A method and system for predicting groundwater pollution diffusion trend

Abstract

The present invention discloses a method and system for predicting the trend of groundwater pollution diffusion, the method comprising: collecting groundwater pollution diffusion information; obtaining water body recharge and discharge information of groundwater pollution area, combining historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data, calculating the pollution diffusion speed of water body recharge and discharge calculation models for different groundwater pollution areas, updating and optimizing control measures, generating pollution diffusion speed calculation of water body recharge and discharge calculation models for different groundwater pollution areas, water pollution harmful substance diffusion area information, and performing graph neural network groundwater pollution big data prediction. The present invention can not only solve the problem of insufficient data collection, but also significantly improve the prediction accuracy, providing a more effective scientific basis for pollution control.

Description

Technical Field

[0001] The present invention relates to the field of groundwater pollution prediction, and in particular to a method and system for predicting the diffusion trend of groundwater pollution. Background Art

[0002] Groundwater pollution is a global environmental problem that poses a serious threat to ecosystems and human health. Due to the concealment and mobility of groundwater, the pollution diffusion trend is difficult to directly observe and predict, especially the diffusion paths of pollutants in different geological structures and fluid movements are complex and changeable. In recent years, with the advancement of data acquisition technology and computing power, groundwater pollution diffusion prediction has gradually introduced big data, physical modeling and machine learning methods. However, how to accurately predict the diffusion trend of groundwater pollution to guide scientific governance remains a key challenge.

[0003] At present, the methods for predicting the spread of groundwater pollution are mainly divided into physical models and data-driven models. The physical model is based on the principles of groundwater dynamics and simulates the diffusion of pollutants in geological media, but its simulation accuracy is limited by the complexity of the groundwater system and the difficulty in obtaining parameters. The data-driven model relies on historical observation data and machine learning algorithms, which can overcome the complexity of the physical model to a certain extent, but the limitation of data collection is a key bottleneck. Most existing methods rely on a single data source (such as pollution concentration, flow rate or topography) and cannot fully integrate multi-source information (such as water level changes, pollution remediation effects, etc.), resulting in inaccurate predictions of the model's pollution diffusion speed, direction and range. In addition, the uneven distribution of the monitoring network leads to regional data loss, making it difficult for the model to cope with dynamic changes under complex geological conditions.

[0004] Incomplete data collection directly affects the efficiency and accuracy of groundwater pollution diffusion prediction. First, the lack of detailed records of the intensity and diffusion boundaries of different pollution sources will result in the model being unable to accurately determine the contribution of the pollution source. Secondly, insufficient collection of dynamic variables in the groundwater system (such as recharge, discharge, and flow direction) makes it difficult for the model to capture the dynamic changes of pollutants in time and space. In addition, existing monitoring technologies lack the ability to perceive deep pollution in real time and cannot fully consider the impact of geological medium heterogeneity on diffusion paths. The above deficiencies cause deviations in existing prediction models, especially when dealing with complex and changeable groundwater systems, the prediction results are often distorted. Therefore, there is an urgent need for a more intelligent and scientific way to predict groundwater pollution and improve the efficiency of groundwater pollution prediction. Summary of the invention

[0005] The present application provides a method and system for predicting the diffusion trend of groundwater pollution, aiming to solve the technical problems of low efficiency in calculating the diffusion rate of groundwater pollution and predicting control measures in the prior art.

[0006] In view of the above problems, an embodiment of the present application provides a method and system for predicting the spread trend of groundwater pollution.

[0007] The first aspect disclosed in the present application provides a method for predicting the spread trend of groundwater pollution, the method comprising: collecting groundwater pollution spread information through a multimodal groundwater pollution information sensor platform, the groundwater pollution spread information including historical groundwater pollution concentrations of different harmful substances, water flow speed and direction, groundwater level changes, pollution source location and intensity data; obtaining source interception and water pollution remediation area information; using the source interception, water pollution remediation area information and the historical groundwater pollution concentrations of different harmful substances, water flow speed and direction, groundwater level changes, pollution source location and intensity data, calculating the pollution spread rate for different source interceptions and water pollution remediation, updating and optimizing the control measures, generating different source interceptions, water pollution remediation effect statistics and water pollution harmful substance spread area information; obtaining Obtain water recharge and discharge information for groundwater pollution areas; utilize the water recharge and discharge information for groundwater pollution areas and the historical groundwater pollution data on different harmful substance concentrations, water flow velocity and direction, groundwater level changes, pollution source locations and intensity to calculate the pollution diffusion rate for different groundwater pollution area water recharge and discharge calculation models, update and optimize control measures, and generate pollution diffusion rate calculations for different groundwater pollution area water recharge and discharge calculation models, as well as water pollution harmful substance diffusion area information; perform graph neural network groundwater pollution big data prediction based on the different source interception, water pollution remediation effect statistics, water pollution harmful substance diffusion area information, and the pollution diffusion rate calculations for different groundwater pollution area water recharge and discharge calculation models, as well as water pollution harmful substance diffusion area information.

[0008] Another aspect disclosed in the present application provides a groundwater pollution diffusion trend prediction system, the system comprising:

[0009] The groundwater pollution data collection module collects groundwater pollution diffusion information through a multi-modal groundwater pollution information sensor platform, wherein the groundwater pollution diffusion information includes the concentration of different harmful substances in historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source location and intensity data;

[0010] The pollution diffusion rate information acquisition module is used to obtain the area information of source interception and water pollution remediation area;

[0011] A control measure updating and optimization module is used to calculate the pollution diffusion rate of different source interception and water pollution remediation, update and optimize the control measures, and generate statistics on the effects of different source interception and water pollution remediation, as well as the diffusion area information of harmful substances in water pollution by using the source interception and water pollution remediation area information and the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data;

[0012] Water body recharge and discharge information acquisition module, used to obtain water body recharge and discharge information in the groundwater pollution area;

[0013] A groundwater pollution diffusion area calculation module is used to calculate the pollution diffusion rate of water body recharge and discharge calculation models for different groundwater pollution areas, update and optimize the control measures, and generate pollution diffusion rate calculation of water body recharge and discharge calculation models for different groundwater pollution areas, and information on the diffusion area of ​​harmful substances in water pollution;

[0014] The graph neural network groundwater pollution big data prediction module is used to perform graph neural network groundwater pollution big data prediction based on the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, pollution diffusion rate calculation of water body recharge and discharge calculation models in different groundwater pollution areas, and diffusion area information of harmful substances in water pollution.

