A three-dimensional contour dynamic drawing method based on a Web platform

Abstract

The application relates to a three-dimensional contour dynamic drawing method based on a Web platform. Based on a semi-variogram index model, attribute weight vectors and height weight vectors are calculated, attribute values and height values of grid points are obtained, interpolation of the whole calculation region is realized, and thus grid point data with coordinates, longitude and latitude, attribute values and height values are obtained. Based on the grid point data, three-dimensional gridding is carried out, triangular meshes are obtained, and finally a three-dimensional contour map is generated. The application can directly load and process sample data, fuse and calculate terrain data and attribute data, and through high-precision four-dimensional interpolation calculation, a continuous three-dimensional terrain model which reflects terrain undulation and contains attribute information is constructed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data visualization, in particular, to a three-dimensional contour dynamic drawing method based on a Web platform. BACKGROUND

[0002] Three-dimensional contour maps are an advanced data visualization tool that displays the variation and distribution characteristics of data through contours in three-dimensional space. Contour maps essentially represent continuous distribution and gradual change of quantitative characteristics with connecting lines of equal value points. These lines connect points on the map with equal values of a particular data set, such as height, temperature, pressure, etc. In three-dimensional contour maps, this representation method is extended to three-dimensional space, making the variation and distribution characteristics of data more intuitive in three-dimensional scale.

[0003] However, current browser-based contour drawing tools are mostly limited to two-dimensional data processing and cannot effectively handle three-dimensional coordinate point data, let alone achieve dynamic display of three-dimensional contours and efficient data output. Traditional three-dimensional contour generation methods can handle three-dimensional data, but when faced with four-dimensional data containing height information and additional attribute values, their processing capacity and display effect are obviously limited. In addition, these methods usually rely on desktop software such as MATLAB, which not only has complex operations but also makes it difficult to achieve real-time interaction and display in a Web environment, limiting its widespread application in practical applications. SUMMARY

[0004] To overcome at least one deficiency in the prior art, the present application provides a three-dimensional contour dynamic drawing method based on a Web platform.

[0005] In a first aspect, a three-dimensional contour dynamic drawing method based on a Web platform is provided, comprising:

[0006] Obtaining sample point data of a calculation region, the sample point data including attribute value data, height data, and coordinate data;

[0007] Determining parameters of a semi-variogram model based on the sample point data; the parameters of the semi-variogram model include nugget value and sill value;

[0008] Calculating semi-variogram values corresponding to any two sample points using the semi-variogram model based on the distance between any two sample points in the sample point data and the parameters of the semi-variogram model; constructing a Gram matrix based on all semi-variogram values;

[0009] Obtaining an attribute weight vector based on the Gram matrix and the attribute value data; obtaining a height weight vector based on the Gram matrix and the height data;

[0010] The sample point data, the attribute weight vector, and the height weight vector are used to divide a calculation region into grids to determine grid point data; the grid point data includes grid point coordinates, grid point longitude, and grid point latitude; covariance between the grid point and the sample point is calculated to form a covariance vector; based on the covariance vector, the attribute weight vector, and the height weight vector, attribute values of the grid point and height values of the grid point are obtained;

[0011] Color values of each grid point are determined; the grid point coordinates, the grid point longitude and latitude, the attribute values of the grid point, the height values of the grid point, and the color values of the grid point form final grid point data;

[0012] The final grid point data is subjected to three-dimensional gridding to obtain a triangular mesh; a three-dimensional contour map is obtained based on the triangular mesh.

[0013] In one embodiment, parameters of a semi-variogram model are determined based on sample point data, including:

[0014] Distances between any two sample points and semi-variances are calculated;

[0015] All distances are sorted in ascending order to obtain a sorted distance sequence and a corresponding semi-variance sequence;

[0016] The maximum value in the distance sequence is taken as a range;

[0017] The distance sequence is divided into multiple intervals, and the mean of all distances in each interval is calculated to form a distance mean array;

[0018] Based on each element in the distance mean array, a theoretical semi-variogram value corresponding to each element is calculated; a theoretical semi-variogram value vector is constructed based on the theoretical semi-variogram value corresponding to each element; the semi-variance sequence forms a semi-variance vector;

[0019] Based on the theoretical semi-variogram value vector and the semi-variance vector, nugget values and sill values are determined.

