Enterprise environmental protection compliance 360-degree self-inspection and self-correction system based on big data

Abstract

The invention discloses an enterprise environmental protection compliance 360-degree self-inspection and self-correction system based on big data, particularly relates to the technical field of environmental protection compliance monitoring, and is used for solving the problem that hidden association possibly existing between environmental protection data cannot be timely found and accurately evaluated in the prior art. Comprehensive monitoring of potential environmental protection risks and compliance problems is realized by integrating and analyzing environmental protection data of enterprises. Risk points are accurately identified from massive environment, operation and compliance data by using data mining and machine learning technologies, and implicit association in the data is explored by using a variational auto-encoder; through combination of time sequence feature anomaly analysis and topological structure complexity analysis, the influence of the implicit association mode on the enterprise compliance is evaluated from multiple dimensions, the comprehensiveness and accuracy of evaluation are ensured, the enterprise is prompted to perform prevention and self-correction before problems occur, and the enterprise compliance is improved. And due to the 360-degree monitoring and self-correcting functions, the efficiency of environmental protection compliance management of enterprises is greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental compliance monitoring technology, and more specifically, to a 360-degree self-inspection and self-correction system for enterprise environmental compliance based on big data. Background Art

[0002] The 360-degree self-examination and self-correction of corporate environmental compliance is an internal management process for enterprises, which aims to comprehensively review and ensure that their business activities comply with relevant environmental laws, regulations and standards. Through this self-examination and self-correction mechanism, enterprises can comprehensively evaluate their environmental compliance from multiple dimensions (such as emission management, resource use and waste disposal), and proactively identify and correct potential environmental risks and violations. By implementing the 360-degree self-examination and self-correction of environmental compliance, enterprises can not only improve their environmental standards and reduce legal risks, but also help enhance their corporate image and strengthen their sustainable development capabilities.

[0003] In a complex corporate environment, there may be hidden correlations between different environmental data. These correlation patterns may not be obvious but have a significant impact on compliance. For example, the time correlation between the efficiency of a particular type of waste treatment and certain production activities. If the potential hidden correlations between environmental data cannot be discovered and accurately assessed in a timely manner, it will have an adverse impact on the judgment of the company's environmental compliance in the self-examination and self-correction tasks, and may result in the development of environmental protection measures that cannot target actual risks and problems.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a 360° self-inspection and self-correction system for enterprise environmental compliance based on big data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A 360-degree self-inspection and self-correction system for corporate environmental compliance based on big data, including a data collection and integration module, a preliminary risk identification module, a hidden association identification module, an association impact assessment module, and a feedback self-inspection and self-correction module;

[0008] Data collection and integration module: covers environmental compliance of all relevant aspects of the enterprise, including production, waste treatment and emission control; collects environmental data, including environmental data, enterprise operation data and compliance data;

[0009] Preliminary risk identification module: using data mining and machine learning technology to preliminarily identify potential environmental risks and compliance issues in the enterprise based on the collected environmental data;

[0010] Implicit association identification module: based on the variational autoencoder, environmental protection data that may have implicit associations are screened out; based on the screened environmental protection data that may have implicit associations, an implicit association environmental protection data group is established;

[0011] Association impact assessment module: By analyzing the time series characteristics of the implicitly associated environmental protection data group, the temporal mutation degree of the implicitly associated pattern in the implicitly associated environmental protection data group is evaluated; by analyzing the topological structure complexity of the implicitly associated environmental protection data group, the potential compliance risk caused by the geometric complexity of the implicitly associated pattern in the implicitly associated environmental protection data group is evaluated;

[0012] Feedback self-inspection and self-correction module: Based on the potential compliance risk caused by the temporal mutation degree and geometric complexity of the implicit correlation pattern in the implicit correlation environmental data group, it is judged whether the adverse impact of the correlation pattern of each implicit correlation environmental data group on environmental compliance is too large, and feedback is given to the self-inspection and self-correction task.

[0013] In a preferred embodiment, data mining and machine learning techniques are used to preliminarily identify potential environmental risks and compliance issues in enterprises based on the collected environmental data, including:

[0014] Extract key features from environmental data and reduce feature redundancy through dimensionality reduction technology: Use feature extraction algorithms and time series feature extraction to extract key features of data; After feature extraction, use feature engineering technology to enhance the expressiveness of features; Use dimensionality reduction technology to reduce data dimensions and retain local and global structures in high-dimensional data; Use features after dimensionality reduction for visualization analysis and projection to display data distribution;

[0015] Select machine learning models based on feature data: Establish a candidate machine learning model library based on the type and characteristics of feature data; Use K-fold cross-validation technology to evaluate the performance of each candidate model, compare the model's accuracy, recall rate, and F1 score indicators to determine the most suitable model;

[0016] Use the selected machine learning model to train on the feature data to identify potential environmental risks and compliance issues: apply the optimized model to the feature data set for model training; use the trained model to predict and classify the company's environmental data to identify potential risk areas and compliance issues; for each identified risk point, combine time series analysis and historical data to assess the potential impact of the risk and generate an early warning report.

[0017] In a preferred embodiment, environmental protection data that may have implicit associations are screened out based on a variational autoencoder; and an implicitly associated environmental protection data group is established based on the screened environmental protection data that may have implicit associations, specifically including:

[0018] Construct a variational autoencoder suitable for environmental data, including an encoder part for mapping high-dimensional data to a low-dimensional latent space and a decoder part for reconstructing data from the latent space;

[0019] Use environmental data to train the variational autoencoder and adjust the network parameters to minimize the reconstruction error and the Kullback-Leibler divergence of the latent space;

[0020] In the low-dimensional latent space of the variational autoencoder, clustering algorithms are applied to identify data groups;

[0021] Analyze the data characteristics in each cluster group and identify the common characteristics in a specific cluster group;

[0022] Based on the identified implicit association patterns, an implicit association environmental protection data set is constructed.