[0015] Beneficial effects:

[0016] The present invention proposes a method and system for predicting the trend of groundwater pollution diffusion. Through a multimodal groundwater pollution information sensor platform, groundwater pollution diffusion information is collected; source interception and water pollution remediation area information is obtained; based on source interception and water pollution remediation area information and historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data, the pollution diffusion rate of different source interceptions and water pollution remediation is calculated, and the control measures are updated and optimized to generate different source interception, water pollution remediation effect statistics and water pollution harmful substance diffusion area information; groundwater pollution area area water supply and discharge information is obtained; based on the groundwater pollution area area water supply and discharge information and historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data , the pollution diffusion rate is calculated for the water body recharge and discharge calculation models of different groundwater pollution areas, and the governance measures are updated and optimized, and the pollution diffusion rate calculation and water pollution harmful substance diffusion area information of different groundwater pollution areas are generated. The graph neural network groundwater pollution big data prediction is carried out in combination with the pollution diffusion rate calculation and water pollution harmful substance diffusion area information of different groundwater pollution areas. It is realized that according to the concentration of different harmful substances in historical groundwater pollution in different terrains, water flow speed and direction, groundwater level changes, pollution source location and intensity, the pollution diffusion rate calculation and governance measures of source interception, water pollution remediation and groundwater pollution area water body recharge and discharge calculation models are updated and optimized, and the technical effect of improving the pollution diffusion rate calculation and governance measure prediction efficiency of groundwater pollution is improved. The present invention can not only solve the problem of insufficient data collection, but also significantly improve the prediction accuracy, and provide a more effective scientific basis for pollution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method of the present invention;

[0018] Figure 2 It is a system module composition diagram of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present application provide a method and system for predicting the spread trend of groundwater pollution, which solves the technical problems of low efficiency in calculating the pollution spread rate and predicting control measures for groundwater pollution, and realizes the technical effect of calculating the pollution spread rate and updating and optimizing control measures for source interception, water pollution remediation, and water body replenishment and discharge calculation models in accordance with different harmful substance concentrations, water flow speed and direction, groundwater level changes, and pollution source locations and intensities in historical groundwater pollution in different terrains, thereby improving the technical effect of improving the efficiency of calculating the pollution spread rate and predicting control measures for groundwater pollution.

[0020] Figure 1 As shown, the embodiment of the present application provides a method for predicting the diffusion trend of groundwater pollution, the method comprising:

[0021] Step S100: When the preset area node is met, the groundwater pollution diffusion information is collected through the multimodal groundwater pollution information sensor platform, and the groundwater pollution diffusion information includes the concentration of different harmful substances in historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source location and intensity data; the area information of source interception and water pollution remediation is obtained;

[0022] Preferably, in different terrain environments, groundwater pollution diffusion information is collected through a multimodal groundwater pollution information sensor platform, and the groundwater pollution diffusion information includes historical groundwater pollution concentrations of different harmful substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity data; different terrains have different source interceptions and water pollution remediation, and the area information of source interception and water pollution remediation areas is obtained. The source interception and water pollution remediation area information includes the spatial location information of different source interceptions and water pollution remediation in different terrains, providing data support for groundwater pollution prediction and rock and soil prediction in different terrains.

[0023] Step S200, using the source interception, water pollution remediation area information and the historical groundwater pollution different harmful substances concentration, water flow speed and direction, groundwater level change, pollution source location and intensity data, the pollution diffusion rate of different source interception and water pollution remediation is calculated, and the control measures are updated and optimized to generate different source interception, water pollution remediation effect statistics and water pollution harmful substances diffusion area information;

[0024] According to the area information of the source interception and water pollution remediation area, the content of groundwater chemical substances is divided using a density clustering model to obtain the concentrated locations of different hazardous substances, and the concentrated locations of different hazardous substances correspond to the area information of the source interception and water pollution remediation area;

[0025] According to the concentrated locations of the different hazardous substances, the density clustering model is used to analyze the pollution diffusion trajectory of the historical groundwater pollution data on the concentrations of different hazardous substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and obtain the results of historical groundwater pollution concentrations of different hazardous substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and pollution diffusion trajectory;

[0026] The concentration of different harmful substances in the historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source location and intensity, pollution diffusion trajectory are statistically evaluated to obtain the distribution locations of groundwater pollution with different hydrogeological characteristics;

[0027] According to the locations of groundwater pollution distribution with different hydrogeological characteristics, the pollution diffusion rate is calculated, and the control measures are updated and optimized for the different source interception and water pollution remediation, so as to generate statistics on the effects of the different source interception and water pollution remediation and information on the diffusion area of ​​harmful substances in water pollution.