[0020] In one embodiment, based on the theoretical semi-variogram value vector and the semi-variance vector, nugget values and sill values are determined, including:

[0021] The product of the transpose of the theoretical semi-variogram value vector and the theoretical semi-variogram value vector is calculated to obtain a covariance matrix;

[0022] The covariance matrix is added to a diagonal matrix to obtain a stabilized covariance matrix;

[0023] The stabilized covariance matrix is subjected to Cholesky decomposition to obtain an inverse matrix;

[0024] The product of the inverse matrix and the transpose of the theoretical semi-variogram value vector is calculated, and multiplied by the semi-variance vector to obtain a final vector;

[0025] The first row of the final vector corresponds to a row vector as a nugget value;

[0026] The second row of the final vector corresponds to a row vector multiplied by the range, plus the nugget value, to obtain a sill value; the sill value minus the nugget value, and then divided by the range, to obtain a bias value.

[0027] In an embodiment, the semi-variogram model is:

[0028]

[0029] Wherein, γ(h) is the semi-variogram value corresponding to the distance h, C0 is the nugget value, C is the bias value, and a is the range.

[0030] In an embodiment, determining the color value of each grid point comprises:

[0031] Obtaining a color band configuration file;

[0032] Determining the color range of each grid point according to the color band configuration file;

[0033] Using the interpolateColor function to determine the color value of the grid point based on the color range.

[0034] In an embodiment, the final grid point data is three-dimensionally gridded to obtain a triangular mesh, comprising:

[0035] Constructing a triangular mesh according to the adjacent relationship of the final grid point data in the longitude and latitude directions.

[0036] In a second aspect, a three-dimensional contour dynamic drawing device based on a Web platform is provided, comprising:

[0037] A sample point data acquisition module is configured to acquire sample point data of a calculation region, the sample point data including attribute value data, height data, and coordinate data;

[0038] A parameter determination module is configured to determine parameters of a semi-variogram model based on the sample point data; the parameters of the semi-variogram model include a nugget value and a bias value;

[0039] A Gram matrix construction module is configured to calculate semi-variogram values corresponding to any two sample points using the semi-variogram model based on the distance between any two sample points in the sample point data and the parameters of the semi-variogram model; and construct a Gram matrix based on all the semi-variogram values;

[0040] A weight vector acquisition module is configured to obtain an attribute weight vector based on the Gram matrix and the attribute value data; and obtain a height weight vector based on the Gram matrix and the height data;

[0041] a meshing module, configured to perform meshing on the calculation region based on the sample point data, the attribute weight vector, and the height weight vector, and determine mesh point data, wherein the mesh point data comprises mesh point coordinates, mesh point longitude and latitude, mesh point attribute values, mesh point height values, and mesh point color values;

[0042] a mesh point data determination module, configured to calculate a covariance between the mesh point and the sample point, and form a covariance vector, and obtain the mesh point attribute values and the mesh point height values based on the covariance vector, the attribute weight vector, and the height weight vector, and determine the color values of each mesh point;

[0043] a three-dimensional meshing module, configured to perform three-dimensional meshing on the final mesh point data, and obtain a triangular mesh, and obtain a three-dimensional contour map based on the triangular mesh.

[0044] In a third aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the three-dimensional contour dynamic drawing method based on a Web platform.

[0045] In a fourth aspect, an embodiment of the present application provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the three-dimensional contour dynamic drawing method based on a Web platform.

[0046] Compared with the prior art, the present application has the following beneficial effects: based on the semi-variogram model, the attribute weight vector and the height weight vector are calculated, and the attribute values and the height values of the mesh points are obtained, interpolation of the entire calculation region is realized, and thus the mesh point data with coordinates, longitude and latitude, attribute values, and height values is obtained; three-dimensional meshing is performed based on the mesh point data, a triangular mesh is obtained, and finally a three-dimensional contour map is generated. The present application can directly load and process sample data, fuse and calculate terrain data and attribute data, perform high-precision four-dimensional interpolation calculation, and construct a continuous three-dimensional terrain model which reflects terrain undulation and contains attribute information. BRIEF DESCRIPTION OF DRAWINGS

[0047] The present application can be better understood by referring to the following description in conjunction with the accompanying drawings, which form a part of this specification. In the drawings:

[0048] Figure 1 a flow chart of the three-dimensional contour dynamic drawing method based on a Web platform is shown;

[0049] Figure 2A schematic diagram showing the boundary points of the sample points and the computational region is shown.

