An adaptive water body monitoring method and device based on water pollution level

Abstract

The application relates to the technical field of water pollution monitoring, and provides a water body monitoring method based on water pollution grades and self-adaption, which comprises the following steps: according to water body identification data, a vector polygon is adopted to extract the vector boundary of the water body, and the vector boundary of the water body is obtained; according to the vector boundary of the water body, an initial triangular net is generated; according to the pollution degree of the water body, a grid subdivision algorithm is adopted to adjust the density of the initial triangular net, and the adjusted grid is obtained; wherein in the adjusted grid, the grid density of the adjusted grid with high pollution degree is greater than the grid density of the adjusted grid with low pollution degree; and according to the water body monitoring result, the adjusted grid is updated. The technical scheme of the application can realize high-density monitoring of the heavily-polluted area and sparse grid of the lightly-polluted area, reduce the database storage pressure and the calculation load, improve the monitoring data processing efficiency and the precision of the water body pollution monitoring.

Description

Technical Field

[0001] This invention relates to the field of water pollution monitoring technology, and in particular to an adaptive water body monitoring method and device based on water pollution level. Background Technology

[0002] In water pollution control, transforming identification results into actionable control units is a crucial step. Currently, grid-based management methods widely used in environmental supervision mainly include two types: those based on fixed grids and those based on administrative boundaries.

[0003] The fixed grid-based control method uses a regular latitude and longitude grid or a fixed-size fish-grid to cover the monitoring area and maps the identification results into the grid; the administrative boundary-based method directly uses the administrative division or river management scope line as the control unit.

[0004] Currently, among the two commonly used control methods, fixed regular grids generate a large number of invalid grids when crossing the shoreline, leading to data redundancy and statistical errors. Furthermore, fixed regular grids also have the problem of not being able to dynamically adjust the grid density according to the severity of pollution. This will further lead to insufficient monitoring accuracy in heavily polluted areas, while wasting computing resources in lightly polluted areas, and the original grids are difficult to update quickly to match new pollution points when the pollution range changes. Summary of the Invention

[0005] This invention provides an adaptive water body monitoring method based on water pollution level, which solves the problems of existing technologies where fixed regular grids generate a large number of invalid grids when crossing the shoreline, leading to data redundancy and statistical errors. It also has the drawback of not being able to dynamically adjust the grid density according to the severity of pollution to quickly update and match new pollution points. This invention enables high-density monitoring of heavily polluted areas and sparse grids in lightly polluted areas, reducing database storage pressure and computational load, and improving the efficiency of monitoring data processing and the accuracy of water pollution monitoring.

[0006] This invention provides an adaptive water body monitoring method based on water pollution level, comprising the following steps.

[0007] Based on the water body identification data, vector polygons are used to extract the vector boundaries of the water bodies, thus obtaining the water body vector boundaries; Generate an initial triangulation network based on the water body vector boundary; Based on the degree of water pollution, a grid subdivision algorithm is used to adjust the density of the initial triangular mesh to obtain an adjusted mesh. Among the adjusted meshes, the mesh density of the adjusted mesh with a higher degree of pollution is greater than that of the adjusted mesh with a lower degree of pollution. The grid was updated and adjusted based on water monitoring results.

[0008] This invention provides an adaptive water body monitoring method based on water pollution levels. Based on water monitoring results, the updated and adjusted grid now includes the following: Boundary constraint processing is applied to the adjusted mesh to obtain a pure water body mesh. This invention provides an adaptive water body monitoring method based on water pollution levels. Boundary constraint processing is applied to the adjusted mesh, including: The boundary of the adjusted mesh is constrained within the vector boundary of the water body; Remove mixed-cell meshes from the adjusted mesh; where a mixed-cell mesh contains invalid meshes that include both water and land cells.

[0009] This invention provides an adaptive water body monitoring method based on water pollution levels. Based on water monitoring results, the updated and adjusted grid now includes the following: Each grid in the adjusted grid is globally uniquely encoded to obtain the encoded water body grid.

[0010] This invention provides an adaptive water body monitoring method based on water pollution levels. Each grid cell in the adjusted grid is globally uniquely encoded to obtain the encoded water body grid, including: Using the Hilbert curve method, each grid in the adjusted grid is globally uniquely encoded to obtain the encoded water body grid.

