Ecological environment anomaly detection and early warning method based on artificial intelligence

Abstract

This invention belongs to the field of environmental monitoring technology. It discloses an artificial intelligence-based method for detecting and warning of ecological and environmental anomalies. By acquiring data on pollutant concentrations, meteorological parameters, and spatial distribution of enterprises at multiple locations within an industrial park, an emission characteristic fingerprint database is constructed based on the production process characteristics of each enterprise. By analyzing the spatiotemporal diffusion trajectory of pollutants, abnormal areas are identified, and reverse source tracing parameters are calculated to construct a spatiotemporal superposition inversion model for multi-source emissions. A multi-enterprise contribution separation matrix is ​​generated based on fingerprint matching degree weighted calculation, achieving accurate separation of emission contributions from each enterprise in a mixed pollution field. By monitoring pollutant concentration fluctuation characteristics in real time and dynamically adjusting the contribution weight allocation, the accuracy of monitoring results is ensured. The responsible parties for excessive emissions are accurately identified, and early warning signals are generated. This invention can achieve accurate identification of pollution sources and determination of responsibility in complex multi-source emission environments, effectively improving the accuracy and impartiality of environmental supervision in industrial parks.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically, to an artificial intelligence-based method for detecting and warning of ecological and environmental anomalies. Background Technology

[0002] In current industrial park environmental monitoring, with the increasing density of enterprises and the growing diversification of industries within the parks, the cross-influence and superposition effects of different enterprise emission sources have become a technical challenge for environmental regulation. Industrial parks typically house dozens or even hundreds of different types of production enterprises, including chemical, pharmaceutical, electronics, and machinery manufacturing industries. These enterprises are spatially adjacent, and their emitted pollutants undergo complex physicochemical processes and meteorological conditions in the atmosphere, forming extremely complex mixed pollution fields. Existing environmental monitoring technologies mainly rely on single-point concentration measurements at a limited number of monitoring points deployed within the park. When monitoring equipment detects that the pollutant concentration in a certain area exceeds the standard, the lack of effective source apportionment techniques makes it impossible to accurately identify the specific emission source causing the exceedance, let alone quantify the specific proportion of each potential emission source's contribution to the monitoring point concentration. Especially when multiple enterprises are engaged in production activities simultaneously, the pollutants emitted by different enterprises mix and superimpose during transport, forming complex pollution plumes that render traditional source tracing methods based on concentration gradients or diffusion models ineffective. Furthermore, different enterprises have different production processes, emission characteristics, and production cycles. Some enterprises may emit continuously and stably, while others emit intermittently in batches. This spatiotemporal heterogeneity further increases the complexity of pollution source identification. In actual environmental law enforcement, because it is impossible to accurately determine the responsible party for excessive emissions, environmental protection departments often have to adopt "one-size-fits-all" management measures. This not only affects the economic interests of enterprises with normal production, but also weakens the scientific nature and credibility of environmental supervision. At the same time, some enterprises that violate emission standards may also take advantage of this technical deficiency to emit excessive emissions when other enterprises are in normal production, hiding their emission responsibility in the overall mixed pollution, forming a "free-rider" phenomenon, which seriously affects the effectiveness and fairness of environmental governance.

[0003] In view of this, the present invention proposes an artificial intelligence-based method for detecting and warning of ecological and environmental anomalies to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based method for detecting and warning of ecological environment anomalies, comprising:

[0005] Acquire time-series data of pollutant concentrations, meteorological parameters, and spatial distribution data of enterprises at multiple locations within the industrial park;

[0006] Based on the production process characteristics and historical emission records of each enterprise, the concentration ratio and temporal fluctuation characteristics of pollutant components are extracted to construct an enterprise emission characteristic fingerprint database.

[0007] Acquire spatiotemporal diffusion trajectory data of pollutants under different meteorological conditions, and identify abnormal areas of propagation paths based on the temporal evolution characteristics of concentration gradients between monitoring points;

[0008] Based on the time difference of arrival of the concentration peak at each monitoring point in the area with abnormal propagation path, the reverse source propagation velocity vector and distance attenuation coefficient of pollutants are calculated.

[0009] Based on the reverse source propagation velocity vector and distance attenuation coefficient, a spatiotemporal superposition inversion model of multi-source emissions is constructed.

[0010] Based on the spatiotemporal overlay inversion model and emission feature fingerprint database, a multi-enterprise contribution separation matrix for each monitoring point is generated by weighted calculation of fingerprint matching degree.

[0011] By monitoring the characteristic spectrum of pollutant concentration fluctuations in real time, emission source characteristic patterns that match those in the emission characteristic fingerprint database are identified.

[0012] Based on the intensity changes and frequency of occurrence of emission source characteristic patterns, the weight allocation coefficients of each enterprise in the contribution separation matrix are dynamically adjusted.

[0013] Based on the adjusted contribution separation matrix, the responsible parties for excessive emissions are identified and graded early warning signals are generated.

[0014] The technical effects and advantages of the artificial intelligence-based method for detecting and warning of ecological environment anomalies in this invention are as follows:

[0015] This invention establishes a unique identification mechanism for enterprise emissions, making each enterprise's emission behavior traceable and identifiable. Even in complex situations where multiple enterprises emit simultaneously and pollutants are highly mixed, the contribution of each emission source can be accurately determined. This invention improves the accuracy and fairness of environmental supervision, avoids misjudgments and injustices caused by traditional management, protects the legitimate rights and interests of law-abiding enterprises, and simultaneously creates a strong deterrent against enterprises that violate emission standards, effectively curbing speculative phenomena that use mixed emissions to cover up illegal activities. In practical applications, environmental protection departments can take differentiated management measures based on accurate liability determination results, conduct precise enforcement against enterprises that exceed emission standards, and provide protection and support to enterprises with normal production, thereby improving the efficiency of environmental governance and social acceptance. In addition, this invention can also achieve rapid response and early warning for pollution incidents, identifying abnormal emission sources in the early stages of pollution spread, gaining valuable time for timely emergency measures, and effectively reducing the scope and severity of environmental pollution accidents. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the artificial intelligence-based ecological environment anomaly detection and early warning method of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This application provides an artificial intelligence-based method for detecting and warning of ecological and environmental anomalies. The implementing entities of the method include, but are not limited to, environmental monitoring platforms, industrial park management systems, pollution source tracing systems, and multi-source data analysis platforms, which can be considered general computing nodes in this application. The detection and warning system includes, but is not limited to, at least one cloud-based environmental anomaly analysis engine, a distributed pollution source tracing system, and an intelligent responsible entity identifier.

[0019] Please see Figure 1 In this embodiment of the invention, the specific implementation process of the artificial intelligence-based ecological environment anomaly detection and early warning method includes:

[0020] The project acquires time-series data on pollutant concentrations, meteorological parameters, and spatial distribution data of enterprises from multiple monitoring points within the industrial park. The time-series data includes real-time concentrations of various pollutants recorded by monitoring stations across the park. Meteorological parameters include environmental factors such as wind speed, wind direction, humidity, and temperature. The spatial distribution data records the geographical coordinates and boundaries of each emitting enterprise. This data is collected in real-time through a distributed monitoring network, forming the foundational data layer for environmental monitoring and ensuring the accuracy and comprehensiveness of subsequent analyses. The time-series pollutant concentration data reflects the dynamic changes in environmental quality, meteorological parameters influence the diffusion paths and rates of pollutants, and the spatial distribution data provides a geographical reference for source tracing analysis. These three types of data collectively constitute the original data foundation for environmental anomaly detection.

[0021] It should be noted that all data acquisition involved in this invention was completed through legal and compliant means, in accordance with national and local environmental monitoring data collection standards, ensuring that the data acquisition equipment has undergone legal metrological certification, the monitoring point settings meet the requirements of environmental monitoring technical specifications, and the data transmission and storage meet relevant confidentiality requirements. The acquisition of enterprise production and emission data has been authorized by the relevant enterprises or carried out in accordance with the law, and the meteorological data comes from authorized channels of official meteorological departments.

[0022] Based on the production process characteristics and historical emission records of each enterprise, the concentration ratios and temporal fluctuation characteristics of pollutant components are extracted to construct an enterprise emission characteristic fingerprint database. This database contains unique emission characteristics of each enterprise, such as pollutant component concentration ratio vectors, frequency domain characteristics of temporal fluctuations, and correlation characteristics with production shifts. These data collectively constitute a "digital identity card" for enterprise emissions. Through in-depth analysis of historical data, the system can identify the unique emission patterns of each enterprise, providing a benchmark for subsequent source tracing analysis.

[0023] The system acquires spatiotemporal diffusion trajectory data of pollutants under different meteorological conditions and identifies anomalous areas of propagation paths based on the temporal evolution characteristics of concentration gradients between monitoring points. It systematically analyzes changes in pollutant concentration gradients between adjacent monitoring points, identifies time points and spatial regions of sudden gradient increases, and determines anomalous propagation areas through cluster analysis, providing spatial constraints for pollution source localization.

