Industrial pollutant emission reduction potential intelligent evaluation system and method based on data mining

Abstract

The invention relates to the technical field of environmental science, in particular to an industrial pollutant emission reduction potential intelligent evaluation system and method based on data mining, and the system comprises a data collection module, a data fusion module, a model construction module, a process optimization module, a data processing module and a data processing module, the system comprises a model building module, a process optimization module in communication connection with the model building module, an environmental protection optimization module in communication connection with the process optimization module, an emission reduction potential evaluation module in communication connection with the environmental protection optimization module, and a decision support module in communication connection with the emission reduction potential evaluation module and used for receiving an emission reduction potential analysis result; and generating an emission reduction priority sequence based on the emission reduction potential analysis result. Through innovative technologies such as multi-dimensional data fusion, intelligent modeling, multi-objective optimization and intelligent decision support, the accuracy, comprehensiveness and practicability of industrial pollutant emission reduction potential evaluation are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental science and technology, and in particular to an intelligent evaluation system and method for industrial pollutant emission reduction potential based on data mining. Background Art

[0002] With the continuous advancement of industrialization, environmental pollution problems are becoming increasingly severe, and the reduction of industrial pollutant emissions has become a global focus. Traditional industrial pollutant reduction methods mainly rely on end-of-pipe treatment and single technology applications, which are difficult to meet the increasingly stringent environmental protection requirements and complex and changing industrial production needs. In recent years, with the rapid development of technologies such as big data and artificial intelligence, data-driven intelligent emission reduction methods have gradually become a research hotspot.

[0003] The existing methods for assessing the potential for reducing industrial pollutants have the following main problems: First, data utilization is insufficient. Most methods only focus on data of a single dimension, such as production data or environmental monitoring data, ignoring the potential correlation between multi-dimensional data, resulting in one-sided or inaccurate assessment results. Second, the model lacks flexibility and adaptability. Many methods use fixed statistical models or empirical models, which are difficult to adapt to the dynamic changes and uncertainties of industrial production. Furthermore, the optimization strategy is single, and it is difficult to achieve multi-objective collaborative optimization. Traditional methods often only focus on the goal of reducing pollutant emissions, ignoring other important factors such as production efficiency and energy consumption. Finally, the decision-making support capabilities are insufficient. Existing methods mostly stay at the data analysis level, lacking in-depth interpretation of the emission reduction potential assessment results and specific and feasible decision-making recommendations.

[0004] These problems seriously restrict the accuracy and practicality of the assessment of industrial pollutant emission reduction potential. There is an urgent need for a new assessment system and method that can comprehensively utilize multi-dimensional data, has adaptive capabilities, can achieve multi-objective optimization, and provide intelligent decision-making support. Summary of the invention

[0005] The present invention aims to solve the above technical problems and proposes an intelligent evaluation system and method for industrial pollutant emission reduction potential based on data mining. The system and method integrate multi-dimensional information such as industrial production data, energy consumption data and environmental monitoring data to build an intelligent evaluation model, realize the coordinated optimization of process parameters and environmental protection measures, and provide reliable decision support.

[0006] The present invention proposes an intelligent evaluation system for industrial pollutant emission reduction potential based on data mining, comprising:

[0007] Data acquisition module for:

[0008] Collect industrial production data, energy consumption data and environmental monitoring data;

[0009] Preprocess the collected data, including data cleaning, standardization, vectorization and integration processing;

[0010] The data fusion module is connected to the data acquisition module for:

[0011] Receiving the preprocessed data sent by the data acquisition module;

[0012] Based on the preprocessed data, an industrial data pool is formed;

[0013] A model building module is connected to the data fusion module for:

[0014] Building a neural network model based on the data in the industrial data pool;

[0015] Clustering or classifying different pollutant emissions, pollution load data and optimization strategies using the neural network model;

[0016] A process optimization module is in communication with the model building module and is used to:

[0017] Receiving the clustering or classification result sent by the model building module;

[0018] Based on the clustering or classification results, generating a process parameter adjustment plan;

[0019] Output the process optimization path that meets the goal of minimizing pollutant emissions;

[0020] The environmental optimization module is connected to the process optimization module for:

[0021] Receiving the process optimization path sent by the process optimization module;

[0022] Based on the process optimization path, adjusting environmental protection unit parameters;

[0023] Output environmental optimization solutions that meet the minimum emission setting;

[0024] The emission reduction potential assessment module is connected to the environmental protection optimization module for:

[0025] Based on the environmental optimization scheme, calculate the emission reduction potential of different pollutants;

[0026] Produce emission reduction potential analysis results;

[0027] A decision support module is in communication with the emission reduction potential assessment module and is used to:

[0028] receiving the emission reduction potential analysis results;

[0029] Based on the emission reduction potential analysis results, generating an emission reduction priority ranking;

[0030] Output intelligent analysis report on industrial pollutant emission reduction potential.

[0031] The intelligent evaluation method of industrial pollutant emission reduction potential based on multi-dimensional data mining includes the following steps:

[0032] S1. Data collection and preprocessing:

[0033] Collect industrial production data, energy consumption data and environmental monitoring data;

[0034] Clean, standardize, vectorize and integrate the collected data;

[0035] S2. Data fusion:

[0036] Integrate the pre-processed multi-dimensional data to form an industrial data pool;

[0037] Analyze the correlation between data of different dimensions;

[0038] S3. Model construction:

[0039] Build a neural network model based on the data in the industrial data pool;

[0040] Clustering or classifying different pollutant emissions, pollution load data and optimization strategies using the neural network model;

[0041] S4. Process Optimization:

[0042] Analyze the relationship between process parameters and pollutant emissions based on model clustering or classification results;

[0043] Under the premise of meeting production requirements, generate a process optimization path that meets the goal of minimizing pollutant emissions;

[0044] S5. Environmental optimization:

[0045] Adjust environmental protection unit parameters according to process optimization path;

[0046] Output environmental optimization solutions that meet the minimum emission setting;

[0047] S6. Assessment of emission reduction potential:

[0048] Simulate pollutant emissions under different emission reduction measures;

[0049] Calculate the emission reduction potential of different pollutants under various emission reduction measures;

[0050] Analyse uncertainties in estimates of emission reduction potential;

[0051] S7.Decision support:

[0052] Prioritize emission reduction measures by taking into account multiple criteria such as technical feasibility, economic benefits and environmental benefits;

[0053] Generate corresponding policy recommendations based on the priority ranking results;

[0054] Generate intelligent analysis reports on industrial pollutant reduction potential.