[0011] According to the present invention, an adaptive water body monitoring method based on water pollution level is provided, wherein the water body identification data includes a probability map and a level map.

[0012] According to the present invention, an adaptive water body monitoring method based on water pollution level is provided, which extracts the vector boundary of the water body using vector polygons based on water body identification data, including: The water body identification data is binarized to obtain a water body mask; The water mask is post-processed to obtain a post-processed mask; the post-processing includes noise removal and void filling. Extract the outline of the water body from the grid of the post-processing mask; The outline of the water body is vectorized to obtain the vector boundary of the water body.

[0013] This invention provides an adaptive water body monitoring method based on water pollution levels. Based on the degree of water pollution, a mesh subdivision algorithm is used to adjust the density of the initial triangular mesh, resulting in an adjusted mesh, including: The number of subdivisions of the grid is determined based on the degree of water pollution in the initial triangulation grid. The initial triangular mesh is subdivided according to the subdivision number and subdivision coefficient to obtain the adjusted mesh. This invention provides an adaptive water body monitoring method based on water pollution levels. Based on water monitoring results, the updated and adjusted grid includes: Based on the water monitoring results, the pollution level change value of each grid is obtained by comparing the adjusted grids at different times; For grids where the pollution level changes beyond a threshold, density adjustments are made.

[0014] The present invention also provides an adaptive water body monitoring device based on water pollution level, comprising the following modules: The boundary extraction module is used to extract the vector boundary of the water body based on the water body identification data using vector polygons, thus obtaining the water body vector boundary. The meshing module is used to generate an initial triangular mesh based on the water body vector boundary; The density adjustment module is used to adjust the density of the initial triangular mesh according to the degree of water pollution using a mesh subdivision algorithm, resulting in an adjusted mesh. In the adjusted mesh, the mesh density of the adjusted mesh with a higher degree of pollution is greater than that of the adjusted mesh with a lower degree of pollution. The update module is used to update and adjust the grid based on water monitoring results.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described adaptive water body monitoring methods based on water pollution levels.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described adaptive water body monitoring methods based on water pollution levels.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the above-described adaptive water body monitoring methods based on water pollution levels.

[0018] The present invention provides an adaptive water body monitoring method based on water pollution levels. This method extracts the vector boundaries of water bodies using vector polygons based on water body identification data; generates an initial triangular mesh based on these boundaries; adjusts the density of the initial triangular mesh using a mesh subdivision algorithm based on the degree of water pollution, resulting in an adjusted mesh; and updates the adjusted mesh based on water body monitoring results. This method achieves high-density monitoring of heavily polluted areas and sparse meshing of lightly polluted areas, reducing database storage pressure and computational load, and improving the efficiency of monitoring data processing and the accuracy of water pollution monitoring. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the adaptive water body monitoring method based on water pollution level provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of the adaptive water body monitoring method and device based on water pollution level provided by the present invention.

[0022] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] The following is combined with Figures 1 to 3 This invention is described.

[0025] Figure 1 This is one of the flowcharts of the adaptive water body monitoring method based on water pollution level provided by the present invention, such as... Figure 1 As shown, the method includes the following: Step 101: Based on the water body identification data, use vector polygons to extract the vector boundary of the water body to obtain the water body vector boundary.

[0026] The present invention provides an adaptive water body monitoring method based on water pollution level. In one embodiment, step 101 includes steps A1 to A4: Step A1: Binarize the water body identification data to obtain a water body mask; Step A2: Post-process the water mask to obtain a post-processed mask; the post-processing includes noise removal and void filling. Step A3: Extract the outline of the water body from the grid of the post-processing mask; Step A4: Vectorize the outline of the water body to obtain the vector boundary of the water body.

[0027] In step A1, in most cases, the water body identification data includes probability maps and grade maps; the water body identification data can also be a binary map obtained after processing.

[0028] The water body identification data is the raster data source used for initializing the network construction of the algorithm in this invention, including water body probability raster maps, pollution level raster maps, and preprocessed binary raster maps. Figure 3 Class; Probability map pixels 0~1 represent the spatial probability of water bodies, and grade map distinguishes between water and land and pollution levels by assigning pixel values.