[0023] In a preferred embodiment, the temporal characteristic anomaly analysis is performed on the implicitly associated environmental protection data group to evaluate the temporal mutation degree of the implicitly associated pattern in the implicitly associated environmental protection data group, specifically:

[0024] Use dynamic time warping algorithm for DTW distance calculation;

[0025] After calculating the DTW distance of each pair of time series, the DTW distance is changed over time to generate a time series similarity curve;

[0026] Calculate the abnormality of the time series similarity curve, and its expression is: Among them, TSCA is the abnormality of the time series similarity curve, T represents the total length of the time series, and α k is the weight coefficient at the kth time point, δ k is the mutation measure at the kth time point, d k represents the actual distance value between time series at time point k, μ k and σ k Respectively represent the mean and standard deviation of the similarity curve at time point k;

[0027] Use mutation detection algorithms to identify mutation points in time series; calculate the posterior probability of a mutation occurring at time point t;

[0028] If the abnormality of the time series similarity curve exceeds a preset threshold and a significant mutation is detected at a critical moment, it is determined that the degree of the time series mutation of the implicit association pattern is serious.

[0029] In a preferred embodiment, the DTW distance calculation formula is: Among them, DTW(A, B) represents the minimum alignment distance between sequences A and B, A and B represent two time series, d(a i, b j ) represents point a in time series A i and point b in time series B j The distance between.

[0030] In a preferred embodiment, the potential compliance risk level caused by the geometric complexity of the implicit association pattern in the implicit association environmental protection data group is evaluated by performing a topological complexity analysis on the implicit association environmental protection data group, specifically:

[0031] The topological cyclic complexity is calculated to quantify the complexity of topological features in the implicitly associated environmental protection data set. The calculation formula is: Where TLC is the topological cyclomatic complexity, F is the number of dimensions of the topological structure, and N is f is the number of persistent entries in the f-th dimension homology, γ f is the weight coefficient of the f-th dimension homology, Persistence f，g is the persistence of the g-th topological feature in the f-th dimension, v f，g and f，g They represent the extinction and generation scales of the g-th topological feature respectively;

[0032] When the topological cyclic complexity is greater than a preset threshold, the potential compliance risk caused by geometric complexity is judged to be serious.

[0033] In a preferred embodiment, based on the potential compliance risk caused by the temporal mutation degree and geometric complexity of the implicit correlation pattern in the implicit correlation environmental data group, it is judged whether the adverse impact of the correlation pattern of each implicit correlation environmental data group on environmental compliance is too large, and feedback is given to the self-examination and self-correction task, specifically:

[0034] When the degree of temporal mutation of the implicit correlation pattern is serious or the degree of potential compliance risk caused by geometric complexity is serious, it is judged that the correlation pattern of the implicit correlation environmental protection data group has too great an adverse impact on environmental protection compliance;

[0035] When the system detects that the association pattern of implicitly associated environmental data groups has too great an adverse impact on environmental compliance, it automatically triggers predefined rules;

[0036] Generate a compliance risk warning, mark it as high risk, and associate the warning with a specific implicitly associated environmental data group;

[0037] Generate detailed compliance risk reports that include risk summaries, data analysis, and recommended actions.

[0038] The technical effects and advantages of the 360° self-inspection and self-correction system for enterprise environmental compliance based on big data of the present invention are as follows:

[0039] 1. By integrating and analyzing the environmental data of enterprises, comprehensive monitoring of potential environmental risks and compliance issues is achieved. By using data mining and machine learning technology, the system can accurately identify risk points from massive amounts of environmental, operational and compliance data, improving the accuracy of enterprises' identification and processing capabilities of environmental risks. In addition, the system also uses advanced variational autoencoders to explore implicit associations in the data, revealing those non-obvious association patterns, and providing enterprises with deeper risk insights.

[0040] 2. By combining time series feature anomaly analysis and topology structure complexity analysis, the system can evaluate the impact of implicit association patterns on corporate compliance from multiple dimensions, ensuring the comprehensiveness and accuracy of the evaluation. These comprehensive analyses not only help companies to promptly discover and respond to potential compliance risks, but also prompt companies to prevent and self-correct before problems arise. Through real-time feedback and the automated generation of self-inspection and self-correction tasks, the system ensures that compliance issues can be quickly incorporated into the processing flow, enhancing the flexibility of companies to respond to changes in environmental regulations. This 360-degree monitoring and self-correction function greatly improves the efficiency of corporate environmental compliance management, helping companies to maintain environmental responsibilities while also protecting their own business interests and market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a structural schematic diagram of a 360° self-inspection and self-correction system for enterprise environmental compliance based on big data in the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Example

[0044] Figure 1 A structural schematic diagram of a 360° self-inspection and self-correction system for enterprise environmental compliance based on big data is given in the present invention. The 360° self-inspection and self-correction system for enterprise environmental compliance based on big data includes a data collection and integration module, a preliminary risk identification module, a hidden association identification module, a correlation impact assessment module and a feedback self-inspection and self-correction module.

[0045] Data collection and integration module: covers the environmental compliance of all relevant aspects of the enterprise, including production, waste treatment and emission control; collects environmental data, including environmental data, enterprise operation data and compliance data.

[0046] Preliminary risk identification module: Utilizes data mining and machine learning techniques to preliminarily identify potential environmental risks and compliance issues in the enterprise based on the collected environmental data.

[0047] Implicit association identification module: based on the variational autoencoder, environmental protection data that may have implicit associations are screened out; based on the screened environmental protection data that may have implicit associations, an implicit association environmental protection data group is established.

[0048] Association impact assessment module: By conducting time series feature anomaly analysis on the implicitly associated environmental protection data group, the degree of temporal mutation of the implicit association pattern in the implicitly associated environmental protection data group is evaluated; by conducting topological structure complexity analysis on the implicitly associated environmental protection data group, the degree of potential compliance risk caused by the geometric complexity of the implicit association pattern in the implicitly associated environmental protection data group is evaluated.

