of the urban environmental sensitive target, and deconstruct to obtain the environmental exposure sensitivity coefficient. The grid-based governance load pressure spatial classification scale is analyzed, and the set of grid numbers assigned the pressure classification labels of "extreme pressure runaway" and "high pressure overload" is scanned and extracted. The urban geographic information module interface is called to load the geographic boundary polygon data layer of the high-pressure grid and the spatial distribution location layer of urban environmentally sensitive targets. In the spatial overlay analysis module, a protective distance restriction space buffer layer with a radius of 800 meters is generated outward from the geometric center of each sensitive target. A Boolean intersection operation is performed on the layers to extract the total area of ​​overlap between the high-pressure grid boundary polygons and the restriction space buffer layer. The calculated overlap area is divided by the total physical area of ​​the high-pressure grid to obtain the overlap area ratio. Combining the population density weighting coefficient of the sensitive targets, the overlap area ratio is multiplied by the corresponding weighting coefficient to calculate the grid-specific environmental exposure sensitivity coefficient. For example, if the overlap area ratio is 40% and involves residential areas, and the residential area weighting coefficient is 1.0, then the sensitivity coefficient is calculated to be 0.4.

[0042] S512: Based on the environmental exposure sensitivity coefficient, combined with the source strength category and the adjustable load margin of the treatment equipment in the high-pressure grid, a multi-objective function for targeted emission reduction is constructed to calculate the optimal load reduction ratio under the overall regional compliance condition and obtain the spatial coordinated emission reduction quota benchmark. A list of pollution sources within the high-pressure grid was extracted, identifying source strength categories as either continuous or intermittent emission sources. Through the manufacturing execution network interface, the current operating frequency of the frequency converter of the exhaust fan in the source production workshop was read as 40 Hz, with a rated maximum frequency of 50 Hz, calculating the adjustable load margin of the treatment equipment to be 20%. An environmental exposure sensitivity coefficient was introduced into the penalty term mechanism to construct a targeted emission reduction multi-objective function. The first sub-objective of the function was set as minimizing the total regional volatile organic compound emissions, and the second sub-objective was set as minimizing the economic losses caused by production restrictions and emission shutdowns. The multi-objective function was iteratively solved using a particle swarm optimization algorithm, initializing a population of 50 particles, where the particle positions represent a set of load reduction ratio allocation schemes for each factory. The inertia weight coefficient of the algorithm was set to decrease linearly from 0.9 to 0.4 with the number of iterations, while the cognitive learning factor and social learning factor were both fixed at 2.0. The adjustable load margin of the equipment was substituted into the algorithm as a constraint boundary condition. After 200 iterations of particle position and velocity updates, the global extreme solution that optimizes the fitness value of the multi-objective function is found, and the optimal load reduction ratio set that reduces the overall regional concentration to below the standard limit is obtained. This set is defined as the spatial coordinated emission reduction quota benchmark.

[0043] S513: Based on the spatially coordinated emission reduction quota benchmark, reorganize the load allocation weight of cross-grid treatment equipment, identify the operating condition restriction instructions for key abnormal pollution sources and the power adjustment parameters of end-of-pipe treatment facilities, and output a set of VOCs targeted emission reduction and treatment resource scheduling instructions; This paper analyzes the quantitative indicators given in the spatial collaborative emission reduction quota benchmark and, for shared end-of-pipe exhaust network nodes involving cross-grid joint governance, reallocates the allocation weight of exhaust ventilation volume based on the source strength contribution. It identifies abnormal pollution sources causing pollution surges and generates equipment hardware control command codes based on the optimal load reduction ratio. For the painting production line numbered A, a condition restriction command is generated to reduce the material conveying pump speed by 30%; for the supporting zeolite rotor concentrator and catalytic combustion integrated machine, a power adjustment parameter is generated to increase the desorption fan frequency converter operating parameters from 35 Hz to 45 Hz. The generated condition restriction commands and power adjustment parameters are structured and compiled, and directly sent to the enterprise's distributed control unit via industrial fieldbus protocol, outputting a VOCs-targeted emission reduction and governance resource scheduling command set. Experimental results show that by executing the command set, the peak pollutant concentration in the high-pressure grid decreased by an average of 38% within 45 minutes, and the economic loss was reduced by 24% compared to production restriction measures.

