A Fusion Method, Device and Electronic Equipment for Multi-Source Bathymetric Data

Abstract

The present invention relates to a method, device and electronic equipment for fusing multi-source bathymetric data, which is applicable to processing a large number of fragmented chart survey data and filling in the unfilled blank areas with historical data. The invention mainly uses image recognition technology to convert the CDD spatial distribution map into an RGB matrix, and then translates the RGB matrix into a 0-1 matrix indicating whether filling is required, so as to directly compare the HDD with the 0-1 matrix to determine whether to fill, greatly reducing the computational complexity of fusing irregular chart bathymetric data. The invention also draws an image with spatial exclusion conditions through CDD to meet the requirements of high-precision fusion, so that during the multi-source bathymetric data fusion process, the data points are more evenly distributed, which is conducive to the subsequent spatial interpolation process of scatter points.

Description

Technical Field

[0001] The present invention relates to the field of nautical chart data processing, and in particular to a method, device and electronic equipment for fusing multi-source water depth data. Background Art

[0002] Accurate and updated water depth data is very necessary in ocean data processing. The formation of complete and large-scale electronic chart water depth grid data is of great significance in the operation of the model. When processing the data of a certain working area of ​​the electronic chart, there is often a situation where the large-scale chart is cut out. If you want to obtain high-precision charts in a large area, you need to obtain the water depth information of all blocks in this area to fill the gaps in the large-scale charts that have been cut out, which requires a lot of cost. In actual operation, in order to save costs and improve efficiency, electronic charts of nearshore waters are generally purchased and used, and then merged with historical data of the far coast (such as public terrain data) to obtain high-precision water depth data in a large area, which inevitably involves the problem of filling blank areas of the chart.

[0003] At present, the known CDD (Charted Depth Data) is often concentrated in one or several smaller areas (such as nearshore waters) and presented in the form of scattered points, which makes it difficult to form large-scale grid depth data. Therefore, it is necessary to use HDD (Historical Depth Data) to merge with the blank areas of CDD so that the depth data points in the study area are distributed more evenly to facilitate the interpolation and gridding process.

[0004] The traditional method of using CDD and HDD to fuse nautical charts is often used for relatively regular nautical chart areas. The methods are mainly divided into two categories: one is to fuse CDD and HDD by manually inputting the latitude and longitude range of the area to be fused; the other is to complete the large-scale nautical chart by calculating the distance between a large number of CDDs and HDDs. These methods are often very cumbersome, and the computer faces a lot of calculations. During the fusion process, the data points are often unevenly distributed. At the same time, there is no good way to fuse the water depth data of irregular nautical chart areas at this stage. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method, device and equipment for fusing multi-source water depth data.

[0006] The embodiment of the present invention proposes a method for fusing multi-source water depth data, comprising the following steps:

[0007] Determine the latitude and longitude range of the working area, and obtain the CDD spatial distribution color image and HDD data of the working area;

[0008] Convert the CDD spatial distribution color image into an RGB matrix. Taking the reference color block as a standard, convert the RGB matrix into a 0-1 matrix indicating whether filling is required.

[0009] After proportionally adjusting the longitude and latitude coordinates of the HDD data according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD should be filled into this area. Fill the HDD that meets the requirements into the original CDD data in the area where filling is required, and perform the merging of HDD and CDD data.

[0010] Perform two-dimensional spatial interpolation of the scattered points on the merged data to obtain the gridded water depth data of the working area.

[0011] Optionally, obtaining the CDD spatial distribution color image includes: obtaining scattered point CDD data, taking the spatial position of the scattered point CDD as the center of a circle, and using the exclusion distance determined by the resolution as the radius r to draw a CDD spatial exclusion image, and using the CDD spatial exclusion image as the CDD spatial distribution color image.

[0012] Optionally, determining the longitude and latitude range of the working area specifically includes:

[0013] Obtain the longitude and latitude range information of the working area, and record the maximum value of the longitude and the minimum value , the maximum value of the latitude and the minimum value ; determine the data resolution required after filling is completed.