[0050] Figure 3 The present invention provides a three-dimensional contour map obtained by the method of this application and a two-dimensional contour map obtained by prior art, wherein (a) is a two-dimensional contour map obtained by prior art and (b) is a three-dimensional contour map obtained by the method of this application. Detailed Implementation

[0051] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0052] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution according to this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0053] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0054] This application provides a method for dynamically drawing three-dimensional contour lines based on a Web platform. Figure 1 A flowchart of a web-based method for dynamically drawing 3D contour lines is shown. See [link / reference]. Figure 1 The methods include:

[0055] Step S1: Obtain sample point data for the calculation area. The sample point data includes attribute value data, height data, and coordinate data. Here, attribute value data can be, for example, temperature, pressure, etc. Figure 2 A schematic diagram of the sample points and the boundary points of the computational region is shown.

[0056] Step S2: Determine the parameters of the semi-variation index model based on the sample point data; the parameters of the semi-variation index model include the nugget value and the bias value.

[0057] Specifically, the semi-variance index model is as follows:

[0058]

[0059] Where γ(h) is the semi-variable value corresponding to distance h, C0 is the nugget value, C is the offset value, and a is the range.

[0060] Step S3, based on the distance between any two sample points in the sample point data and the parameters of the semi-variogram model, a semi-variogram value corresponding to any two sample points is calculated using the semi-variogram model; and based on all the semi-variogram values, a Gram matrix is constructed.

[0061] Step S4, based on the Gram matrix and the attribute value data, an attribute weight vector is obtained; and based on the Gram matrix and the height data, a height weight vector is obtained.

[0062] Here, the attribute weight vector is obtained by multiplying the vector corresponding to the Gram matrix and the attribute value data; and the height weight vector is obtained by multiplying the vector corresponding to the Gram matrix and the height data. The two vectors contain information of each sample point after a series of mathematical operations and optimization, which are used to calculate the weight vector during prediction. In addition, during the generation of the attribute weight vector and the height weight vector, the construction of the Gram matrix and the inverse operation are performed, which to some extent eliminates the noise and uncertainty in the original data. The original data may have measurement errors, outliers and other problems, while the vector obtained by processing the attribute value array can more stably and reliably reflect the essential characteristics of the data.

[0063] Step S5, based on the sample point data, the attribute weight vector, and the height weight vector, the calculation area is divided into grids to determine grid point data; the grid point data includes grid point coordinates, grid point longitude and latitude; the covariance between the grid points and the sample points is calculated to form a covariance vector; based on the covariance vector, the attribute weight vector, and the height weight vector, the attribute value of the grid point and the height value of the grid point are obtained.

[0064] Here, based on the calculation boundary bound, the boundary grid spacing, the sample point coordinates, the attribute value data, the height data, the exponential model object, the attribute weight vector, and the height weight vector, the grid division is performed. The attribute value of the grid point is obtained by multiplying the covariance vector and the attribute weight vector, and the height value of the grid point is obtained by multiplying the covariance vector and the height weight vector.

[0065] Step S6, the color value of each grid point is determined; the grid point coordinates, the grid point longitude and latitude, the attribute value of the grid point, the height value of the grid point, and the color value of the grid point constitute the final grid point data.

[0066] Here, the color value of each grid point is determined, including:

[0067] A color band configuration file is obtained; here, the color band configuration file can be provided by the system or customized by the user.

[0068] Determine the color range of each grid point according to the color band profile; the color band profile includes attribute value range and corresponding color, and the color range corresponding to the attribute value of the grid point is determined in the color band profile.

[0069] The color value of the grid point is determined by color interpolation based on the color range by using the interpolateColor function.

[0070] In step S7, the final grid point data is subjected to three-dimensional gridding to obtain a triangular mesh, and a three-dimensional contour map is obtained based on the triangular mesh.

[0071] Here, the final grid point data is subjected to three-dimensional gridding, including: constructing a triangular mesh according to the adjacent relationship of the final grid point data in the longitude and latitude directions. The grid points adjacent in the longitude and latitude directions are connected to form a plurality of triangles, and the three points of each triangle are the target point, the point adjacent to the target point in latitude, and the point adjacent to the target point in longitude. This method avoids complex global search and judgment process, and realizes fast triangular mesh generation by direct use of local adjacent information, and is especially suitable for large-scale data point processing scenarios. In other embodiments, the three-dimensional gridding can also use the existing Delaunay triangulation method.