[0055] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0056] First, the present invention realizes the deep fusion and mining of multi-dimensional data. By adopting advanced data preprocessing technology and graph neural network methods, the system can effectively integrate heterogeneous data from different sources, fully explore the potential correlation between data, and provide a comprehensive and reliable data foundation for subsequent analysis and decision-making. This multi-dimensional data fusion method significantly improves the comprehensiveness and accuracy of the evaluation, overcoming the limitation of traditional methods that only focus on a single data source.

[0057] Secondly, the present invention constructs an intelligent evaluation model with adaptive capabilities. The recurrent neural network model based on the attention mechanism is adopted, combined with online learning and concept drift detection technology, so that the system can continuously learn and adapt to dynamic changes in industrial production. This adaptive capability greatly improves the stability and reliability of the model, making the evaluation results more in line with the actual production situation.

[0058] Furthermore, the present invention achieves the synergy of process optimization and environmental optimization. By combining advanced algorithms such as reinforcement learning and multi-agent collaborative optimization, the system can meet the pollutant emission reduction target while taking into account multiple goals such as production efficiency and energy consumption. This multi-objective collaborative optimization method effectively solves the contradictions between the various goals in the traditional method and provides enterprises with a more comprehensive and feasible optimization solution.

[0059] In addition, the present invention provides intelligent decision support functions. Based on technologies such as Monte Carlo tree search and fuzzy cognitive graphs, the system can not only evaluate the emission reduction potential, but also analyze uncertainty factors, generate priority rankings and specific policy recommendations. This intelligent decision support function greatly improves the interpretability and practicality of the evaluation results, and provides strong support for managers and decision makers.

[0060] Finally, the system of the present invention has good scalability and maintainability. Through modular design and continuous optimization mechanism, the system can flexibly respond to new demands and technological developments and maintain long-term effectiveness and advancement.

[0061] In general, the intelligent evaluation system and method for industrial pollutant emission reduction potential based on data mining proposed in this invention comprehensively improves the accuracy, comprehensiveness and practicality of industrial pollutant emission reduction potential evaluation through innovative technologies such as multidimensional data fusion, intelligent modeling, multi-objective optimization and intelligent decision support. This not only provides accurate emission reduction guidance for enterprises, but also provides a scientific basis for the government to formulate environmental protection policies, which has important practical significance for promoting industrial green transformation and achieving sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is the main flow chart of the overall system of the present invention.

[0063] Figure 2 It is the internal structure diagram of the data acquisition module of the present invention.

[0064] Figure 3 It is the internal structure diagram of the data fusion module of the present invention.

[0065] Figure 4 It is a diagram of the internal structure of the model building module of the present invention.

[0066] Figure 5 This is a diagram of the internal structure of the process optimization module of the present invention.

[0067] Figure 6 This is a diagram of the internal structure of the environmental optimization module of the present invention.

[0068] Figure 7 This is a diagram of the internal structure of the emission reduction potential assessment module of the present invention.

[0069] Figure 8 It is the internal structure diagram of the decision support module of the present invention. DETAILED DESCRIPTION

[0070] Please refer to Figure 1-8 The system of the present invention comprises a data acquisition module 1, a data fusion module 2, a model building module 3, a process optimization module 4, an environmental optimization module 5, an emission reduction potential assessment module 6 and a decision support module 7. These modules form a complete system through communication connection, and jointly complete the intelligent assessment task of industrial pollutant emission reduction potential.

[0071] The data acquisition module 1 is mainly used to collect industrial production data, energy consumption data and environmental monitoring data, and pre-process the collected data. In practical applications, the data acquisition module 1 can obtain data in a variety of ways, such as real-time collection through sensors, extraction from the enterprise production management system, or acquisition from an environmental monitoring station. Preferably, the frequency of data collection can be adjusted according to specific needs. For example, production data may need to be collected once a minute, while environmental monitoring data may only need to be collected once an hour.

[0072] Data preprocessing is an important function of the data acquisition module 1. The present invention adopts a series of preprocessing techniques, including data cleaning, standardization, vectorization and integrated processing. Among them, data cleaning is mainly used to remove outliers and missing values. For example, the moving average method or the median method can be used to fill the missing values, and the 3σ principle can be used to identify and process outliers. Standardization processing is to unify data of different dimensions to the same scale. Commonly used methods include Z-score standardization and Min-Max standardization. Vectorization processing is to convert non-numeric data into numerical data. For example, One-Hot Encoding can be used to process categorical variables. Integrated processing is to integrate multi-source data together to form a unified data format.

[0073] The data fusion module 2 is communicatively connected with the data acquisition module 1, and is used to receive the pre-processed data and form an industrial data pool based on the data. Data fusion is a complex process, and the present invention adopts a multi-level data fusion strategy. First, for the fusion of the time dimension, the system will synchronize the data collected at different frequencies to a unified time standard. Secondly, for the fusion of the spatial dimension, the system will associate the data from different locations and different devices. Finally, for the fusion of the feature dimension, the system will perform correlation analysis on different types of data (such as production data, energy consumption data, and environmental data) to find out the potential relationship between them.

[0074] In a preferred embodiment, the data fusion module 2 can use a deep learning method to achieve the fusion of multi-source heterogeneous data. For example, a deep autoencoder can be used to learn the low-dimensional representation of data, thereby achieving dimensionality reduction and fusion of data. The specific fusion process can be expressed as the following mathematical formula:

[0075] H=f(W1X+b1),

[0076] Y=g(W2H+b2),

[0077] Among them, X represents the input multi-source heterogeneous data, H represents the learned low-dimensional representation, and Y represents the reconstructed data. W1 and W2 are the weight matrices of the encoder and decoder respectively, b1 and b2 are bias terms, and f and g are activation functions, usually ReLU or Sigmoid functions.

[0078] The model building module 3 is connected to the data fusion module 2 for building a neural network model based on the data in the industrial data pool. The present invention uses advanced deep learning technology to build a model to fully explore the potential rules in the data. Specifically, the model building module 3 will first perform feature engineering to extract meaningful features from the original data. Then, according to the specific requirements of the task, a suitable neural network structure is selected. For example, for time series data, a long short-term memory network (LSTM) can be selected; for image data, a convolutional neural network (CNN) can be selected; for complex multimodal data, an attention mechanism (Attention Mechanism) can be selected, etc.

[0079] A key step in model building is to cluster or classify different pollutant emissions, pollution load data, and optimization strategies. Unsupervised learning methods can be used for clustering, such as K-means or hierarchical clustering algorithms; supervised learning methods can also be used for classification, such as support vector machines (SVM) or random forests. The choice of method depends on the specific data characteristics and task requirements.

[0080] The data acquisition module 1 includes a data source access unit 11 , a data preprocessing unit 12 and a data quality control unit 13 .