[0028] Preferably, the interception of different sources and the remediation of water pollution are regulated and controlled, and the area information of the interception of the source and the remediation of water pollution and the concentration of different harmful substances in the historical groundwater pollution, the water flow speed and direction, the groundwater level change, the location and intensity data of the pollution source are used to calculate the pollution diffusion rate of the interception of different sources and the remediation of water pollution, update and optimize the control measures, and generate statistics on the effects of interception of different sources and the remediation of water pollution and the diffusion area information of harmful substances in water pollution, specifically including: taking the area information of the interception of the source and the remediation of water pollution as the center, dividing the content of groundwater chemical substances according to the scope of the interception of the source and the remediation of water pollution by using the density clustering model, and obtaining the concentrated locations of different harmful substances, and the concentrated locations of different harmful substances correspond to the area information of the interception of the source and the remediation of water pollution;

[0029] In different terrains, the density clustering model is used to analyze the pollution diffusion trajectory of different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity data of historical groundwater pollution. According to the concentrated locations of different harmful substances, a bottom-up condensed hierarchical pollution diffusion trajectory analysis is performed to obtain the results of different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and pollution diffusion trajectories of historical groundwater pollution;

[0030] The hydrogeological characteristics are evaluated by statistically analyzing the concentrations of different harmful substances in the historical groundwater pollution, water flow velocity and direction, groundwater level changes, location and intensity of pollution sources, and pollution diffusion trajectory results, and obtaining the locations of groundwater pollution distribution sites with different hydrogeological characteristics (the locations of groundwater pollution distribution sites with different hydrogeological characteristics correspond to the concentrations of different harmful substances in the historical groundwater pollution, water flow velocity and direction, groundwater level changes, location and intensity of pollution sources, and pollution diffusion trajectory results); providing technical support for calculating the pollution diffusion rate for interception of different sources and remediation of water pollution, and updating and optimizing control measures.

[0031] Set the penetration time of groundwater in different terrain and soil porosity areas; obtain different groundwater pollution spatial distribution information according to the locations of groundwater pollution distribution with different hydrogeological characteristics and the area information of source interception and water pollution remediation areas;

[0032] The spatial distribution information of groundwater pollution of different types is combined with the infiltration time of groundwater in the areas of different terrain rock and soil porosity to calculate the water content of different terrain rock and soil porosity;

[0033] According to the porosity water content and porosity saturation of the rock and soil in different terrains, the pollution diffusion rate of the different source interception and water pollution remediation is calculated, and the control measures are updated and optimized to generate statistics on the effects of the different source interception and water pollution remediation and the diffusion area information of harmful substances in water pollution.

[0034] Preferably, according to the locations of groundwater pollution distribution with different hydrogeological characteristics, the pollution diffusion rate is calculated and the control measures are updated and optimized for the different source interception and water pollution remediation, so as to generate statistics on the effects of the different source interception and water pollution remediation and information on the diffusion area of ​​harmful substances in water pollution.

[0035] Preferably, the environmental management engineer sets the groundwater infiltration time of different terrain rock and soil porosity areas based on the characteristics of source interception and water pollution remediation; calculates the relative groundwater pollution distance between the locations of groundwater pollution distribution with different hydrogeological characteristics and the source interception and water pollution remediation area information, and obtains different groundwater pollution spatial distribution information, which is the relative groundwater pollution distance between the receiving position and the sound source position; uses the different groundwater pollution spatial distribution information as restriction information, and sequentially combines the groundwater infiltration time of different terrain rock and soil porosity areas to calculate the water content of different terrain rock and soil porosity, which corresponds to the different groundwater pollution spatial distribution information;

[0036] According to the porosity water content of the rock and soil in different terrains and the porosity saturation of the rock and soil in different terrains, the percentage of the porosity water content of the rock and soil in different terrains in the porosity saturation of the rock and soil in different terrains is determined respectively, and the pollution diffusion rate of the different source interception and water pollution remediation is calculated, and the control measures are updated and optimized according to the percentage, and the statistics of the effects of the different source interception and water pollution remediation and the diffusion area information of harmful substances in water pollution are generated, and the different source interception, water pollution remediation and the diffusion area information of harmful substances in water pollution correspond to the different source interception and water pollution remediation, and the source interception, water pollution remediation and the diffusion area information of harmful substances in water pollution can be the number of turns of the porosity adjustment knob of the rock and soil in different terrains, which provides support for efficient pollution diffusion rate calculation and updating and optimization of control measures for different source interception and water pollution remediation.

[0037] Step S300, obtaining water recharge and discharge information of groundwater pollution areas; using the water recharge and discharge information of groundwater pollution areas and the historical groundwater pollution concentrations of different harmful substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity data, to calculate the pollution diffusion rate of water recharge and discharge calculation models for different groundwater pollution areas, and to update and optimize the control measures, and to generate pollution diffusion rate calculation of water recharge and discharge calculation models for different groundwater pollution areas, and water pollution harmful substance diffusion area information;

[0038] Based on the water recharge and discharge information of the groundwater pollution area, the density clustering model is used to divide the groundwater chemical content to obtain the concentrated locations of different hazardous substances;

[0039] According to the different hazardous substance concentration locations, the density clustering model is used to analyze the pollution diffusion trajectory of the historical groundwater pollution different hazardous substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity data to obtain the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the different hazardous substance concentration locations;

[0040] According to the results of groundwater pollution diffusion trajectories based on the hydrogeological characteristics of the locations where the different hazardous substances are concentrated, the pollution diffusion rate is calculated and the control measures are updated and optimized for the water body recharge and discharge calculation models in the different groundwater pollution areas, so as to generate the pollution diffusion rate calculation and water pollution harmful substance diffusion area information of the water body recharge and discharge calculation models in the different groundwater pollution areas.

[0041] Preferably, there are water body recharge and discharge calculation models for different groundwater pollution areas in different terrains, and the water body recharge and discharge information of the groundwater pollution area is obtained. The water body recharge and discharge information of the groundwater pollution area includes the spatial position information of the water body recharge and discharge calculation models of different groundwater pollution areas in different terrains, so as to provide data support for groundwater pollution prediction; the water body recharge and discharge information of the groundwater pollution area and the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data are used to predict the groundwater pollution area. The water body recharge and discharge calculation model calculates the pollution diffusion rate, updates and optimizes the control measures, generates pollution diffusion rate calculations for different groundwater pollution areas, and water pollution harmful substance diffusion area information for water body recharge and discharge calculation models, specifically including taking the groundwater pollution area water body recharge and discharge information as the center, dividing the groundwater chemical substance content using a density clustering model according to the groundwater pollution area water body recharge and discharge calculation model, which can clearly identify the coverage range, and obtaining the concentrated locations of different hazardous substances, which correspond to the water body recharge and discharge information of the groundwater pollution area;