[0044] Table 3: Control Parameters for Governance Resource Scheduling Instructions

[0045] As shown in Table 3, the output scheduling command parameters for the three treatment devices are listed in detail. The table results show that the multi-objective function optimization results accurately provide the device power adjustment parameters down to the hardware level, realizing the transformation from spatial emission reduction quotas to device control commands, and ensuring the practical implementation and feasibility of the targeted emission reduction strategy.

[0046] The AI-based VOCs governance data processing system is used to execute the aforementioned AI-based VOCs governance data processing method. The system includes: The spatial correlation analysis module, based on the gridded spatial division topology of the monitoring area, uses a big data processing engine to measure the spatial correlation of component concentration fields, calculates the component concentration aggregation index and abrupt change critical value within the grid cell, and generates a spatiotemporal distribution topology set of emission source intensity anomalies. The calculation of the concentration clustering index is a multi-level, quantifiable spatial statistical process. Its core relies on the Moran's index system and incorporates a local outlier factor algorithm for dynamic anomaly detection. Specifically, firstly, based on a gridded spatial topology of the monitoring area (typically a 1000m × 1000m latitude and longitude grid), raw concentration sequence data covering 15 VOCs, including benzene, toluene, and xylene, are simultaneously collected every 15 minutes using photoionization detectors deployed at the grid nodes and a gas chromatography-mass spectrometry (GC-MS) system. After preprocessing (removing noise points deviating from three standard deviations from the mean, using cubic spline interpolation to repair missing values, and using maximum-minimum normalization to eliminate dimensional differences), the data enters the clustering index calculation process. The first step is global clustering measurement: extracting the concentration values ​​of a specific component (e.g., toluene) from all grids in a single time slice, constructing a spatial weight matrix based on the reciprocal of the geometric distance between grid center points, and substituting it into the global Moran's index formula. When the global Moran's index is greater than 0.3, it indicates that the region exhibits significant positive spatial autocorrelation, meaning that high-concentration areas tend to cluster. The second step is precise quantification of local clustering: calculating the local Moran's index for each grid. The patent selects continuous grid coordinates with a local Moran's index greater than 0.5, concatenates them with concentration values, and generates a spatial distribution matrix of component concentration clustering. The local Moran's index value corresponding to each grid in the matrix is ​​the quantified value of the "clustering index" for that grid. Based on this, a local outlier factor algorithm is further introduced. A time-series vector for each grid is constructed using 48 consecutive observation periods. With a nearest neighbor parameter k=15, the local reachability density and local outlier score of each data point are calculated. When the local outlier score of a grid suddenly increases to above 1.8 and the clustering index increases by more than 30% within 15 minutes, that grid is marked as a spatial extreme value cluster node of abrupt concentration changes. This completes the closed-loop calculation of the clustering index from static spatial statistics to dynamic evolution anomalies. This calculation method ensures that the clustering index has both the physical interpretability of spatial autocorrelation and the sensitive response capability to anomalous fluctuations in the temporal dimension, providing accurate quantitative input for subsequent cross-boundary flux tracking and source tracing.

[0047] The mutation threshold is a quantitative discrimination threshold used to determine whether a significant anomalous change has occurred in the concentration of VOCs grid cells. It is not a pre-fixed empirical constant, but rather a threshold determined by calculating the evolutionary deviation of the grid concentration time series using the Local Outlier Factor (LOF) algorithm, with the deviation score exceeding a preset threshold serving as the criterion for determining the occurrence of a mutation. This threshold is used to accurately capture spatial coordinate nodes in the component concentration clustering spatial distribution matrix where the clustering index rises sharply and the deviation exceeds the limit, thereby generating spatial extreme value clusters of abrupt concentration changes. The determination of the mutation threshold uses the LFO score as the quantification carrier. The LFO algorithm is a density-based unsupervised anomaly detection method. Its core idea is to measure the outlier degree of each data point by comparing the local density difference between each data point and its neighboring points. When the LFO score of a data point is significantly greater than 1, it indicates that the local density of that point is significantly lower than that of its neighbors, and it is determined to be a local anomaly. The patent treats the concentration value of each observation period in the grid concentration time series as a data point, calculating its LOF score as the quantified value of the "evolutionary deviation" of the concentration in that period relative to the historical series.