[0029] The acquisition methods for water body identification data include, but are not limited to: generating probability and grade rasters by spectral inversion / semantic segmentation of remote sensing images; generating global pollution rasters by spatial interpolation of in-situ water quality monitoring points; and generating binary maps by rasterizing river and lake cadastral and inspection ledgers.

[0030] In the probability map, each pixel has a value between 0 and 1, representing the probability that the location is a body of water.

[0031] When performing binarization processing on water body identification data, a threshold needs to be set in advance, such as 0.5. When the value is greater than the threshold, the pixel is located as a water body.

[0032] In the hierarchy map, each pixel is assigned a category number, such as 0 for background, 1 for water, and 2 for vegetation.

[0033] Based on the classification description, determine one or more category numbers corresponding to the water body, and then extract the pixels corresponding to these categories.

[0034] When a body of water is divided into multiple subcategories, such as deep water and shallow water, they all need to be incorporated into a water body mask.

[0035] Step 102: Generate an initial triangular mesh based on the water body vector boundary.

[0036] The present invention provides an adaptive water body monitoring method based on water pollution level. In one embodiment, step 102 adopts the Delaunay triangulation algorithm.

[0037] Specifically, step 102 includes steps D1 to D2: Step D1: Extract all vertices from the vector boundary of the water body in sequence; Step D2: Construct the minimum convex hull containing all vertices; Step D3: Generate a Delaunay triangulation inside the convex hull using the Bowyer-Watson point-by-point insertion algorithm; Step D4: Force the generated Delaunay triangulation back to the vector boundary of the water body, delete the triangles outside the boundary, and ensure that all mesh cells are located inside the water body; Step D5: Optimize the mesh quality by using the edge exchange method to avoid the formation of narrow triangles, thereby obtaining an initial triangular mesh that fits the shoreline of the water body.

[0038] Step D5 is an optional step.

[0039] Step 102 uses a triangular mesh to fit the shoreline, resulting in a high degree of boundary fit. This avoids geometric distortion of the fixed mesh at complex shorelines and improves the accuracy of area statistics.

[0040] Step 103: Based on the degree of water pollution, the initial triangular mesh is adjusted using a mesh subdivision algorithm to obtain an adjusted mesh; wherein, in the adjusted mesh, the mesh density of the adjusted mesh with a higher degree of pollution is greater than the mesh density of the adjusted mesh with a lower degree of pollution.

[0041] The present invention provides an adaptive water body monitoring method based on water pollution level. In one embodiment, step 103 includes steps B1 to B2: Step B1: Determine the number of subdivisions of the grid based on the degree of water pollution in the initial triangular mesh. Step B2: Subdivide the initial triangular mesh according to the number of subdivisions and the subdivision coefficient to obtain the adjusted mesh.

[0042] In step B1, the higher the degree of water pollution, the greater the number of grid subdivisions. For example, when the water pollution level is high, the grid subdivision level is 2.

[0043] In step B2, the subdivision coefficient refers to the rate of change of the grid side length in the triangular mesh after each subdivision.

[0044] For example, when the subdivision factor is 0.5, the original value of the grid side length in the triangulation is L. When the subdivision number is 1, each grid in the triangulation will be subdivided into four smaller grids with a side length of 0.5L. When the subdivision number is 2, each grid in the triangulation will be subdivided into 16 smaller grids with a side length of 0.25L.

[0045] Step 103 is an adaptive pollution intensity grid generation method. The more severe the pollution, the denser the grid. Under the same amount of data, the monitoring resolution of key areas is improved by 2-4 times. Moreover, the land interference grid is eliminated through geometric constraints, making management more precise.

[0046] Step 104: Update and adjust the grid based on the water monitoring results.

[0047] In step 104 above, the water monitoring results are time-series water quality data that are periodically and dynamically collected after the grid deployment is completed. These include: the geographical coordinates of monitoring points, measured water quality indicators such as ammonia nitrogen, etc. They are the input source that drives the dynamic iterative update of the grid after the initial network construction is completed, and belong to different time series and different data sources than the water body identification data used for initial modeling.

[0048] The present invention provides an adaptive water body monitoring method based on water pollution level. In one embodiment, step 104 includes steps C1 to C2: Step C1: Based on the water monitoring results, compare the adjusted grids at different times to obtain the pollution level change value of each grid; Step C2: Adjust the density of grids whose pollution level changes exceed the threshold.

