Method for predicting atmospheric pollution in industrial park

Abstract

The invention relates to the technical field of environmental protection detection, and discloses an industrial park atmospheric pollution prediction method comprising the following steps: S1, collecting meteorological data, emission data, historical air quality data and geographic information data in an industrial park; s2, preprocessing the collected data, including data cleaning, normalization and feature extraction; s3, based on a time sequence analysis method and a machine learning algorithm, constructing a multi-level pollution source prediction model, and identifying and predicting emission characteristics of different pollution sources; s4, through a multi-scale modeling method, modeling is carried out on the emission behavior of a single pollution source on the microscopic scale; and S5, coupling and integration of the models are implemented, and cross-scale pollution prediction is realized. By combining a micro-scale long-short-term memory network model and a macro-scale Gaussian diffusion model, complex emission behaviors of a single pollution source and diffusion paths of pollutants in a regional range can be captured at the same time.

Description

Technical Field

[0001] The invention relates to the technical field of environmental protection detection, and in particular to a method for predicting air pollution in an industrial park. Background Art

[0002] In the current management of air pollution in industrial parks, accurate prediction of the concentration distribution and diffusion behavior of pollutants is crucial for effective environmental governance and decision-making. However, existing air pollution prediction methods have some significant shortcomings when dealing with complex industrial park environments.

[0003] First, traditional pollution prediction methods usually rely on single-scale modeling techniques. For example, although the pollution diffusion model based on the macro scale can simulate the propagation path of pollutants within the regional scope, it is often difficult to capture the complex emission characteristics of a single pollution source, especially when there are nonlinear and long-term dependent data in the time series, its prediction accuracy decreases significantly. On the other hand, although the micro-scale model can model a single pollution source in detail, it is difficult to accurately predict the spatial distribution of pollutants over a large area. Therefore, it is often difficult for a single-scale model to balance accuracy and applicability in practical applications, resulting in a large deviation between the prediction results and the actual situation.

[0004] Secondly, the existing technology generally has the problem of poor model adaptability. Due to the wide variety of pollution sources and complex emission characteristics in industrial parks, as well as the dynamic changes in meteorological conditions and geographical environment over time, traditional models lack sufficient flexibility in the face of these variable factors. The fixed structure of a single model makes it difficult to maintain high accuracy under different environmental conditions, and it is easy to fail in extreme meteorological conditions or complex emission situations, resulting in unreliable prediction results. In addition, traditional models are usually unable to adjust prediction strategies in real time, and lack a dynamic optimization mechanism for model performance, which further limits its effectiveness in practical applications.

[0005] Finally, existing technologies still have problems with insufficient model integration and coupling capabilities. In a complex industrial park environment, a single model often cannot fully reflect the mutual influence and synergy between different pollution sources. Although some technologies attempt to improve prediction accuracy through simple model superposition, the results are often not as expected due to the lack of an effective coupling mechanism. In addition, traditional model integration methods lack the ability to dynamically adjust model weights, which may cause the integrated results to be biased towards a specific model, thereby affecting the accuracy and robustness of the overall prediction.

[0006] To this end, those skilled in the art provide a method for predicting air pollution in an industrial park to solve the problems raised in the above background technology. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a method for predicting air pollution in an industrial park, which solves the limitation of the prior art that a single-scale model is difficult to balance accuracy and applicability.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for predicting air pollution in an industrial park, comprising the following steps: S1. Collect meteorological data, emission data, historical air quality data and geographic information data within the industrial park; S2, preprocessing the collected data, including data cleaning, normalization, and feature extraction; S3. Based on time series analysis methods and machine learning algorithms, a multi-level pollution source prediction model is constructed to identify and predict the emission characteristics of different pollution sources; S4. Through multi-scale modeling methods, the emission behavior of a single pollution source is modeled at the micro scale, and a regional pollution diffusion model is constructed at the macro scale; S5. Implement model coupling and integration, couple microscale models with macroscale models, and realize cross-scale pollution prediction.

[0009] Preferably, the time series analysis in step S3 uses a seasonal ARIMA model, and the formula is:

[0010] in, For time The pollutant concentration, is a constant term, is the autoregression coefficient, is the moving average coefficient, is the white noise error term; The machine learning algorithm in step S3 adopts the random forest algorithm based on the feature Predicting pollutant concentrations The formula is:

[0011] in, For the A decision tree, is the total number of decision trees.