[0024] Based on the time difference of arrival of peak concentrations at various monitoring points within the area with abnormal propagation paths, the reverse source propagation velocity vector and distance attenuation coefficient of pollutants are calculated. The system utilizes the timestamps of peak pollutant concentrations recorded by the monitoring points, combined with the spatial distribution of the monitoring points, to calculate the velocity components of pollutant propagation. Then, through vector decomposition, the residual propagation velocity vector is obtained, enabling preliminary location of the pollution source area. Finally, the attenuation law is determined by fitting the distance-concentration relationship.

[0025] Based on the reverse source propagation velocity vector and distance attenuation coefficient, a spatiotemporal superposition inversion model for multi-source emissions is constructed. This model, grounded in a spatial grid, considers the cumulative contribution of multiple potential emission sources and uses the least squares method to invert the actual emission intensity of each source, minimizing the residual between the theoretical model and monitoring data, thereby achieving an accurate description of complex emission scenarios.

[0026] Based on a spatiotemporal overlay inversion model and an emission feature fingerprint database, a multi-enterprise contribution separation matrix is ​​generated for each monitoring point through fingerprint matching degree weighted calculation. The system calculates the matching degree between the preliminary contribution values ​​obtained from the inversion and the enterprise emission feature fingerprints, and generates a more accurate enterprise contribution separation matrix through weighted processing, thereby achieving precise deconstruction of mixed pollution signals and clarifying the actual contribution ratio of each enterprise.

[0027] By monitoring the characteristic spectrum of pollutant concentration fluctuations in real time, the system identifies emission source characteristic patterns that match those in the emission characteristic fingerprint database. The system performs time-frequency analysis on the real-time collected pollutant concentration data, extracts fluctuation characteristics, and matches them with templates in the emission characteristic fingerprint database to identify the emission characteristics of specific enterprises, providing direct evidence for determining the responsible party.

[0028] Based on the intensity changes and frequency of emission source characteristic patterns, the system dynamically adjusts the weighting coefficients of each enterprise in the contribution separation matrix. Based on the statistical characteristics of the characteristic patterns, the system calculates the dynamic activity index of enterprises, further highlighting the impact of recent activities through time weighting, dynamically optimizing the contribution matrix, and improving the timeliness and accuracy of source tracing results.

[0029] Based on the adjusted contribution separation matrix, the responsible parties for excessive emissions are identified, and tiered early warning signals are generated. The system combines the exceedance status of monitoring points with the enterprise's contribution to the exceedance, calculates each enterprise's actual contribution, identifies the main responsible parties, and generates tiered early warning signals based on the responsibility index, providing precise decision support for environmental regulation.

[0030] In this embodiment of the invention, the detailed implementation steps for constructing an enterprise emission characteristic fingerprint database include:

[0031] The system acquires the concentration monitoring values ​​of multiple pollutant components from each enterprise within a preset time period and normalizes the concentration values ​​of each component. Historical monitoring data related to each enterprise, including SO2 and NO, is extracted from the environmental monitoring database. x Concentration time-series data of various pollutant components such as PM2.5 and VOCs were collected. Normalization was performed using a maximum-minimum standardization method, mapping concentration data of different magnitudes to the [0,1] interval to eliminate the influence of dimensional differences and create a unified data foundation for subsequent feature extraction. The normalization process specifically considered the impact of seasonal variations, employing a sliding window strategy for local normalization to maintain the temporal characteristics and relative variation patterns of the data.

[0032] The system calculates the normalized concentration ratios of each pollutant component to construct a component concentration ratio vector for each enterprise. Different industrial production processes generate specific ratios of pollutants, and these component ratios are important indicators of an enterprise's emission characteristics. The system calculates the relative concentration ratios between major pollutant components to construct a high-dimensional ratio vector. For example, for monitoring data with n major pollutant components, the ratios of C(n,2) = n(n-1) / 2 component pairs are calculated to form a unique "pollutant ratio fingerprint" for each enterprise. This ratio vector is highly robust to background concentrations and dilution effects, remaining relatively stable under different diffusion conditions, and is a key feature for enterprise identification.

[0033] The system extracts the time-series fluctuation curves of pollutant concentration monitoring values ​​within the production cycle and performs Fourier transform on these curves to obtain frequency domain characteristics. Enterprise production activities typically exhibit certain periodicity and regularity, which is directly reflected in the time-series fluctuations of emission data. The system first identifies the typical production cycle length of the enterprise (e.g., 8 hours, 24 hours, or 7 days), and then segments the concentration time-series data within the corresponding cycle. A Fast Fourier Transform (FFT) is used to convert the time-domain signal into a frequency-domain representation; the main features of the spectrum are extracted, including the dominant frequency components, spectral energy distribution, and bandwidth power ratio. These frequency domain features can effectively capture the start-up and shutdown modes, load changes, and emission control characteristics during the enterprise's production process, forming a "time fingerprint" of the enterprise's emissions.

[0034] The time-series correlation coefficient between frequency domain characteristics and the shift changeover time points of the enterprise is calculated and denoted as the production correlation degree. Enterprise emission patterns are usually closely related to production shift arrangements, and this correlation is an important basis for identifying enterprise characteristics. The system obtains the enterprise's production shift schedule, such as the 8:00, 16:00, and 0:00 shift transition points for a three-shift system, and calculates the correlation coefficient between the frequency domain characteristic variation patterns around these time points and the shift changeover. The production correlation degree is calculated using lagged cross-correlation analysis.

[0035] ;

[0036] in, For lag Correlation coefficient at time For frequency domain characteristic time series, Indicates in The shift changeover indicator function is typically a 0-1 function, where 1 indicates a shift change has occurred and 0 indicates no change has occurred. Representing frequency domain characteristic time series The mean, that is, the average level of the frequency domain characteristics at all time points; Indicates the function for indicating shift change The mean value reflects the average frequency of shift changes; Representing frequency domain characteristic time series The standard deviation reflects the amplitude of frequency domain characteristic fluctuations; Indicates the function for indicating shift switching The standard deviation reflects the degree of change in the shift switching pattern.

[0037] High production correlation indicates that a company's emissions are highly synchronized with its production activities. This is strong evidence for identifying emission sources and an important indicator for distinguishing between companies with continuous and intermittent production.

[0038] The component concentration ratio vector, frequency domain features, and production correlation are combined into a multi-dimensional feature vector, which is stored in the emission feature fingerprint database and labeled with the corresponding enterprise identifier and process type. The system organizes the above three types of features in a structured manner to form a comprehensive emission feature fingerprint of the enterprise. To improve retrieval and matching efficiency, dimensionality reduction techniques such as principal component analysis (PCA) are used to process high-dimensional feature vectors, retaining the principal components with the most information. The fingerprint database adopts a hierarchical structure, indexed by process type, enterprise size, and geographical region, and is updated regularly to reflect the impact of enterprise process changes and pollution control measures. In addition to basic features, each fingerprint record also includes a confidence score and timeliness marker to ensure the accuracy and usability of the fingerprint database. The enterprise emission feature fingerprint database becomes the core knowledge base for the system to identify abnormal emissions and trace the responsible parties.

[0039] In this embodiment of the invention, the detailed implementation steps for calculating the reverse source propagation velocity vector and distance attenuation coefficient of pollutants based on the time difference of arrival of the concentration peak at each monitoring point within the abnormal propagation path area include:

[0040] The system identifies the timestamps corresponding to the peak pollutant concentrations at each monitoring point within an abnormal transmission path area and calculates the time difference of arrival (TDOA) between adjacent monitoring points. The system performs peak detection on the time-series concentration data of each monitoring point within the abnormal area, employing a local maximum search combined with a dual-threshold filtering algorithm to accurately identify valid concentration peaks and their corresponding timestamps. The calculation of TDOA between adjacent monitoring points must consider the sampling frequency of the monitoring equipment. For high-frequency data, interpolation techniques are used to improve time resolution, while for low-frequency data, the precise peak time is estimated through concentration curve fitting. The system also distinguishes between single-peak and multi-peak cases. For multi-peak cases, a peak shape matching algorithm is used to ensure the tracking of the same pollution plume's propagation process, avoiding confusion between different pollution events. TDOA is the fundamental data for calculating pollutant propagation speed, and its accuracy directly affects the reliability of the source tracing results.

[0041] The system acquires the spatial distance and azimuth between adjacent monitoring points and calculates the propagation velocity component of pollutants by combining this with the time difference of arrival of peak concentrations. Based on the geographic coordinates of the monitoring points, the system calculates the Euclidean distance and azimuth between adjacent points. Considering the influence of terrain, the distance calculation is corrected using a digital elevation model (DEM) to ensure measurement accuracy. The formula for calculating the propagation velocity component is:

[0042] ;

[0043] in, For the propagation speed component, For spatial distance, This represents the time difference between the arrival of the concentration peak.