[0081] The data source access unit 11 is used to access multiple data sources, including industrial production equipment, energy consumption monitoring equipment, and environmental monitoring equipment. In practical applications, the data source access unit 11 may need to process multiple data interfaces and protocols. For example, for industrial production equipment, it may be necessary to support industrial communication protocols such as OP C UA and Modbus; for environmental monitoring equipment, it may be necessary to support traditional interfaces such as RS485 and 4-20mA. In order to improve the compatibility and scalability of the system, the data source access unit 11 adopts a modular design, and new interface modules can be added as needed.

[0082] The data preprocessing unit 12 is connected to the data source access unit 11 for preprocessing the accessed data. The main purpose of preprocessing is to improve the data quality and lay a foundation for subsequent analysis and modeling. The specific preprocessing steps include data cleaning, denoising, outlier processing and format unification.

[0083] Data cleaning mainly deals with missing values ​​and duplicate values. For missing values, a variety of strategies can be used, such as deleting records containing missing values, filling with mean / median values, or using machine learning methods for predictive filling. The choice of which strategy depends on the proportion and distribution characteristics of the missing values. For example, when the missing ratio is less than 5%, it can be deleted directly; when the missing ratio is between 5% and 15%, you can consider using mean or median filling; when the missing ratio exceeds 15%, you may need to use more complex filling methods, such as KNN-based filling or multiple interpolation.

[0084] Denoising is an important step to reduce random fluctuations in data. The present invention uses a variety of denoising methods, including moving average method, wavelet transform and Kalman filtering. Among them, wavelet transform is particularly suitable for processing non-stationary signals and can effectively separate signals and noise. The mathematical expression of wavelet denoising is as follows:

[0085]

[0086] Where f(t) is the original signal, ψ j,k (t) is the wavelet basis function, c j,k is the wavelet coefficient. The denoising process is to perform threshold processing on the wavelet coefficients to remove the wavelet coefficients corresponding to the noise.

[0087] Outlier processing is another important part of data preprocessing. The present invention adopts a variety of outlier detection methods, including statistical methods (such as the 3σ principle), density-based methods (such as the LOF algorithm), and machine learning-based methods (such as the Isolation Forest algorithm). After detecting an outlier, you can choose to delete, replace, or mark it.

[0088] The purpose of format unification is to enable data from different sources to be processed and analyzed on the same platform. This includes unit conversion, timestamp unification, encoding format unification, etc. For example, for time data, it can be uniformly converted to UTC time; for text data, it can be uniformly converted to UTF-8 encoding.

[0089] The data quality control unit 13 is in communication with the data preprocessing unit 12 and is used to perform quality inspection on the preprocessed data. Quality inspection includes integrity inspection, consistency inspection and validity verification. The integrity inspection is mainly to ensure that all necessary data fields have been filled; the consistency inspection is to ensure that the data is consistent between different tables or systems; and the validity verification is to ensure that the data value is within a reasonable range.

[0090] In practical applications, the data quality control unit 13 can set a series of quality indicators and thresholds. For example, the data integrity rate can be set to be no less than 99%, the data consistency can be set to be no less than 98%, the abnormal value ratio can be set to be no more than 1%, etc. If it is detected that the data quality does not meet the standard, the system will issue an alarm and may trigger the process of data re-collection or manual intervention.

[0091] The data fusion module 2 includes a multi-dimensional data integration unit 21 , a data association analysis unit 22 and a data storage unit 23 .

[0092] The multidimensional data integration unit 21 is used to integrate process data, environmental data, financial data and regulatory data in multiple dimensions. This is a complex process that requires consideration of the time dimension, spatial dimension and feature dimension of the data. In the time dimension, different types of data may have different collection frequencies and need to be time aligned. For example, production data may be minute-level, while financial data may be daily or monthly level, and the system needs to align them to the same time scale. In the spatial dimension, the geographical location and spatial distribution of different data sources need to be considered. In the feature dimension, the heterogeneity of different types of data needs to be handled.

[0093] The present invention uses tensor decomposition technology to achieve the integration of multidimensional data. Specifically, methods such as Tucker decomposition or CP decomposition can be used. Taking Tucker decomposition as an example, its mathematical expression is as follows:

[0094]

[0095] Wherein, χ is the original high-dimensional tensor, G is the core tensor, and A, B, and C are factor matrices corresponding to different dimensions. In this way, high-dimensional data can be compressed into a low-dimensional space while retaining the relationship between the data. The data association analysis unit 22 is connected to the multidimensional data integration unit 21 for analyzing the association relationship between data of different dimensions. This is the core step of data fusion, which aims to discover the potential connection between data and provide a basis for subsequent modeling and analysis. The present invention adopts a variety of association analysis methods, including correlation analysis, principal component analysis (PCA) and canonical correlation analysis (CCA).

[0096] For example, for the association between continuous variables, the Pearson correlation coefficient can be used:

[0097]

[0098] Among them, x i and i are the observed values ​​of two variables, and is their average value. The value of the correlation coefficient r is between -1 and 1. The larger the absolute value, the stronger the correlation. For high-dimensional data, principal component analysis (PCA) can be used to reduce the dimension and find the main direction of change. The basic idea of ​​PCA is to find the direction with the largest data variance, which is the principal component. Mathematically, this is equivalent to solving the eigenvalues ​​and eigenvectors of the covariance matrix:

[0099] Cv=λv,

[0100] Where C is the covariance matrix of the data, λ is the eigenvalue, and v is the corresponding eigenvector.

[0101] The data storage unit 23 is in communication with the data association analysis unit 22 and is used to store the integrated and associated data in structured and semi-structured forms. In practical applications, the data storage unit 23 needs to consider factors such as the size of the data, the access frequency, and the query mode, and select a suitable storage solution.

[0102] For structured data, you can usually use a relational database such as MySQL or PostgreSQL. These databases support complex SQL queries and are suitable for processing data with clear patterns. For semi-structured data, you can consider using a NoSQL database such as MongoDB or Cassandra. These databases have better scalability and flexibility and are suitable for processing dynamically changing data patterns.

[0103] In order to improve the access efficiency of data, the present invention also adopts some optimization technologies. For example, for common queries, appropriate indexes can be established; for large-scale data, partitioning and sharding strategies can be adopted; for hot data, cache technologies such as Redis can be used to speed up access.

[0104] In general, the data acquisition module 1, the data fusion module 2 and their subunits of the present invention together constitute a powerful data processing and fusion system. This system can collect data from multiple sources, perform comprehensive preprocessing and quality control, and then integrate and correlate data of different dimensions to finally form a high-quality industrial data pool.

