3D scene simulation and field data analysis method convenient for ecological environment law enforcement

Abstract

The invention discloses a 3D scene simulation and field data analysis method convenient for ecological environment law enforcement, and belongs to the field of ecological environment management, and the method comprises the steps: determining a law enforcement region of an ecological environment, obtaining the multi-view stereoscopic vision data and point cloud data of the law enforcement region, and building a 3D scene model; a river pollution diffusion path is planned according to the grid distribution condition, interpolation is carried out on each diffusion grid on the river pollution diffusion path, and the overlapped river pollution diffusion paths are fused; calculating the number of pollutant transverse diffusion grids at the positions of the diffusion grids; and importing the 3D scene model into an ecological environment law enforcement platform, carrying out river pollutant concentration data assignment, and obtaining river pollution information of a law enforcement area by a user through clicking a diffusion grid. According to the scheme, the 3D scene model of the ecological law enforcement area is constructed through the visual data and the point cloud data, it is ensured that workers can timely and accurately obtain the environment pollution state of the law enforcement area, and efficient visual ecological environment quality evaluation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of ecological environment management, and particularly to a 3D scene simulation and on-site data analysis method for facilitating ecological environment law enforcement. Background Art

[0002] The ecological environment is closely related to human life and work, and can directly or indirectly affect human life and production activities. Ensuring the quality of the ecological environment on which humans depend for survival is the foundation of current social development. In a complete ecological environment, river water resources play a very important role. The deterioration of the ecological environment often indicates the pollution and deterioration of river water pollution. Therefore, how to monitor the state of river water resources in human life and production activities, and how to supervise polluting source enterprises through the ecological environment conditions of each section of river water resources have become the key to ecological environment law enforcement.

[0003] In the prior art, in the process of ecological environment law enforcement of river water resources, it often relies on random or fixed-point sampling inspections, or uses fixed-point detection equipment for data collection. These methods are inefficient and the detection results are accidental. It is impossible to achieve large-scale visualization of ecological environment law enforcement, and it is impossible to trace the polluting source enterprises and predict and evaluate the pollution diffusion. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the present invention provides a 3D scene simulation and on-site data analysis method for facilitating ecological environment law enforcement, which can assist in the visualization law enforcement of the ecological environment and realize the tracing and diffusion prediction of river pollution sources.

[0005] In order to achieve the above invention purpose, the technical solution adopted by the present invention is as follows: Provide a 3D scene simulation and on-site data analysis method for facilitating ecological environment law enforcement, which includes: Step S1: Determine the law enforcement area of the ecological environment, obtain the multi-view stereo vision data and point cloud data of the law enforcement area, and perform multi-view stereo vision calibration and point cloud registration to obtain a 3D scene model of the law enforcement area of the ecological environment; Step S2: Establish a three-dimensional coordinate system in the 3D scene model, grid the law enforcement area in the 3D scene model, and obtain the coordinates of the grid where the pollution monitoring equipment is located in the three-dimensional coordinate system according to the distribution position of the pollution monitoring equipment in the law enforcement area; Step S3: The pollution monitoring equipment collects on-site river pollution source data, plans the river pollution diffusion path according to the surrounding grid distribution, interpolates the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path, fuses the overlapping river pollution diffusion paths, and corrects the river pollutant concentration data corresponding to the overlapping diffusion grids; Step S4: Based on the river pollutant concentration data corresponding to each diffusion grid on the river pollution diffusion path, calculate the number of lateral diffusion grids of pollutants at the location of the diffusion grid, and represent the dynamic diffusion width of the river pollution source at different positions in the river as the river flows. Step S5: Import the 3D scene model into the ecological environment law enforcement platform, assign the river pollutant concentration data to the diffusion grids in the 3D scene model, and display the pollution prompt colors for the diffusion grids and the lateral diffusion grids. Users can obtain the river pollution information in the law enforcement area by clicking on the diffusion grids on each river pollution diffusion path.