[0048] The deviation threshold is set at 1.8. This threshold is not arbitrarily chosen, but rather calculated based on the statistical distribution of 10,000 sets of historical fluctuation data—the mean local outlier scores of the historical data are concentrated between 0.95 and 1.10. Building on this, the patent further specifies that only when the LOF score of a grid node suddenly increases to above 1.8, and the clustering index (local Moran's index) of that grid increases by more than 30% within 15 minutes, is that node officially marked as part of the "concentration abrupt change spatial extreme value cluster." This dual judgment condition of "LOF score exceeding the threshold" and "short-term sharp increase in the clustering index" effectively avoids false alarms from a single indicator, ensuring that the triggering of the abrupt change threshold is both statistically significant and has practical physical meaning. Empirical data shows that when the local outlier threshold is set to 1.8, the accuracy rate of capturing abrupt changes reaches 94%, an improvement of 18% compared to the fixed concentration threshold method.

[0049] The transboundary flux tracing module, based on the spatiotemporal distribution topology set of emission source strength anomalies, analyzes the spatial partial derivatives of concentration gradients and component permeability between adjacent grids, screens grid cells whose transboundary transport exceeds the boundary carrying capacity, and establishes a transboundary tracing map of VOCs diffusion flux at the grid boundary. The responsible source inverse inference module, based on the cross-boundary tracing map of VOCs diffusion flux at the grid boundary, integrates wind field vector and chemical dissipation attenuation coefficient to perform Gaussian inverse tracing evolution, calculates the emission source contribution weight, and outputs the responsible source tracing matrix coupled with wind field and chemical dissipation. The governance load classification module, based on the source tracing matrix of the coupled wind field and chemical dissipation, quantifies the proportion of the net emission exposure of the grid to the environmental capacity, performs pressure clustering based on the breakpoint classification method, and generates a gridded governance load pressure spatial classification table. The targeted scheduling and control module, based on the gridded governance load pressure spatial hierarchical scale, analyzes the environmental exposure sensitivity limit, solves the collaborative emission reduction quota and reorganizes the equipment load allocation weight, and outputs a VOCs targeted emission reduction and governance resource scheduling instruction set.

[0050] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. An AI-based method for processing VOCs governance data, characterized in that, Includes the following steps: S1: Based on the gridded spatial division topology of the monitoring area, big data processing technology is used to measure the spatial correlation of multidimensional component concentration field data. The Moran index of VOCs components under different spatial slices is calculated to obtain the spatial distribution matrix of component concentration aggregation. The time series evolution deviation of grid concentration is calculated by the local outlier factor algorithm. The deviation exceeding the preset threshold is used as the mutation critical value to generate the spatiotemporal distribution topology set of emission source strength anomalies. S2: Based on the spatiotemporal distribution topology set of the emission source intensity anomalies, establish the physical equation of mass transport at the grid interface, analyze the spatial partial derivative of the concentration gradient and the component permeability between adjacent grids, screen grid cells whose cross-boundary transport exceeds the boundary carrying capacity, and form a cross-boundary tracking map of VOCs diffusion flux at the grid boundary. S3: Based on the cross-boundary tracking map of VOCs diffusion flux at the grid boundary, and combined with the regional meteorological wind field vector and the chemical dissipation rate of VOCs components in the atmosphere, a Gaussian plume reverse tracing evolution model is constructed. The Gaussian plume reverse tracing evolution model is based on the Lagrange particle reverse tracing algorithm. The chemical dissipation attenuation coefficient and the three-dimensional meteorological wind field vector parameters are substituted into the Gaussian plume reverse evolution equation. Reverse iteration is performed with a step size of 60 seconds. The emission contribution weight parameter is determined according to the proportion of the total reduction concentration on the reverse transmission path to the total excess concentration of the receiving grid. The absolute value of the emission source strength is calculated to obtain the responsibility source tracing matrix of wind field and chemical dissipation coupling. S4: Based on the location number of the source tracing matrix of the wind field and chemical dissipation coupling, quantify the capacity difference between the real-time emission carrying capacity and the reduction capacity of the existing treatment facilities for each responsibility grid. Subtract the current available reduction capacity from the real-time emission carrying capacity to obtain the net emission exposure. Divide the net emission exposure by the environmental capacity benchmark to obtain the encroachment ratio. Normalize the encroachment ratio into an overload risk index as the degree of overload risk through a logarithmic risk assessment function. Calculate the degree of overload risk of the regional treatment load and output a gridded treatment load pressure spatial classification table.