[0072] After obtaining the triangular mesh, a Cesium geometry is created based on the triangular mesh, and is encapsulated, and finally the geometry instance is added to the Cesium scene, so that the three-dimensional triangular mesh composed of the data points can be visualized on the three-dimensional earth, and finally a three-dimensional contour map is obtained. Figure 3 The three-dimensional contour map obtained by the method of the present application and the two-dimensional contour map obtained by the prior art are shown, wherein (a) is a two-dimensional contour map obtained by the prior art, and (b) is a three-dimensional contour map obtained by the method of the present application.

[0073] In this embodiment, based on the semi-variogram model, the attribute weight vector and the height weight vector are calculated, and the attribute value and the height value of the grid point are obtained, the interpolation of the entire calculation region is realized, and thus the grid point data with coordinates, longitude and latitude, attribute value, and height value is obtained; the grid point data is subjected to three-dimensional gridding to obtain a triangular mesh, and finally a three-dimensional contour map is generated. The present application can directly load and process sample data, fuse and calculate terrain data and attribute data, and construct a continuous three-dimensional terrain model reflecting terrain undulation and containing attribute information through high-precision four-dimensional interpolation calculation.

[0074] In one embodiment, in step S2, the parameters of the semi-variogram model are determined based on the sample point data, including:

[0075] Step S21, calculate the distance and semi-variance between any two sample points; here, the distance between two sample points (x i ,y i ) and (x j ,y j ) are the coordinates of two sample points, the semi-variance between two sample points, refers to the absolute value of the difference between the attribute values of two sample points.

[0076] Step S22, sort all distances in ascending order to obtain the sorted distance sequence and the corresponding semi-variance sequence; here, sort all distances in ascending order to obtain the sorted distance sequence, each pair of sample points corresponds to a distance and a semi-variance, and after obtaining the sorted distance sequence, the semi-variance sequence semi can be obtained accordingly according to the same sorting.

[0077] Step S23, take the maximum value in the distance sequence as the range;

[0078] Step S24, divide the distance sequence into multiple intervals, calculate the mean of all distances in each interval, and construct a distance mean array lag; here, if the number of elements in the distance sequence is greater than 30, the number of intervals is 30, otherwise, the number of intervals is equal to the number of elements in the distance sequence.

[0079] Step S25, based on each element in the distance mean array, calculate the theoretical semi-variation value corresponding to each element; based on the theoretical semi-variation value corresponding to each element, construct a theoretical semi-variation value vector X; the semi-variance sequence constitutes a semi-variance vector Y;

[0080] Here, the theoretical semi-variation value Where a is the range, and lag[i] represents the ith element in the distance mean array.

[0081] The ith row vector in the theoretical semi-variation value vector X is [1, R(i)].

[0082] Step S26, determine nugget value and bias value based on the theoretical semi-variation value vector and the semi-variance vector.

[0083] In one embodiment, step S26, based on the theoretical semi-variation value vector and the semi-variance vector, determines the nugget value and the bias value, comprising:

[0084] Calculate the product of the transpose of the theoretical semi-variation value vector X and the theoretical semi-variation value vector X to obtain the covariance matrix Z;

[0085] Add the diagonal matrix to the covariance matrix Z to obtain the stabilized covariance matrix; the elements on the diagonal of the diagonal matrix are 1 / interpolation precision, and the interpolation precision is set according to experimental results.

[0086] Cholesky decomposition is performed on the stabilized covariance matrix to obtain the inverse matrix;

[0087] The product of the inverse matrix and the transpose of the theoretical semi-variation value vector X is calculated, and multiplied by the semi-variation vector Y to obtain the final vector W;

[0088] The row vector corresponding to the first row of the final vector is taken as the nugget value;

[0089] The row vector corresponding to the second row of the final vector is multiplied by the range, and then the nugget value is added to obtain the base value; the base value is subtracted by the nugget value, and then divided by the range to obtain the bias value.

[0090] In this embodiment, due to various complex situations of actual data, such as insufficient sample size which may lead to insufficient representativeness of data, and uneven distribution of data which may make the data in some areas too sparse or dense. In the calculation process, these factors are easy to cause rounding errors, and then lead to the problem of numerical instability when solving the inverse matrix or performing eigenvalue decomposition, which seriously affects the accuracy and reliability of subsequent calculation.