[0042] Within the same room with different terrains, the density clustering model is used to analyze the pollution diffusion trajectory of different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity data of the historical groundwater pollution. A bottom-up condensed hierarchical pollution diffusion trajectory analysis is performed according to the different hazardous substance concentration locations to obtain the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the different hazardous substance concentration locations (the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the hazardous substance concentration locations include the recharge and discharge of the groundwater pollution water bodies with hydrogeological characteristics in the different hazardous substance concentration locations);

[0043] According to the results of groundwater pollution diffusion trajectory of the hydrogeological characteristics of the concentrated locations of different hazardous substances, the pollution diffusion rate is calculated, and the control measures are updated and optimized for the water body recharge and discharge calculation models of the different groundwater pollution areas, so as to generate the pollution diffusion rate calculation of the water body recharge and discharge calculation models of the different groundwater pollution areas and the water pollution harmful substance diffusion area information. The groundwater pollution area water body recharge and discharge calculation model pollution diffusion rate calculation and water pollution harmful substance diffusion area information can be the groundwater pollution area water body recharge and discharge rate update optimization results, brightness update optimization results, color contrast update optimization results, which provides technical support for the pollution diffusion rate calculation and control measures update optimization for the water body recharge and discharge calculation models of different groundwater pollution areas.

[0044] Obtaining water recharge, discharge pollution diffusion rate calculation and control measures for the groundwater pollution area, wherein the water recharge, discharge pollution diffusion rate calculation and control measures for the groundwater pollution area include statistical parameters of groundwater flow patterns;

[0045] According to the hydrogeological characteristics of the different hazardous substances concentrated locations, the groundwater pollution diffusion trajectory results are selected from the water body recharge and discharge calculation models of the different groundwater pollution areas, and the different preset groundwater flow patterns are used to calculate the water body recharge and discharge calculation models of the groundwater pollution areas;

[0046] According to the different preset groundwater flow patterns, the water recharge and discharge calculation models for the groundwater pollution area are statistically analyzed to generate pollution diffusion rate calculations and water pollution harmful substance diffusion area information for the different groundwater pollution area water recharge and discharge calculation models.

[0047] Preferably, if there is no groundwater pollution with hydrogeological characteristics in some locations, then there is no need for groundwater flow pattern statistics and groundwater pollution area water body recharge and discharge calculation models. If there is groundwater pollution with hydrogeological characteristics in some locations, then groundwater flow pattern statistics are used, that is, different groundwater pollution area water body recharge and discharge calculation models are used to calculate the pollution diffusion rate and the diffusion area information of harmful substances in water body pollution. According to the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of different hazardous substances concentrated locations, the pollution diffusion rate of the water body recharge and discharge calculation models of the different groundwater pollution areas is calculated, and the control measures are updated and optimized to generate the pollution diffusion rate calculation of the water body recharge and discharge calculation models of the different groundwater pollution areas and the diffusion area information of harmful substances in water body pollution, and also include:

[0048] Obtaining water recharge, discharge pollution diffusion rate calculation and control measures for the groundwater pollution area, wherein the water recharge, discharge pollution diffusion rate calculation and control measures for the groundwater pollution area include statistical parameters of groundwater flow patterns;

[0049] According to the hydrogeological characteristics of the different hazardous substance concentration locations, the groundwater pollution diffusion trajectory results are selected from the different groundwater pollution area area water body recharge and discharge calculation models, and the different preset groundwater flow pattern statistics groundwater pollution area area water body recharge and discharge calculation models are screened. The different preset groundwater flow pattern statistics groundwater pollution area area water body recharge and discharge calculation models are groundwater pollution area area water body recharge and discharge calculation models that receive the groundwater flow pattern statistical parameters; according to the groundwater flow pattern statistical parameters, the groundwater flow pattern statistics of the different preset groundwater flow pattern statistics groundwater pollution area area water body recharge and discharge calculation models generate the pollution diffusion rate calculation of the different groundwater pollution area area water body recharge and discharge calculation models, and the water pollution harmful substance diffusion area information. , the pollution diffusion rate calculation of the groundwater pollution area area water body recharge and discharge calculation model, and the water pollution harmful substance diffusion area information can be the groundwater pollution area area water body recharge and discharge rate update optimization result (that is, it is necessary to first calculate the groundwater pollution area area water body recharge and discharge calculation model within the groundwater flow pattern statistics restriction range, and then calculate the pollution diffusion rate and the water pollution harmful substance diffusion area information according to different groundwater pollution area area water body recharge and discharge calculation models, calculate the pollution diffusion rate of the groundwater pollution area area water body recharge and discharge calculation model, and update and optimize the control measures. If there is no hydrogeological characteristics of groundwater pollution, there is no need for the groundwater flow pattern statistics groundwater pollution area area water body recharge and discharge calculation model), reducing unnecessary groundwater pollution area area water body recharge and discharge costs.

[0050] Step S400, based on the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, pollution diffusion rate calculation of water body recharge and discharge calculation models in different groundwater pollution areas, and diffusion area information of harmful substances in water pollution, graph neural network groundwater pollution big data prediction is performed.

[0051] Preferably, the graph neural network groundwater pollution big data prediction includes:

[0052] 1. Data collection and integration, historical data: including the concentration, flow rate, and direction of different harmful substances in groundwater pollution. Groundwater level change data. Pollution source location and intensity data. Real-time data: obtain real-time pollution information through the dynamic groundwater pollution information sensor platform. Remediation data: area and effect statistics of the pollution remediation area, and information on different governance measures. Form a comprehensive data set through multi-source data fusion and build a graph structure containing node features and edge information.

[0053] 2. Build a graph neural network model. The definition of the graph is as follows: Nodes: represent pollution modules, such as monitoring points or regions. Node features: pollutant concentration, water recharge, discharge, pollution source intensity, etc. Edges: represent the connection between regions, such as groundwater flow path, diffusion direction, etc. Edge features: groundwater flow rate, distance, permeability coefficient, etc. Graph neural network type: select an appropriate graph neural network model (such as GCN, GraphSAGE, GAT) to process graph data.