[0044] Statistical analysis was performed on the calculated velocity components from multiple monitoring points to eliminate outliers. By integrating information from multiple points, a propagation velocity field of pollutants in the abnormal area was constructed, providing dynamic parameters for subsequent source tracing calculations.

[0045] Based on the propagation velocity component and concurrent wind speed and direction data, the residual propagation velocity vector is obtained by separating the dominant meteorological component through vector decomposition. The propagation of pollutants in the atmosphere is significantly influenced by wind fields, but is also affected by diffusion, deposition, and topography. The system acquires meteorological observation data concurrent with the peak concentration propagation period and constructs local wind speed and direction vectors. Through vector decomposition, the observed propagation velocity component is decomposed into downwind and crosswind components and compared with the meteorological wind speed vector. By establishing a contribution ratio model, the contribution of meteorological factors is separated from the total propagation velocity, obtaining the residual propagation velocity vector. This residual vector reflects the influence of non-meteorological factors (such as topography, turbulence, and thermodynamic effects) on pollutant propagation, which is particularly important for source tracing analysis in complex terrain and multi-source emission scenarios.

[0046] The system uses back projection to determine potential source areas of pollutants by back-projecting the residual propagation velocity vector. Based on the acquired residual propagation velocity vector, the system tracks possible sources of pollutants using a back projection algorithm. The back projection employs a multi-point intersection method, plotting the backward extensions of the residual velocity vectors from each monitoring point on a spatial coordinate system and analyzing the intersection areas of these extensions. To handle measurement errors and the randomness of the propagation process, the system uses probabilistic heatmap technology to transform the backward extensions into a spatial probability distribution; the area with the highest probability density is the potential pollution source area. The system also incorporates a map of enterprise distribution within the industrial park to assess the likelihood of each enterprise being located in the source area, thus preliminarily screening potential emission source enterprises.

[0047] Concentration values ​​from each monitoring point within the abnormal transmission path area are extracted. Based on the distance from each monitoring point to the potential source area, a concentration-distance decay curve is fitted, and the distance decay coefficient is calculated. Pollutants are diluted and decayed during transmission, and the relationship between concentration and distance follows a specific pattern. The system collects concentration data from all monitoring points within the abnormal area during the pollution event, combines this data with the distance from each point to the potential source area, constructs a scatter plot, and performs curve fitting. The decay model uses an exponential decay function:

[0048] ;

[0049] in, For distance from the source Concentration at that location, The initial concentration estimated at the source. This is the distance attenuation coefficient.

[0050] The optimal distance attenuation coefficient was determined using a nonlinear regression method. This coefficient reflects the diffusion characteristics of a specific pollutant under current meteorological conditions and is a key parameter for constructing a multi-source emission superposition model. The system also analyzes the residual distribution to verify the model's fitting quality, and introduces piecewise fitting or correction terms that consider topographic factors when necessary to improve the model's accuracy.

[0051] In this embodiment of the invention, the detailed implementation steps for constructing a spatiotemporal superposition inversion model of multi-source emissions based on the reverse source propagation velocity vector and the distance attenuation coefficient include:

[0052] Based on the spatial distribution data of enterprises, a two-dimensional spatial grid model of the industrial park is constructed, and the location coordinates of each enterprise are marked in the spatial grid model. Using Geographic Information System (GIS) technology, the industrial park is divided into a uniform two-dimensional grid. The grid resolution is dynamically adjusted according to the park size and enterprise density, typically ranging from 50 to 200 meters. The location coordinates, factory area, and main emission outlet locations of each enterprise are accurately marked in the grid model. For large enterprises, the model is further subdivided into multiple emission units to improve model accuracy. The grid model uses a UTM projection system to ensure the accuracy of spatial calculations, and a 2.5D model considering elevation changes is constructed by overlaying terrain data, providing a more accurate spatial reference for subsequent diffusion simulations.

[0053] The coverage area of ​​anomalous propagation path regions is marked in the spatial grid model, generating a propagation impact area map containing multiple potential emission sources. The previously identified anomalous propagation path regions are mapped onto the spatial grid, coverage boundaries are drawn, and the spatial distribution of anomaly severity is marked. The propagation impact area map is presented as a heatmap, visually displaying the spatial distribution and gradient changes of pollution concentrations. Based on the back projection results, potential emission source enterprises are marked in the impact area map, and the potential contribution of each enterprise is preliminarily assessed based on the spatial relationship between the enterprises and the anomaly center. The impact area map not only visualizes the spatial data but also serves as a spatial constraint for subsequent model overlay construction, ensuring the model focuses on key areas and improving computational efficiency.

[0054] Based on the propagation impact area map, the propagation path length and azimuth angle from each enterprise's emission source to each monitoring point are calculated. Using a spatial grid model, the shortest path algorithm is employed to calculate the actual propagation distance from each enterprise to the monitoring point, considering the influence of terrain obstacles and urban buildings to avoid simple straight-line distance errors. The azimuth angle calculation considers the angle between the prevailing wind direction and the line connecting the enterprise and the monitoring point, assessing the downwind position of the monitoring point relative to the enterprise. For complex terrain, wind field simulation technology is used to simulate the propagation trajectory of pollutants under actual terrain conditions, obtaining more accurate path length and azimuth data. These spatial relationship parameters are key inputs for constructing the multi-source superposition model, affecting the calculation of the theoretical contribution value of each enterprise to the monitoring point.

[0055] Based on the propagation path length, distance attenuation coefficient, and reverse source propagation velocity vector, the theoretical concentration contribution of each emission source to each monitoring point is calculated. A pollutant transport model from the emission source to the monitoring point is established based on the previously determined distance attenuation coefficient and propagation velocity vector. The theoretical contribution calculation considers three core factors: emission intensity of the emission source, propagation distance attenuation, and directional diffusion. The calculation formula is as follows:

[0056] ;

[0057] in, For enterprises For monitoring points The theoretical concentration contribution value, For enterprises The emission intensity (the variable to be solved), For enterprises For monitoring points The propagation path length, This is the distance attenuation coefficient. It is a directional function. This is the azimuth deflection angle.

[0058] Directional function Based on a Gaussian diffusion model, the system reflects the fan-shaped diffusion characteristics of pollutants under the influence of the prevailing wind direction. The theoretical contribution value is calculated for each enterprise-monitoring point pair, constructing a complete contribution matrix to lay the foundation for subsequent inversion calculations.

[0059] A spatiotemporal superposition inversion model for multi-source emissions is constructed. This model uses the least squares method to invert the actual emission intensity of each emission source, minimizing the sum of squared residuals between the superimposed theoretical concentration contributions of each source and the measured concentrations at monitoring points. The core of the inversion model is solving an inverse problem of multi-source pollution, namely, inferring the emission intensity of each source from known monitoring values. The following equations are established:

[0060] ;

[0061] in, For monitoring points The measured concentration, This represents the measurement error and background concentration terms.

[0062] The constrained least squares method is used to solve the system of equations, with constraints including non-negativity of emission intensity and total emission limits. Considering the ill-conditioned nature of the inverse problem, regularization techniques such as the Tikhonov regularization method are employed to improve the stability of the solution. For large-scale grids, iterative algorithms such as the conjugate gradient method are used to improve computational efficiency. To handle emission variations under different meteorological conditions, the data is categorized by meteorological condition, models are constructed for each category, and the results are compared to verify the consistency of the models under different conditions. The emission intensity obtained from the final inversion is the estimate of the actual emissions of each enterprise during the monitoring period, providing a quantitative basis for subsequent contribution separation and liability determination.

[0063] In this embodiment of the invention, the detailed implementation steps for generating a multi-enterprise contribution separation matrix for each monitoring point based on a spatiotemporal overlay inversion model and an emission feature fingerprint database through fingerprint matching degree weighted calculation include:

[0064] Based on the spatiotemporal overlay inversion model, the preliminary contribution values ​​of each enterprise to each monitoring point are obtained. Based on the previously constructed multi-source emission inversion model, the initial contribution concentration value of each enterprise to each monitoring point is calculated. The preliminary contribution is converted into a percentage, intuitively representing the proportion of each enterprise in the total concentration at the monitoring point. This preliminary result is mainly based on spatial relationships and diffusion models, reflecting the theoretical contribution under purely physical processes. Uncertainty analysis is performed on the inversion results to assess the confidence interval of the preliminary contribution, providing a reference standard for subsequent feature matching. The preliminary contribution matrix, as the basic separation result, will be fused with the feature matching results to balance information from both the physical model and statistical characteristics.

[0065] The measured pollutant component concentration ratios at each monitoring point are extracted and compared with the component concentration ratio vectors of each enterprise in the emission characteristic fingerprint database to calculate the fingerprint matching degree. Pollutant component ratio features identical to those in the characteristic fingerprint database are extracted from the monitoring data to construct feature vectors for each monitoring point. The degree of feature matching is quantified by calculating the similarity between the monitoring point feature vectors and the characteristic fingerprints of each enterprise. The cosine similarity method is used for similarity calculation.