[0006] Further, the calibration formula for multi-view stereo vision in step S1 is: ; where is the camera projection matrix, is the internal parameter matrix, is the translation vector, is the rotation matrix, is the pixel offset caused by radial distortion, u is the pixel point coordinate, r is the distance from the pixel point to the reference pixel point, are the third-order radial distortion coefficients respectively.

[0007] Further, the point cloud registration formula in step S1 is: ; where i is the point cloud number, is the i th source point cloud, is the i th target point cloud, n is the number of point clouds, is the weight coefficient of the point cloud, is the point cloud curvature, , is the regularization coefficient.

[0008] Further, step S3 includes: Step S31: Take the grid where the pollution monitoring device is located as the sub-pollution source grid. The pollution monitoring device collects the on-site river pollution source data, plans the river pollution diffusion path according to the grid distribution around the sub-pollution source grid, and interpolates the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path. Step S32: Based on the coordinates of each diffusion grid on the river pollution diffusion path, evaluate whether there is overlap between the river pollution diffusion paths, fuse the overlapping river pollution diffusion paths into one river pollution diffusion path, and correct the river pollutant concentration data corresponding to the overlapping diffusion grids.

[0009] Further, step S31 includes: Step S311: According to the coordinates of the sub-pollution source grid where the pollution monitoring device is located in the three-dimensional coordinate system , is the distribution coordinate of the sub-pollution source grid on the horizontal plane, is the height coordinate of the sub-pollution source grid; Step S312: Based on the sub-pollution source grid, according to the height coordinates of the adjacent grids around the sub-pollution source grid, screen the minimum value among the height coordinates of the adjacent grids , and use it as the first diffusion grid; establish a river pollution diffusion path grid set with the first diffusion grid and the sub-pollution source grid; Step S313: Return to step S312, based on the first diffusion grid, screen the second diffusion grid adjacent to the first diffusion grid, and add it to the river pollution diffusion path grid set; Step S314: Repeat step S313, based on the n -1 diffusion grid, screen the n diffusion grid, and add it to the river pollution diffusion path grid set until the grid diffusion equation is satisfied and the screening of the diffusion grid stops; The grid diffusion equation is: ; where l is the unit length of the grid, is the diffusion rate of the pollutant in water, is the promotion factor of the diffusion rate in the flowing state, is the river pollutant concentration at the location of the sub-pollution source grid collected by the pollution monitoring device, is the target river pollutant concentration, v is the average flow velocity of the river within the law enforcement area of the ecological environment; Step S315: Connect the diffusion grids in the river pollution diffusion path grid set in sequence starting from the sub-pollution source grid according to the screening order, and form a river pollution diffusion path based on the sub-pollution source grid; Step S316: Based on the river pollutant concentration collected on-site by the pollution monitoring device at the location of the sub-pollution source grid, interpolate the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path; obtain the river pollutant concentration data corresponding to the location of each diffusion grid , ; Among them, is the river pollutant concentration at the location of the n diffusion grid, n on the river pollution diffusion path.

[0010] Furthermore, step S32 includes: Step S321: Obtain the river pollution diffusion paths planned corresponding to the river pollution source data collected on-site by each pollution monitoring device within the law enforcement area, and obtain the coordinates corresponding to each diffusion grid within each river pollution diffusion path, and evaluate whether there is an overlap between two river pollution diffusion paths; If there exists a river pollution diffusion path A and a diffusion grid on the river pollution diffusion path B satisfies , then it is determined that the river pollution diffusion path A overlaps with the river pollution diffusion path B , and the river pollution diffusion path A and the river pollution diffusion path B are merged into one river pollution diffusion path; Otherwise, it is determined that the river pollution diffusion path A does not overlap with the river pollution diffusion path B , and they are used as two independent river pollution diffusion paths; is the coordinate of the A th diffusion grid on the river pollution diffusion path a , a is the number of the diffusion grid on the river pollution diffusion path A , is the coordinate of the B th diffusion grid on the river pollution diffusion path b , b is the number of the diffusion grid on the river pollution diffusion path B ; Step S322: According to the river pollutant concentration data A corresponding to the two overlapping diffusion grids after the merger of the river pollution diffusion path B and the river pollution diffusion path , correct the river pollutant concentration data corresponding to the overlapping diffusion grids after being merged into one river pollution diffusion path; ; Among them, u , w are respectively the numbers of the overlapping diffusion grids on the river pollution diffusion path A and the river pollution diffusion path B ,e It is the diffusion grid number after integrating into a river pollution diffusion path. It is the e river pollutant concentration data of the th diffusion grid after integrating into a river pollution diffusion path.

