Three-dimensional isoline dynamic drawing method based on Web platform

Abstract

The invention relates to a three-dimensional isoline dynamic drawing method based on a Web platform, and the method comprises the steps: calculating an attribute weight vector and a height weight vector based on a semi-variation index model, obtaining an attribute value and a height value of a grid point, achieving the interpolation of a whole calculation region, and obtaining the data of the grid point with coordinates, longitude and latitude, the attribute value and the height value; and performing three-dimensional gridding based on the grid point data to obtain a triangular mesh, and finally generating a three-dimensional contour map. According to the method, sample data can be directly loaded and processed, the topographic data and the attribute data are subjected to fusion calculation, and a continuous three-dimensional topographic model reflecting topographic relief and containing attribute information is constructed through high-precision four-dimensional interpolation calculation.

Description

Technical Field

[0001] The present application relates to the field of data visualization, and in particular to a method for dynamically drawing three-dimensional contour lines based on a Web platform. Background Art

[0002] A 3D contour map is an advanced data visualization tool that uses contour lines in three-dimensional space to display the changing patterns and distribution characteristics of data. A contour map is essentially a type of graph that uses lines connecting points of equal value to represent continuously distributed and gradually changing quantitative characteristics. These lines connect points on the map with equal values ​​of specific data sets (such as height, temperature, pressure, etc.). In a 3D contour map, this representation method is extended to three-dimensional space, allowing the changing patterns and distribution characteristics of data to be more intuitively displayed on a three-dimensional scale.

[0003] However, most of the current browser-based contour drawing tools are limited to the processing of two-dimensional data, and cannot effectively process three-dimensional coordinate point data, let alone achieve dynamic display of three-dimensional contour lines and efficient data output. Although traditional three-dimensional contour line generation methods can process three-dimensional data, their processing capabilities and display effects are obviously limited when faced with four-dimensional data containing height information and additional attribute values. In addition, these methods usually rely on desktop software such as MATLAB, which is not only complicated to operate, but also difficult to achieve real-time interaction and display in a Web environment, limiting their widespread promotion in practical applications. Summary of the invention

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

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

[0006] Obtaining sample point data of the calculation area, the sample point data includes attribute value data, height data and coordinate data;

[0007] Determine the parameters of the semivariogram model based on the sample point data; the parameters of the semivariogram model include the nugget value and the bias value;

[0008] Based on the distance between any two sample points in the sample point data and the parameters of the semivariogram model, the semivariogram model is used to calculate the semivariogram values ​​corresponding to any two sample points; the Gram matrix is ​​constructed based on all the semivariogram values;

[0009] Based on the Gram matrix and the attribute value data, the attribute weight vector is obtained; based on the Gram matrix and the height data, the height weight vector is obtained;

[0010] Based on the sample point data, attribute weight vector, and height weight vector, the calculation area is gridded to determine the grid point data; the grid point data includes the grid point coordinates, grid point longitude, and grid point latitude; the covariance between the grid point and the sample point is calculated to form a covariance vector; based on the covariance vector, attribute weight vector, and height weight vector, the attribute value of the grid point and the height value of the grid point are obtained;

[0011] Determine the color value of each grid point; the grid point coordinates, the longitude and latitude of the grid point, 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;

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

[0013] In one embodiment, determining the parameters of the semivariogram model based on the sample point data includes:

[0014] Calculate the distance and semivariance between any two sample points;

[0015] Sort all distances in ascending order to obtain the sorted distance sequence and the corresponding semivariance sequence;

[0016] Take the maximum value in the distance sequence as the range;

[0017] Divide the distance sequence into multiple intervals, calculate the mean of all distances in each interval, and form a distance mean array;

[0018] Based on each element in the distance mean array, the theoretical semivariance value corresponding to each element is calculated; based on the theoretical semivariance value corresponding to each element, a theoretical semivariance value vector is constructed; the semivariance sequence constitutes a semivariance vector;

[0019] Based on the theoretical semivariance vector and semivariance vector, the nugget value and the partial value are determined.