[0066] ;

[0067] in, For the feature vector of the monitoring point, For enterprise characteristic fingerprints, Represents the dot product of vectors. Represents the vector norm.

[0068] Considering the time factor, data from different time periods is segmented for calculation to capture dynamic changes in emission characteristics. Furthermore, weighted similarity calculations are employed to improve the reliability of feature matching, taking into account the detection limits and measurement errors of different pollutant components. Fingerprint matching, based on the identification results of pollutant "chemical characteristics," overcomes the limitations of purely physical models and is particularly suitable for distinguishing companies with similar emission characteristics but different spatial locations.

[0069] A non-linear mapping is applied to the fingerprint matching score to generate matching score weight coefficients, which increase exponentially with the fingerprint matching score. The relationship between feature matching score and actual contribution is usually non-linear; a higher matching score indicates a higher probability of contribution. An exponential function is used for non-linear mapping to amplify the influence of high matching scores and suppress noise from low matching scores. The formula for calculating the weight coefficients is:

[0070] ;

[0071] in, For matching degree The corresponding weighting coefficients, The parameter used to control the exponential growth rate.

[0072] parameter By calibrating using historical data, with a typical value of 2-5, the weights increase rapidly in the high matching range (e.g., 0.8-1.0), forming an effective discriminant. Adaptive β values ​​are used for different pollutant types and monitoring scenarios to optimize the rationality of weight allocation. This nonlinear mapping mechanism ensures that emission sources with distinct characteristics receive higher influence weights, improving the identification and reliability of the separation results.

[0073] The preliminary contribution value is multiplied by the matching weight coefficient of the corresponding enterprise to obtain the corrected enterprise contribution value. The preliminary contribution value based on the spatial model is combined with the weight coefficient based on feature matching to calculate the weighted corrected contribution value. During the correction process, the uncertainty range of the preliminary contribution value and the reliability of the matching degree are considered simultaneously, and outliers are restricted to avoid extreme results. For special cases such as low-concentration background pollution and difficulty in distinguishing between multiple sources, Bayesian inference methods are used to integrate prior knowledge and observational evidence to improve the robustness of the correction results. The corrected contribution value more comprehensively reflects the actual impact of enterprises on pollution at monitoring points, and is a comprehensive representation of both spatial relationships and emission characteristics.

[0074] The corrected enterprise contribution values ​​are arranged by monitoring point and enterprise dimension to construct a multi-enterprise contribution separation matrix. Each column of the matrix is ​​normalized to ensure the sum of enterprise contributions for each monitoring point is 1. All corrected results are organized into a two-dimensional matrix, with rows representing monitoring points, columns representing enterprises, and cell values ​​representing the corresponding contribution values. To ensure consistency and comparability, each column of the matrix (i.e., the contribution of all enterprises at each monitoring point) is normalized to a sum of 1. Confidence intervals for contribution are also calculated to assess the reliability of the separation results, and warning labels are added for results with high uncertainty. The multi-enterprise contribution separation matrix is ​​the core result of pollution source analysis, intuitively displaying the contribution composition of each enterprise to environmental monitoring data, providing a quantitative basis for liability determination and regulatory decisions. It supports multiple visualization methods, such as heatmaps, radar charts, and bubble charts, facilitating regulators' quick understanding of complex contribution relationships.

[0075] In this embodiment of the invention, the detailed implementation steps for identifying emission source characteristic patterns matching the emission characteristic fingerprint database by real-time monitoring of the characteristic spectrum of pollutant concentration fluctuations include:

[0076] The real-time pollutant concentration time-series data is segmented using a sliding window, with each segment corresponding to a typical production cycle of the enterprise. Continuously acquired pollutant concentration data is also segmented using the sliding window technique, with the window length set to match the enterprise's typical production cycle, such as 8 hours, 24 hours, or a specific process cycle. The window sliding step size is set to 10-25% of the window length to ensure smoothness and sensitivity in continuous analysis. For different pollutants and different concentration ranges, adaptive windowing technology is used, dynamically adjusting window parameters based on signal strength and fluctuation characteristics. Before segmentation, the raw data is preprocessed, including outlier removal, missing value imputation, and trend elimination to ensure the accuracy of subsequent analysis. Sliding window segmentation is a fundamental step in spectral analysis; reasonable window settings directly affect the effectiveness of feature extraction and the accuracy of pattern recognition.

[0077] Wavelet transform is applied to each segment of data to extract multi-scale fluctuation features in the time and frequency domains, generating a real-time fluctuation feature matrix. Compared to traditional Fourier transform, wavelet transform has the advantage of time-frequency localization, enabling it to simultaneously capture the time and frequency characteristics of the signal. Continuous wavelet transform technology is employed, using Morlet wavelets as the base wavelet, to decompose the segmented data into multiple scales. The transform process covers multiple time scales from minute-level to daily-level, comprehensively capturing the fluctuation characteristics of pollutant concentrations. Key features are extracted from the wavelet coefficient matrix, including energy distribution in each frequency band, scale correlation, and singularity distribution, to construct the real-time fluctuation feature matrix. This feature matrix retains both the variation information in the time dimension and the periodic features in the frequency dimension, providing rich feature descriptions for subsequent pattern matching.

[0078] Frequency domain features of each enterprise are extracted from the emission fingerprint database as reference templates. Frequency domain feature templates relevant to the current analysis are extracted from the enterprise emission fingerprint database, including enterprise-specific spectral distribution patterns, energy ratio characteristics, and time-varying patterns. To improve matching efficiency, a subset of potentially relevant enterprise templates is pre-screened based on current environmental conditions and pollutant types, narrowing the search scope. The reference templates are standardized to eliminate the influence of absolute concentration values ​​and highlight the relative characteristics of fluctuation patterns. Furthermore, the reference templates are adaptively adjusted based on current meteorological conditions and diffusion scenarios to simulate characteristic changes under different propagation conditions, improving template adaptability and matching accuracy.

[0079] The cross-correlation coefficient between the real-time fluctuation feature matrix and each reference template is calculated and denoted as the template matching degree. Cross-correlation analysis is used to evaluate the similarity between the real-time fluctuation features and the reference templates. Considering the time offset and scale variation of the fluctuation features, the Dynamic Time Warping (DTW) algorithm is used to calculate the optimal matching path of the feature sequence, overcoming the limitations of simple correlation analysis. During the cross-correlation calculation, a weighted strategy is adopted for features in different frequency bands, emphasizing the importance of enterprise-specific frequency bands. A complete matching degree matrix is ​​generated, showing the matching status of real-time data and each enterprise's template in different time windows, dynamically tracking the changing trend of the matching degree. The matching analysis also considers the similarity between templates, handling possible multi-template aliasing, and improving the discriminative power of identification. As a core indicator for emission source identification, the template matching degree directly reflects the degree to which the emission characteristics of a specific enterprise are reflected in the monitoring data.

[0080] Reference templates with a matching degree greater than a preset matching threshold are selected, and their corresponding enterprise identifiers and matching degree values ​​are combined and marked as emission source feature patterns. Based on the template matching degree distribution, an adaptive matching threshold is set, typically the upper quartile of the matching degree distribution or a fixed threshold (e.g., 0.7), to filter significantly matching enterprise templates. For templates with a matching degree exceeding the threshold, their corresponding enterprise identifier, matching time period, matching degree value, and matching frequency band characteristics are recorded to generate emission source feature pattern labels. These feature patterns are then scored for credibility, considering factors such as the absolute value of the matching degree, relative advantage, and time persistence, and high-credibility identification results are marked. The feature pattern discovery mechanism employs a continuous monitoring strategy to track changes in the frequency and intensity of specific enterprise features, establishing a dynamic profile of enterprise activities. These emission source feature patterns are direct evidence of enterprise emission behavior, providing crucial evidence for subsequent identification of responsible entities and adjustment of contributions.

[0081] In this embodiment of the invention, the detailed implementation steps for dynamically adjusting the weight allocation coefficients of each enterprise in the contribution separation matrix based on the intensity changes and frequency of occurrence of emission source characteristic patterns include:

[0082] The frequency of occurrence of emission source characteristic patterns for each enterprise within a preset time window is recorded as the characteristic pattern frequency. An appropriate time window is set (e.g., the last 24 hours, the last 7 days), and the total number of times each enterprise's characteristic pattern is identified within the window is counted. Considering the cyclical nature of enterprise production, a time distribution analysis is performed on the frequency across different time periods to identify time patterns such as weekday / rest day and day shift / night shift. For frequency statistics, a weighted counting strategy is adopted, adjusting the counts based on the credibility of the characteristic pattern to improve the reliability of the statistical results. The continuity and clustering of characteristic patterns are also analyzed to distinguish between continuous and intermittent emission patterns. These time pattern characteristics provide important basis for assessing enterprise activity. The characteristic pattern frequency directly reflects the activity level of enterprise emission activities and is the fundamental data for adjusting the contribution matrix.