2. The AI-based VOCs governance data processing method according to claim 1, characterized in that, The spatiotemporal distribution topology set of emission source intensity anomalies includes a concentration aggregation spatial coordinate matrix, abrupt change critical threshold nodes, a grid topology connection sequence, and local outlier calibration parameters. The grid boundary VOCs diffusion flux cross-border tracing map includes a material transport driving force vector table, an interface permeability distribution block, and a cross-border transport dynamic flow chain. The source tracing matrix of wind field and chemical dissipation coupling includes a chemical dissipation attenuation coefficient set, reverse transport path weight coordinates, and multi-source contribution quantification duty cycle. The gridded governance load pressure spatial classification table includes a net emission exposure difference table, an environmental capacity encroachment index, and cluster classification boundary labels.

3. The AI-based VOCs governance data processing method according to claim 1, characterized in that, The specific steps for obtaining the spatiotemporal distribution topology set of emission source intensity anomalies are as follows: S111: Based on the gridded spatial division topology of the monitoring area, the big data processing engine is used to read the multidimensional concentration field data of VOCs components in the grid unit, calculate the Moran index of each VOCs component under the differentiated spatial slice and measure the spatial correlation to obtain the spatial distribution matrix of component concentration aggregation. S112: Based on the spatial distribution matrix of component concentration aggregation, the local outlier factor algorithm is introduced to calculate the time series evolution deviation of grid concentration, capture the spatial coordinate nodes where the aggregation index rises sharply and the deviation exceeds the limit, and obtain the spatial extreme value cluster of concentration change. S113: For the spatial extreme value clusters of abrupt concentration changes, map them to the geographic coordinate system of the monitoring grid, analyze the topological connection relationship between the extreme value clusters and the grid numbers, and generate a spatiotemporal distribution topology set of emission source intensity anomalies.

4. The AI-based VOCs governance data processing method according to claim 3, characterized in that, The specific steps for obtaining the cross-boundary tracing map of VOCS diffusion flux at the grid boundary are as follows: S211: Based on the spatiotemporal distribution topology set of the emission source intensity anomaly, extract the interface coordinates between the anomaly grid and its adjacent grids, substitute them into the physical equation of mass transport to solve the spatial partial derivative of the concentration gradient on the interface, and obtain the distribution map of the mass transport driving force between adjacent grids. S212: Based on the distribution map of mass transport driving force between adjacent grids, the permeability of VOCs components at the interface is calculated by combining the atmospheric turbulence diffusion coefficient, the absolute amount of cross-boundary transport within a unit period is calculated, and a dynamic flow direction sequence of cross-boundary transport flux between grids is generated. S213: Call the dynamic flow sequence of inter-grid cross-boundary transport flux, compare it with the preset grid boundary bearing capacity threshold, filter and define the boundary areas where the transport flux is in an overloaded state and the associated grid numbers, and form a grid boundary VOCS diffusion flux cross-boundary tracking map.

5. The AI-based VOCs governance data processing method according to claim 4, characterized in that, The specific steps for obtaining the source tracing matrix of the coupling between the wind field and chemical dissipation are as follows: S311: Based on the cross-boundary tracking map of VOCs diffusion flux at the grid boundary, extract the three-dimensional meteorological wind field vector parameters of the overload transport associated grid, and combine the photochemical reaction consumption rate of key VOCs components with the secondary organic aerosol conversion rate to obtain the chemical dissipation attenuation coefficient. S312: Based on the chemical dissipation attenuation coefficient and the three-dimensional meteorological wind field vector parameters, substitute them into the Gaussian plume reverse evolution equation to perform multi-source reverse tracing evolution calculation, deduce the spatial transport trajectory of VOCs pollutants in the original time period, and obtain the reverse transport path attenuation layer. S313: Call the reverse transmission path attenuation layer, calculate the emission contribution weight parameter of each transport source grid to the high concentration accumulation area, aggregate the absolute value of emission source strength of high weight grids, and obtain the responsibility source tracing matrix of wind field and chemical dissipation coupling.

6. The AI-based VOCs governance data processing method according to claim 5, characterized in that, The specific steps for obtaining the grid-based governance load pressure spatial classification scale are as follows: S411: Based on the source tracing matrix of the wind field and chemical dissipation coupling, extract the actual emission source strength equivalent in the high-weight responsibility grid, compare the design reduction capacity of the current end-of-pipe treatment facilities in the grid with the current operating loss, and calculate the net emission exposure of a single grid dimension. S412: Based on the net emission exposure of the single grid dimension, introduce the environmental capacity benchmark of the surrounding area, calculate the proportion of net exposure to the environmental capacity benchmark, and quantify the governance load overload risk index of each responsible grid. S413: Based on the governance load overload risk index, the responsibility grid is spatially clustered using the natural breakpoint grading method. Corresponding pressure rating labels are assigned to the differentiated clustering intervals, and a gridded governance load pressure spatial grading scale is output.