[0012] Preferably, the micro-scale modeling in step S4 is performed by using a long short-term memory network to model the time series data, and its basic unit calculation formula is:

[0013] in, For the moment The hidden state of Go out for John, is the memory unit state, Represents element-wise multiplication.

[0014] Preferably, the macro-scale modeling in step S4 uses a Gaussian diffusion model to predict the concentration distribution of pollutants in the region, and the formula is:

[0015] in, For coordinates The pollutant concentration at is the emission amount from pollution sources, and are the lateral and longitudinal diffusion coefficients, is the wind speed.

[0016] Preferably, the coupling model in step S5 predicts the pollutant concentration within the regional range by combining the micro-scale long short-term memory network output with the macro-scale Gaussian diffusion model input.

[0017] Preferably, the data preprocessing in step S2 includes feature extraction, and the feature extraction formula is:

[0018] in, For the moment The characteristic value of For the past The original data at each time point, is the window size.

[0019] Preferably, the pollution source prediction model in step S3 uses a multi-level regression analysis, and the regression formula is:

[0020] in, is the predicted pollutant concentration, is the input feature, is the regression coefficient, is the error term.

[0021] Preferably, the macro-scale model in step S4 is also combined with topographic data through a terrain correction factor Adjust the concentration prediction value, and the correction formula is:

[0022] in, is the corrected concentration value, is a correction factor related to terrain.

[0023] Preferably, the cross-scale pollution prediction model in step S5 outputs the final pollutant concentration prediction value by weighted averaging the prediction results of each model, and the formula is:

[0024] in, is the final predicted value, For the The predicted value of the model, is the corresponding weight.

[0025] A prediction system for air pollution in an industrial park, used for a prediction method for air pollution in an industrial park, comprising: Data collection module, which is used to collect real-time and historical weather conditions, pollutant emissions, air quality, and geographic feature data from weather stations, emission monitoring equipment, historical data storage, and geographic information systems within the industrial park; The data processing and feature extraction module is used to pre-process the collected data, including cleaning, normalization and other steps, and extract key features from it; The model training and prediction module is used to build and train the pollutant concentration prediction model; Model coupling and integration module, which is used to couple micro-scale and macro-scale models to achieve cross-scale pollution prediction from a single pollution source to the entire industrial park; Real-time prediction and dynamic adjustment module, used to dynamically adjust and update the prediction model according to real-time data; Visualization and early warning module, used to visualize the prediction results and generate pollution warning information; The management and decision support module is used to provide pollution source management and decision support services to industrial park managers.

[0026] The present invention provides a method for predicting air pollution in an industrial park, which has the following beneficial effects: 1. By combining the micro-scale long short-term memory network model and the macro-scale Gaussian diffusion model, the present invention can simultaneously capture the complex emission behavior of a single pollution source and the diffusion path of pollutants in the regional scope. Compared with the traditional single-scale prediction model, this method significantly improves the accuracy of pollutant concentration prediction, especially when processing data with nonlinearity and long-term dependence, the effect is more obvious.

[0027] 2. The present invention adopts a cross-scale coupling and integration method. By weighted averaging the prediction results of different models, the system can flexibly respond to complex situations under different pollution sources and environmental conditions. Different from the problem that a single model in the prior art is prone to fail under specific conditions, this method can dynamically adjust the weights of each model to ensure that reliable prediction results can still be provided in a changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0030] Embodiment 1: Please see attached Figure 1 The embodiment of the present invention provides a method for predicting air pollution in an industrial park, comprising the following steps: S1. Collect meteorological data, emission data, historical air quality data and geographic information data within the industrial park; Specifically, this step first collects current meteorological data through various sensors and monitoring equipment installed in the industrial park. These data include key parameters such as temperature, humidity, wind speed, wind direction, and precipitation, which can effectively reflect the impact of atmospheric conditions on the diffusion of pollutants. At the same time, through the emission monitoring system in the industrial park, the emission data of various pollution sources are obtained in real time, including the emission of specific pollutants such as sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM2.5 and PM10), and volatile organic compounds. In addition, the system also extracts historical air quality data from the historical database to ensure that the prediction model can draw on past data trends and laws to further improve the accuracy of the prediction. In order to improve the analysis of the diffusion path and impact range of pollutants, this step also integrates the geographic information data of the industrial park, including detailed spatial information such as topography, vegetation cover, and water distribution. These geographic information helps to more accurately simulate the diffusion behavior of pollutants under different terrain conditions, and provides more comprehensive basic data for subsequent modeling and prediction. By comprehensively considering meteorological conditions, emission data, historical air quality and geographic information data, this step provides multi-dimensional and accurate data input for subsequent pollution prediction modeling, thereby ensuring the reliability and effectiveness of the prediction model.