[0083] The average template matching degree corresponding to the characteristic patterns of each emission source is calculated within a preset time window and denoted as the average matching intensity. Statistical analysis is performed on the matching degrees of all characteristic patterns of the same enterprise within the time window, calculating the mean, standard deviation, and distribution characteristics. The average matching intensity reflects the significance and stability of the enterprise's emission characteristics in the monitoring data. Time-series analysis of the matching intensity is conducted to identify trends and patterns of intensity changes and assess the dynamic characteristics of the enterprise's emission behavior. For the calculation of matching intensity, the influence of different pollutants and different monitoring conditions is considered, and normalization is used to ensure data comparability. The average matching intensity, combined with characteristic frequency, comprehensively describes both the quantitative and qualitative dimensions of the enterprise's emission activities, providing a comprehensive basis for the calculation of the activity index.

[0084] The dynamic activity index for each enterprise is calculated by multiplying the frequency of characteristic patterns by the average matching intensity. The activity index is a core indicator for quantifying the comprehensive impact of enterprise emissions activities, combining two key dimensions: activity frequency and intensity. The calculation formula is as follows:

[0085] ;

[0086] in, For enterprises The dynamic activity index, For the frequency of feature patterns, This represents the average matching strength.

[0087] The original activity index is standardized to ensure comparability between values ​​from different companies. The activity index calculation also considers the impact of company size and industry characteristics, adjusting for these factors through industry standardization coefficients to ensure fair assessment for large and small companies with similar emission behaviors. The dynamic activity index directly reflects the actual environmental impact of a company's recent emissions activities and is a key reference value for adjusting the contribution matrix.

[0088] The dynamic activity index is weighted by time decay, giving higher weight to recent data to obtain time-weighted activity. A time decay function is used, assigning weights based on the freshness of the data, with recent data receiving greater influence. The decay function employs an exponential decay model:

[0089] ;

[0090] in, For time points The weight, For the current time, This is the attenuation coefficient.

[0091] Appropriate attenuation coefficients are set based on pollutant characteristics and regulatory needs, typically ensuring that data from the past 7 days accounts for more than 80% of the total weight. Time-weighted calculations consider the uneven distribution of data, employing interpolation techniques for sparse data periods to ensure the rationality of weight allocation. Time-weighted activity emphasizes the timeliness of corporate emission behavior, enabling the contribution matrix to quickly reflect changes in corporate emission status, thus improving the timeliness and targeting of regulation.

[0092] Based on the initial weights of the corresponding enterprises in the time-weighted activity and contribution separation matrix, weight allocation coefficients are calculated using linear interpolation, and the contribution separation matrix is ​​updated. The time-weighted activity is then converted into weight adjustment coefficients, and the final weight allocation coefficients are calculated using linear interpolation combined with the original contribution matrix. The interpolation process considers the confidence level of the original contribution and the reliability of the activity index, finding a reasonable balance between the two. The formula for calculating the adjustment coefficients is:

[0093] ;

[0094] in, For enterprises The final weighting coefficients, The normalized time-weighted activity level. The original contribution after normalization. These are the balancing parameters.

[0095] Balance parameters The value is typically set between 0.3 and 0.7, and dynamically adjusted based on actual regulatory experience and historical data verification results. The contribution separation matrix is ​​updated using weighted allocation coefficients and normalized to ensure the sum of enterprise contributions at each monitoring point is 1. The updated contribution matrix better reflects the recent actual emission behavior of enterprises, balancing the results of static model analysis and dynamic activity monitoring, and providing a more accurate quantitative basis for identifying responsible parties.

[0096] In this embodiment of the invention, the detailed implementation steps for identifying abnormal areas of propagation paths based on the temporal evolution characteristics of concentration gradients between monitoring points include:

[0097] The spatial concentration gradient is calculated as the pollutant concentration difference between adjacent monitoring points in a multi-site monitoring network. Based on the spatial distribution of the monitoring network, adjacent monitoring point pairs are identified, and the pollutant concentration difference between each pair at the same time is calculated. The Delaunay triangulation method is used to define adjacent points, ensuring the integrity of spatial coverage and the rationality of point pair relationships. The concentration difference calculation considers measurement errors and data time asynchrony, improving calculation accuracy through data interpolation and error propagation analysis. The original concentration difference values ​​are normalized to eliminate the influence of differences in the magnitude of different pollutants, facilitating comprehensive analysis of multiple pollutants. The spatial concentration gradient reflects the spatial rate of change of pollutant concentration and is fundamental data for identifying the direction and intensity of pollution propagation. Areas with abnormal gradient values ​​usually indicate the presence of local emission sources or special propagation phenomena.

[0098] Time series analysis was performed on the spatial concentration gradient to extract the evolution curve of the rate of change of the concentration gradient over time. Temporal analysis was conducted on the concentration gradient sequences of each pair of adjacent monitoring points to calculate the time derivative (rate of change) of the gradient and construct the gradient evolution curve. A sliding window technique was used to calculate the trend and fluctuation characteristics of the gradient within the window. Particular attention was paid to abrupt gradient changes; dynamic thresholds were set to identify gradient abrupt changes that significantly deviated from background changes. To improve the robustness of the analysis, wavelet analysis and empirical mode decomposition were used to separate the fluctuation components of different scales in the gradient signal, focusing on analyzing mid-to-high frequency changes related to pollution emissions. The gradient evolution curve reveals the dynamic changes in the spatial distribution of pollutants over time and is a key temporal feature for identifying abnormal propagation behavior.

[0099] The process involves identifying the time nodes of gradient surges in the evolution curve and statistically analyzing the synchronicity of gradient surge events among monitoring point pairs. Based on the gradient evolution curve, a peak detection algorithm is used to identify the time nodes of gradient surges. The surge determination criteria include two dimensions: absolute threshold and relative rate of change, ensuring the capture of anomalous events at different scales. A time alignment analysis is performed on gradient surge events across the entire monitoring network to calculate the temporal distribution and spatial correlation of events. Synchronicity assessment uses a time window method, statistically analyzing the simultaneous occurrence of gradient surges in multiple point pairs within a specific time window (e.g., 30 minutes). A synchronicity matrix is ​​generated to describe the temporal correlation of gradient surge events among point pairs. High synchronicity areas typically indicate the presence of a common pollution source and are an important indicator for identifying anomalous areas.

[0100] Spatial clustering is performed on monitoring point pairs exhibiting gradient abrupt increases in synchronicity. The spatial regions covered by the clustered monitoring point sets are marked as candidate anomaly regions. Based on the synchronicity matrix, graph-theoretic clustering methods, such as spectral clustering, are used to organize monitoring points with high temporal synchronicity due to gradient abrupt increases into connected subgraphs, each representing a potential influence region. The clustering process considers the spatial proximity and gradient correlation of monitoring points to ensure the spatial continuity and physical rationality of the partitioning results. For each clustering result, the core region and influence boundary are calculated, and the spatial extent of the candidate anomaly regions is determined using the convex hull algorithm or density contour line method. For sparsely populated areas, spatial interpolation techniques are used to extend the boundaries of the anomaly regions, improving the integrity of the coverage. Candidate anomaly regions represent spatial areas where abnormal behavior occurs during pollutant propagation and are the focus of subsequent analysis.

[0101] The consistency index of concentration gradient directions within candidate anomaly areas is calculated. Candidate anomaly areas with a consistency index greater than a preset consistency threshold are identified as anomaly areas in the transmission path. The gradient directions of each monitoring point pair within the candidate anomaly areas are analyzed to assess the concentration of directional distribution and the significance of the dominant direction. The consistency index is calculated using directional statistics, treating the gradient direction as a unit vector and calculating the ratio of the length of the composite vector to the number of vectors. The consistency index ranges from [0,1], with values ​​closer to 1 indicating greater directional consistency. An appropriate consistency threshold (typically 0.6-0.8) is set to screen areas with highly consistent gradient directions. These areas usually indicate a clear pollutant transmission path and a relatively definite emission source location. Combining the dominant gradient direction with meteorological wind field data, the possible causes of transmission anomalies are further assessed, distinguishing between meteorological-driven and localized emission-induced anomalies. The finally identified anomaly areas in the transmission path are key areas for subsequent source tracing analysis and responsibility determination. An anomaly area report is generated, including key information such as spatial extent, anomaly severity, duration, and possible sources of impact.