[0020] In one embodiment, based on the theoretical semi-variance vector and the semi-variance vector, determining the nugget value and the bias value includes:

[0021] Calculate the transpose of the theoretical semivariance vector and the product of the theoretical semivariance vector to obtain the covariance matrix;

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

[0023] Perform Cholesky decomposition on the stabilized covariance matrix to obtain the inverse matrix;

[0024] Calculate the product of the inverse matrix and the transpose of the theoretical semivariance vector, and multiply it by the semivariance vector to get the final vector;

[0025] The row vector corresponding to the first row of the final vector is used as the nugget value;

[0026] The row vector corresponding to the second row of the final vector is multiplied by the range and then added to the nugget value to obtain the sill value; the nugget value is subtracted from the sill value and then divided by the range to obtain the bias value.

[0027] In one embodiment, the semivariogram model is:

[0028]

[0029] Among them, γ(h) is the semivariance value corresponding to distance h, C 0 is the nugget value, C is the bias value, and a is the range.

[0030] In one embodiment, determining the color value of each grid point includes:

[0031] Get the ribbon profile;

[0032] Determine the color range of each grid point according to the color band profile;

[0033] The interpolateColor function is used to perform color interpolation based on the color range to determine the color value of the grid point.

[0034] In one embodiment, the final grid point data is three-dimensionally meshed to obtain a triangular mesh, including:

[0035] The final grid point data is used to construct a triangular mesh according to the adjacent relationship in the longitude and latitude directions.

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

[0037] A sample point data acquisition module is used to acquire sample point data of the calculation area, the sample point data includes attribute value data, height data and coordinate data;

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

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

[0040] The weight vector acquisition module is used to obtain the attribute weight vector based on the Gram matrix and the attribute value data; and obtain the height weight vector based on the Gram matrix and the height data;

[0041] A grid division module is used to divide the calculation area into grids based on sample point data, attribute weight vectors, and height weight vectors, and determine grid point data; the grid point data includes grid point coordinates, grid point longitude, and latitude;

[0042] The grid point data determination module is used to calculate the covariance between the grid point and the sample point 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; the color value of each grid point is determined; the grid point coordinates, the longitude and latitude of the grid point, 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;

[0043] The three-dimensional meshing module is used to perform three-dimensional meshing on the final grid point data to 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. When the computer program is executed by a processor, it implements the above-mentioned three-dimensional contour line dynamic drawing method based on the Web platform.

[0045] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, the above-mentioned three-dimensional contour line dynamic drawing method based on the Web platform is implemented.

[0046] Compared with the prior art, the present application has the following beneficial effects: based on the semivariogram model, the present application calculates the attribute weight vector and the height weight vector, and obtains the attribute value and height value of the grid point, realizes the interpolation of the entire calculation area, and obtains the grid point data with coordinates, longitude and latitude, attribute value, and height value; based on the grid point data, three-dimensional gridding is performed to obtain a triangular grid, and finally a three-dimensional contour map is generated. The present application can directly load and process sample data, fuse and calculate the terrain data with the attribute data, and after high-precision four-dimensional interpolation calculation, construct a continuous three-dimensional terrain model that reflects both terrain undulations and attribute information. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0048] Figure 1A flowchart of a three-dimensional contour line dynamic drawing method based on a Web platform is shown;

[0049] Figure 2 A schematic diagram of sample points and boundary points of the calculation area is shown;

[0050] 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 the two-dimensional contour map obtained by the prior art, and (b) is the three-dimensional contour map obtained by the method of the present application. DETAILED DESCRIPTION

[0051] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0052] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure closely related to the scheme according to the present application is shown in the drawings, while other details that are not closely related to the present application are omitted.

[0053] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0054] The present application provides a method for dynamically drawing three-dimensional contour lines based on a Web platform. Figure 1 A flowchart of a three-dimensional contour line dynamic drawing method based on a Web platform is shown in FIG. Figure 1 , methods include:

[0055] Step S1, obtaining sample point data of the calculation area, the sample point data including attribute value data, height data and coordinate data. Here, the attribute value data may be, for example, temperature, pressure, etc. Figure 2 A schematic diagram of sample points and boundary points of the calculation area is shown.

[0056] Step S2, determining the parameters of the semivariogram model based on the sample point data; the parameters of the semivariogram model include the nugget value and the bias value.