[0054] 3. Model input and feature extraction, input features: node feature matrix: including pollutant concentration, water flow speed, direction, water level change, etc. Graph adjacency matrix: defines the connection relationship between nodes. Feature processing: Use graph neural network to extract node embeddings to capture the characteristics of pollution propagation. Update node status and learn the dynamic trend of pollution diffusion.

[0055] 4. Diffusion trend calculation, prediction target: Calculate the diffusion speed, range and concentration of pollutants. Simulate the impact of different pollution remediation measures on diffusion trends. Propagation simulation: Use time series prediction (such as methods based on time series graph neural networks) to simulate the changes in pollution diffusion over time. Comprehensively consider dynamic factors such as groundwater recharge and discharge.

[0056] 5. Model training and optimization, training data: historical observation data is used as training set. Pollution diffusion information at different time points. Loss function: use mean square error (MSE) or other regression loss to minimize the difference between the predicted value and the true value. Optimization method: use Adam or SGD optimizer. Select the best model parameters through cross validation.

[0057] 6. Output and analysis, prediction results: Time series changes in the pollution module area, pollutant concentration and diffusion range. Simulation results of the optimization plan for governance measures. Visualization: Map showing the trend of pollution diffusion. Dynamic change simulation diagram.

[0058] 7. Application and decision support, pollution control optimization: Optimize pollution control measures based on prediction results. Provide statistical support for groundwater remediation effects. Early warning system: Use models to predict diffusion speed and range, and issue pollution warnings in advance. Dynamic update: The model can be updated through real-time data to continuously improve prediction accuracy.

[0059] When the prediction of groundwater pollution big data by graph neural network through the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, and pollution diffusion rate calculation of water body recharge and discharge calculation models for different groundwater pollution areas, and diffusion area information of harmful substances in water pollution fails to reach the preset accuracy, the parameters of the source interception and water pollution remediation historical data that fail to reach the preset accuracy and the water body recharge and discharge calculation model for the groundwater pollution area that fail to reach the preset accuracy are obtained;

[0060] Adjust the model parameters of the source interception and water pollution remediation historical data that do not reach the preset accuracy, and the parameters of the water body recharge and discharge calculation model for the groundwater pollution area that does not reach the preset accuracy, and calculate them multiple times to obtain the variance of the multiple calculation results;

[0061] When the variance of the multiple calculation results does not reach the preset accuracy, a density clustering model is generated to monitor the fault signal;

[0062] The density clustering model monitoring fault signal is sent to the groundwater monitoring human-computer interaction terminal.

[0063] Preferably, according to the different source interception, water pollution remediation effect statistics, and water pollution harmful substance diffusion area information, and the different groundwater pollution area water body recharge, discharge calculation model pollution diffusion rate calculation, and water pollution harmful substance diffusion area information, the source interception, water pollution remediation and groundwater pollution area water body recharge, discharge calculation model in different terrains are used to perform graph neural network groundwater pollution big data prediction, to provide support for the realization of graph neural network groundwater pollution big data prediction, and also include: when the different source interception, water pollution remediation effect statistics, and water pollution harmful substance diffusion area information, and the different groundwater pollution area water body recharge, discharge calculation model pollution diffusion rate calculation, and water pollution harmful substance diffusion area information are used to perform graph neural network groundwater pollution big data prediction. When the prediction fails to reach the preset accuracy, the parameters of the calculation model for source interception that fails to reach the preset accuracy, historical data on water pollution remediation, and area water recharge and discharge for groundwater pollution areas that fail to reach the preset accuracy are obtained; the model parameters are adjusted and calculated multiple times for the parameters of the calculation model for source interception that fails to reach the preset accuracy, historical data on water pollution remediation, and area water recharge and discharge for groundwater pollution areas that fail to reach the preset accuracy, and the variance of the multiple calculation results is obtained by looping 10 times; if the variance of the multiple calculation results is looped 10 times and the variance of the multiple calculation results does not reach the preset accuracy, a density clustering model monitoring fault signal is generated; the density clustering model monitoring fault signal is sent to the groundwater monitoring human-computer interaction terminal to provide support for relevant technical personnel to carry out equipment maintenance in a timely manner.

[0064] When the variance of the results of multiple calculations does not reach the preset accuracy, the concentration location of hazardous substances in the density clustering model is obtained according to the source interception that does not reach the preset accuracy, the historical data of water pollution remediation, and the parameters of the water body recharge and discharge calculation model of the groundwater pollution area that does not reach the preset accuracy;

[0065] The data collection location of groundwater pollution with hydrogeological characteristics is replaced according to the concentration location of hazardous substances in the density clustering model.

[0066] Preferably, if the variance of multiple calculation results is looped and executed 10 times, and the variance of multiple calculation results does not reach the preset accuracy, it indicates that there may be hidden dangers of groundwater pollution area water body recharge, discharge calculation model failure, source interception, and water pollution remediation failure. According to the source interception and water pollution remediation historical data that do not reach the preset accuracy, and the parameters of the groundwater pollution area water body recharge and discharge calculation model that do not reach the preset accuracy, the density clustering model's hazardous substance concentration position is obtained, and the density clustering model's hazardous substance concentration position is unable to provide groundwater pollution area area water body recharge, discharge services, and different terrain rock and soil irrigation service location; according to the density clustering model's hazardous substance concentration position, the data collection position of the hydrogeological characteristic groundwater pollution is replaced to provide technical support for ensuring the groundwater pollution area area water body recharge, discharge services and different terrain rock and soil irrigation services.