[0049] Feedback self-inspection and self-correction module: Based on the potential compliance risk caused by the temporal mutation degree and geometric complexity of the implicit correlation pattern in the implicit correlation environmental data group, it is judged whether the adverse impact of the correlation pattern of each implicit correlation environmental data group on environmental compliance is too large, and feedback is given to the self-inspection and self-correction task.

[0050] Covering environmental compliance in all relevant aspects of the company, including production, waste treatment and emission control, specifically:

[0051] Environmental protection data collection in the production process of enterprises includes raw material consumption, energy usage, waste gas emissions, wastewater treatment, etc.

[0052] Obtained from the company's production equipment, various environmental data in the production process are monitored and recorded in real time through the installed Industrial Internet of Things (IoT) sensors. For example, the discharge of wastewater is monitored through a flow meter, and the composition and emission rate of exhaust gas are monitored through a flue gas analyzer.

[0053] The sensor data is transmitted to the central data server using wireless transmission protocols (such as LoRa, ZigBee). During the data transmission process, AES-256 encryption technology is used to ensure data security.

[0054] Environmental protection data in the waste treatment process include the types, quantities, treatment methods and characteristics of the residues after treatment of solid waste.

[0055] Automatically record and track waste through the Waste Management System (WMS). Every waste treatment process is updated in real time through barcode scanning and RFID (radio frequency identification) technology to ensure that the treatment process is transparent and traceable.

[0056] Combine waste treatment data with the operating data of treatment equipment (such as temperature and pressure) and store and integrate data through a data warehouse.

[0057] Environmental protection data in the emission control link include air pollutant emission data, noise pollution levels, concentrations of toxic substances in wastewater, etc.

[0058] Obtained from the enterprise emission monitoring system, which includes online monitoring equipment (such as pollutant online monitoring instruments) to collect emission data in real time.

[0059] The data is transferred to the corporate environmental compliance database through a dedicated network (such as VPN), and big data processing platforms such as Hadoop are used for data storage and preliminary processing to ensure the integrity and availability of the data.

[0060] Integrate data from different links, use ETL (Extract, Transform, Load) tools to extract, transform and load data, and form a unified data model. This data model will contain all relevant environmental parameters to support subsequent data analysis and risk identification.

[0061] Collect environmental data, which includes environmental data, enterprise operation data and compliance data, specifically:

[0062] Environmental data includes air quality, soil quality, water quality monitoring data, etc. Specific parameters may include carbon dioxide, sulfur oxides, nitrogen oxides, PM2.5 concentrations, as well as chemical oxygen demand (COD) and heavy metal content in water bodies.

[0063] By cooperating with local or national environmental monitoring stations, real-time environmental data is obtained. At the same time, remote sensing technology and satellite image data are used to monitor the surrounding environment. Drone technology is also used to collect high-precision environmental data of specific areas.

[0064] Upload all environmental data to the cloud platform and use a distributed database (such as Cassandra) for storage to ensure data scalability and high availability.

[0065] Enterprise operation data includes production process data, raw material procurement and consumption data, energy usage records, equipment operation status, etc.

[0066] Obtain operational data from the company's ERP system (Enterprise Resource Planning) to ensure real-time data synchronization. The SCADA system (Supervisory Control and Data Acquisition) of production equipment also provides detailed operational data.

[0067] Use API interface to integrate data from ERP and SCADA systems into the environmental data management platform to ensure data consistency and real-time performance.

[0068] Compliance data includes relevant environmental laws and regulations, industry standards, internal corporate environmental policies and operating procedures, etc.

[0069] Get the latest regulatory and standards data in real time with automated regulatory tracking systems, and work with third-party compliance data providers to gain access to global compliance information.

[0070] The system automatically prompts and updates new regulatory requirements to ensure that the company's compliance data is always up to date.

[0071] Before being integrated into the enterprise's data lake, environmental data needs to be processed initially, including data cleaning, denoising, format conversion, etc. These operations are performed through big data processing frameworks (such as Apache Spark) and stored in efficient file formats (such as Parquet or ORC) to support subsequent large-scale analysis tasks.

[0072] Using data mining and machine learning technologies, we can preliminarily identify potential environmental risks and compliance issues in enterprises based on the collected environmental data, including:

[0073] Extract key features from environmental data and reduce feature redundancy through dimensionality reduction technology:

[0074] By analyzing the collected environmental data (including environmental data, corporate operation data, and compliance data), we can identify the types of data that are closely related to environmental risks and compliance issues. Specifically, we can extract core indicators such as emissions, waste treatment frequency, and energy consumption patterns, which play a key role in the company's environmental performance.

[0075] Use feature extraction algorithms such as TF-IDF (for text data), PCA (Principal Component Analysis, for continuous data) and time series feature extraction (for time-dependent data) to extract key features of the data. Through these techniques, determine the features that best reflect the environmental status of the enterprise. For example, when processing emission data, PCA is used to convert multidimensional emission data into several main components to ensure that only the most explanatory variables are retained.

[0076] After feature extraction, feature engineering techniques such as polynomial feature combination and interactive feature construction are used to enhance the expressiveness of features. For example, the relationship between energy consumption and emissions of enterprises in different seasons is calculated and incorporated into the model as new features. These new features can capture complex environmental behavior patterns and help identify potential risks.

[0077] Use advanced dimensionality reduction techniques such as t-SNE (t-Distributed Stochastic Neighbor Embedding) or UMAP (Uniform Manifold Approximation and Projection), which can not only reduce the data dimension but also preserve the local and global structures in high-dimensional data. In particular, when analyzing high-dimensional environmental data (such as the distribution of air pollutant concentrations), t-SNE can help identify potential connections between different pollution sources.

