Pollution plume classification methods, systems, equipment and storage media

Abstract

This invention provides a method, system, device, and storage medium for classifying pollution plumes, belonging to the field of intelligent groundwater pollution management technology. The pollution plume classification method includes: acquiring point pollution data; dividing the point pollution data into multiple independent pollution plumes; performing pollution migration simulation on the pollution plumes to obtain corresponding simulated pollution plumes; predicting the duration for which the concentration of the simulated pollution plume at the plant boundary exceeds a preset concentration, obtaining a duration prediction result; and classifying the pollution plumes based on the duration prediction result. This invention considers the changes in pollution plumes over time, performs migration simulation on the divided pollution plumes, and predicts the duration for which the concentration exceeds the standard at the plant boundary, simulating and predicting the future pollution trend of the pollution plumes to classify them. This improves the accuracy and precision of pollution level classification, effectively narrows down the key areas for enterprise pollution management, and improves the efficiency of enterprise pollution management.

Description

Technical Field

[0001] This invention relates to the field of groundwater pollution management technology, specifically to a pollution plume classification method, a pollution plume classification system, an electronic device, and a readable storage medium. Background Technology

[0002] Based on the results of the first round of national surveys on soil and groundwater pollution, groundwater pollution is widespread in industrial enterprises, especially in the oil and petrochemical industries, where the detection rate of organic pollution in groundwater is high, necessitating urgent control and remediation. Since pollution distribution across an enterprise is often uneven, and concentrations vary significantly across polluted areas, tiered management of pollution plumes can effectively reduce management workload and facilitate the concentration of control efforts, representing a future trend in enterprise site pollution management.

[0003] Existing groundwater pollution classification technologies are mainly based on the multi-factor pollution risk assessment classification of groundwater using the analytic hierarchy process (AHP), and focus on the regional assessment of groundwater and the assessment classification of enterprise sites. However, the above schemes do not consider time factors and the concentration at the plant boundary, resulting in low accuracy and reliability of pollution classification results, which is not conducive to enterprises concentrating their superior resources on pollution control. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and storage medium for classifying pollutant plumes to solve the aforementioned technical problems.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for classifying pollution plumes, comprising:

[0006] Acquire point pollution data; wherein, the point pollution data includes point pollution distribution data and point pollution concentration data;

[0007] Based on the point pollution data, multiple independent pollution plumes were identified;

[0008] The pollution plume was simulated to obtain the corresponding simulated pollution plume;

[0009] The duration for which the concentration in the simulated pollution plume's outlet boundary area exceeds a preset concentration is predicted, resulting in a duration prediction result; wherein, the outlet boundary area concentration is the pollution concentration in the area where the pollution plume exceeds the preset boundary.

[0010] Pollution plumes are classified based on duration prediction results.

[0011] Optionally, based on the point pollution data, multiple independent pollution plumes are divided, including:

[0012] The point pollution data is interpolated using the inverse distance interpolation method to obtain patch pollution distribution data and patch pollution concentration data;

[0013] Based on the sheet-like pollution distribution data and the sheet-like pollution concentration data, several areas with high pollution concentrations were identified.

[0014] Determine whether there is any overlap between adjacent areas with high pollution concentrations;

[0015] Adjacent high-concentration areas in non-overlapping regions are treated as independent pollution plumes.

[0016] Optionally, the method further includes:

[0017] If it is determined that there is an overlapping area between adjacent high-concentration pollution areas, then based on the sheet-like pollution concentration data, the maximum concentration of the overlapping area and the maximum concentration of the high-concentration area are determined; wherein, the maximum concentration of the high-concentration area is the maximum concentration of the high-concentration pollution area that includes the overlapping area.

[0018] The maximum concentration in the overlapping area is compared with the maximum concentration in the high-value area. If the maximum concentration in the overlapping area is less than a preset multiple of the maximum concentration in the high-value area, the high-value area containing the overlapping area is segmented to obtain an independent pollution plume.

[0019] Optionally, the method further includes:

[0020] If the maximum concentration in the overlapping area is greater than or equal to the maximum concentration in the high-value area by a preset multiple, then the high-value areas containing the overlapping area will be merged to obtain an independent pollution plume.

[0021] Optionally, the method further includes:

[0022] Based on the data on the distribution of pollution plumes and sheet-like pollution, as well as the concentration data of sheet-like pollution, the concentration of pollution plumes at the plant boundary is determined.

[0023] Determine whether the concentration of the pollutant plume at the factory boundary is greater than the preset concentration;

[0024] If the concentration of the pollutant plume at the plant's exit boundary is greater than the preset concentration, the pollutant plume will be classified as a Level 1 pollutant plume.

[0025] Optionally, the step of simulating pollution migration of the pollution plume to obtain the corresponding simulated pollution plume includes:

[0026] If the concentration of the contamination plume at the factory boundary is less than or equal to the preset concentration, the shape information and simulation parameters of the contamination plume are determined based on the defined contamination plume and the sheet-like contamination data.

[0027] Based on the shape information and simulation parameters of the pollution plume, a migration simulation of the pollution plume is performed to obtain the corresponding simulated pollution plume.