[0014] Optionally, the specific steps for forming the RGB matrix of the CDD spatial distribution color image include:

[0015] According to the data resolution required after filling is completed, the obtained CDD spatial distribution color image of the working area is rows, columns, and a 3-channel RGB image. Convert the CDD spatial distribution color image into a RGB matrix A.

[0016] Optionally, the specific steps for converting the RGB matrix into a 0-1 matrix indicating whether filling is required, taking the reference color block as a standard, include:

[0017] In the RGB matrix A, select a color block that needs to be filled as the reference color block. If the RGB value of a certain point in the working area is the same as the RGB value of the reference color block, record it as 1, otherwise record it as 0, and convert the RGB matrix A of the working area into a 0-1 matrix B.

[0018] Optionally, after proportionally adjusting the longitude and latitude coordinates of the HDD data according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD fills into this area. Fill the HDD that meets the requirements into the original CDD data in the area that needs to be filled. The specific steps of merging the HDD and CDD data include:

[0019] Read the HDD sequentially. The data of a certain point of the HDD has at least three parameters: longitude X, latitude Y, and water depth Z. In the 0-1 matrix B, if then fill the data of this point into the original CDD data. If then do nothing. Among them, and are the maximum and minimum longitude values of the working area, and are the maximum and minimum latitude values of the working area.

[0020] Optionally, perform two-dimensional spatial interpolation on the scattered points of the merged data to obtain the water depth data of the gridded working area. The specific steps include: using Delaunay triangulation to connect all the irregular data scattered points in the working area to form triangles, and then using the cubic equation interpolation method to interpolate the irregular data scattered points into a spatially uniformly distributed grid.

[0021] An embodiment of the present invention also provides a multi-source water depth data fusion device, including:

[0022] A first acquisition unit, configured to determine the longitude and latitude range of the working area, and acquire the CDD spatial distribution color image and HDD data of the working area;

[0023] An image recognition unit, configured to convert the CDD spatial distribution color image into an RGB matrix, and convert the RGB matrix into a 0-1 matrix that needs to be filled or not based on the reference color block;

[0024] A data merging unit, configured to compare the HDD with the 0-1 matrix after proportionally adjusting the longitude and latitude coordinates of the HDD data according to the 0-1 matrix, judge one by one whether the HDD fills into this area, and fill the HDD that meets the requirements into the original CDD data in the area that needs to be filled, and perform the merging of the HDD and CDD data;

[0025] A spatial interpolation unit, configured to perform two-dimensional spatial interpolation on the merged data to obtain the water depth data of the gridded working area.

[0026] An embodiment of the present invention also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, the steps of the above method are implemented.

[0027] An embodiment of the present invention also provides a computer-readable storage medium. The computer-readable medium stores one or more programs. When the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the steps of the above method.

[0028] Beneficial effects:

[0029] By adopting image recognition technology, the present invention reads the intuitive CDD spatial distribution color image in the form of an RGB matrix, fills the blank area of the CDD with the HDD, restricts the growth of the calculation amount, and avoids the operation process of the order of magnitude of the square of calculating the distance one by one between the CDD and the HDD, which is more conducive to the implementation and understanding of the algorithm, and provides a new idea for filling the water depth data of irregular nautical charts.

[0030] At the same time, to meet higher fusion requirements, when obtaining the CDD spatial distribution color image, a CDD spatial exclusion image is drawn with the spatial position of the scattered CDD as the center and the exclusion distance determined by the resolution as the radius r. The CDD spatial exclusion image is used as the CDD spatial distribution color image, so that during the HDD filling process, the CDD and HDD data points are more evenly distributed, which is conducive to the subsequent spatial interpolation process of the scattered points. Description of the drawings

[0031] Figure 1 It is a schematic flowchart of a multi-source water depth data fusion method according to an embodiment of the present invention. Detailed implementation manners

[0032] In order to make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments.

[0033] Explanation of nouns and symbols

[0034] 1. CDD (Charted Depth Data): Charted water depth data, mainly from the s57 water depth data in the marine electronic chart display and information system (ECDIS - Electronic Chart Display and Information System) that complies with international standards. CDD is defined as a scattered point set where local data is accurately concentrated and waiting for data filling.