[0102] In this embodiment of the invention, the detailed implementation steps for determining the responsible party for excessive emissions and generating tiered early warning signals based on the adjusted contribution separation matrix include:

[0103] The system identifies pollutant concentration exceedances at various monitoring sites, obtaining the exceedance multiple and duration of exceedances at each site. Based on national and local environmental standards, pollutant concentration data at each monitoring site are assessed for exceedance. Exceedance analysis considers differentiated standards for different time periods, such as daily averages, hourly values, and limits for specific time periods. The exceedance multiple (the ratio of measured value to standard value) and duration of exceedance are calculated to comprehensively assess the severity of exceedances. For cases involving multiple exceedances, a comprehensive index method is used to calculate the overall exceedance level, ensuring a reasonable assessment of the impact of various pollutants. The system also analyzes the temporal distribution characteristics of exceedances, identifying periodic and sudden exceedances to provide time clues for subsequent liability determination. Exceedances are the direct trigger for environmental early warnings; the degree and duration of exceedances are directly related to the determination of the warning level.

[0104] The contribution values ​​of each enterprise corresponding to the monitoring points exceeding the standards are extracted from the adjusted contribution separation matrix. From the updated multi-enterprise contribution separation matrix, the row data corresponding to the monitoring points exceeding the standards is extracted to obtain the contribution ratio of each enterprise to the exceeding points. For cases with multiple exceeding points, an enterprise-monitoring point contribution heatmap is generated to visually display the spatial distribution of each enterprise's influence. Confidence assessments are performed on the contribution values, and warning markers are added for results with high uncertainty to ensure the reliability of decision-making. The contribution values ​​are the direct basis for identifying responsible parties, reflecting the relative role of each enterprise in the exceeding of standards at the monitoring points, and serve as the quantitative basis for environmental liability determination.

[0105] Multiply each company's contribution value by the concentration exceedance factor to calculate each company's absolute contribution to the excessive concentration. Then convert the relative contribution value into a specific excessive concentration to calculate the actual excessive concentration caused by each company. The calculation formula is as follows:

[0106] ;

[0107] in, For enterprises The absolute contribution For enterprises The proportion of contribution, For the actual concentration measured at the monitoring point, These are standard limits.

[0108] For each monitoring point exceeding the standard, the enterprise's contribution is calculated separately, and weighted according to the importance of the monitoring point (such as population density and sensitive areas) to comprehensively assess the environmental impact of the enterprise's emissions. The absolute contribution directly reflects the actual degree of impact of the enterprise's emissions on environmental exceedances and is the core quantitative indicator for liability determination.

[0109] Enterprises with absolute contributions are ranked in descending order, and those whose contributions exceed a preset responsibility threshold are marked as primary responsible entities. Based on the ranking of absolute contributions, the group of enterprises with the most significant impact on exceeding standards is identified. The responsibility threshold is set using a contribution distribution analysis method, typically taking 70-80% of the total exceedances as the primary responsibility coverage target, or using the Pareto principle to determine a critical few enterprises. The reliability of the list of primary responsible entities is assessed, and a credibility score is assigned to each responsibility determination result by combining model uncertainty and feature matching confidence. For cases where responsibility boundaries are unclear, fuzzy clustering is used to classify enterprises into three categories: high responsibility, medium responsibility, and low responsibility, avoiding mechanical threshold division. The list of primary responsible entities is a key target for environmental regulatory enforcement, directly related to the subsequent severity of penalties and rectification requirements.

[0110] The frequency of occurrence of each major responsible entity at multiple monitoring points exceeding emission standards was statistically analyzed to calculate the cross-regional impact coefficient. The spatial scope of influence of the major responsible entities was analyzed, counting the number of times each enterprise was identified as a major responsible party at different emission points. The calculation of the cross-regional impact coefficient considered the spatial distribution and representativeness of the monitoring points to assess the breadth of the spatial impact of enterprise emissions. A spatial weighting model was adopted, assigning differentiated weights to monitoring points at different distances and orientations to more accurately assess the spatial impact patterns of enterprises. The cross-regional impact coefficient is an important dimension of environmental risk assessment; a high coefficient indicates that enterprise emissions have an impact on a large area, usually foreshadowing more severe environmental risks and higher warning levels.

[0111] A comprehensive responsibility index is constructed based on absolute contribution, duration of exceeding limits, and cross-regional impact coefficient. Multi-dimensional responsibility assessment indicators are integrated to create a comprehensive comprehensive responsibility index. The calculation employs a weighted summation method.

[0112] ;

[0113] in, As a comprehensive responsibility index, This is the normalized absolute contribution. The duration of exceeding the standard after normalization. This is the cross-regional influence coefficient. , and The weighting coefficients and .

[0114] The weighting coefficients are set based on regulatory policy guidance and pollutant characteristics, typically assigning the highest weight to the absolute contribution (approximately 0.5-0.6). The calculation results are standardized so that the final index is distributed in the [0,10] range, facilitating grading and result interpretation. The comprehensive responsibility index fully reflects multiple dimensions of a company's environmental impact and serves as a scientific basis for early warning grading.

[0115] The comprehensive responsibility index is divided into multiple warning level intervals, and the warning level is determined based on the interval to which the comprehensive responsibility index belongs. A mapping relationship between the comprehensive responsibility index and the warning level is established, typically divided into a four-level warning system: Level 1 (Red, Severe), Level 2 (Orange, Relatively Severe), Level 3 (Yellow, Moderate), and Level 4 (Blue, Mild). The level division takes into account historical statistical distribution and management experience, and sets reasonable threshold boundaries. For index values ​​in the critical area, fuzzy membership degree calculation is used to assess the probability of belonging to different levels, avoiding decision-making fluctuations caused by hard thresholds. The warning level is directly linked to the emergency response mechanism; different levels trigger different regulatory measures and emergency plans, making it a core indicator of environmental risk management.

[0116] Based on the historical exceedance records of the main responsible entities, the current warning level is revised to generate a final warning level. The historical environmental performance of the responsible entities is analyzed, including past exceedance frequency, penalty records, and rectification status, to adjust the initial warning level. Revision rules include: multiple exceedances within a year will upgrade the warning level; completion of rectification and long-term compliance may result in an appropriate downgrade; and the warning level will be raised during special sensitive periods (such as major events or periods of severe air pollution emergencies). Rule engine technology is used to automate the complex revision logic, ensuring consistency and interpretability of the revision process. The final warning level comprehensively considers both the current exceedance situation and historical performance, more comprehensively reflecting the company's environmental credit status and contributing to precise enforcement and categorized supervision.

[0117] The system triggers corresponding warning signals based on the final warning level, and combines the main responsible parties, absolute contributions, and the final warning level to generate a tiered warning report. Based on the final determined warning level, the corresponding warning signal is automatically triggered and sent to environmental regulatory departments and enterprise managers through multiple channels, including SMS, email, and mobile application push notifications. Simultaneously, a detailed tiered warning report is generated, containing four core parts: an overview of the environmental situation (monitoring points exceeding standards, degree of exceedance, and scope of impact), an analysis of responsible parties (list of main responsible enterprises, ranking of contributions, and key evidence), a risk assessment (warning level, ongoing forecasts, and potential impacts), and response recommendations (regulatory measures, enterprise rectification requirements, and follow-up monitoring arrangements). The report adopts a tiered presentation method, providing appropriate levels of detail for managers at different levels to facilitate rapid decision-making. The warning report serves as the basis for initiating environmental emergency response, supporting environmental departments in achieving scientific and precise pollution control and risk prevention.

[0118] In this embodiment of the invention, the detailed implementation steps for obtaining the residual propagation velocity vector by stripping the dominant meteorological component through vector decomposition based on the propagation velocity component and wind speed and direction data during the same period include:

[0119] The average wind speed and prevailing wind direction for the time period corresponding to the peak concentration arrival time are obtained to construct a meteorological wind speed vector. Meteorological observation data concurrent with pollutant propagation are obtained from meteorological monitoring stations, including wind speed, wind direction, temperature stratification, and atmospheric stability. For areas with complex terrain, a numerical meteorological model is used to simulate the local wind field, generating high-resolution three-dimensional wind field data. The average wind speed and prevailing wind direction for the key period of pollutant propagation (from source emission to detection at the monitoring point) are calculated to construct a wind speed vector representing the meteorological driving force. The wind speed vector is represented in polar coordinates (wind speed magnitude and wind direction angle) and converted to component form in a rectangular coordinate system for easy subsequent vector calculations. The meteorological wind speed vector is the main driving force of pollutant propagation, and accurate wind field data is a key input for source tracing calculations.

[0120] The propagation velocity component is decomposed into a downwind component parallel to the prevailing wind direction and a crosswind component perpendicular to the prevailing wind direction. The observed pollutant propagation velocity components are then vector-decomposed to separate the downwind and crosswind velocity components. The decomposition process is based on the principle of vector projection, calculating the projection of the propagation velocity onto the prevailing wind direction (downwind component) and the component perpendicular to the prevailing wind direction (crosswind component). The calculation formula is as follows:

[0121] ;

[0122] ;

[0123] in, For tailwind weight, Crosswind weight, For the magnitude of the propagation speed, For the direction and angle of dissemination, The prevailing wind direction.