[0049] In step C1 above, based on the water monitoring results, the cross-temporal grid matching is completed by comparing the adjusted grids at different times using the unique spatial encoding of the Hilbert grid set by this invention.

[0050] Specifically, this includes: interpolating newly collected water monitoring results to generate a global pollution raster, which is then mounted onto existing triangular grid cells via grid coding. Using the Hilbert curve code of the same grid as a unique index, the pollution attribute parameters of the grid are retrieved from historical and current time series. Numerical or level difference calculations are performed on each grid to obtain the pollution degree change value for each individual grid. This change value is compared with a preset mutation threshold, and only grids exceeding the threshold are individually subdivided and densified, achieving local grid updates without requiring full-area grid reconstruction. A change detection mechanism is introduced to capture pollution spread trends, support historical status retrospection, facilitate source tracing, and enable timely intervention.

[0051] The present invention provides an adaptive water body monitoring method based on water pollution level. In one embodiment, between step 103 and step 104, step 103-4A is further included: Step 103-4A: Perform boundary constraint processing on the adjusted mesh to obtain a pure water body mesh.

[0052] This includes applying boundary constraints to the adjusted mesh, including: The boundary of the adjusted mesh is constrained within the vector boundary of the water body; Remove mixed-cell meshes from the adjusted mesh; where a mixed-cell mesh contains invalid meshes that include both water and land cells.

[0053] A hybrid pixel grid refers to an invalid grid that contains both water and land pixels within its grid cells.

[0054] In step 103-4A, a mesh construction method that conforms to the water body boundary is used to eliminate land interference.

[0055] The present invention provides an adaptive water body monitoring method based on water pollution level. In one embodiment, step 103-4B is further included between step 103 and step 104: Step 103-4B: Perform a globally unique encoding on each grid in the adjusted grid to obtain the encoded water body grid.

[0056] Each grid cell in the adjusted grid is globally uniquely encoded to obtain the encoded water body grid, including: Using the Hilbert curve method, each grid in the adjusted grid is globally uniquely encoded to obtain the encoded water body grid.

[0057] Each grid in the adjusted grid is globally uniquely encoded based on the Hilbert curve, which facilitates historical data tracing and status updates, enabling a shift from post-event identification to in-event early warning.

[0058] This invention provides an adaptive water body monitoring method based on water pollution levels. The method involves extracting the vector boundaries of water bodies using vector polygons based on water body identification data; generating an initial triangular mesh based on these boundaries; adjusting the density of the initial triangular mesh using a mesh subdivision algorithm based on the degree of water pollution to obtain an adjusted mesh; and updating the adjusted mesh based on water body monitoring results. This method achieves high-density monitoring of heavily polluted areas and sparse meshing of lightly polluted areas, reducing database storage pressure and computational load, and improving the efficiency of monitoring data processing and the accuracy of water pollution monitoring.

[0059] The following describes the adaptive water body monitoring method and apparatus based on water pollution level provided by the present invention. The adaptive water body monitoring method and apparatus based on water pollution level described below can be referred to in correspondence with the adaptive water body monitoring method based on water pollution level described above.

[0060] Figure 2 This is a schematic diagram of the process of the adaptive water body monitoring method and device based on water pollution level provided by the present invention, as shown below. Figure 2 As shown, the device includes the following: The boundary extraction module 201 is used to extract the vector boundary of the water body based on the water body identification data using vector polygons, so as to obtain the water body vector boundary. Meshing module 202 is used to generate an initial triangular mesh based on the water body vector boundary; The density adjustment module 203 is used to adjust the density of the initial triangular mesh according to the degree of water pollution using a mesh subdivision algorithm to obtain the adjusted mesh; wherein, in the adjusted mesh, the mesh density of the adjusted mesh with a high degree of pollution is greater than the mesh density of the adjusted mesh with a low degree of pollution. Update module 204 is used to update and adjust the grid based on water monitoring results.

[0061] This invention provides an adaptive water body monitoring method and device based on water pollution level. The method involves extracting the vector boundaries of the water body using vector polygons based on water body identification data; generating an initial triangular mesh based on these boundaries; adjusting the density of the initial triangular mesh using a mesh subdivision algorithm based on the degree of water pollution to obtain an adjusted mesh; and updating the adjusted mesh based on water body monitoring results. This achieves high-density monitoring of heavily polluted areas and sparse meshing of lightly polluted areas, reducing database storage pressure and computational load, and improving the efficiency of monitoring data processing and the accuracy of water pollution monitoring.