[0078] In the process of dimensionality reduction, redundant features are identified and eliminated, such as the interference of features with high correlation on the model. By calculating the mutual information between features, the most critical features for risk identification are retained to ensure the simplicity and efficiency of the model. For example, in the case of high correlation between the concentrations of multiple pollutants, only the most representative features are retained to reduce the interference of redundant information.

[0079] Use the reduced-dimensional features for visual analysis, and display the data distribution through two-dimensional or three-dimensional projections to help corporate management understand potential environmental risks. Combined with the SHAP value (Shapley Additive Explanations), analyze the importance of each feature in the model to ensure the interpretability and transparency of the model.

[0080] Select the appropriate machine learning model based on the feature data:

[0081] According to the type and characteristics of feature data, a library of candidate machine learning models is established, including Random Forest, Gradient Boosting Machine (GBM), XGBoost, Support Vector Machine (SVM) and deep learning models (such as Convolutional Neural Network, CNN). Each model performs differently in different data characteristics and risk identification tasks, so it is necessary to select the optimal model according to the specific application scenario.

[0082] K-fold cross-validation technology is used to evaluate the performance of each candidate model, and the accuracy, recall, F1 score and other indicators of the model are compared to determine the most suitable model. For example, in the prediction of energy consumption and emissions, random forests may show higher robustness, while in complex nonlinear data, deep learning models may be more suitable.

[0083] Use hyperparameter optimization techniques, such as Bayesian Optimization or GridSearch, to tune the key parameters of the model to achieve optimal performance. For example, in the GBM model, adjust the learning rate, tree depth and other parameters to improve recognition accuracy while controlling the complexity of the model and avoiding overfitting.

[0084] Use the selected machine learning model to train on the feature data to identify potential environmental risks and compliance issues:

[0085] Apply the optimized model to the feature data set for model training. Use a distributed computing framework (such as Apache Spark) to accelerate the training process of large-scale data and ensure that the model can efficiently process massive amounts of environmental data. During the training process, monitor the convergence of the model in real time and ensure the stability and accuracy of the model by adjusting the training batch size and learning rate.

[0086] When training the model, consider the imbalance of environmental risk data, for example, the number of compliant data is far greater than the number of non-compliant data. Use data resampling techniques (such as SMOTE, Synthetic Minority Over-sampling Technique) or weighted loss functions to balance the sample distribution between different categories and improve the model's ability to identify minority classes (such as risk events).

[0087] Using the trained model, the environmental data of enterprises is predicted and classified to identify potential risk areas and compliance issues. For each identified risk point, time series analysis and historical data are combined to assess the potential impact of the risk and generate early warning reports to help enterprises take measures in advance.

[0088] Environmental protection data that may have implicit associations are screened based on variational autoencoders; based on the screened environmental protection data that may have implicit associations, an implicitly associated environmental protection data group is established, specifically including:

[0089] Construct a variational autoencoder (VAE) suitable for environmental data, including an encoder part for mapping high-dimensional data to a low-dimensional latent space, and a decoder part for reconstructing data from the latent space:

[0090] The encoder is responsible for mapping high-dimensional environmental data to a low-dimensional latent space. A multi-layer perceptron (MLP) is used, and its input layer receives environmental data. Assume that the input data dimension is The input dimension is n. The encoder performs nonlinear transformation through multiple hidden layers and finally maps to the mean μ and variance σ of the latent space. 2 ,Right now:

[0091] z is a latent variable, which represents the result of mapping high-dimensional input data to low-dimensional latent space through the encoder. z is the core of VAE and is used to capture the potential structure or pattern of the input data.

[0092] Indicates that the latent variable z follows a Gaussian distribution with a mean of μ(x) and a variance of σ 2 (x).

[0093] μ(x): The mean of the input data x calculated by the encoder. It represents the "center" position of the input data in the latent space.

[0094] σ 2 (x): The variance of the input data x calculated by the encoder. It indicates the dispersion of the data in the latent space and affects the fluctuation range of the latent variable z.

[0095] The decoder is used to reconstruct the input data x′ from the latent space, which is done by back propagation. The decoder is mirror-symmetric to the encoder, and its output layer generates reconstructed data of the same dimension as the input data. The goal is to minimize the reconstruction error ||xx′||.

[0096] The total loss function of VAE consists of the reconstruction error and the Kullback-Leibler (KL) divergence, which is defined as:

[0097] The total loss function of VAE, which represents the objective function that the model needs to minimize, consists of two main parts: reconstruction error and KL divergence.

[0098] The reconstruction error term represents the logarithmic probability that the input data x is successfully reconstructed given the latent variable z.

[0099] q(z|x): posterior distribution, which represents the probability distribution of the latent variable z given the input data x. This distribution is calculated by the encoder.

[0100] log p(x|z): The logarithm of the conditional probability, which represents the probability of reconstructing the input data x given the latent variable z.

[0101] KL(q|z|x)||p(z)): Kullback-Leibler (KL) divergence, which measures the difference between two probability distributions.

[0102] p(z): Prior distribution, usually assumed to be a standard normal distribution It represents the prior assumption about the latent variable z.

[0103] KL divergence: This term is used to regularize the distribution of the latent space, making the posterior distribution q(z|x) close to the prior distribution p(z), which helps prevent overfitting.

[0104] The KL divergence regularization term controls the distribution of the latent space, making it close to the standard normal distribution Helps stabilize training and avoid model overfitting.

[0105] Use environmental data to train the variational autoencoder and adjust the network parameters to minimize the reconstruction error and the Kullback-Leibler divergence of the latent space:

[0106] Normalize the input environmental data to ensure that the values ​​of each dimension of the data are within the same order of magnitude.

[0107] A Gaussian filter is used to eliminate random noise in the data and ensure the quality of the input data.