[0091] In order to solve this problem, a small positive definite matrix is added to the covariance matrix to ensure that the added covariance matrix has positive definiteness. For example, the value of the element on the diagonal can be dynamically adjusted according to the size of the interpolation accuracy, when the interpolation accuracy requirement is higher, the value of the element on the diagonal is appropriately increased, to more strongly ensure the positive definiteness of the covariance matrix. In this way, the problem of singularity or non-positive definiteness of the covariance matrix can be effectively solved, and the stability and reliability of the calculation are improved. The parameters in the W vector can more accurately reflect the characteristics of the data, and provide more accurate basic data for subsequent interpolation calculation.

[0092] Further, the three-dimensional contour lines obtained by processing can be directly printed or exported as a txt file on the web side, which expands the use range of the data and meets the needs of different users in different scenarios.

[0093] The same inventive concept as the method for dynamically drawing three-dimensional contour lines based on a Web platform is adopted, and the embodiment further provides a device for dynamically drawing three-dimensional contour lines based on a Web platform, which comprises:

[0094] A sample point data acquisition module is configured to acquire sample point data of a calculation region, the sample point data including attribute value data, height data and coordinate data;

[0095] A parameter determination module is configured to determine parameters of a semi-variation exponential model based on the sample point data; the parameters of the semi-variation exponential model include a nugget value and a bias value;

[0096] a Gram matrix construction module, configured to calculate, based on distances between any two sample points in the sample point data and parameters of the semi-variogram model, semi-variogram values corresponding to any two sample points by using the semi-variogram model, and construct a Gram matrix based on all the semi-variogram values;

[0097] a weight vector acquisition module, configured to obtain an attribute weight vector based on the Gram matrix and the attribute value data, and obtain a height weight vector based on the Gram matrix and the height data;

[0098] a grid division module, configured to divide the calculation region into grids based on the sample point data, the attribute weight vector, and the height weight vector, and determine grid point data, wherein the grid point data comprises grid point coordinates, grid point longitude and latitude;

[0099] a grid point data determination module, configured to calculate covariances between the grid points and the sample points to form a covariance vector, obtain attribute values of the grid points and height values of the grid points based on the covariance vector, the attribute weight vector, and the height weight vector, and determine color values of each grid point, wherein the grid point coordinates, the grid point longitude and latitude, the attribute values of the grid points, the height values of the grid points, and the color values of the grid points form final grid point data;

[0100] a three-dimensional gridding module, configured to perform three-dimensional gridding on the final grid point data to obtain a triangular mesh, and obtain a three-dimensional contour map based on the triangular mesh.

[0101] The three-dimensional contour dynamic drawing device based on the Web platform of this embodiment has the same inventive concept as the three-dimensional contour dynamic drawing method based on the Web platform described above, and thus the specific implementation of the device can be seen from the embodiment part of the three-dimensional contour dynamic drawing method based on the Web platform described above, and the technical effects thereof correspond to those of the method described above, which will not be described here.

[0102] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the three-dimensional contour dynamic drawing method based on the Web platform described above.

[0103] The embodiment of the present application provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the three-dimensional contour dynamic drawing method based on the Web platform described above.

[0104] In summary, the present application has the following technical effects:

[0105] The application can directly load and process sample data, fuse and calculate terrain data and attribute data, construct a continuous three-dimensional terrain model which reflects terrain undulation and contains attribute information through high-precision four-dimensional interpolation calculation. On this basis, the size and distribution of the attribute value are directly expressed by color coding, and then dynamically displayed on the Web. At the same time, the system can automatically extract contour information and export it as a txt data file, improving the data processing efficiency and visualization effect.

[0106] The above merely describes various embodiments of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the application, which should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A method for dynamically drawing three-dimensional contour lines based on a Web platform, characterized in that, The method comprises the following steps: obtaining sample point data of a calculation area, the sample point data comprising attribute value data, height data and coordinate data; determining parameters of a semi-variogram exponential model based on the sample point data; the parameters of the semi-variogram exponential model comprising a nugget value and a sill value; calculating semi-variogram values corresponding to any two sample points by using the semi-variogram exponential model based on distances between any two sample points in the sample point data and the parameters of the semi-variogram exponential model; constructing a Gram matrix based on all the semi-variogram values; obtaining an attribute weight vector based on the Gram matrix and the attribute value data; obtaining a height weight vector based on the Gram matrix and the height data; performing grid division on the calculation area based on the sample point data, the attribute weight vector and the height weight vector to determine grid point data; the grid point data comprising grid point coordinates, grid point longitude and latitude; calculating covariances between grid points and sample points to form a covariance vector; obtaining attribute values of the grid points and height values of the grid points based on the covariance vector, the attribute weight vector and the height weight vector; determining color values of each grid point; the grid point coordinates, the grid point longitude and latitude, the attribute values of the grid points, the height values of the grid points and the color values of the grid points forming final grid point data; performing three-dimensional gridding on the final grid point data to obtain a triangular mesh; obtaining a three-dimensional contour map based on the triangular mesh.

