Wind element visualization method based on hybrid interpolation

Abstract

The invention belongs to the technical field of wind element visualization, and discloses a wind element visualization method based on hybrid interpolation. According to the method, regular grid conversion is carried out on wind vector data, hierarchical calculation is carried out based on dynamic hierarchy and fragmentation loading thoughts, the characteristics of a local wind field are displayed more finely while the overall trend is guaranteed, a 3 * 3 fragmentation grid range is set, the position of a fragmentation grid center point is calculated and serves as an integral initial point, and the integral point of the local wind field is calculated along the wind direction and against the wind direction. Acquiring position data of n sampling points, performing wind speed interpolation calculation according to wind area characteristic intensity, adopting a bicubic interpolation algorithm when the intensity is smaller than a set threshold value, adopting a radial basis interpolation algorithm when the intensity is larger than or equal to the set threshold value to enhance precision, and performing speed integral calculation on the interpolated sampling points based on a fourth-order Runge-Kutta method to obtain a wind speed integral calculation result; and the calculation amount is reduced and the calculation efficiency is improved while the precision is ensured by dynamically adjusting the integral step length according to the wind speed change trend.

Description

Technical Field

[0001] This invention relates to the field of wind element visualization technology, and in particular to a wind element visualization method based on hybrid interpolation. Background Technology

[0002] Meteorological visualization is the process of presenting complex meteorological data intuitively through graphics, images, animations, and other forms to help people better understand and analyze meteorological phenomena. It not only displays the spatiotemporal distribution characteristics of meteorological data but also reveals the patterns and trends of meteorological changes, providing a scientific basis for weather forecasting, disaster warnings, and climate research. Wind element visualization is an important tool in meteorology, oceanography, and environmental science for displaying the distribution of wind speed and direction. By presenting wind speed and direction graphically, researchers can intuitively understand the structure and dynamic characteristics of wind fields.

[0003] Modern meteorological data acquisition technologies capture wind element data characterized by high resolution, multidimensionality, strong spatiotemporal correlation, and dramatic changes. Therefore, wind element visualization faces a series of challenges, including efficient loading of large-scale data, complete display of dynamic characteristics, and highlighting local features. Traditional visualization methods include the arrow method and the static streamline method. However, when the data volume is large, the arrow method suffers from overlapping, resulting in cluttered visualizations that affect observation. Furthermore, the arrows are independent of each other, failing to express the continuity and flow trend of the wind field. In addition, while the static streamline method can display the overall trend of the wind field, it lacks precision in depicting local details, especially in areas with drastic wind speed changes or vortex structures. It also lacks dynamic adaptability, making it difficult to adjust the drawing strategy in real time based on the view range, map level, and user interaction, leading to significant differences in visualization effects at different scales and affecting analytical consistency.

[0004] In the visualization of wind elements, traditional static visualization methods are insufficient to meet the needs of real-time monitoring and dynamic analysis due to the significant intermittency and fluctuation of wind elements. For example, wind speed and direction may change significantly in a short period of time, and static streamlines or arrow methods cannot reflect these changes in a timely manner. Therefore, developing visualization methods that can be updated in real time and respond dynamically, while ensuring information integrity and improving visualization effects, is one of the urgent problems that needs to be studied and solved. Summary of the Invention

[0005] Currently, traditional wind element visualization methods using arrows are inefficient when processing large-scale data. When data points are too dense, they cannot clearly and intuitively display the overall trend of the wind vector field while preserving local features. Static streamline methods cannot dynamically adjust according to map zoom levels, limiting their application in interactive maps. Furthermore, global integration calculations are costly, making real-time interaction difficult for large-scale, high-resolution data. To address these issues, this invention provides a wind element visualization method based on hybrid interpolation. This method aims to overcome the bottlenecks of traditional methods in terms of real-time performance, accuracy, and expressiveness, improving the visualization accuracy, efficiency, and effectiveness of two-dimensional regular grid vector data to meet the high requirements of practical applications.