[0062] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute an adaptive water body monitoring method based on water pollution levels.

[0063] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the adaptive water body monitoring method based on water pollution level provided by the above methods.

[0065] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the adaptive water body monitoring method based on water pollution level provided by the above methods.

[0066] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive water body monitoring method based on water pollution level, characterized in that, include: Based on the water body identification data, vector polygons are used to extract the vector boundaries of the water bodies, thus obtaining the water body vector boundaries; An initial triangulation network is generated based on the water body vector boundary; Based on the degree of water pollution, a grid subdivision algorithm is used to adjust the density of the initial triangular mesh to obtain an adjusted mesh; wherein, in the adjusted mesh, the mesh density of the adjusted mesh with a higher degree of pollution is greater than the mesh density of the adjusted mesh with a lower degree of pollution. The adjusted grid is updated based on the water monitoring results.

2. The adaptive water body monitoring method based on water pollution level according to claim 1, characterized in that, The process of updating the adjusted grid based on water monitoring results also includes, prior to: The adjusted mesh is subjected to boundary constraint processing to obtain a pure water body mesh.

3. The adaptive water body monitoring method based on water pollution level according to claim 2, characterized in that, The boundary constraint processing of the adjusted mesh includes: The boundary of the adjusted mesh is constrained within the vector boundary of the water body; Remove the mixed-cell grid from the adjusted grid; wherein the mixed-cell grid includes invalid grids containing both water and land cells.

4. A water body monitoring method based on water pollution level according to claim 1 or 2, characterized in that, The process of updating the adjusted grid based on water monitoring results also includes, prior to: Each grid in the adjusted grid is globally uniquely encoded to obtain the encoded water body grid.

5. The adaptive water body monitoring method based on water pollution level according to claim 4, characterized in that, The step of globally uniquely encoding each grid in the adjusted grid to obtain the encoded water body grid includes: The Hilbert curve method is used to globally uniquely encode each grid in the adjusted grid to obtain the encoded water body grid.

6. The adaptive water body monitoring method based on water pollution level according to claim 1, characterized in that, The water body identification data includes probability maps and grading maps.

7. The adaptive water body monitoring method based on water pollution level according to claim 6, characterized in that, The step of extracting the vector boundary of the water body using vector polygons based on water body identification data includes: The water body identification data is binarized to obtain a water body mask; The water mask is post-processed to obtain a post-processed mask; wherein, the post-processing includes noise removal and void filling; Extract the outline of the water body from the grid of the post-processing mask; The outline of the water body is vectorized to obtain the vector boundary of the water body.

8. The adaptive water body monitoring method based on water pollution level according to claim 1, characterized in that, Based on the degree of water pollution, a grid subdivision algorithm is used to adjust the density of the initial triangular mesh to obtain an adjusted mesh, including: The number of subdivisions of the grid is determined based on the degree of water pollution in the initial triangular mesh. The initial triangular mesh is subdivided according to the number of subdivisions and the subdivision coefficient to obtain the adjusted mesh.

9. The adaptive water body monitoring method based on water pollution level according to claim 1, characterized in that, The step of updating the adjusted grid based on water monitoring results includes: Based on the water monitoring results, the pollution level change value of each grid is obtained by comparing the adjusted grids at different times; For grids where the pollution level change exceeds a threshold, density adjustment is performed.

10. A water body monitoring device based on water pollution level adaptive monitoring, characterized in that, include: The boundary extraction module is used to extract the vector boundary of the water body based on the water body identification data using vector polygons, thus obtaining the water body vector boundary. A meshing module is used to generate an initial triangular mesh based on the water body vector boundary; The density adjustment module is used to adjust the density of the initial triangular mesh according to the degree of water pollution using a mesh subdivision algorithm to obtain an adjusted mesh; wherein, in the adjusted mesh, the mesh density of the adjusted mesh with a higher degree of pollution is greater than the mesh density of the adjusted mesh with a lower degree of pollution. An update module is used to update the adjusted grid based on water monitoring results.

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