[0108] The model was trained using the Adam optimizer (learning rate set to 0.001), and the optimizer parameters were adjusted to β1 = 0.9, β2 = 0.999. The mini-batch strategy was used during training, and the batch size was set to 128 to balance training efficiency and memory usage.

[0109] The model hyperparameters (such as latent space dimension and number of network layers) are optimized through grid search. The choice of latent space dimension is determined by the balance between reconstruction accuracy and KL divergence, usually between 10 and 50, and the optimal value is selected through cross-validation.

[0110] A 5-fold cross-validation technique is used to calculate the reconstruction error and KL divergence of each fold to ensure the generalization ability of the model on different data sets. For each fold of data, the loss changes on the training and validation sets are recorded to ensure the stability and consistency of the model.

[0111] In the low-dimensional latent space of the variational autoencoder, a clustering algorithm is applied to identify data groups that may indicate unknown or non-significant data associations:

[0112] Extract the latent variable z from the trained VAE model, i.e. the low-dimensional latent space representation, The latent variable z represents the main features of the input environmental data and removes redundant information in high-dimensional data.

[0113] Clustering algorithm selection: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is used in latent space. DBSCAN is selected because it can identify clusters of arbitrary shapes and can handle noisy data. The main parameters of DBSCAN are ∈ (maximum neighborhood radius) and MinPts (minimum number of samples), where ∈ is determined by estimating the K distance graph of the data points, and MinPts is generally selected as twice the dimension of the latent space.

[0114] According to the setting of ∈, dense data groups in the latent space are identified. These groups may reflect the potential associations between environmental data, and DBSCAN can effectively identify these groups and mark out noise data points.

[0115] The clustering effect is evaluated by calculating the Silhouette Coefficient. The Silhouette Coefficient value is between -1 and 1. The closer it is to 1, the better the clustering effect.

[0116] Analyze the data characteristics in each cluster group and identify common characteristics or patterns in specific cluster groups. These characteristics or patterns may indicate implicit connections between environmental data:

[0117] Perform detailed analysis on the data in each cluster group to identify common environmental characteristics in the group (such as pollutant emission patterns, waste treatment strategies). Use association rule mining algorithms (such as Apriori) to further identify implicit association rules between data. For example, if the association between specific emissions and waste treatment methods frequently appears in a certain group, this pattern may indicate the existence of implicit environmental risks.

[0118] Use t-SNE to visualize the clustering results of the latent space in two or three dimensions. By displaying the clustering results on a two-dimensional plane, combined with specific environmental indicators (such as carbon dioxide emissions and wastewater treatment rates), users can intuitively understand these implicit associations. Statistical tests (such as chi-square tests) are used to verify the consistency of features in cluster groups to ensure that the identified patterns are statistically significant and can explain the potential associations between environmental data.

[0119] Based on the identified implicit correlation patterns, an implicit correlation environmental protection data group is constructed to integrate environmental protection data with similar implicit characteristics:

[0120] According to the identified implicit association patterns, corresponding implicit association environmental protection data groups are constructed. Each implicit association environmental protection data group contains data points that show similar characteristics in the latent space, which may reflect similar environmental protection risks or compliance issues in practical applications. For example, the data in a specific group may correspond to high energy consumption and high emission patterns in a specific season, which are integrated into an independent implicit association environmental protection data group.

[0121] The constructed implicitly associated environmental protection data group is stored in a distributed database and integrated with the enterprise's environmental protection management system. By establishing indexes and data tags, subsequent analysis and monitoring are facilitated.

[0122] As new data is added, the VAE model is retrained regularly and the clustering algorithm is reapplied to update the implicitly associated data group. Through this dynamic update mechanism, the system can reflect changes in corporate operations and environmental conditions in real time, improving the accuracy and timeliness of environmental compliance monitoring.

[0123] By analyzing the abnormal time series characteristics of the implicitly associated environmental protection data group, the degree of temporal mutation of the implicitly associated pattern in the implicitly associated environmental protection data group is evaluated, specifically:

[0124] Environmental protection data usually contains a large amount of time series data, such as the change of pollutant emissions over time, the daily record of wastewater treatment effects, etc. These data may contain complex implicit correlation patterns that are difficult to directly identify through traditional statistical analysis methods. To this end, time series feature anomaly analysis is used to reveal and evaluate the time series mutations of these implicit correlation patterns.

[0125] In order to calculate the similarity between different time series, the dynamic time warping (DTW) algorithm is used. DTW can effectively handle the nonlinear changes of time series and is suitable for analyzing time series data with different time scales or rates.

[0126] DTW distance calculation:

[0127] DTW(A, B) represents the minimum alignment distance between sequences A and B, calculated by minimizing the cumulative distance of the alignment paths.

[0128] Among them, A and B represent two time series respectively. Assume that A={a1,a2,...,a n} and B = {b1, b2, ..., b m}, where n and m are the sequence lengths of A and B respectively.

[0129] d(a i , b j ): represents point a in time series A i and point b in time series B jThe distance between them is usually calculated using the Euclidean distance:

[0130] After calculating the DTW distance of each pair of time series, these distance values ​​are changed over time to generate a "time series similarity curve", which represents the change in similarity between time series in the implicitly associated environmental protection data set at a given time point.

[0131] In order to evaluate the severity of the time series mutation, the abnormality of the time series similarity curve is calculated to measure the degree of abnormal mutation in the time series similarity curve. The expression is: Among them, TSCA is the abnormality of the time series similarity curve, which is used to quantify the abnormality of the entire time series similarity curve.

[0132] T: represents the total length of the time series.

[0133] α k : The weight coefficient of the kth time point, used to emphasize the importance of the time point. For example, for the mutation moment, α k The weight coefficients can be adjusted by Bayesian Optimization to minimize the prediction error.

[0134] δ k : The mutation metric at the kth time point is defined as:

[0135] d k : Represents the actual distance value (DTW distance) between time series at time point k.