[0067] In summary, the method and system for predicting groundwater pollution diffusion trend provided by the embodiments of the present application have the following technical effects:

[0068] The present application provides a method and system for predicting the spread trend of groundwater pollution, which realizes the calculation of pollution spread rate, updating and optimizing the treatment measures for source interception, water pollution remediation and groundwater pollution area water body recharge and discharge calculation model according to the concentration of different harmful substances in historical groundwater pollution in different terrains, water flow speed and direction, groundwater level changes, pollution source location and intensity, and improves the technical effect of improving the calculation of pollution spread rate and treatment measures for groundwater pollution. By adopting the method of obtaining the area water body recharge and discharge pollution spread rate calculation and treatment measures for groundwater pollution area; according to the hydrogeological characteristics of different hazardous substances concentrated locations, the groundwater pollution diffusion trajectory results are selected from the area water body recharge and discharge calculation models of different groundwater pollution areas, and the statistical groundwater pollution area water body recharge and discharge calculation models of different groundwater pollution areas are generated. The area water body recharge and discharge calculation model of different groundwater pollution areas generates pollution spread rate calculation and water pollution harmful substance diffusion area information, thereby reducing unnecessary groundwater pollution area water body recharge and discharge costs. The present invention can not only solve the problem of insufficient data collection, but also significantly improve prediction accuracy, providing a more effective scientific basis for pollution control.

[0069] Based on the same inventive concept as the method for predicting the diffusion trend of groundwater pollution in the above-mentioned embodiment, Figure 2 As shown, the embodiment of the present application provides a groundwater pollution diffusion trend prediction system, the system comprising:

[0070] The groundwater pollution data collection module collects groundwater pollution diffusion information through a multi-modal groundwater pollution information sensor platform, wherein the groundwater pollution diffusion information includes the concentration of different harmful substances in historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source location and intensity data;

[0071] The pollution diffusion rate information acquisition module is used to obtain the area information of source interception and water pollution remediation area;

[0072] A control measure updating and optimization module is used to calculate the pollution diffusion rate of different source interception and water pollution remediation, update and optimize the control measures, and generate statistics on the effects of different source interception and water pollution remediation, as well as the diffusion area information of harmful substances in water pollution by using the source interception and water pollution remediation area information and the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data;

[0073] Water body recharge and discharge information acquisition module, used to obtain water body recharge and discharge information in the groundwater pollution area;

[0074] A groundwater pollution diffusion area calculation module is used to calculate the pollution diffusion rate of water body recharge and discharge calculation models for different groundwater pollution areas, update and optimize the control measures, and generate pollution diffusion rate calculation of water body recharge and discharge calculation models for different groundwater pollution areas, and information on the diffusion area of ​​harmful substances in water pollution;

[0075] The graph neural network groundwater pollution big data prediction module is used to perform graph neural network groundwater pollution big data prediction based on the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, pollution diffusion rate calculation of water body recharge and discharge calculation models in different groundwater pollution areas, and diffusion area information of harmful substances in water pollution.

[0076] Furthermore, the system also includes:

[0077] A first hazardous substance concentrated location acquisition module is used to divide the groundwater chemical substance content using a density clustering model according to the source interception and water pollution remediation area information, and obtain different hazardous substance concentrated locations, wherein the different hazardous substance concentrated locations correspond to the source interception and water pollution remediation area information;

[0078] The first pollution diffusion trajectory analysis module is used to perform pollution diffusion trajectory analysis on the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity data using a density clustering model according to the different hazardous substance concentration locations, and obtain historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and pollution diffusion trajectory results;

[0079] The hydrogeological characteristic evaluation module is used to evaluate the hydrogeological characteristics by collecting statistics on the concentration of different harmful substances in the historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and pollution diffusion trajectories, and obtain the locations of groundwater pollution distribution with different hydrogeological characteristics;

[0080] The final update optimization module is used to calculate the pollution diffusion rate, update and optimize the control measures for the different source interception and water pollution remediation according to the locations of groundwater pollution distribution with different hydrogeological characteristics, and generate statistics on the effects of different source interception and water pollution remediation, as well as information on the diffusion area of ​​harmful substances in water pollution.

[0081] The module for setting the infiltration time of groundwater in the porosity area is used to set the infiltration time of groundwater in the porosity area of ​​different terrains and soils;

[0082] A groundwater pollution spatial distribution information acquisition module is used to obtain different groundwater pollution spatial distribution information according to the locations of groundwater pollution distribution locations with different hydrogeological characteristics and the area information of source interception and water pollution remediation areas;

[0083] A module for obtaining the porosity and water content of rock and soil in different terrains is used to combine the spatial distribution information of groundwater pollution with the penetration time of groundwater in the area of ​​the rock and soil porosity in different terrains to calculate the porosity and water content of rock and soil in different terrains;

[0084] A dynamic hazardous substance concentration location acquisition module is used to divide the groundwater chemical substance content using a density clustering model according to the water body recharge and discharge information of the groundwater pollution area, and obtain the concentration locations of different hazardous substances;

[0085] The dynamic pollution diffusion trajectory analysis module is used to perform pollution diffusion trajectory analysis on the concentrations of different harmful substances in the historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source locations and intensity data using a density clustering model according to the different hazardous substance concentration locations, and obtain the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the different hazardous substance concentration locations; based on the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the different hazardous substance concentration locations, the pollution diffusion rate is calculated for the water body recharge and discharge calculation models of the different groundwater pollution areas, and the control measures are updated and optimized, so as to generate the pollution diffusion rate calculation of the water body recharge and discharge calculation models of the different groundwater pollution areas, and the water pollution harmful substance diffusion area information.

[0086] A module for obtaining water recharge, discharge pollution diffusion rate calculation, and control measures for groundwater pollution areas, which is used to obtain water recharge, discharge pollution diffusion rate calculation, and control measures for groundwater pollution areas, wherein the water recharge, discharge pollution diffusion rate calculation, and control measures for groundwater pollution areas include statistical parameters of groundwater flow patterns;

[0087] A model calculation module is used to select different preset groundwater flow patterns to calculate the groundwater pollution area water body recharge and discharge calculation models from the water body recharge and discharge calculation models of the different groundwater pollution areas according to the hydrogeological characteristics of the different hazardous substances concentrated locations and the groundwater pollution diffusion trajectory results;

[0088] The water body recharge and discharge calculation module is used to calculate the water body recharge and discharge calculation model of the groundwater pollution area according to the different preset groundwater flow patterns, and generate the pollution diffusion rate calculation of the water body recharge and discharge calculation model of the different groundwater pollution area areas, and the diffusion area information of harmful substances in water pollution.