[0105] The model building module 3 includes a feature extraction unit 31, a model selection unit 32, a model training unit 33 and a model evaluation unit 34. These units work together to complete the conversion process from raw data to a usable model.

[0106] The feature extraction unit 31 is the first step in model construction, and its main task is to extract key features from the industrial data pool. In the scenario of industrial pollutant emission reduction potential assessment, feature extraction is particularly important because the original data often contains a lot of redundant and noisy information. The present invention uses a variety of feature extraction techniques, including but not limited to statistical feature extraction, time-frequency domain feature extraction, and automatic feature extraction based on deep learning.

[0107] Taking time series data as an example, the system of the present invention can extract the following features:

[0108] 1. Statistical characteristics: mean, variance, skewness, kurtosis, etc.

[0109] 2. Time domain characteristics: maximum value, minimum value, peak value, RMS value, etc.

[0110] 3. Frequency domain features: spectral features obtained through fast Fourier transform (FFT).

[0111] 4. Time-frequency domain features: Time-frequency joint distribution features obtained using wavelet transform.

[0112] For complex nonlinear features, the present invention preferably uses an autoencoder for automatic feature extraction. The mathematical expression of the autoencoder is as follows:

[0113] h=f(Wx+b),

[0114] x ′ =g(W ′ h+b ′ ),

[0115] Among them, x is the input data, h is the extracted features, and x ′ is the reconstructed data. W and W ′ is the weight matrix, b and b ′ is the bias term, f and g are activation functions. By minimizing the reconstruction error ∥ xx ′ ∥ 2 , the autoencoder can learn an effective representation of the data. The model selection unit 32 is communicated with the feature extraction unit 31, and its main function is to select a suitable neural network model structure according to data characteristics and task requirements. In the application scenario of industrial pollutant emission reduction potential assessment, the system of the present invention needs to process various types of data and tasks, so model selection is crucial. In a preferred embodiment of the present invention, the model selection unit 32 adopts an automatic model selection algorithm based on meta-learning. The algorithm first pre-trains a set of models on a large amount of historical data, and then quickly selects the most suitable model structure based on the characteristics of the new task. Specifically, for a given task T and data set D, the objective function of model selection can be expressed as:

[0116]

[0117] Among them, M * is the optimal model, and P(M|T,D) is the posterior probability of the model given the task and dataset. This probability can be estimated by Bayesian inference or gradient-based methods.

[0118] The model training unit 33 is in communication with the model selection unit 32 and is used to train the selected neural network model using historical data. During the training process, the system of the present invention uses a variety of optimization techniques to improve training efficiency and model performance.

[0119] First, for large-scale data sets, the present invention adopts a distributed training strategy. Specifically, the parameter server architecture is used to store model parameters on a central server, while data and calculations are distributed to multiple working nodes. This approach can significantly improve the training speed, especially for large neural network models.

[0120] Secondly, in order to solve the common class imbalance problem in industrial data, the present invention adopts the comprehensive oversampling and undersampling technique (SMOTEENN). This method first uses the SMOTE algorithm to generate synthetic samples of the minority class, and then uses the Edited Nearest Neighbors (ENN) algorithm to clean up the samples near the boundary, thereby obtaining a more balanced training set.

[0121] Finally, in order to improve the generalization ability of the model, the present invention adopts regularization technology in the training process. Specifically, a combination of L1 and L2 regularization, namely Elastic Net regularization, is used:

[0122]

[0123] Among them, L0 is the original loss function, |w|1 and are the L1 norm and L2 norm of the parameters respectively, and λ1 and λ2 are regularization coefficients. This regularization method can achieve the effect of feature selection and preventing overfitting at the same time. The model evaluation unit 34 is communicated with the model training unit 33, and is used to evaluate the performance of the trained model and optimize the model according to the evaluation results. In the scenario of industrial pollutant emission reduction potential assessment, model evaluation needs to consider multiple indicators, including accuracy, stability, and interpretability. The system of the present invention uses cross-validation technology to evaluate the generalization performance of the model. Specifically, using K-fold cross-validation, the data set is divided into K subsets, and K-1 subsets are used as training sets each time, and the remaining subset is used as a validation set. This process is repeated K times, and the average performance is finally taken as the evaluation result of the model. For regression tasks, the present invention mainly uses the root mean square error (RMSE) and the coefficient of determination (R 2 ) as the evaluation indicator:

[0124]

[0125] Among them, y i is the true value, is the predicted value, is the average of the true values.

[0126] For classification tasks, the present invention mainly uses accuracy, precision, recall and F1 score as evaluation indicators. In multi-classification problems, these indicators are calculated using macro-average and micro-average.

[0127] Based on the evaluation results, the model evaluation unit 34 will also perform model optimization. The optimization methods include but are not limited to:

[0128] 1. Hyperparameter tuning: Use the Bayesian optimization algorithm to automatically search for the optimal hyperparameter combination.

[0129] 2. Model ensembles: combining multiple base models into a more powerful model, such as random forests or gradient boosted trees.

[0130] 3. Knowledge distillation: Transferring the knowledge of large and complex models into small and simple models to improve the efficiency and deployability of the models.

[0131] Next, the present invention will describe in detail the internal structure and working principle of the process optimization module 4. For example, the process optimization module 4 includes a process parameter analysis unit 41, an optimization target setting unit 42, a multi-target optimization unit 43 and a process path generation unit 44.

[0132] The main task of the process parameter analysis unit 41 is to analyze the relationship between process parameters and pollutant emissions. In industrial production, this relationship is usually nonlinear and dynamically changing, so advanced data analysis technology is required. The system of the present invention adopts a feature importance analysis method based on a gradient boosting tree. This method can effectively capture the nonlinear interactions between features and has strong robustness to outliers.

[0133] The calculation formula of feature importance is as follows:

[0134]

[0135] Among them, I j is the importance score of feature j, M is the number of trees, and L m is the number of leaf nodes in the mth tree, is the importance of feature j at node t. The optimization target setting unit 42 is connected to the process parameter analysis unit 41 for setting the optimization target of minimizing pollutant emissions. In practical applications, the optimization target is usually multidimensional, including not only pollutant emissions, but also production efficiency, energy consumption and other indicators. The system of the present invention adopts a multi-objective optimization framework based on fuzzy set theory. Specifically, for each target f i (x), define a membership function μ i (f i (x)), indicating the degree of goal achievement. Then, the multi-objective optimization problem can be transformed into maximizing the overall membership: maxμ(x)=min i＝1,…,k μ i (f i (x)), where k is the number of objectives. This method can effectively balance the weights between different objectives and is particularly suitable for dealing with complex optimization problems in industrial production. The multi-objective optimization unit 43 is connected in communication with the optimization target setting unit 42 to achieve multi-objective optimization of minimizing pollutant emissions while meeting production requirements. The system of the present invention adopts an improved multi-objective particle swarm optimization algorithm (MOPSO) to solve this problem.