[0136] μ k and σ k : Respectively represent the mean and standard deviation of the similarity curve at time point k, calculated using historical data or the sliding window method. This metric indicates the degree of deviation of the distance value at the current time point from the historical mean.

[0137] After calculating TSCA, mutation detection algorithms, such as Bayesian Online Change Point Detection (BOCPD), are used to identify mutation points in the time series. These mutation points reflect significant changes in the implicit association patterns within the time series data group, thus indicating possible environmental compliance risks.

[0138] Calculate the posterior probability of a mutation occurring at time point t, which is expressed as:

[0139]

[0140] P(Change Point|d1,...,dt ): The posterior probability of a mutation occurring at time point t.

[0141] P (Change Point): Prior probability, which indicates the probability of a mutation occurring at any point in time, usually set based on historical data.

[0142] P(d1, ..., d t|Change Point): Under the condition of assuming a mutation, the sequence d1, ..., d t The likelihood function of .

[0143] P(d1, ..., d t ): Observe the sequence d1, ..., d t The marginal probability of is used for normalization calculation.

[0144] According to the results of time series similarity curve anomaly (TSCA) and mutation detection, the degree of temporal mutation of the implicit association pattern in the implicit association environmental protection data group is evaluated, and its possible adverse impact is judged by comparing with the environmental protection compliance standards.

[0145] If the abnormality of the time series similarity curve exceeds the preset threshold and a significant mutation is detected at a critical moment, it is determined that the degree of temporal mutation of the implicit association pattern is serious, and the degree of temporal mutation of the implicit association pattern in the implicit association environmental protection data group is large, and corresponding measures need to be taken.

[0146] By analyzing the topological complexity of the implicitly associated environmental protection data set, the potential compliance risk caused by the geometric complexity of the implicitly associated patterns in the implicitly associated environmental protection data set is evaluated, specifically:

[0147] Implicitly associated environmental data sets may have complex geometric structures and topological properties, which are often not revealed by traditional statistical analysis. Through topological data analysis (TDA), topological features (such as rings and holes) can be extracted from the data. These features can reflect the geometric complexity of the data and help assess potential compliance risks.

[0148] Topological data analysis studies the topological structure of data to reveal its geometric features at different scales. Persistent Homology is one of the core methods of TDA, which can detect and quantify the topological features of data at different scales, such as rings, holes, cavities, etc.

[0149] The generation and disappearance of persistent coherence can be expressed by the formula PH f ={(s g , v g )} reflects, PHf is a set of persistent entries of order f homology, representing the generation and extinction of topological features. f represents the dimension of homology, for example:

[0150] f=0: represents a connected branch (such as an independent cluster of data).

[0151] f=1: indicates a ring structure (such as a one-dimensional cycle).

[0152] f=2: represents a hole structure (such as a two-dimensional cavity).

[0153] s g Indicates the generation scale of the g-th topological feature, which usually refers to a threshold of data complexity (such as density threshold).

[0154] v g represents the extinction scale of the g-th topological feature, that is, the scale at which the topological feature no longer exists.

[0155] Persistence is defined as g =v g -s g .

[0156] In order to quantify the complexity of topological features in implicitly associated environmental protection data sets, the topological cyclic complexity is calculated. By analyzing the persistent homology of the data set, the topological cyclic structure in the data is quantified and its potential risk to compliance is evaluated.

[0157] The topological cyclomatic complexity calculation formula is: TLC is the topological cyclic complexity, which is used to quantify the complexity of the topological cyclic structure in a data set.

[0158] F: The number of dimensions of the topological structure, usually ranging from 0 to 2, representing features such as connected branches, loops, and holes in the analyzed data set.

[0159] N f : The number of persistent entries of the f-th dimension homology, that is, the number of topological features detected in the f-th dimension.

[0160] γ f : Weight coefficient of the f-th dimension homology, which is used to adjust the importance of topological features of different dimensions according to specific application scenarios. For example, in some environmental monitoring scenarios, the ring structure may be more important than the connected branches, so a higher weight can be given to γ1. The value of the weight coefficient is determined by Bayesian Optimization or other hyperparameter optimization techniques to improve the correlation between TLC and actual compliance risk.

[0161] Persistence f，g : The persistence of the g-th topological feature in the f-th dimension, defined as the difference between the extinction scale and the generation scale of the feature: Persistence f，g =v f，g -s f，g .

[0162] v f，g and f，g They represent the extinction and generation scales of the g-th topological feature respectively.

[0163] By interpreting the topological cyclomatic complexity, we can identify data groups with high geometric complexity and analyze the topological features in these groups. For example, high topological cyclomatic complexity may indicate the presence of multiple complex loops or holes in the environmental data group, which may cause compliance issues.

[0164] The greater the topological cyclic complexity, the greater the degree of potential compliance risk caused by the geometric complexity of the implicit association pattern in the implicit association environmental protection data set.

[0165] When the topological cyclic complexity is greater than a preset threshold, the potential compliance risk caused by geometric complexity is judged to be serious.

[0166] Preset threshold setting for topological cyclomatic complexity: This threshold is determined based on compliance cases in historical environmental protection data and their corresponding TLC values. By analyzing multiple environmental protection data groups that are known to have compliance risks, the distribution of their topological cyclomatic complexity is statistically analyzed. The median or upper quartile of the distribution is selected as the initial threshold, and through empirical adjustments in practical applications, the threshold can both identify potential risks and reduce the false positive rate. The setting of the threshold is intended to balance the detection accuracy of complexity with the requirements of actual risk management.

[0167] Preset threshold setting for abnormality of time series similarity curve: This threshold is set based on the time series mutation pattern successfully identified in historical data. By analyzing a large number of cases where significant time series mutations have occurred and caused compliance issues, the distribution of their TSCA values ​​is calculated. The upper quartile of the statistical distribution is selected as the initial threshold to ensure sensitive detection. At the same time, combined with the opinions of field experts and actual application feedback, the threshold is further fine-tuned to ensure that possible compliance risks can be identified in a timely manner at critical moments.