[0089] The module for obtaining the preset accuracy is used to obtain the parameters of the source interception that fails to reach the preset accuracy, the water pollution remediation historical data, and the groundwater pollution area water supply and discharge calculation model that fails to reach the preset accuracy when the graph neural network groundwater pollution big data prediction fails to reach the preset accuracy through the different source interception, water pollution remediation effect statistics, and the water pollution harmful substance diffusion area information and the different groundwater pollution area water supply and discharge calculation model pollution diffusion rate calculation and water pollution harmful substance diffusion area information;

[0090] The variance module of multiple calculation results is used to adjust the model parameters of the source interception that does not reach the preset accuracy, the historical data of water pollution remediation, and the parameters of the groundwater pollution area area water supply and discharge calculation model that does not reach the preset accuracy, and calculate them multiple times to obtain the variance of the multiple calculation results;

[0091] A density clustering model monitoring fault signal generation module is used to generate a density clustering model monitoring fault signal when the variance of multiple calculation results does not reach a preset accuracy;

[0092] The density clustering model monitoring fault signal sending module is used to send the density clustering model monitoring fault signal to the groundwater monitoring human-computer interaction terminal.

[0093] The density clustering model hazardous substance concentration location acquisition module is used to obtain the density clustering model hazardous substance concentration location according to the source interception that does not reach the preset accuracy, the water pollution remediation historical data, and the parameters of the groundwater pollution area water supply and discharge calculation model that does not reach the preset accuracy when the variance of multiple calculation results does not reach the preset accuracy;

[0094] The data collection position replacement module is used to replace the data collection position of groundwater pollution with hydrogeological characteristics according to the concentration position of hazardous substances in the density clustering model.

[0095] Further, it is obvious that those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

[0096] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent 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 method for predicting the diffusion trend of groundwater pollution, characterized in that: The method includes: Collect groundwater pollution diffusion information through a multimodal groundwater pollution information sensor platform, including historical groundwater pollution concentrations of different harmful substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity data; Obtaining source interception and water pollution remediation area information; using the source interception and water pollution remediation area information and the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data, calculating the pollution diffusion rate for different source interception and water pollution remediation, updating and optimizing the control measures, and generating different source interception, water pollution remediation effect statistics and water pollution harmful substance diffusion area information; Obtaining water recharge and discharge information for groundwater pollution areas; using the water recharge and discharge information for groundwater pollution areas and the historical groundwater pollution data on different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity, to calculate the pollution diffusion rate for different groundwater pollution areas and to update and optimize control measures for water recharge and discharge calculation models, and to generate pollution diffusion rate calculations for different groundwater pollution areas and water pollution harmful substance diffusion area information for water recharge and discharge calculation models; According to the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, pollution diffusion rate calculation of water body recharge and discharge calculation model in different groundwater pollution areas, and diffusion area information of harmful substances in water pollution, the graph neural network groundwater pollution big data prediction is performed; The monitoring points or areas are taken as nodes, pollutant concentration, water recharge, discharge, and pollution source intensity are taken as node features, groundwater flow path and diffusion direction are taken as edges, and groundwater flow velocity, distance, and permeability coefficient are taken as edge features; the node feature matrix is ​​taken as input feature, and the node feature matrix includes pollutant concentration, water flow velocity, direction, and water level change. The connection relationship between nodes is taken as the adjacency matrix of the graph, and the graph neural network is used to extract node embedding to capture the characteristics of pollution propagation; the diffusion speed, range, and concentration of pollutants are taken as the output parameters of the graph neural network.

2. A method for predicting the diffusion trend of groundwater pollution according to claim 1, characterized in that: By using the source interception, water pollution remediation area information and the historical groundwater pollution different harmful substances concentration, water flow speed and direction, groundwater level change, pollution source location and intensity data, the pollution diffusion rate of different source interception and water pollution remediation is calculated, and the control measures are updated and optimized, and the statistics of different source interception, water pollution remediation effects and water pollution harmful substances diffusion area information are generated, including: According to the area information of the source interception and water pollution remediation area, the content of groundwater chemical substances is divided using a density clustering model to obtain the concentrated locations of different hazardous substances, and the concentrated locations of different hazardous substances correspond to the area information of the source interception and water pollution remediation area; According to the concentrated locations of the different hazardous substances, the density clustering model is used to analyze the pollution diffusion trajectory of the historical groundwater pollution data on the concentrations of different hazardous substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and obtain the results of historical groundwater pollution concentrations of different hazardous substances, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and pollution diffusion trajectory; The hydrogeological characteristics are evaluated by statistically analyzing the concentrations of different harmful substances in the historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source locations and intensity, and pollution diffusion trajectories, and obtaining the locations of groundwater pollution distribution with different hydrogeological characteristics. According to the locations of groundwater pollution distribution with different hydrogeological characteristics, the pollution diffusion speed is calculated for the interception of different sources and water pollution remediation, and the control measures are updated and optimized to generate statistics on the effects of interception of different sources and water pollution remediation, as well as information on the diffusion area of ​​harmful substances in water pollution.

3. A method for predicting the diffusion trend of groundwater pollution according to claim 2, characterized in that: According to the locations of groundwater pollution distribution with different hydrogeological characteristics, the pollution diffusion rate of the different source interception and water pollution remediation is calculated, and the control measures are updated and optimized to generate statistics on the effects of the different source interception and water pollution remediation, as well as the diffusion area information of harmful substances in water pollution, including: Set the penetration time of groundwater in different terrain and soil porosity areas; obtain different groundwater pollution spatial distribution information according to the locations of groundwater pollution distribution with different hydrogeological characteristics and the area information of source interception and water pollution remediation areas; The spatial distribution information of different groundwater pollution is combined with the penetration time of groundwater in the area of ​​rock and soil porosity in different terrains to calculate the water content of rock and soil porosity in different terrains; based on the water content of rock and soil porosity in different terrains and the porosity saturation of rock and soil in different terrains, the pollution diffusion rate of the different source interception and water pollution remediation is calculated, and the control measures are updated and optimized to generate statistics on the effects of different source interception and water pollution remediation and information on the diffusion area of ​​harmful substances in water pollution.