[0011] Further, step S4 includes: Step S41: Calculate the number of lateral diffusion grids at the location of the diffusion grid according to the river pollutant concentration data corresponding to each diffusion grid on the river pollution diffusion path. ; Among them, is the basic concentration of pollutants in the river, is the diffusion velocity of pollutants in water under the disturbed state; Step S42: Based on the number of lateral diffusion grids N, taking grids on both sides perpendicular to the river pollution diffusion path with each diffusion grid as the midpoint in turn as the lateral diffusion grids, and the lateral diffusion grids represent the dynamic diffusion width of the river pollution source at different positions in the river as the river flows.

[0012] Further, step S5 includes: Step S51: Import the 3D scene model into the ecological environment law enforcement platform, assign the river pollutant concentration data to the corresponding diffusion grids on each river pollution diffusion path in the 3D scene model, and set each diffusion grid and the corresponding lateral diffusion grids to be displayed in the pollution prompt color. Step S52: When the user clicks on each diffusion grid, the river pollutant concentration data at the current position can be displayed. At the same time, the coordinates and numbers of the pollution monitoring devices installed at the locations of the upstream sub-pollution source grids are also displayed.

[0013] The beneficial effects of the present invention are as follows: This solution constructs a 3D scene model of the ecological law enforcement area through visual data and point cloud data, integrates the pollution data collected by pollution monitoring devices with the 3D scene model, uses the grid technology to predict the diffusion of river pollution sources in the 3D scene model, and can realize the integration of pollution data collected by pollution monitoring devices at multiple points, improving the accuracy of pollution source diffusion prediction. According to the predicted pollution source diffusion path, pollution tracing and tracking can be carried out, and the supervision of the sewage discharge of pollution source enterprises can be realized through the pollution tracing situation.

[0014] Using the pollution concentration data real-time monitored by pollution monitoring equipment, the diffusion path can be real-time updated on the ecological environment law enforcement platform to ensure that the staff can timely and accurately obtain the environmental pollution status of the law enforcement area, realizing efficient visual ecological environment quality assessment, which serves as an important auxiliary tool for ecological environment law enforcement and management. Brief Description of the Drawings

[0015] Figure 1 It is a flowchart of a 3D scene simulation and on-site data analysis method for facilitating ecological environment law enforcement. Specific Embodiments

[0016] The following describes the specific embodiments of the present invention to facilitate the understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions made using the concept of the present invention are within the scope of protection.

[0017] As Figure 1 shown, a 3D scene simulation and on-site data analysis method for facilitating ecological environment law enforcement includes: Step S1: Determine the law enforcement area of the ecological environment, and obtain multi-view stereo vision data and point cloud data of the law enforcement area. In this embodiment, the multi-view stereo vision data and point cloud data of the law enforcement area can be realized through a GIS drone, and the calibration of multi-view stereo vision and point cloud registration are performed. During the calibration of multi-view stereo vision and point cloud registration, the visual pixels are aligned with the point cloud to obtain a 3D scene model of the law enforcement area of the ecological environment.

[0018] The calibration formula for multi-view stereo vision is: ; Wherein, is the camera projection matrix, is the internal parameter matrix, is the translation vector, is the rotation matrix, is the pixel offset caused by radial distortion, u is the pixel point coordinate, r is the distance from the pixel point to the reference pixel point, are the third-order radial distortion coefficients respectively.

[0019] The point cloud registration formula is: ; Wherein, i is the number of the point cloud, is the i th source point cloud, is the i th target point cloud, n is the number of point clouds, is the weight coefficient of the point cloud, is the point cloud curvature, , is the regularization coefficient.