[0028] Optionally, the migration simulation of the contamination plume based on its shape information and simulation parameters to obtain a corresponding simulated contamination plume includes:

[0029] Based on the shape information and simulation parameters of the contamination plume, a migration simulation of the contamination plume is performed to obtain the corresponding contamination plume model.

[0030] Determine whether the fitting rate between the actual pollution concentration of the pollution plume and the simulated pollution concentration of the corresponding pollution plume model is greater than the preset fitting rate;

[0031] If so, the current migration simulation is considered successful, and the contamination plume model is saved as the simulated contamination plume corresponding to that contamination plume.

[0032] Optionally, classifying the pollution plume based on the duration prediction result includes:

[0033] If the predicted duration for which the concentration of the simulated pollution plume in the factory boundary area is greater than the preset concentration is less than the first duration, then the pollution plume corresponding to the simulated pollution plume is classified as a first-level pollution plume.

[0034] If the predicted duration for which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration is greater than the first duration but less than the second duration, then the pollution plume corresponding to the simulated pollution plume is classified as a pollution plume of the second level.

[0035] If the predicted duration for which the concentration in the simulated pollution plume at the plant boundary is greater than the preset concentration is greater than the second duration, then the pollution plume corresponding to the simulated pollution plume is classified as a third-level pollution plume.

[0036] In a second aspect of the present invention, a plume classification system is provided, the system comprising:

[0037] The data acquisition module is used to acquire point pollution data; wherein, the point pollution data includes point pollution distribution data and point pollution concentration data;

[0038] The pollution plume segmentation module is used to segment multiple independent pollution plumes based on the point pollution data;

[0039] The migration simulation module is used to simulate the migration of contaminant plumes and obtain the corresponding simulated contaminant plumes.

[0040] The duration prediction module is used to predict the duration during which the concentration of the simulated pollution plume in the boundary area is greater than a preset concentration, and to obtain the duration prediction result; wherein, the concentration in the boundary area is the pollution concentration in the area where the pollution plume exceeds the preset boundary.

[0041] The classification module is used to classify pollution plumes based on duration prediction results.

[0042] Optionally, the pollution plume segmentation module includes a data processing module and a region segmentation module;

[0043] The data processing module is used for:

[0044] The point pollution data is interpolated using the inverse distance interpolation method to obtain patch pollution distribution data and patch pollution concentration data;

[0045] Based on the sheet-like pollution distribution data and the sheet-like pollution concentration data, several areas with high pollution concentrations were identified.

[0046] The region division module is used for:

[0047] Determine whether there is any overlap between adjacent areas with high pollution concentrations;

[0048] Adjacent high-concentration areas in non-overlapping regions are treated as independent pollution plumes.

[0049] Optionally, the region division module is further configured to:

[0050] If it is determined that there is an overlapping area between adjacent high-concentration pollution areas, then based on the sheet-like pollution concentration data, the maximum concentration of the overlapping area and the maximum concentration of the high-concentration area are determined; wherein, the maximum concentration of the high-concentration area is the maximum concentration of the high-concentration pollution area that includes the overlapping area.

[0051] The maximum concentration in the overlapping area is compared with the maximum concentration in the high-value area: if the maximum concentration in the overlapping area is less than a preset multiple of the maximum concentration in the high-value area, the high-value area containing the overlapping area is segmented to obtain an independent pollution plume.

[0052] If the maximum concentration in the overlapping area is greater than or equal to the maximum concentration in the high-value area by a preset multiple, then the high-value areas containing the overlapping area will be merged to obtain an independent pollution plume.

[0053] Optionally, the migration simulation module is specifically used for:

[0054] Based on the segmented contamination plume and the sheet-like contamination data, the shape information and simulation parameters of the contamination plume are determined.

[0055] Based on the shape information and simulation parameters of the pollution plume, a migration simulation of the pollution plume is performed to obtain the corresponding simulated pollution plume.

[0056] Optionally, the level division module is specifically used for:

[0057] If the predicted duration for which the concentration of the simulated pollution plume in the factory boundary area is greater than the preset concentration is less than the first duration, then the pollution plume corresponding to the simulated pollution plume is classified as a first-level pollution plume.

[0058] If the predicted duration for which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration is greater than the first duration but less than the second duration, then the pollution plume corresponding to the simulated pollution plume is classified as a pollution plume of the second level.

[0059] If the predicted duration for which the concentration in the simulated pollution plume at the plant boundary is greater than the preset concentration is greater than the second duration, then the pollution plume corresponding to the simulated pollution plume is classified as a third-level pollution plume.

[0060] A third aspect of this application provides an electronic device configured to perform the above-described pollutant plume classification method.

[0061] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, are configured by the processor to perform the above-described contamination plume classification method.

[0062] This invention acquires point pollution data, identifies pollution plumes based on this data, and then simulates the migration of these plumes to obtain simulated pollution plumes. The duration for which the concentration of the simulated pollution plume exceeds a preset concentration at the plant boundary is predicted, yielding a duration prediction result. Based on this duration prediction result, the pollution plumes are classified into different levels. In other words, this invention classifies pollution plumes into levels, effectively narrowing down the key areas for enterprise pollution management. By considering the changes in pollution plumes over time, it simulates the migration of the identified plumes to predict future pollution trends, predicts the duration for which the concentration exceeds the standard at the plant boundary, and classifies the pollution plumes based on the duration prediction result. This improves the accuracy and precision of pollution level classification, facilitating groundwater pollution management for enterprises and increasing their pollution management efficiency.