[0136] In the MOPSO algorithm, each particle represents a possible solution, and its position and velocity are updated according to the following formula:

[0137]

[0138] in, and are the velocity and position of particle i at time t, pbest iis the individual optimal position of particle i, gbest is the global optimal position, w is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers.

[0139] The process path generation unit 44 is in communication with the multi-objective optimization unit 43 and is used to generate a specific process optimization path according to the optimization result. This process involves converting the abstract optimization result into an operable process parameter adjustment scheme. The system of the present invention adopts a rule extraction method based on a decision tree to convert the optimization result into a series of IF-THEN rules.

[0140] For example, a typical rule might be:

[0141] IF (temperature>80℃) AND (pressure<5MPa) THEN (adjust valve opening = -10%)

[0142] This form of rule is both intuitive and easy to understand, and convenient for actual operation in industrial sites.

[0143] The environmental protection optimization module 5 includes a total emission calculation unit 51, an environmental protection parameter adjustment unit 52, an effect evaluation unit 53 and an optimization scheme generation unit 54. These units work together to achieve refined regulation of environmental protection parameters.

[0144] The main task of the total emission calculation unit 51 is to calculate the current total pollutant emission. The system of the present invention adopts a hybrid calculation method based on material balance and emission factor. For pollutants that can be directly monitored, online monitoring data is used; for pollutants that are difficult to monitor directly, the emission factor method is used for estimation. The calculation formula for the total emission is as follows:

[0145]

[0146] Where E is the total emissions, A i is the activity level (such as raw material usage or product output), EF i is the emission factor, ER i is the emission reduction efficiency. The environmental parameter adjustment unit 52 is connected to the total emission calculation unit 51 in communication, and is used to adjust the environmental unit parameters according to the total emission minimization target. The system of the present invention adopts a parameter adjustment strategy based on model predictive control (MPC). The core idea of ​​MPC is to solve the optimal control problem in a rolling time domain:

[0147]

[0148]

[0149] Wherein, y is the system output (such as pollutant emissions), r is the reference trajectory, u is the control input (such as the operating parameters of environmental protection equipment), x is the system state, N is the prediction time domain, and Q and R are weight matrices. The effect evaluation unit 53 is connected to the environmental parameter adjustment unit 52 for evaluating the environmental effect after parameter adjustment. The system of the present invention adopts a comprehensive evaluation method that not only considers the pollutant emission reduction effect, but also considers factors such as energy consumption and cost. The evaluation indicators include:

[0150] 1. Pollutant emission reduction rate:

[0151] Here, E0 represents the emissions before adjustment, while E represents the emissions after adjustment;

[0152] 2. Energy efficiency: pollutant emission reduction / energy consumption;

[0153] 3. Cost-effectiveness ratio: pollutant emission reduction / operating cost;

[0154] The optimization scheme generating unit 54 is in communication connection with the effect evaluation unit 53, and is used to generate the final environmental protection optimization scheme according to the evaluation result. The system of the present invention adopts a case-based reasoning (CBR) method to generate the optimization scheme. The basic idea of ​​CBR is to use historical experience to solve new problems, which is very consistent with the actual situation in industrial production.

[0155] The CBR workflow includes the following four steps:

[0156] 1. Retrieve: Retrieve the historical case that is most similar to the current situation from the case library.

[0157] 2. Reuse: Apply the retrieved cases to the current problem.

[0158] 3. Revise: Make necessary adjustments to the plan based on the current situation.

[0159] 4. Retain: Add the new solution to the case library for future use.

[0160] In a preferred embodiment of the present invention, the case similarity is calculated using weighted Euclidean distance:

[0161]

[0162] in, and are the values ​​of the two cases on the i-th feature. i is the weight of the feature.

[0163] Next, the present invention will describe in detail the internal structure and working principle of the emission reduction potential assessment module 6. The emission reduction potential assessment module 6 includes a scenario simulation unit 61, a potential calculation unit 62, an uncertainty analysis unit 63 and a result visualization unit 64.

[0164] The main task of the scenario simulation unit 61 is to simulate the pollutant emissions under different emission reduction measures. The system of the present invention adopts the Monte Carlo simulation method to generate a large number of possible scenarios. Specifically, for each key parameter, a probability distribution function is defined. Then, different parameter combinations are generated by random sampling, and each combination represents a possible scenario.

[0165] For example, for the efficiency η of a certain emission reduction measure, we can assume that it follows a normal distribution N(μ,σ 2 ).

[0166] Then, in each simulation, the value of this parameter can be generated as follows:

[0167] η=μ+σ·Z,

[0168] Here, Z is a standard normally distributed random variable.

[0169] The potential calculation unit 62 is connected to the scenario simulation unit 61 for calculating the emission reduction potential of different pollutants under various emission reduction measures. The system of the present invention adopts a method based on Data Envelopment Analysis (DEA) to evaluate the emission reduction potential. DEA is a non-parametric method that can handle multiple input and multiple output problems at the same time and is very suitable for evaluating the efficiency of complex systems.

[0170] In the DEA model, each decision making unit (DMU) represents an emission reduction measure. The input can be cost, energy consumption, etc., and the output is the emission reduction of various pollutants. The mathematical model of DEA can be expressed as:

[0171]

[0172] Where θ is the efficiency score, y rj and x ij are the rth output and ith input of the jth DMU (decision making unit), u r and v i is the weight to be solved.

[0173] The uncertainty analysis unit 63 is in communication connection with the potential calculation unit 62, and is used to analyze the uncertainty factors of the emission reduction potential estimation. In the actual application of industrial pollutant emission reduction, there are many sources of uncertainty, including data errors, model errors, parameter uncertainties, etc. The system of the present invention adopts a global sensitivity analysis method to quantify these uncertainties.

[0174] Specifically, the Sobol method, a global sensitivity analysis method based on variance, was used. The Sobol method can calculate the contribution of each input parameter to the output variance. The calculation formula of the First-order Sobol index is as follows:

[0175]

[0176] Where Y is the model output, X i is the i-th input parameter, V represents variance, and E represents expectation. i The larger the value, the more significant the effect of the parameter on the output.

[0177] The result visualization unit 64 is in communication with the uncertainty analysis unit 63 and is used to visually display the emission reduction potential analysis results in the form of charts. The system of the present invention uses a variety of visualization techniques, including but not limited to:

[0178] 1. Heat map: used to show the emission reduction effects of different emission reduction measures on various pollutants.