[0031] S2, preprocessing the collected data, including data cleaning, normalization, and feature extraction; The data preprocessing in step S2 includes feature extraction, and the feature extraction formula is:

[0032] in, For the moment The characteristic value of For the past The original data at each time point, is the window size; Specifically, in this step, the collected meteorological data, emission data, historical air quality data and geographic information data are first cleaned, and outliers and missing values ​​are removed or corrected during the processing to ensure the integrity and accuracy of the data. Next, data normalization is performed to convert data of different dimensions to the same range to eliminate the dimensional differences between different data sources and ensure that various types of data can be compared and analyzed on the same scale in the subsequent modeling process.

[0033] Subsequently, feature extraction is performed to extract representative feature values ​​from the original data by defining a reasonable window size. This formula extracts feature values ​​that reflect the trend of data changes by calculating the average value over a period of time. The feature values ​​can better capture the temporal changes of the data and serve as the input of the subsequent prediction model. This feature extraction method not only simplifies the complexity of the original data, but also improves the computational efficiency and prediction accuracy of the model, ensuring that the final prediction results can accurately reflect the dynamic changes of air pollution.

[0034] S3. Based on time series analysis methods and machine learning algorithms, a multi-level pollution source prediction model is constructed to identify and predict the emission characteristics of different pollution sources; The time series analysis in step S3 uses the seasonal ARIMA model with the formula:

[0035] in, For time The pollutant concentration, is a constant term, is the autoregression coefficient, is the moving average coefficient, is the white noise error term; The machine learning algorithm in step S3 adopts the random forest algorithm based on the features Predicting pollutant concentrations The formula is:

[0036] in, For the A decision tree, is the total number of decision trees; The pollution source prediction model uses multi-level regression analysis, and the regression formula is:

[0037] in, is the predicted pollutant concentration, is the input feature, is the regression coefficient, is the error term; Specifically, in this step, the seasonal ARIMA model is first used to perform a time series analysis on the historical pollutant concentration data. By modeling the past pollutant concentrations, this method can capture the trend and periodic characteristics of pollutant concentrations over time. In actual operation, the system automatically determines the optimal autoregressive order, difference order, and moving average order based on historical data, and then applies these parameters to the seasonal ARIMA model to predict pollutant concentrations in future time periods. During the specific implementation process, the model is continuously iterated and optimized until the model parameters that best match the characteristics of the historical data are found. Based on these parameters, the system can predict future pollutant concentrations and provide a reference for subsequent pollution control measures.

[0038] Next, the system uses the random forest algorithm for machine learning modeling. The algorithm performs regression analysis on different input features (such as meteorological data, emission data, etc.) by constructing multiple decision trees. In actual implementation, the system first randomly extracts subsets from all features and constructs multiple decision trees based on these subsets. Each decision tree predicts pollutant concentrations based on different combinations of features, and finally a more robust prediction result is obtained by averaging the prediction results of all decision trees. The system will automatically adjust the number and depth of decision trees according to the complexity and diversity of the data to ensure the accuracy and generalization of the prediction.

[0039] In addition, the system also uses a multi-level regression analysis method to build a comprehensive pollution source prediction model. During the implementation process, the system first performs a regression analysis on the emission characteristics of different pollution sources and calculates the regression coefficient of each characteristic. Then, the system combines the outputs of each regression model to form the final pollutant concentration prediction value. In the specific implementation, the system will gradually adjust the structure and parameters of the regression model according to the characteristics of the pollution source and the emission data to ensure that it can accurately reflect the impact of different pollution sources on air quality.