[0063] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0064] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0065] Figure 1 This is a schematic flowchart of a pollution plume classification method provided in an embodiment of the present invention;

[0066] Figure 2This is a schematic diagram of the overall process of a pollution plume classification method provided in an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram showing the pollution plume distribution at Company A's site;

[0068] Figure 4 This is a schematic diagram of the pollution plume distribution at Company B's site;

[0069] Figure 5 This is a schematic diagram of the pollution plume at site C of enterprise;

[0070] Figure 6 This is a schematic diagram of the pollution plume at site D of enterprise;

[0071] Figure 7 This is a schematic diagram of the pollution plume at site E of enterprise;

[0072] Figure 8 This is a schematic diagram of the architecture of a pollution plume classification system provided in an embodiment of the present invention. Detailed Implementation

[0073] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this application.

[0075] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0076] Example 1

[0077] Please refer to Figure 1 , Figure 1 This is a schematic flowchart of a pollution plume classification method provided in this embodiment.

[0078] Step S100: Obtain point pollution data; wherein, point pollution data includes point pollution distribution data and point pollution concentration data.

[0079] Point-source pollution data refers to enterprise pollution data, containing only one type of pollution data. This can include point-source pollution distribution data and point-source pollution concentration data, as well as basic geological imagery, groundwater pollutant types, and a hydrogeological simulation parameter database. The hydrogeological simulation parameter database includes a public simulation database and an enterprise-specific hydrogeological database. The public simulation parameter database includes industry standards, guidelines, and literature. As a public dataset, the parameters in the public simulation parameter database represent empirical ranges within each standard, rather than single values, and these empirical ranges will not change in the simulation. The enterprise-specific database can be directly entered based on the lithological logging content in the site investigation report, without professional restrictions. The system automatically updates the enterprise-specific database data according to the enterprise's hydrogeological conditions.

[0080] Step S200: Based on the point pollution data, divide the data into multiple independent pollution plumes.

[0081] Specifically, this embodiment uses the inverse distance interpolation method to interpolate single-point pollution concentration data to obtain multiple sheet-like pollution data with an area of ​​not less than 10m*10m. The sheet-like pollution data includes sheet-like pollution distribution data and sheet-like pollution concentration data. Based on the sheet-like pollution distribution data and sheet-like pollution concentration data, multiple high-concentration pollution areas can be identified. Further, it is determined whether there are overlapping areas between the high-concentration pollution areas. If there are no overlapping areas, the adjacent high-concentration pollution areas without overlapping areas are regarded as independent pollution plumes. If there are overlapping areas, the maximum concentration of the overlapping area and the maximum concentration of the high-concentration area are determined based on the sheet-like pollution concentration data. The maximum concentration of the high-concentration area is the maximum concentration of the high-concentration pollution area containing the overlapping area. It is determined whether the maximum concentration of the overlapping area is less than a preset multiple of the maximum concentration of the high-concentration area. If the maximum concentration of the overlapping area is less than a preset multiple of the maximum concentration of the high-concentration area, the high-concentration pollution area corresponding to the overlapping area is segmented to obtain an independent pollution plume. If the maximum concentration of the overlapping area is greater than or equal to a preset multiple of the maximum concentration of the high-concentration area, the high-concentration pollution areas corresponding to the overlapping area are merged to obtain an independent pollution plume. The preset multiple can be set according to the company's pollution management requirements, and can be 50%.

[0082] It should be noted that in this embodiment, the maximum concentration of the leftmost high-concentration region among adjacent high-concentration regions is compared with the maximum concentration of the overlapping region to segment or merge adjacent high-concentration regions into independent pollution plumes. If a high-concentration region has overlapping areas on both its left and right sides with adjacent high-concentration regions, the left side of the high-concentration region is analyzed first, followed by the right side, to delineate an independent pollution plume.

[0083] Step S300: Simulate the pollution migration of the pollution plume to obtain the corresponding simulated pollution plume.

[0084] It should be understood that this embodiment aims to improve the efficiency of enterprise pollution management by classifying and grading enterprise pollution. Before step S300, it further includes determining the concentration of the pollution plume at the factory boundary based on the concentration data of the pollution plume and sheet-like pollution. The concentration at the factory boundary is the pollution concentration in the area of ​​the pollution plume that exceeds a preset boundary. For example, the preset boundary can be the boundary of the enterprise factory, and the concentration at the factory boundary is the pollution concentration in the part of the pollution plume that exceeds the boundary of the enterprise factory. After determining the concentration at the factory boundary, it is further determined whether the concentration at the factory boundary exceeds a preset concentration. If so, the pollution plume corresponding to the concentration at the factory boundary is classified as a first-level pollution plume; if not, based on the classified pollution plume and the sheet-like pollution data, the shape information and simulation parameter data of the pollution plume are determined. Based on the shape information and simulation parameters, the migration simulation of the pollution plume is performed to obtain a simulated pollution plume. The shape information of the pollution plume includes "I"-shaped pollution plumes and "L"-shaped pollution plumes.