[0179] 2. Sankey diagram: used to show the source, flow and emission reduction path of pollutants.

[0180] 3. Radar chart: used to compare the performance of different emission reduction schemes in multiple dimensions.

[0181] 4. Butterfly plot: used to display the results of uncertainty analysis and intuitively show the sensitivity of each parameter.

[0182] These visualization methods can not only intuitively display complex analysis results, but also help decision makers quickly identify key issues and potential opportunities.

[0183] Finally, the present invention will describe in detail the internal structure and working principle of the decision support module 7. The decision support module 7 includes a multi-criteria decision unit 71, a priority ranking unit 72, a policy suggestion generating unit 73 and a report generating unit 74.

[0184] The main task of the multi-criteria decision-making unit 71 is to comprehensively consider multiple criteria such as technical feasibility, economic benefits and environmental benefits. The system of the present invention adopts the Analytic Hierarchy Process (AHP) to deal with this multi-criteria decision-making problem. The steps of AHP include:

[0185] 1. Establish a hierarchical model;

[0186] 2. Construct a judgment matrix;

[0187] 3. Calculate the weight vector and the maximum eigenvalue;

[0188] 4.Consistency test;

[0189] 5. Hierarchical single sorting and consistency test;

[0190] 6. Total hierarchical sorting and consistency check;

[0191] In the construction of the judgment matrix, a 1-9 scale is used to quantify expert opinions. For example, if the importance of criterion A relative to criterion B is strongly important, the corresponding judgment matrix element a_{ij}=7.

[0192] The priority sorting unit 72 is in communication connection with the multi-criteria decision making unit 71, and is used to sort the emission reduction measures according to the multi-criteria evaluation results. The system of the present invention adopts an improved TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method for sorting.

[0193] The basic idea of ​​TOPSIS is to select a solution that is closest to the ideal solution and farthest from the negative ideal solution. The specific steps include:

[0194] 1. Construct a normalized decision matrix;

[0195] 2. Construct a weighted normalized decision matrix;

[0196] 3. Determine the ideal solution and the negative ideal solution;

[0197] 4. Calculate the distance of each solution to the ideal solution and the negative ideal solution;

[0198] 5. Calculate the relative closeness of each solution;

[0199] 6. Sort by relative proximity;

[0200] In a preferred embodiment of the present invention, in order to deal with the uncertainty of decision information, fuzzy set theory is introduced to expand the traditional TOPSIS into fuzzy TOPSIS.

[0201] The policy suggestion generating unit 73 is in communication connection with the priority sorting unit 72, and is used to generate corresponding policy suggestions according to the priority sorting result. The system of the present invention adopts a method based on knowledge graph and rule reasoning to generate policy suggestions.

[0202] First, a knowledge graph containing information on environmental protection policies, technical measures, economic means, etc. is constructed. Then, based on the priority sorting results, a rule reasoning engine is used to generate specific policy recommendations. For example, if a certain emission reduction technology has a high priority but there are economic barriers, the system may recommend the implementation of relevant financial subsidy policies.

[0203] The report generation unit 74 is in communication with the policy recommendation generation unit 73 and is used to integrate all analysis results and generate a comprehensive intelligent analysis report on the potential for industrial pollutant emission reduction. The system of the present invention uses natural language generation (NLG) technology to automatically generate report text.

[0204] The NLG process consists of the following steps:

[0205] 1. Content determination: Decide what information should be included in the report;

[0206] 2. Document planning: organizing the structure and sequence of information;

[0207] 3. Micro-planning: deciding how to present information at the sentence and paragraph level;

[0208] 4. Surface implementation: generating actual text;

[0209] In the system of the present invention, some templates and terminology libraries in specific fields are also integrated to ensure that the generated reports meet the professional standards in the industrial and environmental protection fields.

[0210] Through the above detailed description, it can be seen that the system of the present invention is comprehensive, intelligent and practical in the assessment of industrial pollutant emission reduction potential. The system can not only accurately assess the emission reduction potential, but also provide optimized emission reduction plans and policy recommendations, which has important practical application value for improving the efficiency of industrial pollutant emission reduction and improving environmental quality.

[0211] The system of the present invention also includes a data updating module, a model iteration module and a system performance monitoring module. The introduction of these modules enables the system to have the ability of self-updating and continuous optimization, greatly improving the practicality and reliability of the system.

[0212] The main function of the data update module is to receive new data in real time and update the industrial data pool. In the practical application of industrial pollutant emission reduction, the timeliness of data is crucial. The system of the present invention adopts an incremental update strategy that can continuously integrate new data into the existing data set without affecting the normal operation of the system.

[0213] Specifically, the data update module adopts a sliding update mechanism based on a time window. Set a fixed-size time window, such as 30 days. When new data arrives, the system will add it to the end of the window and remove the old data at the beginning of the window. This method not only ensures the freshness of the data, but also avoids the storage and computing pressure caused by the unlimited growth of data. Mathematically, this process can be expressed as:

[0214] D new =(D old \D oldest )∪D latest ,

[0215] Among them, D new is the updated dataset, D old is the original data set, D oldest is the old data at the beginning of the time window, D latest is the newly arrived data. The model iteration module is in communication with the data update module, and is used to regularly iterate and optimize the neural network model according to the updated data. The system of the present invention adopts an online learning method, which enables the model to be continuously adjusted and improved with the arrival of new data. The core idea of ​​online learning is to update the model parameters immediately whenever a new data sample is received, rather than waiting for a large amount of data to be accumulated before performing batch updates. The system of the present invention adopts the Stochastic Gradient Descent (SGD) algorithm to implement online learning. For each new data sample (x i ,y i ), the update rule of the model parameter θ is:

[0216]

[0217] Where η is the learning rate, L is the loss function, is the gradient of the loss function with respect to the parameters.

[0218] In order to solve the model drift problem that may occur in online learning, the system of the present invention also introduces an adaptive mechanism based on concept drift detection. When a significant change in the input data distribution is detected, the system will trigger a comprehensive model retraining to ensure the accuracy and stability of the model.

[0219] The system performance monitoring module is connected to the model iteration module for monitoring the operating status of each module of the system and triggering system maintenance and upgrade when necessary. The system of the present invention adopts a layered performance monitoring architecture, including the following levels:

[0220] 1. Infrastructure layer: monitor the usage of hardware resources, such as CPU utilization, memory usage, disk I / O, etc.

[0221] 2. Middleware layer: monitor the performance indicators of middleware such as database and message queue.

[0222] 3. Application layer: monitor the response time, throughput, error rate and other indicators of each functional module.

[0223] 4. Business layer: monitor business indicators such as model prediction accuracy and adoption rate of decision recommendations.