[0057] Specifically, the semivariogram model is:

[0058]

[0059] Among them, γ(h) is the semivariance value corresponding to distance h, C 0 is the nugget value, C is the bias value, and a is the range.

[0060] Step S3, using the semivariogram model to calculate the semivariogram values ​​corresponding to any two sample points based on the distance between any two sample points in the sample point data and the parameters of the semivariogram model; and constructing a Gram matrix based on all the semivariogram values.

[0061] Step S4, 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.

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

[0063] Step S5, gridding the calculation area based on the sample point data, attribute weight vector, and height weight vector to determine the grid point data; the grid point data includes the grid point coordinates, grid point longitude and latitude; calculating the covariance between the grid point and the sample point to form a covariance vector; based on the covariance vector, attribute weight vector, and height weight vector, obtaining the attribute value of the grid point and the height value of the grid point.

[0064] Here, grid division is performed based on the calculated boundary bound, boundary grid spacing, sample point coordinates, attribute value data, height data, index model object, attribute weight vector and height weight vector. The covariance vector is multiplied by the attribute weight vector to obtain the attribute value of the grid point, and the covariance vector is multiplied by the height weight vector to obtain the height value of the grid point.

[0065] Step S6, determining the color value of each grid point; the grid point coordinates, the longitude and latitude of the grid point, 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] Get the color ribbon configuration file; here, the color ribbon configuration file can be provided by the system or customized by the user.

[0068] The color range of each grid point is determined according to the color band configuration file; the color band configuration file includes an attribute value range and corresponding colors, and the color range corresponding to the attribute value of the grid point is determined in the color band configuration file.

[0069] The interpolateColor function is used to perform color interpolation based on the color range to determine the color value of the grid point.

[0070] Step S7, three-dimensionally meshing the final grid point data to obtain a triangular mesh; and obtaining a three-dimensional contour map based on the triangular mesh.

[0071] Here, the final grid point data is three-dimensionally gridded, including: constructing a triangular grid for the final grid point data according to the adjacent relationship in the longitude and latitude directions. Connecting adjacent grid points in the longitude and latitude directions to form multiple triangles, 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 the complex global search and judgment process, and realizes rapid triangular mesh generation through the direct use of local adjacent information, which is particularly suitable for processing scenarios of large-scale data points. In other embodiments, three-dimensional gridding can also use the existing Delaunay triangulation method.

[0072] After obtaining the triangular mesh, create a Cesium geometry based on the triangular mesh and encapsulate it. Finally, add this geometry instance to the Cesium scene. You can then visualize the three-dimensional triangular mesh composed of these data points on the three-dimensional globe and finally obtain a three-dimensional contour map. 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 the two-dimensional contour map obtained by the prior art, and (b) is the three-dimensional contour map obtained by the method of the present application.

[0073] In this embodiment, based on the semivariogram model, the attribute weight vector and the height weight vector are calculated, and the attribute value and height value of the grid point are obtained to realize the interpolation of the entire calculation area, thereby obtaining the grid point data with coordinates, longitude and latitude, attribute value, and height value; three-dimensional gridding is performed based on the grid point data to obtain a triangular grid, and finally a three-dimensional contour map is generated. This application can directly load and process sample data, fuse terrain data with attribute data, and construct a continuous three-dimensional terrain model that reflects both terrain undulations and attribute information through high-precision four-dimensional interpolation calculations.

[0074] In one embodiment, step S2, determining the parameters of the semivariogram model based on the sample point data, includes:

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

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

[0077] Step S23, taking 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 form 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-variance value corresponding to each element; construct a theoretical semi-variance value vector X based on the theoretical semi-variance value corresponding to each element; the semi-variance sequence constitutes a semi-variance vector Y;

[0080] Here, the theoretical semivariance Among them, a is the variation range, and lag[i] represents the i-th element in the distance mean array.

[0081] The i-th row vector in the theoretical semivariance vector X is [1, R(i)].