[0020] Step S2: Establish a three-dimensional coordinate system within the 3D scene model, grid the law enforcement area within the 3D scene model, and obtain the coordinates of the grid where the pollution monitoring equipment is located in the three-dimensional coordinate system according to the distribution positions of the pollution monitoring equipment within the law enforcement area.

[0021] Step S3: The pollution monitoring equipment collects on-site river pollution source data, plans the river pollution diffusion path according to the grid distribution, interpolates the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path, fuses the overlapping river pollution diffusion paths, and corrects the river pollutant concentration data corresponding to the overlapping diffusion grids. Specifically, it includes the following steps: Step S31: Take the grid where the pollution monitoring equipment is located as the sub-pollution source grid. The pollution monitoring equipment collects on-site river pollution source data, plans the river pollution diffusion path according to the grid distribution around the sub-pollution source grid, and interpolates the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path. Specifically, it includes the following steps: Step S311: According to the coordinates of the sub-pollution source grid where the pollution monitoring equipment is located , is the distribution coordinate of the sub-pollution source grid on the horizontal plane, is the height coordinate of the sub-pollution source grid; Step S312: Based on the sub-pollution source grid, screen the minimum value among the height coordinates of the adjacent grids around the sub-pollution source grid as the first diffusion grid; use the first diffusion grid and the sub-pollution source grid to establish a river pollution diffusion path grid set; Step S313: Return to Step S312, based on the first diffusion grid, screen the second diffusion grid adjacent to the first diffusion grid and add it to the river pollution diffusion path grid set; Step S314: Repeat Step S313, based on the n -1 diffusion grid, screen the n th diffusion grid and add it to the river pollution diffusion path grid set until the grid diffusion equation is satisfied and the screening of the diffusion grid stops; The grid diffusion equation is: ; where l is the unit length of the grid, is the diffusion rate of pollutants in water, is the promotion factor of the diffusion rate in the flowing state, is the river pollutant concentration at the location of the sub-pollution source grid collected by the pollution monitoring equipment, is the target river pollutant concentration, v is the average flow velocity of the river within the law enforcement area of the ecological environment; Step S315: Connect the diffusion grids in the river pollution diffusion path grid set in sequence starting from the sub-pollution source grid according to the screening order to form a river pollution diffusion path based on the sub-pollution source grid; Generally, there are four adjacent grids around the sub-pollution source grid. The grid with the lowest height among the four adjacent grids is selected as the river flow direction reference. Thus, based on the diffusion effect of pollutant concentration in water, the river pollution diffusion path can be analyzed. Since the pollution source data in the river is dynamically changing, each time the pollution monitoring equipment collects the on-site pollution source data at its own location, a corresponding river pollution diffusion path can be formed, realizing the dynamic analysis of on-site data and the dynamic generation of the river pollution diffusion path.

[0022] Step S316: Based on the river pollutant concentration collected on-site by the pollution monitoring equipment at the location of the sub-pollution source grid , interpolate the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path; obtain the river pollutant concentration data corresponding to the location of each diffusion grid , ; Among them, is the river pollutant concentration at the location of the n th diffusion grid, n is on the river pollution diffusion path.