[0224] In order to achieve comprehensive and efficient performance monitoring, the system of the present invention adopts an anomaly detection algorithm based on time series analysis. The algorithm first establishes a time series model for each performance indicator, such as the autoregressive integrated moving average (ARIMA) model:

[0225] y t =c+φ1y t-1 +…+φ p y t-p +θ1ε t-1 +…+θ q ε t-q +ε t ,

[0226] Among them, y t is the performance index value at time t, c is a constant term, φ i and θ i is the model parameter, ε t is white noise. This formula represents an autoregressive moving average (ARMA) model, which combines autoregressive (AR) and moving average (MA) components to describe patterns in time series data.

[0227] The system then compares the difference between the actual observations and the model predictions in real time. If the difference exceeds the preset threshold, an abnormal alarm will be triggered. The threshold is set using an adaptive method that can automatically adjust according to the fluctuations of historical data to reduce false positives and false negatives.

[0228] Through this all-round, multi-level performance monitoring mechanism, the system of the present invention can timely discover and solve potential problems, ensuring stable operation and continuous optimization of the system.

[0229] Next, the present invention will elaborate on the intelligent evaluation method of industrial pollutant emission reduction potential based on multi-dimensional data mining. The method includes seven main steps: data collection and preprocessing, data fusion, model construction, process optimization, environmental optimization, emission reduction potential evaluation and decision support.

[0230] In the data collection and preprocessing steps, the system first collects industrial production data, energy consumption data and environmental monitoring data through various channels. The data sources may include the production management system and energy management system within the enterprise, as well as external environmental monitoring stations. In order to ensure the quality and consistency of the data, the method of the present invention adopts a series of preprocessing techniques.

[0231] For example, for time series data, the system uses sliding window median filtering to remove outliers. Assuming the time window size is w, the filtering result at time t is:

[0233]

[0234] Among them, x t is the original data, y t is the filtered data.

[0235] For categorical variables, the system will use frequency encoding technology for preprocessing. Specifically, for each value of the categorical variable, the frequency of its occurrence in the training set is used to replace the original category label. This method not only retains the category information, but also introduces the importance information of the category. In the data fusion step, the system integrates the preprocessed multi-dimensional data to form an industrial data pool. This process involves complex operations such as time alignment, spatial matching, and feature fusion. The method of the present invention adopts a data fusion technology based on graph neural network (GNN). In this method, different types of data are represented as nodes in the graph, and the relationships between nodes (such as time correlation, spatial proximity, etc.) are represented as edges. Then, the transmission and fusion of information are achieved through graph convolution operations. Specifically, for node v i , whose representation at the kth layer is It can be updated by the following formula:

[0236]

[0237] in, is the neighbor set of node i, c ij is the normalization constant, W (k) and b (k) is a learnable parameter and σ is the activation function.

[0238] In the model building step, the system builds a neural network model based on the data in the industrial data pool. Considering the complexity and dynamics of the industrial pollutant emission reduction problem, the method of the present invention adopts an attention-based recurrent neural network model.

[0239] The core idea of ​​this model is to dynamically adjust the importance weights of different input features through the attention mechanism. Specifically, for the hidden state $h_t$ at time $t$, its update formula is:

[0240] h t =LSTM([x t ;c t ],h t-1 ),

[0241] Among them, x t is the input at the current moment, c t is the context vector calculated by the attention mechanism:

[0242]

[0243] The score function here can be a simple dot product or a more complex multi-layer perceptron. In the process optimization step, the system generates a process optimization path that meets the goal of minimizing pollutant emissions based on the clustering or classification results of the model. The method of the present invention adopts a process optimization strategy based on reinforcement learning. Specifically, a deep Q-network (DQN) is used to learn the optimal process parameter adjustment strategy. In DQN, the state space S includes the current process parameters and environmental states, the action space A includes possible parameter adjustment operations, and the reward function R is defined based on pollutant emissions and production efficiency. The update rule of the Q function is:

[0244]

[0245] Among them, α is the learning rate and γ is the discount factor.

[0246] In the environmental optimization step, the system adjusts the environmental unit parameters according to the process optimization path and outputs an environmental optimization solution that satisfies the setting of minimizing the total amount of emissions. The method of the present invention adopts a multi-agent collaborative optimization method. Each environmental unit is modeled as an agent, and these agents achieve global optimization through communication and collaboration.

[0247] Specifically, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm based on shared counterfactual reasoning is adopted. For agent i, its policy function μ i and Q function Q i The update rules are:

[0248]

[0249] y i =r i +γQ ′ i (s ′ ,a ′ 1,...,a ′ N )|a′ j =μ′ j (s′ j ),

[0250] Where D is the experience replay buffer, Q′ i and μ′ j is the target network.

[0251] In the emission reduction potential assessment step, the system simulates pollutant emissions under different emission reduction measures, calculates the emission reduction potential of different pollutants under various emission reduction measures, and analyzes the uncertainty factors of emission reduction potential estimation. The method of the present invention adopts an emission reduction scheme assessment strategy based on Monte Carlo Tree Search (MCTS).

[0252] The four main steps of MCTS include: selection, expansion, simulation, and backpropagation. In the selection phase, the upper confidence bound (UCB) algorithm is used to balance exploration and exploitation:

[0253]

[0254] Among them, w i is the cumulative reward of node i, n i is the number of visits to node i, N is the number of visits to the parent node, and c is the exploration parameter.

[0255] Finally, in the decision support step, the system comprehensively considers multiple criteria such as technical feasibility, economic benefits and environmental benefits, prioritizes emission reduction measures, generates corresponding policy recommendations, and outputs an intelligent analysis report on the potential for industrial pollutant emission reduction. The method of the present invention adopts a decision support model based on fuzzy cognitive map (FCM).

[0256] FCM is a directed graph structure, where nodes represent key concepts and edges represent causal relationships between concepts. The weight of the edge is represented by a fuzzy number, reflecting the strength and uncertainty of the relationship. The state update rule of FCM is:

[0257]

[0258] in, is the activation degree of node i at time t, w j i is the weight from node j to node i, f is the activation function, and the sigmoid function is usually chosen.

[0259] Through the above detailed description, it can be seen that the method of the present invention is comprehensive, intelligent and practical in the assessment of industrial pollutant emission reduction potential. The method can not only accurately assess the emission reduction potential, but also provide optimized emission reduction plans and policy recommendations, which has important practical application value for improving the efficiency of industrial pollutant emission reduction and improving environmental quality.