[0085] This embodiment utilizes a water-soluble organic pollutant migration and diffusion model to simulate the migration of a pollutant plume, obtaining a simulated pollutant plume. Specifically, based on the plume shape information and simulation parameters, the water-soluble organic pollutant migration and diffusion model is used to simulate the migration of the plume, resulting in a plume model. The model then determines whether the fitting rate between the actual pollutant concentration in the plume and the simulated pollutant concentration in the plume model is greater than a preset fitting rate. If yes, the current migration simulation is considered successful, and the plume model is saved as a simulated plume. If not, the current migration simulation is considered a failure, and the simulation is repeated until the fitting rate between the actual pollutant concentration in the plume and the simulated pollutant concentration in the plume model is greater than a preset fitting rate. Finally, the plume model is saved as a simulated plume.

[0086] It should be noted that for Type I pollution plumes, a single migration simulation is sufficient. The migration simulation yields a Type I pollution plume model. The pollution concentrations at three simulated concentration sampling points uniformly distributed along the centerline of the Type I pollution plume model are taken. These three sampling points can be either measured or interpolated concentrations. The fit rate between the actual pollution concentrations at these three sampling points and the simulated pollution concentrations of the Type I pollution plume model is determined. If not, three more simulated concentration sampling points uniformly distributed along the centerline of the Type I pollution plume model are selected, and the fit rate between the actual pollution concentrations at these three sampling points and the simulated pollution concentrations of the Type I pollution plume model is determined again. If the actual pollution concentration at the three simulated concentration sampling points is greater than the preset fitting rate, the fitting rate of the simulated pollution concentration of the "I"-type pollution plume model is greater than the preset fitting rate. If so, the current pollution plume model is saved as a simulated pollution plume. If so, the current migration simulation is considered successful, and the current pollution plume model is used as a simulated pollution plume. For "L"-type pollution plumes, it is necessary to determine the two groundwater flow directions of the "L"-type pollution plume, divide the "L"-type pollution plume into two "I"-type pollution plumes, and perform migration simulations for the two "I"-type pollution plumes respectively. It is then determined whether the fitting rate of the simulated pollution concentration of the two "I"-type pollution plume models is greater than the preset fitting rate. If so, the current migration simulation is considered successful, and the current pollution plume model is used as a simulated pollution plume.

[0087] Step S400: Predict the duration for which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration, and obtain the duration prediction result; wherein, the concentration in the factory boundary area is the pollution concentration in the area where the pollution plume exceeds the preset boundary.

[0088] This embodiment can use a fully automated growth curve analyzer (bioscreen) to predict the time when the concentration in the discharge boundary area of ​​the simulated pollution plume exceeds the preset concentration, and obtain the predicted duration for the concentration in the discharge boundary area of ​​each simulated pollution plume to be greater than the preset concentration.

[0089] Step S500: Classify the pollution plume based on the duration prediction results.

[0090] Specifically, if the predicted duration for which the concentration of the simulated pollutant plume at the plant boundary is greater than the preset concentration is less than the first duration, then the pollutant plume corresponding to the simulated pollutant plume is classified as a first-level pollutant plume; if the predicted duration for which the concentration of the simulated pollutant plume at the plant boundary is greater than the preset concentration is greater than the first duration but less than the second duration, then the pollutant plume corresponding to the simulated pollutant plume is classified as a second-level pollutant plume; if the predicted duration for which the concentration of the simulated pollutant plume at the plant boundary is greater than the preset concentration is greater than the second duration, then the pollutant plume corresponding to the simulated pollutant plume is classified as a third-level pollutant plume.

[0091] For example, the first duration can be 2 years, and the second duration can be 8 years. According to the scheme of this application, if the predicted duration for which the concentration of the simulated pollution plume at the plant boundary is greater than the preset concentration is 1.5 years, then the pollution plume corresponding to the simulated pollution plume is classified as a first-level pollution plume; if the predicted duration for which the concentration of the simulated pollution plume at the plant boundary is greater than the preset concentration is 6 years, then the pollution plume corresponding to the simulated pollution plume is classified as a second-level pollution plume; if the predicted duration for which the concentration of the simulated pollution plume at the plant boundary is greater than the preset concentration is 9 years, then the pollution plume corresponding to the simulated pollution plume is classified as a third-level pollution plume.