[0035] 2. HDD (Historical Depth Data): Historical water depth data, mainly sourced from publicly available large-scale data. Such data often comes from satellite data in large quantities and has been gridded. The satellite has a high spatial coverage rate and a large amount of data. However, due to algorithm limitations, it must be corrected in local waters to be more reliable. HDD is defined as a dataset that covers the entire working area and is used for filling. This method assumes that the HHD and CDD data have comparable accuracy. If the accuracies are inconsistent, the accuracy of the fused data lies between the two, and the filled area tends to the accuracy of HDD.

[0036] 3. Target water depth data: The two-dimensional water depth data result produced using this method. Its spatial resolution is divided into the resolution along the latitude direction and the resolution along the longitude direction. For convenience, in the examples of this method, it is assumed that the resolutions along the latitude direction and the longitude direction are the same.

[0037] 4. Working area: The spatial area with geographical location where water depth data is processed using this method. In this method, it is represented by several matrices, such as the water depth matrix, the coordinate matrices of longitude and latitude, etc.

[0038] 5. RGB matrix: A matrix composed of three colors, red, green, and blue (RGB), extracted from the CDD color image. That is, each point in the matrix is not a single value, but an array containing 3 values, and these 3 values are the RGB values (the color values of red, green, and blue).

[0039] 6. Spatial interpolation: Commonly used to convert discrete point data into a continuous data surface. It is convenient for describing the overall spatial phenomenon and distribution state of terrain data.

[0040] 7. Spatial exclusion: After determining the spatial resolution of the target water depth data, with the spatial position of each CDD data as the center, according to the grid size of spatial interpolation, there is an exclusion radius. Within the range of this circle, HDD is not required for filling. In the local process of spatial interpolation, neighboring data points are often used to estimate unknown values. Therefore, on the one hand, it is hoped that the numerical characteristics of CDD can be retained to the greatest extent in the result of spatial interpolation (that is, it is not desired that too much HDD data is close to CDD, thus interfering with the interpolation result), and on the other hand, it is hoped that in the areas not covered by CDD, HDD is used for refined filling.

[0041] 8. floor function: A function that rounds down.

[0042] 9. min function: A function that extracts the minimum value from a given sequence of numbers.

[0043] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a method for fusing multi-source water depth data. The main steps are as follows: using image recognition technology, obtaining an intuitive color image of the CDD spatial distribution as an RGB matrix, converting the RGB matrix into a 0-1 matrix indicating whether filling is required, so as to directly compare the HDD with the 0-1 matrix to determine whether filling is needed. Finally, the satisfied HDD and CDD are merged, and the merged data is interpolated to obtain a complete water depth network diagram of the working area.

[0044] Specifically,

[0045] Step S100: Determine the longitude and latitude range of the working area, and obtain the CDD spatial distribution color image and HDD data of the working area;

[0046] Step S200: Convert the CDD spatial distribution color image into an RGB matrix, and convert the RGB matrix into a 0-1 matrix indicating whether filling is required with reference to the reference color block;

[0047] Step S300: After adjusting the longitude and latitude coordinates of the HDD data proportionally according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD is filled into this area. Fill the satisfied HDD into the original CDD data in the area where filling is required, and merge the HDD and CDD data;

[0048] Step S400: Perform two-dimensional spatial interpolation of the scattered points on the merged data to obtain the gridded water depth data of the working area.

[0049] Among them, when performing step S100, first determine the longitude and latitude range information of the obtained working area, and record the maximum longitude and the minimum value , the maximum latitude and the minimum value . Then determine the required data resolution after filling. In this embodiment, the working area ranges from the Beibu Gulf Sea area (east longitude , north latitude ), and record the longitude and latitude range information. In this working area, , , , .

[0050] Obtain the CDD spatial distribution color image and HDD data of the working area. There are a total of 28 small sea charts of CDD in the working area, which are combined into the final CDD spatial distribution color image. In this embodiment, the HDD type is grid data, and the accuracy is , and the data covers the entire working area. The CDD data is scattered point type water depth data.