Claims

1. An intelligent evaluation system for industrial pollutant emission reduction potential based on data mining, characterized by: include: Data acquisition module for: Collect industrial production data, energy consumption data and environmental monitoring data; Preprocess the collected data, including data cleaning, standardization, vectorization and integration processing; The data fusion module is connected to the data acquisition module for: Receiving the preprocessed data sent by the data acquisition module; Based on the preprocessed data, an industrial data pool is formed; A model building module is connected to the data fusion module for: Building a neural network model based on the data in the industrial data pool; Clustering or classifying different pollutant emissions, pollution load data and optimization strategies using the neural network model; A process optimization module is in communication with the model building module and is used to: Receiving the clustering or classification result sent by the model building module; Based on the clustering or classification results, generating a process parameter adjustment plan; Output the process optimization path that meets the goal of minimizing pollutant emissions; The environmental optimization module is connected to the process optimization module for: Receiving the process optimization path sent by the process optimization module; Based on the process optimization path, adjusting environmental protection unit parameters; Output environmental optimization solutions that meet the minimum emission setting; The emission reduction potential assessment module is connected to the environmental protection optimization module for: Based on the environmental optimization scheme, calculate the emission reduction potential of different pollutants; Produce emission reduction potential analysis results; A decision support module is in communication with the emission reduction potential assessment module and is used to: receiving the emission reduction potential analysis results; Based on the emission reduction potential analysis results, generating an emission reduction priority ranking; Output intelligent analysis report on industrial pollutant emission reduction potential.

2. The system according to claim 1, characterized in that The data acquisition module comprises: A data source access unit, used to access multiple data sources, including industrial production equipment, energy consumption monitoring equipment and environmental monitoring equipment; A data preprocessing unit, which is in communication with the data source access unit and is used to preprocess the accessed data, including data cleaning, denoising, outlier processing and format unification; The data quality control unit is in communication with the data preprocessing unit and is used to perform quality detection on the preprocessed data, including integrity check, consistency check and validity verification.

3. The system according to claim 1, characterized in that The data fusion module comprises: Multidimensional data integration unit, used to integrate process data, environmental data, financial data and regulatory data in multiple dimensions; A data association analysis unit, which is in communication connection with the multi-dimensional data integration unit and is used to analyze the association relationship between data of different dimensions; The data storage unit is communicatively connected with the data association analysis unit and is used for storing the integrated and associated data in structured and semi-structured forms.

4. The system according to claim 1, characterized in that The model building module includes: A feature extraction unit, used to extract key features from the industrial data pool; A model selection unit, which is in communication with the feature extraction unit and is used to select a suitable neural network model structure according to data features and task requirements; A model training unit, in communication with the model selection unit, for training the selected neural network model using historical data; The model evaluation unit is communicatively connected to the model training unit and is used to evaluate the performance of the trained model and optimize the model according to the evaluation results.

5. The system according to claim 1, characterized in that The process optimization module includes: Process parameter analysis unit, used to analyze the relationship between process parameters and pollutant emissions; An optimization target setting unit, which is in communication with the process parameter analysis unit and is used to set an optimization target for minimizing pollutant emissions; A multi-objective optimization unit, which is in communication with the optimization target setting unit and is used to achieve multi-objective optimization for minimizing pollutant emissions while meeting production requirements; The process path generation unit is in communication with the multi-objective optimization unit and is used to generate a specific process optimization path according to the optimization result.

6. The system according to claim 1, characterized in that The environmental optimization module includes: Total emission calculation unit, used to calculate the current total amount of pollutant emissions; An environmental parameter adjustment unit, which is in communication with the total emission calculation unit and is used to adjust the environmental parameter of the unit according to the total emission minimization target; An effect evaluation unit, which is in communication with the environmental parameter adjustment unit and is used to evaluate the environmental effect after the parameter adjustment; The optimization scheme generating unit is connected to the effect evaluation unit for generating a final environmental protection optimization scheme according to the evaluation result.

7. The system according to claim 1, characterized in that The emission reduction potential assessment module includes: Scenario simulation unit, used to simulate pollutant emissions under different emission reduction measures; A potential calculation unit, which is in communication with the scenario simulation unit and is used to calculate the emission reduction potential of different pollutants under various emission reduction measures; an uncertainty analysis unit, in communication with the potential calculation unit, for analyzing uncertainty factors in the emission reduction potential estimation; The result visualization unit is connected to the uncertainty analysis unit for visually displaying the emission reduction potential analysis results in the form of charts.

8. The system according to claim 1, characterized in that The decision support module includes: Multi-criteria decision-making unit, used to comprehensively consider multiple criteria such as technical feasibility, economic benefits and environmental benefits; a priority sorting unit, which is in communication connection with the multi-criteria decision-making unit and is used to prioritize the emission reduction measures according to the multi-criteria evaluation results; A policy suggestion generating unit, which is in communication connection with the priority sorting unit and is used to generate corresponding policy suggestions according to the priority sorting result; The report generation unit is connected to the policy recommendation generation unit for integrating all analysis results to generate a comprehensive intelligent analysis report on the potential for industrial pollutant emission reduction.

9. The system according to claim 1, characterized in that Also includes: Data update module, used to receive new data in real time and update the industrial data pool; A model iteration module, which is in communication with the data update module and is used to regularly iterate and optimize the neural network model according to the updated data; The system performance monitoring module is connected to the model iteration module for monitoring the operating status of each module of the system and triggering system maintenance and upgrade when necessary.

10. An intelligent evaluation method for industrial pollutant emission reduction potential based on multi-dimensional data mining, using the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Data collection and preprocessing: Collect industrial production data, energy consumption data and environmental monitoring data; Clean, standardize, vectorize and integrate the collected data; S2. Data fusion: Integrate the pre-processed multi-dimensional data to form an industrial data pool; Analyze the correlation between data of different dimensions; S3. Model construction: Build a neural network model based on the data in the industrial data pool; Clustering or classifying different pollutant emissions, pollution load data and optimization strategies using the neural network model; S4. Process Optimization: Analyze the relationship between process parameters and pollutant emissions based on model clustering or classification results; Under the premise of meeting production requirements, generate a process optimization path that meets the goal of minimizing pollutant emissions; S5. Environmental optimization: Adjust environmental protection unit parameters according to process optimization path; Output environmental optimization solutions that meet the minimum emission setting; S6. Assessment of emission reduction potential: Simulate pollutant emissions under different emission reduction measures; Calculate the emission reduction potential of different pollutants under various emission reduction measures; Analyse uncertainties in estimates of emission reduction potential; S7.Decision support: Prioritize emission reduction measures by taking into account multiple criteria such as technical feasibility, economic benefits and environmental benefits; Generate corresponding policy recommendations based on the priority ranking results; Generate intelligent analysis reports on industrial pollutant reduction potential.

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