[0092] Figure 2 This is a schematic diagram of the overall process of this embodiment. First, point pollution data is acquired. Inverse distance interpolation is used to interpolate the point pollution data to determine high-concentration pollution areas. It is then determined whether adjacent high-concentration pollution areas overlap. If there is overlap, it is determined whether the maximum concentration of the overlapping area is less than a preset multiple of the maximum concentration of the high-concentration area. If so, the high-concentration pollution area corresponding to the overlapping area is segmented to obtain an independent pollution plume. If not, the high-concentration pollution areas corresponding to the overlapping area are merged to obtain an independent pollution plume. If there is no overlap, adjacent high-concentration pollution areas are treated as independent pollution plumes. It is then determined whether the concentration at the factory boundary of the pollution plume is greater than a preset concentration. If so, the pollution plume is classified as a first-level pollution plume. If not, based on the shape information and simulation parameters of the pollution plume, pollution migration simulation is performed on the pollution plume. During the simulation, a simulation point is selected on the center line of the pollution plume. At the concentration sampling point, it is determined whether the fitting rate between the actual pollution concentration of the pollution plume and the simulated pollution concentration of the pollution plume model is greater than the preset fitting rate. If not, a new simulated pollution concentration is selected. If so, the pollution plume model is saved as a simulated pollution plume. The time for the concentration at the factory boundary of the simulated pollution plume to be greater than the preset concentration is further predicted to obtain the duration prediction result. If the predicted duration for the concentration at the factory boundary of the simulated pollution plume to be greater than the preset concentration is less than the first duration, the pollution plume corresponding to the simulated pollution plume is classified as a first-level pollution plume. If the predicted duration for the concentration at the factory boundary of the simulated pollution plume to be greater than the preset concentration is greater than the first duration but less than the second duration, the pollution plume corresponding to the simulated pollution plume is classified as a second-level pollution plume. If the predicted duration for the concentration at the factory boundary of the simulated pollution plume to be greater than the preset concentration is greater than the second duration, the pollution plume corresponding to the simulated pollution plume is classified as a third-level pollution plume.

[0093] Example 1: Obtain point pollution data for site A of enterprise A. Use inverse distance interpolation to interpolate the point pollution data to obtain patchy pollution concentration data and patchy pollution distribution data. Further identify multiple high-concentration pollution areas, such as... Figure 3As shown, there is no overlap between two adjacent high-concentration pollution areas. Therefore, the adjacent high-concentration pollution areas are regarded as two independent pollution plumes. Both pollution plumes are "I" type pollution plumes. The preset concentration is set to 20. Based on the sheet pollution concentration data and sheet pollution distribution data, it is determined that the concentration of the two "I" type pollution plumes in the factory boundary area exceeding the preset boundary both exceed 20. Therefore, no migration simulation is required, and the two "I" type pollution plumes are classified as the first level.

[0094] Example 2: Point pollution data from Company B's site is interpolated using inverse distance interpolation to obtain patchy pollution concentration data and patchy pollution distribution data. This further identifies multiple high-concentration pollution areas, such as... Figure 4 As shown, there is an overlap between two adjacent high-concentration pollution areas. The maximum concentration of the high-concentration pollution area on the left is 28, and the maximum concentration of the overlapping area is 20. Obviously, 20 ≥ 50% * 28. Therefore, the two high-concentration pollution areas are merged to obtain an independent pollution plume, which is a "Type I" pollution plume. The preset concentration is set to 20. Based on the sheet pollution concentration data and sheet pollution distribution data, it is determined that the concentration of the "Type I" pollution plume in the factory boundary area beyond the preset boundary exceeds 20. Therefore, the "Type I" pollution plume does not need to be simulated for migration and is classified as the first level.

[0095] Example 3: Point pollution data from site C of enterprise C is interpolated using inverse distance interpolation to obtain patchy pollution concentration data and patchy pollution distribution data. This further identifies multiple high-concentration pollution areas, such as... Figure 5As shown, there are three high-concentration pollution areas from left to right, and adjacent high-concentration pollution areas overlap. The maximum concentration of the leftmost high-concentration pollution area is 28. The maximum concentration of the overlapping area between the leftmost and middle high-concentration pollution areas is 20. Obviously, 20 ≥ 50% * 28, so the leftmost and middle high-concentration pollution areas are merged. The maximum concentration of the merged high-concentration pollution area is 28. The maximum concentration of the overlapping area between the merged and rightmost high-concentration pollution areas is 12. Obviously, 12 < 50% * 28, so the merged high-concentration pollution area and the rightmost high-concentration pollution area are divided into two independent pollution plumes, and both pollution plumes are "I" type pollution plumes. The preset concentration is set to 20, based on the patchy pollution concentration data and patchy pollution distribution data. If the concentration of the left-side "I"-shaped pollution plume exceeding the preset boundary in the factory boundary area exceeds 20, then the left-side "I"-shaped pollution plume does not need to be simulated for migration and is classified as Level 1. Based on the sheet pollution concentration data and sheet pollution distribution data, if the concentration of the right-side "I"-shaped pollution plume exceeding the preset boundary in the factory boundary area does not exceed the preset concentration, then migration simulation is performed on the "I"-shaped pollution plume. Three simulated concentration sampling points are selected on the center line of the pollution plume, and the preset fitting rate is set to 80%. The simulation fitting rate is calculated, and the achievable fitting rate is found to be 86%, which meets the accuracy requirements. The current pollution plume model is saved as the simulated pollution plume. The duration for which the concentration of the simulated pollution plume in the factory boundary area exceeds the preset concentration is predicted. If it is found that the concentration of the "I"-shaped pollution plume in the factory boundary area does not exceed 20 within 8 years from the current moment, then the right-side "I"-shaped pollution plume is classified as Level 3.