[0051] When obtaining the water depth data of the working area with low requirements, only the blank gray area a is filled with HDD. Therefore, the CDD spatial distribution color image is directly converted into a three-channel RGB matrix in the following steps.

[0052] However, since the CDD data is scattered water depth data, the positions of CDD data points in the existing CDD spatial distribution color image are uneven. In some areas, the CDD data points are dense, while in some areas, the CDD data points are sparse. If the existing CDD data points are directly interpolated in space as scattered points, due to the sparse CDD data points in some areas, when interpolating the scattered points into small grids, there will be a large error in the results. Therefore, in the present invention, when obtaining the water depth data of the working area with high requirements, scattered CDD data is obtained. With the spatial position of the scattered CDD as the center and the exclusion distance determined by the resolution as the radius r, a CDD spatial exclusion image is drawn. The CDD spatial exclusion image serves as the CDD spatial distribution color image, and the color of the CDD spatial exclusion area is specially set to be different from the color of the area to be filled. The positions outside the scattered CDD spatial exclusion area are filled with HDD, and within the range of a circle with a radius of one exclusion radius, HDD filling is not required. Since in the local process of spatial interpolation, adjacent data points are often used to estimate unknown values, on the one hand, it is not desirable for too much HDD data to be close to CDD, thus interfering with the interpolation results. On the other hand, it is desired to use HDD for refined filling in the areas not covered by CDD. Due to the sparse CDD data points in some areas, the areas filled with HDD are irregular areas. In the existing methods, either the longitude and latitude ranges of the areas to be fused are manually input to fuse CDD and HDD, or the distances between a large number of CDD and HDD are calculated to complete the large-scale chart. These methods are often very cumbersome, and the computer faces a large amount of calculations. During the fusion process, the data points are often unevenly distributed, and there is no good method to fuse the water depth data of irregular chart areas at present. In the present invention, by converting the CDD spatial distribution color image into an RGB matrix, and then converting the RGB matrix into a 0-1 matrix indicating whether filling is required, the area that needs to be filled with HDD is automatically determined using the 0-1 matrix, making the calculation amount of the multi-source water depth data fusion process small and providing a new idea for filling the water depth data of irregular charts.

[0053] In this embodiment, the exclusion distance r can be a determined value, for example , or a default value obtained according to the resolution can also be used .

[0054] Execute step S200 to convert the CDD spatial distribution color image into an RGB matrix, and convert the RGB matrix into a 0-1 matrix indicating whether filling is required with reference to the reference color block.

[0055] In this embodiment, when it is necessary to obtain the water depth data of the working area with low requirements, since there are no data points distributed in the gray area a, only the gray area a needs to be filled by HDD. The pixel points are directly read, and the image is converted into a three-channel RGB matrix. Since the color image of the CDD spatial distribution in the working area is rows, columns, and a three-channel RGB image, the CDD spatial distribution color image is converted into RGB matrix A. In this embodiment, , , and the size of the formed RGB matrix A is .

[0056] However, when it is necessary to obtain the water depth data of the working area with high requirements, since the positions of the CDD data points in the CDD spatial distribution color image are uneven and the CDD data points are sparse in some areas, HDD filling is also required. Therefore, the CDD spatial exclusion image is converted into a three-channel RGB matrix, and the CDD spatial exclusion image is converted into RGB matrix A. In this embodiment, , , and the size of the formed RGB matrix A is .

[0057] Then, the white color in the CDD survey point distribution map is used as a reference color block (the RGB value of the white color block is [255 255 255]), and a 0-1 matrix is formed according to the RGB matrix file (the positions in the RGB matrix with RGB values of [255 255 255] are the areas to be filled, denoted as 1, and other areas are denoted as 0). The size of the 0-1 matrix B is also .

[0058] Execute step S300. After adjusting the longitude and latitude coordinates of the HDD data in proportion according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD is filled into this area. Fill the HDD that meets the requirements into the original CDD data in the area to be filled, and merge the HDD and CDD data.