[0023] Step S32: According to the coordinates corresponding to each diffusion grid on the river pollution diffusion path, evaluate whether the river pollution diffusion paths overlap, and fuse the overlapping river pollution diffusion paths into one river pollution diffusion path, and correct the river pollutant concentration data corresponding to the overlapping diffusion grids. Specifically, it includes the following steps: Step S321: Obtain the river pollution diffusion paths planned corresponding to the on-site river pollution source data collected by each pollution monitoring equipment within the law enforcement area, and obtain the coordinates corresponding to each diffusion grid within each river pollution diffusion path, and evaluate whether two river pollution diffusion paths overlap; If there exists a river pollution diffusion path A and a river pollution diffusion path B where the diffusion grids on them satisfy , then determine the river pollution diffusion path A coincides with the river pollution diffusion path B Merge the river pollution diffusion path A with the river pollution diffusion path B into one river pollution diffusion path; Otherwise, determine that the river pollution diffusion path A does not coincide with the river pollution diffusion path B and use them as two independent river pollution diffusion paths; is the coordinate of the A th a diffusion grid on the river pollution diffusion path, a is the number of the diffusion grid on the river pollution diffusion path A , is the coordinate of the B th b diffusion grid on the river pollution diffusion path, b is the number of the diffusion grid on the river pollution diffusion path B ; Step S322: According to the river pollution diffusion path A and the river pollution diffusion path B The river pollutant concentration data corresponding to the two overlapping diffusion grids after fusion , correct the river pollutant concentration data corresponding to the overlapping diffusion grids after merging into one river pollution diffusion path; ; Among them, u , w are the numbers of the overlapping diffusion grids on the river pollution diffusion path A and the river pollution diffusion path B respectively, e is the number of the diffusion grid after merging into one river pollution diffusion path, is the river pollutant concentration data of the e th diffusion grid after merging into one river pollution diffusion path; By correcting the river pollutant concentration data of the overlapping diffusion grids, the fusion of the river pollution source data collected by multiple pollution monitoring devices is realized, and the accuracy of pollution diffusion path prediction and pollution data interpolation is improved.

[0024] Step S323: Until each overlapping river pollution diffusion path is merged and the river pollutant concentration data corresponding to the overlapping diffusion grids is corrected, output all river pollution diffusion paths.

[0025] Step S4: Based on the river pollutant concentration data corresponding to each diffusion grid on the river pollution diffusion path, calculate the number of lateral diffusion grids of pollutants at the location of the diffusion grid, and represent the dynamic diffusion width of the river pollution source at different positions in the river as the river flows. Specifically, it includes the following steps: Step S41: According to the river pollutant concentration data corresponding to each diffusion grid on the river pollution diffusion path, calculate the number of lateral diffusion grids of pollutants at the location of the diffusion grid; ; Among them, is the basic concentration of pollutants in the river, is the diffusion velocity of pollutants in water under the disturbed state; Step S42: Based on the number of lateral diffusion grids N , taking each diffusion grid as the midpoint, successively take grids on both sides perpendicular to the river pollution diffusion path as lateral diffusion grids, and the lateral diffusion grids represent the dynamic diffusion width of the river pollution source at different positions in the river as the river flows.

[0026] Step S5: Import the 3D scene model into the ecological environment law enforcement platform, assign river pollutant concentration data to the diffusion grids in the 3D scene model, and display the pollution prompt colors for the diffusion grids and the lateral diffusion grids. Users can obtain the river pollution information in the law enforcement area by clicking on the diffusion grids on each river pollution diffusion path. Specifically, it includes the following steps: Step S51: Import the 3D scene model into the ecological environment law enforcement platform, assign river pollutant concentration data to the corresponding diffusion grids on each river pollution diffusion path in the 3D scene model, and set each diffusion grid and the corresponding lateral diffusion grid to display the pollution prompt color; In this embodiment, the diffusion grids can be set to red and the lateral diffusion grids can be set to yellow. When the staff uses the ecological environment law enforcement platform to obtain the river ecological information in the law enforcement area, they can intuitively obtain the river pollution diffusion range at each moment and the pollution diffusion influence width at different positions; Step S52: When the user clicks on each diffusion grid, the river pollutant concentration data at the current position can be displayed. At the same time, the coordinates and numbers of the pollution monitoring devices installed at the locations of the upstream sub-pollution source grids are also displayed.

[0027] The present invention constructs a 3D scene model of the ecological law enforcement area through visual data and point cloud data, fuses the pollution data collected by pollution monitoring devices with the 3D scene model, uses grid technology to predict the diffusion of river pollution sources in the 3D scene model, and can realize the fusion of pollution data collected by pollution monitoring devices at multiple positions, improving the accuracy of pollution source diffusion prediction. According to the predicted pollution source diffusion path, pollution source tracing and tracking can be carried out, and the supervision of pollution discharge by pollution source enterprises can be realized through the pollution source tracing situation.