[0096] Example 4: Point pollution data from site D of enterprise. Inverse distance interpolation is used to interpolate the point pollution data to obtain patchy pollution concentration data and patchy pollution distribution data. This further identifies multiple high-concentration pollution areas, such as... Figure 6As shown, adjacent high-concentration pollution areas overlap. The maximum concentration in the high-concentration pollution area on the left is 28, and the maximum concentration in the overlapping area is 22. Obviously, 22 ≥ 50% * 28. Merging two adjacent high-concentration pollution areas results in an independent pollution plume, which is a "I"-type pollution plume. The preset concentration is set to 20. Based on the sheet-like pollution concentration data and sheet-like pollution distribution data, it is determined that the concentration in the factory boundary area of ​​this "I"-type pollution plume does not exceed 20 beyond the preset boundary. Therefore, a single migration simulation is performed on this "I"-type pollution plume. Three evenly distributed simulated concentration sampling points are initially selected along the centerline of the pollution plume, and the preset concentration is set. With a fitting rate of 80%, the simulation fitting rate was calculated, yielding a achievable fitting rate of 66%. Obviously, the fitting rate is lower than 80%, which does not meet the accuracy requirements. Three uniformly distributed simulated concentration sampling points were selected again at the center line of the pollution plume, and the simulation fitting rate was calculated again, yielding a achievable fitting rate of 90%, which meets the accuracy requirements. The current pollution plume model was saved as a simulated pollution plume. The duration for which the concentration of the simulated pollution plume in the factory boundary area exceeds the preset concentration was predicted. It was found that the concentration in the factory boundary area of ​​the "I" type pollution plume will exceed 20 starting from the 3rd year and 6th month from the current moment. Therefore, the "I" type pollution plume is classified as the second level.

[0097] Example 5: Point pollution data from site E of enterprise. Inverse distance interpolation is used to interpolate the point pollution data to obtain patchy pollution concentration data and patchy pollution distribution data. This further identifies multiple high-concentration pollution areas, such as... Figure 7As shown, adjacent high-concentration pollution areas overlap. The maximum concentration in the high-concentration area on the left is 28, and the maximum concentration in the overlapping area is 20. Obviously, 20 ≥ 50% * 28. Therefore, the two adjacent high-concentration pollution areas are merged to obtain an independent pollution plume. As shown in the figure, this pollution plume is an "L"-shaped pollution plume. The preset concentration is set to 20. Based on the sheet pollution concentration data and sheet pollution distribution data, it is determined that the concentration in the factory boundary area of ​​this "L"-shaped pollution plume does not exceed 20 beyond the preset boundary. Therefore, this "L"-shaped pollution plume needs to be divided into two "I"-shaped pollution plumes for migration simulation. Three evenly distributed simulated concentration sampling points are selected on the center line of the left "I"-shaped pollution plume. The preset fitting rate is set to 80%. The simulation fitting rate is calculated, and the fitting rate of the "I" type pollution plume on the left is 86%. Three evenly distributed simulated concentration sampling points are selected on the center line of the "I" type pollution plume on the right. The simulation fitting rate is calculated, and the fitting rate of the "I" type pollution plume on the right is 90%, which meets the accuracy requirements. The two "I" type simulated pollution plumes are merged into an "L" type simulated pollution plume. The duration for which the concentration of the simulated pollution plume exceeds the preset concentration at the factory boundary is predicted. It is found that the concentration of the "L" type pollution plume at the factory boundary will start to exceed 20 in the first year and 6 months from the current moment. Therefore, the "L" type pollution plume is classified as the first level.

[0098] This embodiment acquires point pollution data, identifies pollution plumes based on this data, and then simulates the migration of these plumes to obtain simulated pollution plumes. The duration for which the concentration of the simulated pollution plume exceeds a preset concentration at the plant boundary is predicted, yielding a duration prediction result. Based on this duration prediction result, the pollution plumes are classified into different levels. In other words, this embodiment of the invention classifies pollution plumes into levels, effectively narrowing down the key areas for enterprise pollution management. Considering the changes in pollution plumes over time, migration simulations are performed on the identified plumes to simulate future pollution trends. The prediction of the duration for which the concentration exceeds the standard at the plant boundary is then used to classify the pollution plumes, improving the accuracy and precision of pollution level classification, which is beneficial for enterprise groundwater pollution management and increases the efficiency of enterprise pollution management.

[0099] Example 2

[0100] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the architecture of a pollution plume classification system 200 provided in an embodiment of this application.

[0101] The data acquisition module 210 is used to acquire point pollution data; wherein, the point pollution data includes point pollution distribution data and point pollution concentration data.

[0102] The pollution plume segmentation module 220 is used to segment multiple independent pollution plumes based on point pollution data.

[0103] The migration simulation module 230 is used to simulate the migration of contaminant plumes to obtain corresponding simulated contaminant plumes. Specifically, based on the segmented contaminant plume and sheet-like contaminant data, the migration simulation module 230 determines the shape information and simulation parameters of the contaminant plume; based on the shape information and simulation parameters of the contaminant plume, it performs a migration simulation of the contaminant plume to obtain the corresponding simulated contaminant plume.

[0104] The duration prediction module 240 is used to predict the duration during which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration, and to obtain the duration prediction result; wherein, the concentration in the factory boundary area is the pollution concentration in the area where the pollution plume exceeds the preset boundary.