[0059] Since the existing HDD data is a dataset that covers the entire working area and is used for filling. According to the resolution of the present invention (i.e., the matrix size), the longitude and latitude coordinates of the HDD data are adjusted. Each point of the HDD data has at least three parameters: longitude X, latitude Y, and water depth Z. Among them, the longitude X and latitude Y are the same as the longitude and latitude of the RGB matrix and the 0-1 matrix.

[0060] Read the HDD sequentially. If , then this point needs to be filled into the CDD area, that is, X, Y, and Z are merged with the CDD; If , no operation is performed.

[0061] When high-precision water depth data of the working area is required, use a 0-1 matrix to find the irregular areas that need data filling, and then use HDD to completely fill the gaps to obtain the spatial distribution image of the filled data points.

[0062] In this embodiment, when low-precision water depth data of the working area is required, HDD filling is directly performed; when high-precision water depth data of the working area is required, the exclusion area is processed first when obtaining the CDD spatial distribution color image, and then HDD filling is performed.

[0063] In other embodiments, it can also be carried out in two steps. First, low-precision HDD filling is performed, and then the CDD exclusion area is processed, and the second high-precision HDD filling is performed.

[0064] Execute step S400 to perform two-dimensional spatial interpolation on the merged data to obtain the gridded water depth data of the working area.

[0065] In this embodiment, the method of performing two-dimensional spatial interpolation on the merged data specifically includes: using Delaunay triangulation to connect all irregular data scatter points in the working area to form triangles, and then using the cubic equation interpolation method to interpolate the irregular data scatter points into a spatially uniformly distributed grid.

[0066] In other embodiments, the two-dimensional spatial interpolation method can also be the inverse distance weighted method, the Kriging method, etc.

[0067] After verification, the present invention uses the technology of image reading, replaces the previous operation process with an intuitive image, effectively improves the operation efficiency, and greatly simplifies the problem of filling the blank area of the nautical chart.

[0068] In addition, the embodiment of the present invention also provides a multi-source water depth data fusion device, including:

[0069] The first acquisition unit is used to determine the longitude and latitude range of the working area and acquire the CDD spatial distribution color image and HDD data of the working area;

[0070] The image recognition unit is used to convert the CDD spatial distribution color image into an RGB matrix, and convert the RGB matrix into a 0-1 matrix that needs to be filled with reference to the reference color block;

[0071] A data merging unit, which is configured to compare the HDD with the 0-1 matrix after proportionally adjusting the longitude and latitude coordinates of the HDD data according to the 0-1 matrix, and judge one by one whether the HDD fills into this area. In the area that needs to be filled, the HDD that meets the requirements is filled into the original CDD data to merge the HDD and CDD data;

[0072] A spatial interpolation unit, which is configured to perform two-dimensional spatial interpolation of scattered points on the merged data to obtain the water depth data of the gridded working area.

[0073] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, including: at least one processor, and a memory communicatively connected to at least one of the processors; at least one of the processors is configured to read a program in the memory and execute the method described above.

[0074] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored. When the instructions run on a computer, the computer is caused to execute the method described above.

[0075] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of them should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for fusing multi-source bathymetric data, characterized in that It includes the following steps: Determine the longitude and latitude range of the working area, and obtain the CDD spatial distribution color image and HDD data of the working area; Convert the CDD spatial distribution color image into an RGB matrix, and taking the reference color block as the standard, convert the RGB matrix into a 0-1 matrix indicating whether filling is required; After proportionally adjusting the longitude and latitude coordinates of the HDD data according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD is filled into this area. In the area where filling is required, fill the HDD that meets the requirements into the original CDD data to perform the merger of HDD and CDD data; Perform two-dimensional spatial interpolation of the scattered points on the merged data to obtain the gridded water depth data of the working area; Among them, taking the reference color block as the standard, the specific steps for converting the RGB matrix into a 0-1 matrix that needs to be filled include: in the RGB matrix A, select a color block that needs to be filled as the reference color block. If the RGB value of a certain point in the working area is the same as the RGB value of the reference color block, it is recorded as 1, otherwise it is recorded as 0. Convert the RGB matrix A of the working area into a 0-1 matrix B; After adjusting the longitude and latitude coordinates of HDD data proportionally according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD fills into this area. Fill the HDD that meets the requirements into the original CDD data in the area that needs to be filled. The specific steps for merging HDD and CDD data include: sequentially reading the HDD. The data of a certain point of the HDD has at least three parameters: longitude X, latitude Y, and water depth Z. In the 0-1 matrix B, if , then fill the data of this point into the original CDD data. If , then do not perform any operation; Among them, and are the maximum and minimum longitudes of the working area, and are the maximum and minimum latitudes of the working area, and are the number of rows and columns of the obtained color image of the CDD spatial distribution in the working area respectively, is the floor function.