4. A method for predicting the diffusion trend of groundwater pollution according to claim 1, characterized in that: Using the water body recharge and discharge information of the groundwater pollution area and the concentration of different harmful substances in the historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source location and intensity data, the water body recharge and discharge calculation models of different groundwater pollution areas are used to calculate the pollution diffusion rate, update and optimize the control measures, and generate the pollution diffusion rate calculation of the water body recharge and discharge calculation models of different groundwater pollution areas and the diffusion area information of harmful substances in water pollution, including: Based on the water recharge and discharge information of the groundwater pollution area, the density clustering model is used to divide the groundwater chemical content to obtain the concentrated locations of different hazardous substances; based on the concentrated locations of different hazardous substances, the density clustering model is used to analyze the pollution diffusion trajectory of the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source locations and intensity data to obtain the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of different hazardous substance concentrated locations; According to the results of groundwater pollution diffusion trajectories based on the hydrogeological characteristics of the locations where the different hazardous substances are concentrated, the pollution diffusion rate is calculated and the control measures are updated and optimized for the water body recharge and discharge calculation models in the different groundwater pollution areas, so as to generate the pollution diffusion rate calculation and water pollution harmful substance diffusion area information of the water body recharge and discharge calculation models in the different groundwater pollution areas.

5. A method for predicting the diffusion trend of groundwater pollution according to claim 4, characterized in that: According to the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the different hazardous substances concentrated locations, the pollution diffusion rate of the water body recharge and discharge calculation models of the different groundwater pollution areas is calculated, and the control measures are updated and optimized, and the pollution diffusion rate calculation of the water body recharge and discharge calculation models of the different groundwater pollution areas and the diffusion area information of water pollution harmful substances are generated, including: Obtaining the calculation of the diffusion rate of water body recharge and discharge pollution and treatment measures for the groundwater pollution area, wherein the calculation of the diffusion rate of water body recharge and discharge pollution and treatment measures for the groundwater pollution area include statistical parameters of the flow pattern of groundwater; selecting different preset groundwater flow pattern statistics of the groundwater pollution area water body recharge and discharge calculation models from the water body recharge and discharge calculation models for the different groundwater pollution areas according to the groundwater pollution diffusion trajectory results of the hydrogeological characteristics of the concentrated locations of the different hazardous substances; According to the different preset groundwater flow patterns, the water recharge and discharge calculation models for the groundwater pollution area are statistically analyzed to generate pollution diffusion rate calculations and water pollution harmful substance diffusion area information for the different groundwater pollution area water recharge and discharge calculation models.

6. A method for predicting the diffusion trend of groundwater pollution according to claim 1, characterized in that: Also includes: When the prediction of groundwater pollution big data by graph neural network through the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, and pollution diffusion rate calculation of water body recharge and discharge calculation models for different groundwater pollution areas, and diffusion area information of harmful substances in water pollution fails to reach the preset accuracy, the parameters of the source interception and water pollution remediation historical data that fail to reach the preset accuracy and the water body recharge and discharge calculation model for the groundwater pollution area that fail to reach the preset accuracy are obtained; Adjust the model parameters of the source interception and water pollution remediation historical data that do not reach the preset accuracy, and the parameters of the water body recharge and discharge calculation model for the groundwater pollution area that does not reach the preset accuracy, and calculate them multiple times to obtain the variance of the multiple calculation results; When the variance of the multiple calculation results does not reach the preset accuracy, a density clustering model monitoring fault signal is generated; and the density clustering model monitoring fault signal is sent to the groundwater monitoring human-computer interaction terminal; When the variance of multiple calculation results fails to reach the preset accuracy, the concentration location of hazardous substances in the density clustering model is obtained based on the historical data of source interception and water pollution remediation that fail to reach the preset accuracy, and the parameters of the water recharge and discharge calculation model for the groundwater pollution area that fails to reach the preset accuracy; the data collection location of the hydrogeological characteristic groundwater pollution is replaced according to the concentration location of hazardous substances in the density clustering model.

7. A groundwater pollution diffusion trend prediction system, characterized in that: The system includes: The groundwater pollution data collection module collects groundwater pollution diffusion information through a multi-modal groundwater pollution information sensor platform, wherein the groundwater pollution diffusion information includes the concentration of different harmful substances in historical groundwater pollution, water flow speed and direction, groundwater level changes, pollution source location and intensity data; The pollution diffusion rate information acquisition module is used to obtain the area information of source interception and water pollution remediation area; A control measure updating and optimization module is used to calculate the pollution diffusion rate of different source interception and water pollution remediation, update and optimize the control measures, and generate statistics on the effects of different source interception and water pollution remediation, as well as the diffusion area information of harmful substances in water pollution by using the source interception and water pollution remediation area information and the historical groundwater pollution different harmful substance concentrations, water flow speed and direction, groundwater level changes, pollution source location and intensity data; Water body recharge and discharge information acquisition module, used to obtain water body recharge and discharge information in the groundwater pollution area; A groundwater pollution diffusion area calculation module is used to calculate the pollution diffusion rate of water body recharge and discharge calculation models for different groundwater pollution areas, update and optimize the control measures, and generate pollution diffusion rate calculation of water body recharge and discharge calculation models for different groundwater pollution areas, and information on the diffusion area of ​​harmful substances in water pollution; The graph neural network groundwater pollution big data prediction module is used to perform graph neural network groundwater pollution big data prediction based on the statistics of interception at different sources, water pollution remediation effects, diffusion area information of harmful substances in water pollution, pollution diffusion rate calculation of water body recharge and discharge calculation models in different groundwater pollution areas, and diffusion area information of harmful substances in water pollution.

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