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

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

[0084] Calculate the transpose of the theoretical semivariance vector X and the product of the theoretical semivariance vector X to obtain the covariance matrix Z;

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

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

[0087] Calculate the product of the inverse matrix and the transpose of the theoretical semivariance vector X, and multiply it with the semivariance vector Y to obtain the final vector W;

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

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

[0090] In this embodiment, due to various complex situations that often exist in actual data, such as insufficient sample size, which may lead to insufficient representativeness of the data, and uneven data distribution, which may make the data in some areas too sparse or dense. In the calculation process, these factors are prone to rounding errors, which in turn cause the covariance matrix to be singular or non-positive definite. Once this happens in the covariance matrix, numerical instability will occur when solving the inverse matrix or performing eigenvalue decomposition, which seriously affects the accuracy and reliability of subsequent calculations.

[0091] To solve this problem, a small positive definite matrix is ​​added to the covariance matrix to ensure that the added covariance matrix is ​​positive definite. For example, the values ​​of the elements on the diagonal can be dynamically adjusted according to the size of the interpolation accuracy. When the interpolation accuracy requirement is high, the values ​​of the elements on the diagonal are appropriately increased to more strongly ensure the positive definiteness of the covariance matrix. In this way, the singular or non-positive definite problems that may occur in the covariance matrix can be effectively solved, and the stability and reliability of the calculation can be 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 calculations.

[0092] Furthermore, the processed three-dimensional contour lines can be directly printed on the web or exported as txt files. This function expands the scope of data use and meets the data needs of different users in different scenarios.

[0093] Using the same inventive concept as the method for dynamically drawing three-dimensional contour lines based on a Web platform, this embodiment also provides a corresponding device for dynamically drawing three-dimensional contour lines based on a Web platform, including:

[0094] A sample point data acquisition module is used to acquire sample point data of the calculation area, the sample point data includes attribute value data, height data and coordinate data;

[0095] A parameter determination module is used to determine the parameters of the semivariogram model based on the sample point data; the parameters of the semivariogram model include a nugget value and a bias value;

[0096] A Gram matrix construction module is used to calculate the semivariance values ​​corresponding to any two sample points using the semivariance index model based on the distance between any two sample points in the sample point data and the parameters of the semivariance index model; and to construct a Gram matrix based on all the semivariance values;

[0097] The weight vector acquisition module is used to obtain the attribute weight vector based on the Gram matrix and the attribute value data; and obtain the height weight vector based on the Gram matrix and the height data;

[0098] A grid division module is used to divide the calculation area into grids based on sample point data, attribute weight vectors, and height weight vectors, and determine grid point data; the grid point data includes grid point coordinates, grid point longitude, and latitude;

[0099] The grid point data determination module is used to calculate the covariance between the grid point and the sample point 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; the color value of each grid point is determined; the grid point coordinates, the longitude and latitude of the grid point, 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;

[0100] The three-dimensional meshing module is used to perform three-dimensional meshing 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 line dynamic drawing device based on the Web platform of this embodiment has the same inventive concept as the three-dimensional contour line dynamic drawing method based on the Web platform mentioned above. Therefore, the specific implementation method of the device can be seen in the implementation example part of the three-dimensional contour line dynamic drawing method based on the Web platform mentioned above, and its technical effect corresponds to the technical effect of the above method, which will not be repeated here.

[0102] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned three-dimensional contour line dynamic drawing method based on the Web platform is implemented.

[0103] An embodiment of the present application provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, the above-mentioned three-dimensional contour line dynamic drawing method based on the Web platform is implemented.

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

[0105] This application can directly load and process sample data, fuse terrain data with attribute data, and build a continuous three-dimensional terrain model that reflects both terrain undulations and attribute information through high-precision four-dimensional interpolation calculations. On this basis, color coding is used to intuitively express the size and distribution of attribute values, 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 data processing efficiency and visualization effects.

[0106] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A three-dimensional contour line dynamic drawing method based on a Web platform, characterized in that: include: Acquire sample point data of the calculation area, wherein the sample point data includes attribute value data, height data and coordinate data; Determining parameters of a semivariogram model based on the sample point data; the parameters of the semivariogram model include a nugget value and a skew value; Based on the distance between any two sample points in the sample point data and the parameters of the semivariogram model, the semivariogram model is used to calculate the semivariogram values ​​corresponding to any two sample points; a Gram matrix is ​​constructed based on all the semivariogram values; Based on the Gram matrix and the attribute value data, an attribute weight vector is obtained; based on the Gram matrix and the height data, a height weight vector is obtained; Gridding 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 includes grid point coordinates, grid point longitude and latitude; Calculate the covariance between the grid point and the sample point to form a covariance vector; obtain the attribute value of the grid point and the height value of the grid point 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 longitude and latitude of the grid point, 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; Performing three-dimensional meshing on the final grid point data to obtain a triangular mesh; A three-dimensional contour map is obtained based on the triangular mesh.