2. The method of claim 1, wherein, In the method, determining the parameters of the semi-variogram exponential model based on the sample point data comprises the following steps: calculating distances between any two sample points and semi-variances; sorting all the distances in ascending order to obtain a sorted distance sequence and a corresponding semi-variance sequence; taking a maximum value in the distance sequence as a range; dividing the distance sequence into multiple intervals, calculating the mean of all distances in each interval to form a distance mean array; calculating a theoretical semi-variogram value corresponding to each element in the distance mean array based on the element; constructing a theoretical semi-variogram value vector based on the theoretical semi-variogram value corresponding to each element; the semi-variance sequence forming a semi-variance vector; determining the nugget value and the sill value based on the theoretical semi-variogram value vector and the semi-variance vector.

3. The method of claim 2, wherein, In the method, determining the nugget value and the sill value based on the theoretical semi-variogram value vector and the semi-variance vector comprises the following steps: calculating the product of the transpose of the theoretical semi-variogram value vector and the theoretical semi-variogram value vector to obtain a covariance matrix; adding the covariance matrix to a diagonal matrix to obtain a stabilized covariance matrix; performing Cholesky decomposition on the stabilized covariance matrix to obtain an inverse matrix; calculating the product of the inverse matrix and the transpose of the theoretical semi-variogram value vector, and multiplying the product by the semi-variance vector to obtain a final vector; taking a row vector corresponding to the first row of the final vector as the nugget value; multiplying a row vector corresponding to the second row of the final vector by the range, adding the nugget value to the product to obtain a base value; subtracting the nugget value from the base value and dividing the result by the range to obtain the sill value.

4. The method of claim 1, wherein, The semi-variogram model is: Wherein, γ(h) is the semi-variogram value corresponding to the distance h, C0 is the nugget value, C is the sill value, and a is the range.

5. The method of claim 1, wherein, Wherein, Determine the color value of each grid point, including: Obtain the color band configuration file; Determine the color range of each grid point according to the color band configuration file; Adopt the interpolateColor function to determine the color value of the grid point based on the color range.

6. The method of claim 1, wherein, Wherein, Carry out three-dimensional gridding on the final grid point data to obtain a triangular mesh, including: According to the adjacent relationship in the longitude and latitude directions, build a triangular mesh for the final grid point data.

7. A device for dynamically drawing a three-dimensional contour line based on a Web platform, characterized in that, Including: A sample point data acquisition module is configured to acquire sample point data of a calculation region, the sample point data including attribute value data, height data, and coordinate data; A parameter determination module is configured to determine parameters of a semi-variogram model based on the sample point data; the parameters of the semi-variogram model include a nugget value and a sill value; A Gram matrix construction module is configured to calculate semi-variogram values corresponding to any two sample points by using the semi-variogram model based on distances between any two sample points in the sample point data and the parameters of the semi-variogram model, and construct a Gram matrix based on all semi-variogram values; A weight vector acquisition module is configured to obtain an attribute weight vector based on the Gram matrix and the attribute value data, and obtain a height weight vector based on the Gram matrix and the height data; A grid division module is configured to divide a calculation region into grids based on the sample point data, the attribute weight vector, and the height weight vector to determine grid point data; the grid point data includes grid point coordinates, grid point longitudes, and grid point latitudes; A grid point data determination module is configured to calculate covariances between grid points and sample points to form a covariance vector, and obtain attribute values of grid points and height values of grid points based on the covariance vector, the attribute weight vector, and the height weight vector; Determine the color value of each grid point; the grid point coordinates, the grid point longitudes and latitudes, the attribute values of the grid points, the height values of the grid points, and the color values of the grid points constitute final grid point data; A three-dimensional gridding module is configured to carry out three-dimensional gridding on the final grid point data to obtain a triangular mesh, and obtain a three-dimensional contour map based on the triangular mesh.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the three-dimensional contour dynamic drawing method based on a Web platform according to any one of claims 1-6.

9. A computer program product, characterised in that, The computer program / instructions are executed by a processor to implement the three-dimensional contour dynamic drawing method based on a Web platform according to any one of claims 1-6.

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