[0124] The decomposition results reflect the relative effects of wind field driving and other factors (such as turbulent diffusion and topographic influence) in the spread of pollutants. The downwind component mainly reflects the wind field driving effect, while the crosswind component mainly reflects the influence of non-wind field factors.

[0125] The ratio of the downwind component to the meteorological wind speed vector is calculated as the meteorological contribution ratio coefficient. This coefficient quantifies the actual impact of the wind field on pollutant propagation. The calculation formula is:

[0126] ;

[0127] in, Contribution coefficient to meteorological conditions For tailwind weight, This refers to the magnitude of meteorological wind speed.

[0128] Statistical analysis was conducted on the meteorological contribution ratio coefficients under different stability conditions to establish an empirical parameter database applicable to local conditions. Under typical stable atmospheric conditions... Value close to 1, under unstable conditions A smaller value reflects the impact of atmospheric diffusion on propagation. The meteorological contribution ratio coefficient is a key parameter for separating the dominant meteorological components and directly affects the accuracy of residual vector calculation.

[0129] Based on the meteorological contribution ratio, the scaling value of the meteorological wind speed vector is subtracted from the propagation velocity component to obtain the residual propagation velocity vector. Based on the meteorological contribution ratio, the actual contribution of the meteorological wind field to propagation is calculated, and this portion is separated from the total propagation velocity to obtain the residual propagation velocity vector. The calculation formula is as follows:

[0130] ;

[0131] in, Let the residual propagation velocity vector be... To observe the propagation velocity vector, Contribution coefficient to meteorological conditions This is the meteorological wind speed vector.

[0132] The residual vector is calculated in full vector form, preserving directional information to ensure the accuracy of spatial relationships. Uncertainty analysis is performed on the residual vector to assess the impact of meteorological data errors and propagation observation errors on the residual calculation, providing an error range estimate for subsequent source tracing. The residual propagation velocity vector represents the pollutant propagation characteristics caused by non-meteorological factors, typically pointing to the actual pollution source location, and is a key directional indicator for source tracing analysis.

[0133] The residual propagation velocity vector is directionally corrected by removing components aligned with terrain blocking directions, resulting in a corrected residual propagation velocity vector. Combining a digital elevation model (DEM) and 3D building data, the topographic features of the study area are analyzed to identify terrain features that may block or guide pollutant propagation. The residual vector is then corrected for terrain influence, removing components significantly affected by terrain channel or blocking effects. The correction employs a terrain projection filtering method, calculating the angle between the residual vector and the main terrain lines, weakening components parallel to terrain height gradients. The correction results are validated through historical case analysis to evaluate the effectiveness of the correction method, adjusting correction parameters as necessary. The corrected residual propagation velocity vector more accurately points to the actual pollution source location, reducing terrain-induced bias and improving source tracing accuracy, particularly showing significant effectiveness in complex terrain areas.

[0134] In this embodiment of the invention, the detailed implementation steps for constructing a spatiotemporal superposition inversion model of multi-source emissions include:

[0135] A set of equations was established to determine the concentration contribution of each emission source to each monitoring point, with the emission intensity of each emission source as the variable to be solved. Based on the previously determined propagation velocity vector and distance attenuation coefficient, a transmission relationship model from the emission source to the monitoring point was constructed. For each monitoring point, a linear equation incorporating the contributions of all potential emission sources was established:

[0136] ;

[0137] in, For monitoring points The measured concentration, For emission sources The emission intensity (to be solved), To start from the source Time The transmission coefficient, For the regional background concentration, This is the error term.

[0138] The transmission coefficient Tij comprehensively considers factors such as spatial distance, propagation time, meteorological conditions, and topographical influences, and represents the proportional relationship between emission intensity and monitored concentration. Combining the equations for all monitoring points forms a complete system of linear equations, in the form of… ,in To monitor the concentration vector, For the transmission matrix, For emission intensity vector, For the background concentration vector, Let be the error vector. This system of equations forms the mathematical foundation of inversion calculations, and is solved by... Vectors are used to obtain the actual emission intensity of each emission source.

[0139] A distance attenuation coefficient is introduced to construct a concentration attenuation function, which describes the exponential decay of pollutant concentration with increasing propagation distance. Based on the previously calculated distance attenuation coefficient, a model of pollutant concentration change during spatial propagation is constructed. The attenuation function adopts an exponential decay form, and parameters are adjusted in conjunction with meteorological conditions and pollutant characteristics. The formula for calculating the transport coefficient Tij is:

[0140] ;

[0141] in, This is the distance attenuation coefficient. For transmission distance, Here is the directional diffusion function. It is the azimuth angle. For atmospheric stability, For height correction function, This represents the height difference between the source point and the monitoring point.

[0142] Differentiated attenuation model parameter libraries were established for different types of pollutants and varying atmospheric conditions. For conventional pollutants such as PM2.5 and SO2, classic Gaussian diffusion model parameters were used; for special pollutants such as VOCs, the effects of chemical reactions and photolysis processes were considered, and reaction kinetic correction terms were introduced. The concentration attenuation function is a core component in the construction of the transfer matrix and directly affects the accuracy of the inversion results.

[0143] The meteorological conditions of the industrial park were classified based on meteorological parameter data, and concentration contribution equations were established for each type of meteorological condition. Based on the meteorological parameter data, cluster analysis was used to divide the meteorological conditions during the study period into several typical categories, such as strong wind stable type, weak wind unstable type, and temperature inversion type. The classification considered key factors such as wind speed, wind direction, atmospheric stability, and mixing layer height, and hierarchical clustering or K-means algorithms were used for automatic classification. A transfer matrix and equations were established for each meteorological type to reflect the differences in pollutant propagation patterns under different meteorological conditions. This classification method avoids model distortion caused by the mixing of different meteorological conditions and improves the relevance and accuracy of the inversion results. Furthermore, the differences in model parameters under each meteorological type were analyzed, and a meteorological-model parameter mapping relationship was established to provide a basis for parameter selection for real-time applications.

[0144] Adding nonnegativity constraints and total emission constraints to the concentration contribution equations creates a constrained optimization problem. Adding constraints with practical physical meaning to the inversion problem ensures the rationality of the solution. The constraints include: emission intensity nonnegativity constraint. Ensure physical feasibility; total quantity constraints ( ), reflecting regional emission limits; relative change constraints ( This limits the deviation range of emission intensity from historical or baseline values. For enterprises with known process characteristics, emission ratio constraints are added to ensure that the emission ratios of different pollutant components conform to the process characteristics. By combining the equations and constraints, a complete constrained optimization problem is constructed, with the objective function being to minimize the sum of squared residuals.

[0145] This constrained optimization framework ensures that the inversion results are both mathematically optimal and meet the requirements of physical and policy rationality.

[0146] The constrained optimization problem is solved using the Lagrange multiplier method to obtain the inversion emission intensity of each emission source under different meteorological conditions, generating a spatiotemporal superposition inversion model of multi-source emissions. The constrained optimization problem is then transformed into an unconstrained problem using the Lagrange multiplier method, and the Lagrange function is constructed as follows:

[0147] ;

[0148] in, The Lagrangian function is a combination of the objective function and constraints, used to transform constrained optimization problems into unconstrained optimization problems. This is an emission intensity vector, containing the emission intensity values ​​of each emission source (enterprise). , are the main variables to be solved; Let be a Lagrange multiplier vector, containing and Multiple multipliers are used to introduce constraints in mathematics. The maximum total emissions allowed in the region are determined based on environmental capacity or discharge permits. It is the sum of the emission intensities of all emission sources; This indicates other possible constraints, such as relative change constraints or emission ratio constraints.

[0149] The optimal solution is obtained through problem-solving. For large-scale problems, efficient solution techniques such as quadratic programming or interior-point methods are employed to improve computational efficiency. Uncertainty analysis is performed on the inversion results to assess the impact of input data errors and model structure on the results, and confidence intervals for emission intensity are calculated. To verify the reliability of the inversion results, a cross-validation method is used, employing data from a subset of monitoring points for inversion and using the remaining points to verify the prediction accuracy. The final inverted emission intensity constitutes a spatiotemporal superposition model of multi-source emissions. This model can explain the pollution distribution observed by the monitoring network and predict the pollution levels in unmonitored areas, providing a comprehensive spatial analysis tool for environmental management.

[0150] This invention achieves AI-based detection and early warning of ecological and environmental anomalies through multi-point data acquisition, construction of enterprise emission characteristic fingerprints, identification of propagation path anomalies, reverse source tracing calculation, spatiotemporal overlay inversion, contribution separation, feature pattern recognition, and identification of responsible parties. The systematic source tracing method of this invention can accurately locate pollution sources and quantify the contribution of each enterprise, effectively identifying the responsible parties for excessive emissions, and providing scientific and precise technical support for environmental supervision.