[0168] Based on the potential compliance risk caused by the temporal mutation degree and geometric complexity of the implicit correlation pattern in the implicit correlation environmental protection data group, it is judged whether the adverse impact of the correlation pattern of each implicit correlation environmental protection data group on environmental protection compliance is too large, and feedback is given to the self-examination and self-correction tasks, specifically:

[0169] When the degree of temporal mutation of the implicit correlation pattern is serious or the degree of potential compliance risk caused by geometric complexity is serious, it is judged that the correlation pattern of the implicit correlation environmental protection data group has too great an adverse impact on environmental protection compliance.

[0170] Topological Cyclic Complexity (TLC) and Time Series Similarity Curve Anomaly (TSCA) are both core parameters for quantifying implicit correlation patterns in environmental data. TLC identifies topological features such as loops and holes in the data by measuring the geometric complexity of the data. The complexity of these features often indicates potential compliance risks. For example, multiple complex topological structures may reflect the failure of coordination in multiple links within the system, resulting in overall environmental control failure. On the other hand, TSCA captures abnormal mutations in time series data and accurately identifies abnormal behavior of time series data through DTW and mutation detection algorithms. When TSCA exceeds the preset threshold and occurs at a critical moment, it indicates that the system has undergone sudden or systematic changes within a specific period of time, which may trigger major environmental incidents.

[0171] These two parameters analyze the data from different perspectives (geometric complexity and temporal changes). When any indicator indicates that the risk is serious, it means that the implicit correlation pattern in the data may have a significant adverse impact on environmental compliance. By considering the complexity of both time series and topology, the risk of environmental data groups can be assessed more comprehensively and accurately. The logic behind this is that it covers different dimensions of the data, ensuring that hidden risks that may be overlooked by a single analysis method can be discovered in a timely manner under multidimensional analysis, thereby providing more reliable decision-making support in environmental compliance management. Ultimately, when the risk level in any dimension exceeds the threshold, it needs to be given sufficient attention and appropriate measures should be taken to intervene to prevent compliance issues from occurring.

[0172] When the system detects that the association pattern of the implicitly associated environmental data group has too great an adverse impact on environmental compliance, the system will automatically trigger the predefined rule. This rule can be set as a trigger condition in the environmental compliance management platform, similar to the "IF-THEN" logic.

[0173] Pre-set trigger rules in the compliance management system. For example:

[0174] Rule 1: IF TLC>Threshold_TLC THEN Trigger Compliance Alert

[0175] Rule 2: IF TSCA>Threshold_TSCA THEN Trigger Compliance Alert

[0176] When any of the above rules is triggered, the system will generate a compliance risk warning, marked as "high risk", and associate the warning with a specific implicitly associated environmental data group.

[0177] Automatically generate a detailed compliance risk report that includes the following:

[0178] Risk Summary: An overview of the risks detected, including specific values ​​of TLC or TSCA and where they exceed thresholds.

[0179] Data analysis: Detailed insights into hidden patterns of associations that trigger risk, explaining why these patterns may have a negative impact on compliance.

[0180] Recommended actions: Based on historical data and compliance policies, the system generates automated recommendations, such as strengthening monitoring of specific links and adjusting operating parameters.

[0181] The report format can be pre-set to ensure that all key information can be automatically populated, ensuring the standardization and actionability of the report.

[0182] After the compliance risk report is generated, the system will automatically create a new self-inspection and self-correction task in the company's compliance management platform or task management system (such as the enterprise resource planning ERP system or the compliance management system CMS).

[0183] The task will describe the detected risk, the relevant data groups, potential compliance issues, and recommended self-inspection measures. The task will be associated with the generated compliance risk report. The system automatically assigns priorities to tasks based on the risk level. For example, tasks with greater risks of timing mutation or geometric complexity can be set as "high priority". The system can automatically assign tasks to relevant responsible persons or teams, such as environmental compliance department, production operation department, etc. according to the preset role assignment rules.

[0184] After the self-examination and self-correction task is created, the system will monitor the progress of the task in real time, including the status updates of task acceptance, processing, and completion. The system can send real-time notifications to relevant persons in charge through email, SMS or internal notification system to remind them of the urgency of the task and the progress of processing. During the execution of the task, the responsible team will feedback the results to the system and record the specific improvement measures and effects. If the task feedback shows that the compliance issue has been resolved, the system will automatically close the task; if it is not resolved or new issues arise, the system will re-evaluate based on the new data and may generate new tasks.

[0185] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0186] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0187] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0189] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0190] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0192] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0193] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0194] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A 360° self-inspection and self-correction system for corporate environmental compliance based on big data, characterized by: It includes data collection and integration module, preliminary risk identification module, implicit association identification module, association impact assessment module and feedback self-examination and self-correction module; Data collection and integration module: covers environmental compliance in all relevant aspects of the enterprise, including production, waste treatment and emission control; Collect environmental data, including environmental data, enterprise operation data and compliance data; Preliminary risk identification module: using data mining and machine learning technology to preliminarily identify potential environmental risks and compliance issues in the enterprise based on the collected environmental data; Implicit association identification module: Based on the variational autoencoder, it screens environmental data that may have implicit associations; Based on the environmental protection data that may have implicit associations obtained through screening, an implicitly associated environmental protection data group is established; Association impact assessment module: By analyzing the time series characteristics of the implicitly associated environmental protection data group, the temporal mutation degree of the implicitly associated pattern in the implicitly associated environmental protection data group is evaluated; by analyzing the topological structure complexity of the implicitly associated environmental protection data group, the potential compliance risk caused by the geometric complexity of the implicitly associated pattern in the implicitly associated environmental protection data group is evaluated; Feedback self-inspection and self-correction module: Based on the potential compliance risk caused by the temporal mutation degree and geometric complexity of the implicit correlation pattern in the implicit correlation environmental data group, it is judged whether the adverse impact of the correlation pattern of each implicit correlation environmental data group on environmental compliance is too large, and feedback is given to the self-inspection and self-correction task.