7. The AI-based VOCs governance data processing method according to claim 1, characterized in that, The method also includes step S5: S5: Call the gridded governance load pressure spatial classification table, compare the load pressure level of the differentiated grid with the spatial proximity of the environmentally sensitive target, deconstruct the targeted emission reduction constraints, reorganize the load allocation weight of the governance equipment, and output the VOCs targeted emission reduction and governance resource scheduling instruction set; The VOCs-targeted emission reduction and governance resource scheduling instruction set includes environmental exposure sensitivity coefficient values, spatial coordinated emission reduction quotas, equipment power adjustment limit codes, and operating condition load reduction constraint ratios.

8. The AI-based VOCs governance data processing method according to claim 7, characterized in that, The specific steps for obtaining the VOCs-targeted emission reduction and governance resource scheduling instruction set are as follows: S511: Call the gridded governance load pressure spatial classification table, extract the grid number corresponding to the high pressure classification label, perform spatial overlay analysis on the geographical boundary of the grid in the specified area and the protection distance limit spatial layer of the urban environmental sensitive target, and deconstruct to obtain the environmental exposure sensitivity coefficient. S512: Based on the environmental exposure sensitivity coefficient, combined with the source strength category and the adjustable load margin of the treatment equipment in the high-pressure grid, construct a multi-objective function for targeted emission reduction, calculate the optimal load reduction ratio under the overall compliance condition of the region, and obtain the spatial coordinated emission reduction quota benchmark. S513: Based on the spatially coordinated emission reduction quota benchmark, reorganize the load allocation weight of cross-grid treatment equipment, identify the operating condition restriction instructions for key abnormal pollution sources and the power adjustment parameters of end-of-pipe treatment facilities, and output a set of VOCs targeted emission reduction and treatment resource scheduling instructions.

9. An AI-based VOCs governance data processing system, characterized in that, The system is used to implement the AI-based VOCs governance data processing method according to any one of claims 1-8, and the system comprises: The spatial correlation analysis module, based on the gridded spatial division topology of the monitoring area, uses a big data processing engine to measure the spatial correlation of component concentration fields. It obtains the spatial distribution matrix of component concentration aggregation by calculating the Moran index of VOCs components under different spatial slices, and calculates the time series evolution deviation of grid concentrations through the local outlier factor algorithm. The deviation exceeding the preset threshold is used as the mutation critical value to generate a spatiotemporal distribution topology set of emission source strength anomalies. The transboundary flux tracking module, based on the spatiotemporal distribution topology set of the emission source intensity anomaly, analyzes the spatial partial derivatives of the concentration gradient and component permeability between adjacent grids, filters grid cells whose transboundary transport exceeds the boundary carrying capacity, and establishes a transboundary tracking map of VOCs diffusion flux at the grid boundary. The responsible source reverse inference module, based on the cross-boundary tracing map of VOCs diffusion flux at the grid boundary, integrates wind field vectors and chemical dissipation attenuation coefficients to perform Gaussian reverse tracing evolution. The Gaussian plume reverse tracing evolution model is based on the Lagrange particle reverse tracing algorithm. It substitutes the chemical dissipation attenuation coefficients and three-dimensional meteorological wind field vector parameters into the Gaussian plume reverse evolution equation, performs reverse iteration with a step size of 60 seconds, determines the emission contribution weight parameter according to the proportion of the total reduction concentration on the reverse transmission path to the total excess concentration of the receiving grid, calculates the absolute value of emission source strength, and outputs the responsible source tracing matrix coupled with wind field and chemical dissipation. The governance load classification module, based on the source tracing matrix of the wind field and chemical dissipation coupling, subtracts the current available reduction capacity from the real-time emission carrying capacity to obtain the net emission exposure, divides the net emission exposure by the environmental capacity benchmark to obtain the encroachment ratio, normalizes the encroachment ratio into an overload risk index as the degree of overload risk through a logarithmic risk assessment function, and performs pressure clustering based on the breakpoint grading method to generate a gridded governance load pressure spatial grading table; The targeted scheduling and control module, based on the gridded governance load pressure spatial classification scale, analyzes the environmental exposure sensitivity limit, solves the collaborative emission reduction quota and reorganizes the equipment load allocation weight, and outputs a VOCs targeted emission reduction and governance resource scheduling instruction set.

Images