[0028] Using the pollution concentration data real-time monitored by pollution monitoring devices, the diffusion path can be real-time updated on the ecological environment law enforcement platform, ensuring that staff can timely and accurately obtain the environmental pollution status of the law enforcement area and realizing efficient visual ecological environment quality assessment, which serves as an important auxiliary tool for ecological environment law enforcement and management.

Claims

1. A 3D scene simulation and on-site data analysis method for ecological environmental law enforcement, characterized in that: include: Step S1: determine the enforcement area of ​​the ecological environment, obtain multi-view stereoscopic vision data and point cloud data of the enforcement area, and perform multi-view stereoscopic vision calibration and point cloud registration to obtain a 3D scene model of the enforcement area of ​​the ecological environment; Step S2: establishing a three-dimensional coordinate system in the 3D scene model, gridding the law enforcement area in the 3D scene model, and obtaining the coordinates of the grid where the pollution monitoring equipment is located in the three-dimensional coordinate system according to the distribution position of the pollution monitoring equipment in the law enforcement area; Step S3: The pollution monitoring equipment collects the river pollution source data at the site, and plans the river pollution diffusion path according to the distribution of the surrounding grids, and interpolates the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path, merges the overlapping river pollution diffusion paths, and corrects the river pollutant concentration data corresponding to the overlapping diffusion grids; Step S4: based on the river pollutant concentration data corresponding to each diffusion grid on the river pollution diffusion path, the number of pollutant lateral diffusion grids at the location of the diffusion grid is calculated to represent the dynamic diffusion width of the river pollution source at different locations in the river as the river flows; Step S5: Import the 3D scene model into the ecological environment law enforcement platform, assign river pollutant concentration data to the diffusion grid in the 3D scene model, and display the pollution prompt color on the diffusion grid and the lateral diffusion grid. The user can obtain the river pollution information in the law enforcement area by clicking on the diffusion grid on each river pollution diffusion path.

2. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 1 is characterized in that: The calibration formula of multi-view stereo vision in step S1 is: ； in, is the camera projection matrix, is the internal parameter matrix, is the translation vector, is the rotation matrix, is the pixel offset caused by radial distortion, u is the pixel coordinate, r is the distance from the pixel to the reference pixel, are the third-order radial distortion coefficients respectively.

3. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 2 is characterized in that: The point cloud registration formula in step S1 is: ； in, i is the number of the point cloud, For the i Source point cloud, For the i target point cloud, n is the number of point clouds, is the weight coefficient of the point cloud, is the point cloud curvature, , is the regularization coefficient.

4. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 1 is characterized in that: The step S3 comprises: Step S31: The grid where the pollution monitoring device is located is used as a sub-pollution source grid. The pollution monitoring device collects the river pollution source data on site, and plans the river pollution diffusion path according to the grid distribution around the sub-pollution source grid, and interpolates the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path; Step S32: According to the coordinates corresponding to each diffusion grid on the river pollution diffusion path, evaluate whether the river pollution diffusion paths overlap, merge the overlapping river pollution diffusion paths into one river pollution diffusion path, and correct the river pollutant concentration data corresponding to the overlapping diffusion grids.

5. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 4 is characterized in that: The step S31 comprises: Step S311: Based on the coordinates of the sub-pollution source grid where the pollution monitoring equipment is located in the three-dimensional coordinate system , is the distribution coordinate of the sub-pollution source grid in the horizontal plane, is the height coordinate of the sub-pollution source grid; Step S312: Based on the sub-pollution source grid, according to the height coordinates of the adjacent grids around the sub-pollution source grid, select the minimum value among the height coordinates of the adjacent grids , as the first diffusion grid; using the first diffusion grid and the sub-pollution source grid to establish a river pollution diffusion path grid set; Step S313: Return to step S312, based on the first diffusion grid, select a second diffusion grid adjacent to the first diffusion grid, and add the second diffusion grid to the river pollution diffusion path grid set; Step S314: Repeat step S313, n -1 diffusion grid as the basis, filter the n The diffusion grid is added to the grid set of river pollution diffusion paths until the grid diffusion equation is satisfied and the diffusion grid screening stops; The grid diffusion equation is: ;in, l is the unit length of the grid, is the diffusion rate of pollutants in water, is the diffusion rate promoting factor under the flow state, The river pollutant concentration at the location of the sub-pollution source grid collected by the pollution monitoring equipment, is the target river pollutant concentration, v The average flow rate of rivers in the ecological environment enforcement area; Step S315: connecting the diffusion grids in the river pollution diffusion path grid set in sequence starting from the sub-pollution source grids according to the order of screening, to form a river pollution diffusion path based on the sub-pollution source grids; Step S316: Based on the river pollutant concentration collected on-site by the pollution monitoring equipment at the location of the sub-pollution source grid , interpolate the river pollutant concentration corresponding to each diffusion grid on the river pollution diffusion path; obtain the river pollutant concentration data corresponding to the location of each diffusion grid , ； in, For the n The concentration of pollutants in the river where the diffusion grid is located, n It is on the diffusion path of river pollution.

6. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 5 is characterized in that: The step S32 comprises: Step S321: Obtain the river pollution diffusion path planned based on the river pollution source data collected at the scene by each pollution monitoring device in the law enforcement area, and obtain the coordinates corresponding to each diffusion grid in each river pollution diffusion path, and evaluate whether two river pollution diffusion paths overlap; If there is a river pollution diffusion path A Diffusion pathways of river pollution B The diffusion grid on , then determine the river pollution diffusion path A Diffusion pathways of river pollution B Overlap, spreading the river pollution path A Diffusion pathways of river pollution B merged into a river pollution diffusion path; Otherwise, determine the river pollution diffusion path A Diffusion pathways of river pollution B They do not overlap, being two independent pollution diffusion pathways for the river; The diffusion path of river pollution A Previous a The coordinates of the diffusion grid, a The diffusion path of river pollution A The number of the upper diffusion grid, The diffusion path of river pollution B Previous b The coordinates of the diffusion grid, b The diffusion path of river pollution B The number of the upper diffusion grid; Step S322: Based on the river pollution diffusion path A Diffusion pathways of river pollution B River pollutant concentration data corresponding to the two diffusion grids that overlap after fusion , the river pollutant concentration data corresponding to the overlapping diffusion grids after being merged into a river pollution diffusion path are corrected; ； in, u , w They are the river pollution diffusion paths A Diffusion pathways of river pollution B The diffusion grid number that overlaps above, e The diffusion grid numbers after being merged into a river pollution diffusion path. After merging into a river pollution diffusion path e River pollutant concentration data for a diffusion grid; Step S323: After each overlapping river pollution diffusion path is merged and the river pollutant concentration data corresponding to the overlapping diffusion grids are corrected, all river pollution diffusion paths are output.

7. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 6 is characterized in that: The step S4 comprises: Step S41: Calculate the number of pollutant lateral diffusion grids at the location of the diffusion grid according to the river pollutant concentration data corresponding to each diffusion grid on the river pollution diffusion path; ； in, is the basic concentration of pollutants in the river, is the diffusion rate of pollutants in water under disturbance; Step S42: Based on the number of lateral diffusion grids N, take each diffusion grid as the midpoint and sequentially take the two sides perpendicular to the river pollution diffusion path. The grids are used as lateral diffusion grids, which represent the dynamic diffusion width of river pollution sources at different positions in the river as the river flows.

8. The 3D scene simulation and on-site data analysis method for facilitating ecological environmental law enforcement according to claim 7 is characterized in that: The step S5 comprises: Step S51: importing the 3D scene model into the ecological environment law enforcement platform, assigning river pollutant concentration data to the corresponding diffusion grids on each river pollution diffusion path in the 3D scene model, and setting each diffusion grid and the corresponding lateral diffusion grid to display in a pollution prompt color; Step S52: The user clicks on each diffusion grid to display the river pollutant concentration data at the current location. At the same time, the coordinates and numbers of the pollution monitoring equipment installed at the location of the upstream sub-pollution source grid are also displayed.

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