[0105] The classification module 250 is used to classify pollution plumes based on the duration prediction results to obtain pollution plumes of different levels. Specifically, if the classification module 250 determines that the predicted duration for which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration is less than a first duration, then the pollution plume corresponding to the simulated pollution plume is classified as a first-level pollution plume; if the predicted duration for which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration is greater than the first duration but less than a second duration, then the pollution plume corresponding to the simulated pollution plume is classified as a second-level pollution plume; if the predicted duration for which the concentration in the factory boundary area of ​​the simulated pollution plume is greater than the preset concentration is greater than the second duration, then the pollution plume corresponding to the simulated pollution plume is classified as a third-level pollution plume.

[0106] The aforementioned plume segmentation module 220 specifically includes a data processing module 221 and a region segmentation module 222.

[0107] Data processing module 221 is used to interpolate point pollution data using inverse distance interpolation to obtain patch pollution distribution data and patch pollution concentration data;

[0108] Based on the data on the distribution and concentration of patchy pollution, several areas with high pollution concentrations were identified.

[0109] Region partitioning module 222 is used for:

[0110] Determine whether there is any overlap between adjacent areas with high pollution concentrations;

[0111] Adjacent high-concentration areas in non-overlapping regions are treated as independent pollution plumes;

[0112] If it is determined that there is an overlapping area between adjacent high-concentration pollution areas, the maximum concentration of the overlapping area and the maximum concentration of the high-concentration area are determined based on the patchy pollution concentration data; wherein, the maximum concentration of the high-concentration area is the maximum concentration of the high-concentration pollution area that includes the overlapping area.

[0113] The maximum concentration in the overlapping area is compared with the maximum concentration in the high-value area: if the maximum concentration in the overlapping area is less than a preset multiple of the maximum concentration in the high-value area, the high-value area containing the overlapping area is segmented to obtain an independent pollution plume.

[0114] If the maximum concentration in the overlapping area is greater than or equal to the maximum concentration in the high-value area by a preset multiple, then the high-value areas containing the overlapping area will be merged to obtain an independent pollution plume.

[0115] It should be understood that this device corresponds to the above-described pollution plume classification method embodiment and is capable of performing the various steps involved in the above method embodiment. The specific functions of this device can be found in the description above, and detailed descriptions are omitted here to avoid repetition. The device includes at least one software functional module that can be stored in memory or embedded in the device's operating system (OS) in the form of software or firmware.

[0116] Example 3

[0117] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0118] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0119] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0120] Example 4

[0121] This invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, are adapted to perform a program with steps of a polluted plume classification method.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0127] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0128] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0129] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of plume ranking, characterized in that, The method comprises the following steps: acquiring point pollution data; wherein the point pollution data comprises point pollution distribution data and point pollution concentration data; based on the point pollution data, a plurality of independent pollution plumes are divided out; pollution migration simulation is performed on the pollution plumes to obtain corresponding simulated pollution plumes; the duration for which the concentration of the area outside the factory boundary of the simulated pollution plume is greater than a preset concentration is predicted to obtain a duration prediction result; wherein the concentration of the area outside the factory boundary is the pollution concentration of the area where the pollution plume exceeds the preset boundary; based on the duration prediction result, the pollution plume is graded; wherein the pollution migration simulation performed on the pollution plume to obtain the corresponding simulated pollution plume comprises: if the concentration of the area outside the factory boundary of the pollution plume is less than or equal to the preset concentration, the pollution plume shape information and simulation parameters of the pollution plume are determined based on the divided pollution plume and sheet pollution data; wherein the sheet pollution data is determined based on the point pollution data; based on the pollution plume shape information and simulation parameters of the pollution plume, migration simulation is performed on the pollution plume to obtain the corresponding simulated pollution plume; wherein the migration simulation performed on the pollution plume based on the pollution plume shape information and simulation parameters of the pollution plume to obtain the corresponding simulated pollution plume comprises: based on the pollution plume shape information and simulation parameters of the pollution plume, migration simulation is performed on the pollution plume to obtain the corresponding pollution plume model; it is judged whether the fitting rate between the actual pollution concentration of the pollution plume and the simulated pollution concentration of the corresponding pollution plume model is greater than a preset fitting rate; if yes, it is determined that the current migration simulation is successful, and the pollution plume model is saved as the simulated pollution plume corresponding to the pollution plume.

2. The plume ranking method of claim 1, wherein, The method comprises the following steps: interpolation is performed on the point pollution data by using the inverse distance interpolation method to obtain sheet pollution distribution data and sheet pollution concentration data; based on the sheet pollution distribution data and the sheet pollution concentration data, a plurality of high-concentration areas of pollution are determined; it is judged whether there is an overlapping area between adjacent high-concentration areas of pollution; adjacent high-concentration areas of pollution without overlapping areas are taken as independent pollution plumes.

3. The plume ranking method of claim 2, wherein, The method further comprises the following steps: if it is determined that there is an overlapping area between adjacent high-concentration areas of pollution, the maximum concentration of the overlapping area and the maximum concentration of the high-concentration area are determined based on the sheet pollution concentration data; wherein the maximum concentration of the high-concentration area is the maximum concentration of the high-concentration area of pollution containing the overlapping area; the maximum concentration of the overlapping area and the maximum concentration of the high-concentration area are compared, and if the maximum concentration of the overlapping area is less than a preset multiple of the maximum concentration of the high-concentration area, the high-concentration area of pollution containing the overlapping area is segmented to obtain an independent pollution plume.