[0040] S4. Through multi-scale modeling methods, the emission behavior of a single pollution source is modeled at the micro scale, and a regional pollution diffusion model is constructed at the macro scale; The micro-scale modeling in step S4 models the time series data by using a long short-term memory network, and its basic unit calculation formula is:

[0041] in, For the moment The hidden state of Go out for John, is the memory unit state, Represents element-wise multiplication; The macroscale modeling in step S4 uses a Gaussian diffusion model to predict the contaminant concentration in the region. The concentration distribution of is given by:

[0042] in, For coordinates The pollutant concentration at is the emission amount from pollution sources, and are the lateral and longitudinal diffusion coefficients, is the wind speed; The macro-scale model also combines topographic data with topographic correction factors Adjust the concentration prediction value, and the correction formula is:

[0043] in, is the corrected concentration value, is the correction factor related to terrain; Specifically, in this step, the long short-term memory network (LSTM) is first used to model the emission behavior of a single pollution source at a microscopic scale. As a deep learning model suitable for time series data, LSTM can capture and memorize the long-term dependence and short-term volatility in the emission data of pollution sources. In the specific implementation, the LSTM model receives time series data from pollution sources, processes the data through multiple hidden layers, and finally outputs the predicted pollutant emissions.

[0044] In each hidden layer, the LSTM unit can effectively process complex time series relationships through dynamic adjustment of the input gate, forget gate, and output gate. In system applications, the model is trained to learn the patterns in historical emission data and then predict future emissions. Since LSTM has the advantage of processing nonlinear and long-term dependent data, the model can accurately capture the complex emission behavior of pollution sources and provide reliable input for subsequent regional pollution diffusion predictions.

[0045] The system then models the concentration distribution of pollutants in the region at a macroscopic scale using the Gaussian diffusion model, which predicts the concentration of pollutants at different geographic locations by mathematically describing the diffusion process of pollutants.

[0046] In actual operation, the system calculates the concentration distribution of pollutants in a specific area based on the emission of pollution sources, wind speed and atmospheric diffusion coefficient. The model can accurately simulate the diffusion path of pollutants in the atmosphere, especially the impact of changes in wind speed and diffusion coefficient on pollutant concentration. In the application process, the system combines real-time meteorological data and geographic information to continuously adjust the model parameters to ensure that the prediction results are consistent with the actual situation.

[0047] In addition, the macro-scale model also combines topographic data to correct the output of the Gaussian diffusion model by introducing a terrain correction factor. This correction step takes into account the obstruction or guidance of the topography in the region on the diffusion of pollutants. During implementation, the system calculates the correction factor based on the terrain features (such as mountains, rivers, buildings, etc.) and applies it to the prediction results of the Gaussian diffusion model. In this way, the system can more accurately reflect the impact of actual geographical conditions on the diffusion of pollutants, making the prediction of pollutant concentrations closer to reality.

[0048] S5. Implement model coupling and integration, couple microscale models with macroscale models, and realize cross-scale pollution prediction.

[0049] The coupled model in step S5 predicts the pollutant concentration in the regional range by combining the micro-scale LSTM network output with the macro-scale Gaussian diffusion model input; The cross-scale pollution prediction model in step S5 outputs the final pollutant concentration prediction value by weighted averaging the prediction results of each model. The formula is:

[0050] in, is the final predicted value, For the The predicted value of the model, is the corresponding weight; Specifically, in this step, a cross-scale coupling model is first formed by organically combining the micro-scale long short-term memory network (LSTM) model with the macro-scale Gaussian diffusion model. Specifically, the LSTM model is used to predict the emission behavior of a single pollution source and output the predicted pollutant emissions; the output result is then passed as input data to the Gaussian diffusion model to predict the diffusion and concentration distribution of pollutants in the regional range. Through this coupling method, the system can combine detailed emission data at the micro level with diffusion behavior at the macro level, thereby providing more accurate and comprehensive pollution prediction results.

[0051] On this basis, the system further adopts a cross-scale pollution prediction model to perform a weighted average of multiple prediction results to obtain the final regional pollutant concentration prediction value. In actual operation, the system will evaluate the historical performance of each model to dynamically adjust the weight of each model to ensure that the weighted average result best matches the actual situation.

[0052] Through this multi-model integration method, the system can effectively combine the advantages of models at different scales, thereby improving the accuracy and robustness of predictions. Ultimately, the cross-scale pollution prediction model not only achieves comprehensive predictions from a single pollution source to the entire region, but also provides managers with a scientific basis for formulating effective pollution control strategies.