2. The method according to claim 1, characterized in that in, Determining the parameters of the semivariogram model based on the sample point data includes: Calculate the distance and semivariance between any two sample points; Sort all distances in ascending order to obtain the sorted distance sequence and the corresponding semivariance sequence; Take the maximum value in the distance sequence as the range; Divide the distance sequence into multiple intervals, calculate the mean of all distances in each interval, and form a distance mean array; Based on each element in the distance mean array, the theoretical semivariance value corresponding to each element is calculated; based on the theoretical semivariance value corresponding to each element, a theoretical semivariance value vector is constructed; the semivariance sequence constitutes a semivariance vector; The nugget value and the bias value are determined based on the theoretical semi-variance vector and the semi-variance vector.

3. The method according to claim 2, characterized in that in, Determining the nugget value and the bias value based on the theoretical semivariance vector and the semivariance vector includes: Calculate the transpose of the theoretical semivariance value vector and the product of the theoretical semivariance value vector to obtain a covariance matrix; The covariance matrix is ​​added to the diagonal matrix to obtain a stabilized covariance matrix; Performing Cholesky decomposition on the stabilized covariance matrix to obtain an inverse matrix; Calculate the product of the inverse matrix and the transpose of the theoretical semivariance vector, and multiply it by the semivariance vector to obtain a final vector; The row vector corresponding to the first row of the final vector is used as the nugget value; The row vector corresponding to the second row of the final vector is multiplied by the variation range, and then added with the nugget value to obtain the base value; the nugget value is subtracted from the base value, and then divided by the variation range to obtain the bias value.

4. The method according to claim 1, characterized in that The semivariogram model is: Among them, γ(h) is the semi-variation value corresponding to the distance h, C0 is the nugget value, C is the partial platform value, and a is the range.

5. The method according to claim 1, characterized in that in, Determine the color value of each grid point, including: Get the ribbon profile; determining a color range for each grid point according to the color band configuration file; The interpolateColor function is used to perform color interpolation based on the color range to determine the color value of the grid point.

6. The method according to claim 1, characterized in that in, The final grid point data is three-dimensionally meshed to obtain a triangular mesh, including: A triangular mesh is constructed for the final grid point data according to the adjacent relationship in the longitude and latitude directions.

7. A three-dimensional contour line dynamic drawing device based on a Web platform, characterized in that: include: A sample point data acquisition module, used to acquire sample point data of a calculation area, wherein the sample point data includes attribute value data, height data and coordinate data; A parameter determination module, used to determine the parameters of the semivariogram model based on the sample point data; the parameters of the semivariogram model include a nugget value and a skew value; A Gram matrix construction module is used to calculate the semivariance values ​​corresponding to any two sample points using the semivariance index model based on the distance between any two sample points in the sample point data and the parameters of the semivariance index model; and to construct a Gram matrix based on all the semivariance values; A weight vector acquisition module, used 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, used to grid the calculation area based on the sample point data, the attribute weight vector, and the height weight vector, and determine grid point data; the grid point data includes grid point coordinates, grid point longitude and latitude; A grid point data determination module is used to calculate the covariance between the grid point and the sample point to form a covariance vector; based on the covariance vector, the attribute weight vector, and the height weight vector, obtain the attribute value of the grid point and the height value of the grid point; Determine the color value of each grid point; the grid point coordinates, the longitude and latitude of the grid point, 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; The three-dimensional meshing module is used to perform three-dimensional meshing on the final mesh 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 when the computer program is executed by a processor, the method for dynamically drawing three-dimensional contour lines based on a Web platform as described in any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements the three-dimensional contour line dynamic drawing method based on a Web platform as described in any one of claims 1 to 6.

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