2. According to the big data-based 360° self-inspection and self-correction system for corporate environmental compliance according to claim 1, it is characterized in that: Using data mining and machine learning technologies, we can preliminarily identify potential environmental risks and compliance issues in enterprises based on the collected environmental data, including: Extract key features from environmental data and reduce feature redundancy through dimensionality reduction technology: Use feature extraction algorithms and time series feature extraction to extract key features of data; After feature extraction, use feature engineering technology to enhance the expressiveness of features; Use dimensionality reduction technology to reduce data dimensions and retain local and global structures in high-dimensional data; Use features after dimensionality reduction for visualization analysis and projection to display data distribution; Select machine learning models based on feature data: Establish a candidate machine learning model library based on the type and characteristics of feature data; Use K-fold cross-validation technology to evaluate the performance of each candidate model, compare the model's accuracy, recall rate, and F1 score indicators to determine the most suitable model; Use the selected machine learning model to train on the feature data to identify potential environmental risks and compliance issues: apply the optimized model to the feature data set for model training; use the trained model to predict and classify the company's environmental data to identify potential risk areas and compliance issues; for each identified risk point, combine time series analysis and historical data to assess the potential impact of the risk and generate an early warning report.

3. According to claim 2, a 360° self-inspection and self-correction system for corporate environmental compliance based on big data is characterized in that: Based on the variational autoencoder, environmental protection data with potential hidden correlations are screened; Based on the environmental protection data that may have implicit associations obtained through screening, an implicitly associated environmental protection data group is established, specifically including: Construct a variational autoencoder suitable for environmental data, including an encoder part for mapping high-dimensional data to a low-dimensional latent space and a decoder part for reconstructing data from the latent space; Use environmental data to train the variational autoencoder and adjust the network parameters to minimize the reconstruction error and the Kullback-Leibler divergence of the latent space; In the low-dimensional latent space of the variational autoencoder, clustering algorithms are applied to identify data groups; Analyze the data characteristics in each cluster group and identify the common characteristics in a specific cluster group; Based on the identified implicit association patterns, an implicit association environmental protection data set is constructed.

4. According to claim 3, a 360° self-inspection and self-correction system for corporate environmental compliance based on big data, characterized in that: By analyzing the abnormal time series characteristics of the implicitly associated environmental protection data group, the degree of temporal mutation of the implicitly associated pattern in the implicitly associated environmental protection data group is evaluated, specifically: Use dynamic time warping algorithm for DTW distance calculation; After calculating the DTW distance of each pair of time series, the DTW distance is changed over time to generate a time series similarity curve; Calculate the abnormality of the time series similarity curve, and its expression is: Among them, TSCA is the abnormality of the time series similarity curve, T represents the total length of the time series, and α k is the weight coefficient at the kth time point, δ k is the mutation measure at the kth time point, d k represents the actual distance value between time series at time point k, μ k and σ k Respectively represent the mean and standard deviation of the similarity curve at time point k; Use mutation detection algorithms to identify mutation points in time series; calculate the posterior probability of a mutation occurring at time point t; If the abnormality of the time series similarity curve exceeds the preset threshold and a significant mutation is detected at a critical moment, it is determined that the degree of the time series mutation of the implicit association pattern is serious.

5. According to claim 4, a 360° self-inspection and self-correction system for corporate environmental compliance based on big data is characterized in that: The DTW distance calculation formula is: Among them, DTW(A, B) represents the minimum alignment distance between sequences A and B, A and B represent two time series, d(a i , b j ) represents point a in time series A i and point b in time series B j The distance between.

6. The 360° self-inspection and self-correction system for enterprise environmental compliance based on big data according to claim 5 is characterized in that: By analyzing the topological complexity of the implicitly associated environmental protection data set, the potential compliance risk caused by the geometric complexity of the implicitly associated patterns in the implicitly associated environmental protection data set is evaluated, specifically: The topological cyclic complexity is calculated to quantify the complexity of topological features in the implicitly associated environmental protection data set. The calculation formula is: Where TLC is the topological cyclomatic complexity, F is the number of dimensions of the topological structure, and N is f is the number of persistent entries in the f-th dimension homology, γ f is the weight coefficient of the f-th dimension homology, Persistence f，g is the persistence of the g-th topological feature in the f-th dimension, υ f，g and f，g They represent the extinction and generation scales of the g-th topological feature respectively; When the topological cyclic complexity is greater than a preset threshold, the potential compliance risk caused by geometric complexity is judged to be serious.

7. The 360° self-inspection and self-correction system for enterprise environmental compliance based on big data according to claim 6 is characterized in that: Based on the potential compliance risk caused by the temporal mutation degree and geometric complexity of the implicit correlation pattern in the implicit correlation environmental protection data group, it is judged whether the adverse impact of the correlation pattern of each implicit correlation environmental protection data group on environmental protection compliance is too large, and feedback is given to the self-examination and self-correction tasks, specifically: When the degree of temporal mutation of the implicit correlation pattern is serious or the degree of potential compliance risk caused by geometric complexity is serious, it is judged that the correlation pattern of the implicit correlation environmental protection data group has too great an adverse impact on environmental protection compliance; When the system detects that the association pattern of implicitly associated environmental data groups has too great an adverse impact on environmental compliance, it automatically triggers predefined rules; Generate a compliance risk warning, mark it as high risk, and associate the warning with a specific implicitly associated environmental data group; Generate detailed compliance risk reports that include risk summaries, data analysis, and recommended actions.

Images