[0151] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0152] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0153] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. An artificial intelligence-based method for detecting and warning of ecological environment anomalies, characterized in that, include: Acquire time-series data of pollutant concentrations, meteorological parameters, and spatial distribution data of enterprises at multiple locations within the industrial park; Based on the production process characteristics and historical emission records of each enterprise, the concentration ratios and temporal fluctuation characteristics of pollutant components are extracted to construct an enterprise emission characteristic fingerprint database, including: Acquire the concentration monitoring values ​​of multiple pollutant components of each enterprise within a preset time period, and normalize the concentration values ​​of each component. Calculate the normalized concentration ratio relationship between each pollutant component, and construct the component concentration ratio vector of the enterprise. Extract the time-series fluctuation curve of the pollutant concentration monitoring value during the production cycle, and perform Fourier transform on the time-series fluctuation curve to obtain frequency domain characteristics; Calculate the temporal correlation coefficient between the frequency domain characteristics and the time point of the enterprise's production shift switching, and denot it as the production correlation degree; The component concentration ratio vector, the frequency domain feature, and the production correlation degree are combined into a multi-dimensional feature vector, stored in the emission feature fingerprint database, and labeled with the corresponding enterprise identifier and process type; Acquire spatiotemporal diffusion trajectory data of the pollutants under different meteorological conditions, and identify abnormal areas of propagation paths based on the temporal evolution characteristics of concentration gradients between monitoring points; Based on the time difference of arrival of the concentration peak at each monitoring point within the abnormal propagation path area, the reverse source propagation velocity vector and distance attenuation coefficient of the pollutants are calculated, including: Identify the timestamps corresponding to the peak pollutant concentrations at each monitoring point within the abnormal area of ​​the propagation path, and calculate the time difference between the peak concentration arrival times of adjacent monitoring points; The spatial distance and azimuth between adjacent monitoring points are obtained, and the propagation velocity component of the pollutants is calculated by combining the time difference of arrival of the concentration peak. Based on the propagation velocity component and the wind speed and direction data of the same period, the meteorological dominant component is stripped off by vector decomposition to obtain the residual propagation velocity vector. The residual propagation velocity vector is back-projected to determine the possible source region of the pollutant; Extract the concentration values ​​of each monitoring point in the abnormal area of ​​the propagation path, and fit a concentration-distance decay curve based on the distance from the monitoring point to the possible source area and calculate the distance decay coefficient. Based on the reverse source propagation velocity vector and the distance attenuation coefficient, a spatiotemporal superposition inversion model for multi-source emissions is constructed. Based on the spatiotemporal overlay inversion model and the emission feature fingerprint database, a multi-enterprise contribution separation matrix for each monitoring point is generated through fingerprint matching degree weighted calculation, including: Based on the spatiotemporal superposition inversion model, the preliminary contribution values ​​of each enterprise to each monitoring point are obtained. Extract the measured pollutant component concentration ratios at each monitoring point and calculate the similarity between these ratios and the component concentration ratio vectors of each enterprise in the emission feature fingerprint database to obtain the fingerprint matching degree. The fingerprint matching degree is nonlinearly mapped to generate matching degree weight coefficients; The preliminary contribution value is multiplied by the matching weight coefficient of the corresponding enterprise to obtain the corrected enterprise contribution value. The corrected enterprise contribution values ​​are arranged according to monitoring points and enterprise dimensions to construct the multi-enterprise contribution separation matrix. By monitoring the characteristic spectrum of pollutant concentration fluctuations in real time, emission source characteristic patterns matching those in the emission characteristic fingerprint database are identified, including: The real-time collected pollutant concentration time-series data is segmented using a sliding window. Wavelet transform is performed on each segment of data to extract multi-scale fluctuation features in the time-frequency domain and generate a real-time fluctuation feature matrix. Frequency domain features of each enterprise are extracted from the emission feature fingerprint database and used as reference templates; Calculate the cross-correlation coefficient between the real-time fluctuation feature matrix and each of the reference templates, and denot it as the template matching degree; Select a reference template whose template matching degree is greater than a preset matching threshold, and mark the corresponding enterprise identifier and matching degree value as the emission source feature pattern; Based on the intensity changes and frequency of occurrence of the emission source characteristic patterns, the weight allocation coefficients of each enterprise in the contribution separation matrix are dynamically adjusted. Based on the adjusted contribution separation matrix, the responsible parties for excessive emissions are identified and graded early warning signals are generated.

2. The method for detecting and warning of ecological environment anomalies based on artificial intelligence according to claim 1, characterized in that, The step of constructing a spatiotemporal superposition inversion model for multi-source emissions based on the reverse source propagation velocity vector and the distance attenuation coefficient includes: Based on the spatial distribution data of the enterprises, a two-dimensional spatial grid model of the industrial park is constructed, and the location coordinates of each enterprise are marked in the spatial grid model; The coverage area of ​​the abnormal area of ​​the propagation path is marked in the spatial grid model to generate a propagation impact area map containing multiple potential emission sources; Based on the propagation impact area map, calculate the propagation path length and azimuth angle from each enterprise's emission source to each monitoring point; Based on the propagation path length, the distance attenuation coefficient, and the reverse source tracing propagation velocity vector, calculate the theoretical concentration contribution of each emission source to each monitoring point; A spatiotemporal superposition inversion model for the multi-source emissions is constructed. The spatiotemporal superposition inversion model uses the least squares method to invert the actual emission intensity of each emission source, so that the sum of the squared residuals between the superimposed theoretical concentration contribution values ​​of each emission source and the measured concentration values ​​at the monitoring points is minimized.

3. The method for detecting and warning of ecological environment anomalies based on artificial intelligence according to claim 1, characterized in that, The step of dynamically adjusting the weight allocation coefficients of each enterprise in the contribution separation matrix based on the intensity changes and frequency of occurrence of the emission source characteristic patterns includes: The frequency of occurrence of the emission source characteristic patterns for each enterprise within a preset time window is recorded as the characteristic pattern frequency. Calculate the average template matching degree corresponding to each emission source characteristic pattern within the preset time window, and record it as the average matching intensity. The dynamic activity index of each enterprise is calculated based on the product of the frequency of the characteristic pattern and the average matching strength. The dynamic activity index is weighted by time decay to obtain time-weighted activity. Based on the initial weights of the corresponding enterprises in the time-weighted activity and contribution separation matrix, the weight allocation coefficients are calculated by linear interpolation, and the contribution separation matrix is ​​updated.

4. The method for detecting and warning of ecological environment anomalies based on artificial intelligence according to claim 1, characterized in that, The method of identifying abnormal regions of propagation paths based on the temporal evolution characteristics of concentration gradients between monitoring points includes: Calculate the pollutant concentration difference between adjacent monitoring points in the multi-point system as the spatial concentration gradient; Time series analysis was performed on the spatial concentration gradient to extract the evolution curve of the rate of change of the concentration gradient over time. Identify the time nodes of gradient abrupt increases in the evolution curve and statistically analyze the synchronicity of gradient abrupt increase events between each monitoring point pair; Spatial clustering is performed on monitoring point pairs with gradient abrupt increase synchronization, and the spatial region covered by the clustered monitoring point set is marked as a candidate anomaly region. Calculate the consistency index of the concentration gradient direction within the candidate abnormal region, and determine the candidate abnormal region with the consistency index greater than the preset consistency threshold as the propagation path abnormal region.

5. The method for detecting and warning of ecological environment anomalies based on artificial intelligence according to claim 1, characterized in that, The step of obtaining the residual propagation velocity vector by stripping the dominant meteorological component through vector decomposition based on the propagation velocity component and the wind speed and direction data of the same period includes: Obtain the average wind speed and prevailing wind direction for the time period corresponding to the arrival time difference of the concentration peak, and construct a meteorological wind speed vector; The propagation speed component is decomposed into a downwind component parallel to the prevailing wind direction and a crosswind component perpendicular to the prevailing wind direction. Calculate the ratio of the downwind component to the meteorological wind speed vector, and use it as the meteorological contribution ratio coefficient; Based on the meteorological contribution ratio coefficient, the scaling value of the meteorological wind speed vector is subtracted from the propagation speed component to obtain the residual propagation speed vector; The direction of the residual propagation velocity vector is corrected to obtain the corrected residual propagation velocity vector.

6. The method for detecting and warning of ecological environment anomalies based on artificial intelligence according to claim 2, characterized in that, The construction of the spatiotemporal superposition inversion model for the multi-source emissions includes: Establish a set of equations for the concentration contribution of each emission source to each monitoring point, where the emission intensity of each emission source is the variable to be solved in the set of equations. The concentration decay function is constructed by introducing the distance attenuation coefficient. Based on the meteorological parameter data, the industrial park is classified according to meteorological conditions, and the concentration contribution equation set is established for different meteorological condition types. Add nonnegativity constraints and total emissions constraints to the concentration contribution equations to form a constrained optimization problem; The constrained optimization problem is solved using the Lagrange multiplier method to obtain the inversion emission intensity of each emission source under different meteorological conditions, and to generate the spatiotemporal superposition inversion model of the multi-source emissions.

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