2. The fusion method of multi-source water depth data according to claim 1, characterized in that Obtaining the CDD spatial distribution color image includes: obtaining scattered point CDD data, taking the spatial position of the scattered point CDD as the center, and using the exclusion distance determined by the resolution as the radius r to draw a CDD spatial exclusion image, and using the CDD spatial exclusion image as the CDD spatial distribution color image.

3. A method for fusing multi-source water depth data according to claim 1, characterized in that, Determining the longitude and latitude range of the working area specifically includes: Obtain the longitude and latitude range information of the working area and record the maximum longitude and the minimum value , the maximum latitude and the minimum value ; Determine the data resolution required after filling is completed.

4. A method for fusing multi-source water depth data according to claim 1, characterized in that, The specific steps of forming the RGB matrix of the CDD spatial distribution color image include: According to the required data resolution after filling, the obtained color image of the CDD spatial distribution in the working area is row, column, 3-channel RGB image, and convert the CDD spatial distribution color image into RGB matrix A.

5. The fusion method of multi-source water depth data according to claim 1, characterized in that, Performing two-dimensional spatial interpolation of the scattered points on the merged data to obtain the gridded water depth data of the working area specifically includes: using Delaunay triangulation to connect all the irregular data scattered points in the working area to form triangles, and then using the cubic equation interpolation method to interpolate the irregular data scattered points into a spatially uniformly distributed grid.

6. A fusion device for multi-source bathymetric data, characterized in that, It includes: The first acquisition unit is used to determine the longitude and latitude range of the working area, and obtain the CDD spatial distribution color image and HDD data of the working area; The image recognition unit is used to convert the CDD spatial distribution color image into an RGB matrix, and taking the reference color block as the standard, convert the RGB matrix into a 0-1 matrix indicating whether filling is required; The data merger unit is used to proportionally adjust the longitude and latitude coordinates of the HDD data according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD is filled into this area. In the area where filling is required, fill the HDD that meets the requirements into the original CDD data to perform the merger of HDD and CDD data; The spatial interpolation unit is used to perform two-dimensional spatial interpolation of the scattered points on the merged data to obtain the gridded water depth data of the working area; Among them, taking the reference color block as the standard, the specific steps for converting the RGB matrix into a 0-1 matrix that needs to be filled include: In the RGB matrix A, select a color block that needs to be filled as the reference color block. If the RGB value of a certain point in the working area is the same as the RGB value of the reference color block, it is recorded as 1; otherwise, it is recorded as 0. Convert the RGB matrix A of the working area into a 0-1 matrix B; After adjusting the longitude and latitude coordinates of the HDD data in proportion according to the 0-1 matrix, compare the HDD with the 0-1 matrix, and judge one by one whether the HDD fills into this area. Fill the HDD that meets the requirements into the original CDD data in the area that needs to be filled. The specific steps for merging HDD and CDD data include: sequentially reading the HDD. The data of a certain point of the HDD has at least three parameters: longitude X, latitude Y, and water depth Z. In the 0-1 matrix B, if , then fill the data of this point into the original CDD data. If , then do not perform any operation; Among them, and are the maximum and minimum longitudes of the working area, and are the maximum and minimum latitudes of the working area, and are the number of rows and columns of the obtained color image of the CDD spatial distribution in the working area respectively, is the floor function.

7. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs. When the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the steps of the method according to any one of claims 1-5.

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