[0053] Embodiment 2: Please see attached Figure 2 The embodiment of the present invention provides a prediction system for air pollution in an industrial park, comprising: Data collection module, which is used to collect real-time and historical weather conditions, pollutant emissions, air quality, and geographic feature data from weather stations, emission monitoring equipment, historical data storage, and geographic information systems within the industrial park; The data processing and feature extraction module is used to pre-process the collected data, including cleaning, normalization and other steps, and extract key features from it; The model training and prediction module is used to build and train the pollutant concentration prediction model; Model coupling and integration module, which is used to couple micro-scale and macro-scale models to achieve cross-scale pollution prediction from a single pollution source to the entire industrial park; Real-time prediction and dynamic adjustment module, used to dynamically adjust and update the prediction model according to real-time data; Visualization and early warning module, used to visualize the prediction results and generate pollution warning information; The management and decision support module is used to provide pollution source management and decision support services to industrial park managers.

[0054] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A prediction method for air pollution in industrial parks, It is characterized in that The following steps are involved: S1. Collect meteorological data, emission data, historical air quality data and geographic information data within the industrial park; S2, preprocessing the collected data, including data cleaning, normalization, and feature extraction; S3. Based on time series analysis methods and machine learning algorithms, a multi-level pollution source prediction model is constructed to identify and predict the emission characteristics of different pollution sources; S4. Through multi-scale modeling methods, the emission behavior of a single pollution source is modeled at the micro scale, and a regional pollution diffusion model is constructed at the macro scale; S5. Implement model coupling and integration, couple microscale models with macroscale models, and realize cross-scale pollution prediction.

2. A method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The time series analysis in step S3 uses a seasonal ARIMA model, and the formula is: ； in, For time The pollutant concentration, is a constant term, is the autoregression coefficient, is the moving average coefficient, is the white noise error term; The machine learning algorithm in step S3 adopts the random forest algorithm based on the feature Predicting pollutant concentrations The formula is: ； in, For the A decision tree, is the total number of decision trees.

3. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The micro-scale modeling in step S4 is performed by using a long short-term memory network to model the time series data, and its basic unit calculation formula is: ； in, For the moment The hidden state of Go out for John, is the memory unit state, Represents element-wise multiplication.

4. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The macro-scale modeling in step S4 uses a Gaussian diffusion model to predict the concentration distribution of pollutants in the region, and the formula is: ； in, For coordinates The pollutant concentration at is the emission amount of pollution source, and are the lateral and longitudinal diffusion coefficients, is the wind speed.

5. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The coupling model in step S5 predicts the pollutant concentration within the region by combining the micro-scale LSTM network output with the macro-scale Gaussian diffusion model input.

6. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The data preprocessing in step S2 includes feature extraction, and the feature extraction formula is: ； in, For the moment The characteristic value of For the past The original data at each time point, is the window size.

7. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The pollution source prediction model in step S3 uses multi-level regression analysis, and the regression formula is: ； in, is the predicted pollutant concentration, is the input feature, is the regression coefficient, is the error term.

8. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The macro-scale model in step S4 is also combined with topographic data through the terrain correction factor Adjust the concentration prediction value, and the correction formula is: ； in, is the corrected concentration value, is a correction factor related to terrain.

9. The method for predicting air pollution in an industrial park according to claim 1, It is characterized in that The cross-scale pollution prediction model in step S5 outputs the final pollutant concentration prediction value by weighted averaging the prediction results of each model, and the formula is: ； in, is the final predicted value, For the The predicted value of the model, is the corresponding weight.

10. A prediction system for air pollution in an industrial park, used in the prediction method for air pollution in an industrial park according to any one of claims 1 to 9, It is characterized in that include: Data collection module, which is used to collect real-time and historical weather conditions, pollutant emissions, air quality, and geographic feature data from weather stations, emission monitoring equipment, historical data storage, and geographic information systems within the industrial park; The data processing and feature extraction module is used to pre-process the collected data, including cleaning, normalization and other steps, and extract key features from it; The model training and prediction module is used to build and train the pollutant concentration prediction model; Model coupling and integration module, which is used to couple micro-scale and macro-scale models to achieve cross-scale pollution prediction from a single pollution source to the entire industrial park; Real-time prediction and dynamic adjustment module, used to dynamically adjust and update the prediction model according to real-time data; Visualization and early warning module, used to visualize the prediction results and generate pollution warning information; The management and decision support module is used to provide pollution source management and decision support services to industrial park managers.

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