4. The plume ranking method of claim 3, wherein, The method further comprises the following steps: if the maximum concentration of the overlapping area is greater than or equal to a preset multiple of the maximum concentration of the high-concentration area, the high-concentration area of pollution containing the overlapping area is merged to obtain an independent pollution plume.

5. The plume ranking method of claim 2, wherein, The method further comprises the following steps: based on the pollution plume, the sheet pollution distribution data and the sheet pollution concentration data, the concentration of the area outside the factory boundary of the pollution plume is determined; it is judged whether the concentration of the area outside the factory boundary of the pollution plume is greater than a preset concentration; If the concentration of the plume at the factory boundary area is greater than the preset concentration, the plume is divided into a first level of plume.

6. The plume ranking method of claim 1, wherein, The plume is graded based on the time length prediction result, comprising: If the predicted time length of the simulated plume with the concentration at the factory boundary area greater than the preset concentration is less than a first time length, the plume corresponding to the simulated plume is divided into a first level of plume; If the predicted time length of the simulated plume with the concentration at the factory boundary area greater than the preset concentration is greater than the first time length but less than a second time length, the plume corresponding to the simulated plume is divided into a second level of plume; If the predicted time length of the simulated plume with the concentration at the factory boundary area greater than the preset concentration is greater than the second time length, the plume corresponding to the simulated plume is divided into a third level of plume.

7. A plume ranking system characterized in that, The system comprises: A data acquisition module for acquiring point pollution data; wherein the point pollution data comprises point pollution distribution data and point pollution concentration data; A plume division module for dividing a plurality of independent plumes based on the point pollution data; A migration simulation module for simulating the migration of the plume to obtain corresponding simulated plumes; A time length prediction module for predicting the time length of the simulated plume with the concentration at the factory boundary area greater than the preset concentration to obtain a time length prediction result; wherein the concentration at the factory boundary area is the pollution concentration of the plume beyond the preset boundary; A level division module for grading the plume based on the time length prediction result; The migration simulation module is specifically configured to: Determine the plume shape information and simulation parameters of the plume based on the divided plume and sheet pollution data; wherein the sheet pollution data is determined based on the point pollution data; Simulate the migration of the plume based on the plume shape information and simulation parameters of the plume to obtain corresponding simulated plumes; When the migration simulation module is configured to simulate the migration of the plume based on the plume shape information and simulation parameters of the plume to obtain corresponding simulated plumes, it is specifically configured to: Simulate the migration of the plume based on the plume shape information and simulation parameters of the plume to obtain a corresponding plume model; Determine whether the fitting rate between the actual pollution concentration of the plume and the simulated pollution concentration of the corresponding plume model is greater than a preset fitting rate; If yes, it is determined that the current migration simulation is successful, and the plume model is saved as the simulated plume corresponding to the plume.

8. The plume ranking system of claim 7, wherein, The plume division module comprises a data processing module and a region division module; The data processing module is configured to: Interpolate the point pollution data using the inverse distance interpolation method to obtain sheet pollution distribution data and sheet pollution concentration data; Determine a plurality of high pollution concentration regions based on the sheet pollution distribution data and the sheet pollution concentration data; The region division module is configured to: Determine whether there is an overlapping region between adjacent high pollution concentration regions; Divide adjacent high pollution concentration regions without overlapping regions as independent plumes.

9. The plume ranking system of claim 8, wherein, The region division module is further configured to: If it is determined that there is an overlapping area between adjacent high-value areas of pollution concentration, then based on the sheet pollution concentration data, the maximum concentration of the overlapping area and the maximum concentration of the high-value area are determined; wherein the maximum concentration of the high-value area is the maximum concentration of the pollution concentration high-value area containing the overlapping area; The maximum concentration of the overlapping area and the maximum concentration of the high-value area are compared: if the maximum concentration of the overlapping area is less than a preset multiple of the maximum concentration of the high-value area, then the pollution concentration high-value area containing the overlapping area is segmented to obtain an independent plume; If the maximum concentration of the overlapping area is greater than or equal to a preset multiple of the maximum concentration of the high-value area, then the pollution concentration high-value area containing the overlapping area is merged to obtain an independent plume.

10. The plume ranking system of claim 7, wherein, The level division module is specifically configured to: If the predicted duration for which the concentration of the factory boundary area of the simulated plume is greater than the preset concentration is less than the first duration, then the plume corresponding to the simulated plume is divided into a plume of the first level; If the predicted duration for which the concentration of the factory boundary area of the simulated plume is greater than the preset concentration is greater than the first duration but less than the second duration, then the plume corresponding to the simulated plume is divided into a plume of the second level; If the predicted duration for which the concentration of the factory boundary area of the simulated plume is greater than the preset concentration is greater than the second duration, then the plume corresponding to the simulated plume is divided into a plume of the third level.

11. An electronic device, comprising: It includes: A processor and a memory, the memory stores machine readable instructions executable by the processor, and the machine readable instructions are executed by the processor to perform the plume grading method in any one of claims 1-6.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions for causing a machine to perform the plume grading method in any one of claims 1-6.

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