[0006] To achieve the above objectives, the present invention provides a wind element visualization method based on hybrid interpolation, comprising the following steps: Step 1: Data Grid Processing: Define the wind vector data on a two-dimensional discrete data grid, and parse the wind vector data into ordered point data. Each grid point contains the grid point's position coordinates, wind speed, and wind direction information. Perform orthogonal decomposition on the wind speed and wind direction to obtain the X-axis velocity component. and y-axis velocity components The formula for orthogonal decomposition is expressed as follows: ,in, This is the wind speed value. This refers to the wind direction value. Step 2: Perform multi-level control: Perform local streamline control and map hierarchy control on the processed grid data to determine the display view range and data display density rules; Step 3: Divide the grid data within the view area into m×m grids, determine the center position of each grid, and starting from the center position of the grid, set the number of sampling points to n along the wind direction and the counter-wind direction, and perform wind speed interpolation calculation for each sampling point; For wind vector data within the segmented grid, the characteristic intensity value is calculated. When the characteristic intensity value is less than the set threshold, the bicubic interpolation algorithm is used to interpolate the wind speed of the sampling point data. When the feature intensity value is greater than or equal to the set threshold, the radial basis interpolation algorithm is used to calculate the wind speed of the sampling point data; Step 4: Perform velocity integration: For the multiple sampling point data obtained from wind speed interpolation calculation, perform velocity integration using the fourth-order Runge-Kutta method to generate streamline position points; The speed integral step size is adaptively adjusted based on the wind speed angle. Step 5: Complete streamline control: Smoothly draw a series of streamline position points generated by velocity integration using the cubic Bézier curve method to generate airflow streamlines.

[0007] Furthermore, it also includes step 6: if the view range or map level changes, the data within the view range is redrawn according to steps 1 to 5 to achieve dynamic drawing and updating of wind vector data.

[0008] Furthermore, step 2 involves local streamline control and map hierarchy control of the processed grid data, including the following steps: Step 2.1: Define the data rendering range based on the data range and view range. Only render the data within the view range. When the view range changes, update the data within the view range in real time to improve data rendering efficiency. Step 2.2: Control the density and number of streamline rendering by map layer. Map layer and number of streamline are positively correlated.

[0009] Furthermore, the threshold for setting the feature intensity value in step 3 is 0.7.

[0010] Furthermore, step 3 employs a bicubic interpolation algorithm to calculate wind speed interpolation for the sampled data, including the following steps: Step 3.1: Data preparation: First, obtain wind vector data within a 3×3 grid area. Each grid point contains location coordinates, wind speed, and wind direction information. Step 3.2: Determining the center point of the patch grid: Calculate the position of the center point of the patch grid as the initial point for integration, and acquire the position data of n sampling points along the wind direction and against the wind direction; Step 3.3: Feature intensity analysis: Extract the wind direction angle and wind speed of 9 sampling points in the piecewise grid, calculate the variance values, and normalize them according to the variance weights of wind speed and wind direction to obtain feature intensity values ​​in the range of 0-1; Step 3.4: When the feature intensity value is less than 0.7, the bicubic interpolation algorithm is used to calculate the wind speed interpolation of the sampling point data; The function is calculated by weighting the 16 nearest sampling points in the rectangular grid. The value at point (x, y) is expressed by the bicubic interpolation formula as follows: ,in, and It is the coordinate offset relative to the target pixel position. It is a coefficient calculated by interpolation based on the values ​​of neighboring pixels.

[0011] Furthermore, in step 3, the radial basis function (RBF) interpolation algorithm is used to calculate the wind speed interpolation of the sampled data. The RBF interpolation algorithm uses the distance between the target point and known data points as input and constructs an interpolation function using radial basis functions. The interpolation function is expressed as follows: ,in, Represents the target point. Represents known data points. Represents the weighting coefficient. It is a radial basis function, where P is the number of grids contained in the piecewise mesh, and the value of P is m×m.

[0012] Furthermore, in step 4, the wind speed angle is the angle between the lines connecting the streamline position point to the two points before and after it, and the wind speed angle is expressed as... ,in, , , These represent the velocity vectors at the (i-1)th, ith, and (i+1)th positions on the streamline, respectively. The adjustment rule for the velocity integral step size is expressed as follows: ,in, It is the velocity integration step size at the (k+1)th position on the streamline. Let be the velocity integration step size at the k-th position on the streamline.

[0013] Furthermore, The value is 0.8°. The value is 5° Beneficial Effects: This invention provides a wind element visualization method based on hybrid interpolation. Compared with existing technologies, this invention performs regular grid transformation on wind vector data, performs hierarchical calculations based on dynamic hierarchy and piecewise loading, and displays local wind field characteristics more precisely while maintaining the overall trend. A 3×3 piecewise grid is set, and wind speed interpolation is performed on the vector data within each piecewise grid based on the wind region's characteristic intensity. When the intensity is less than a set threshold, a bicubic interpolation algorithm is used; when the intensity is greater, a radial basis interpolation algorithm is used to enhance accuracy. Velocity integration is performed on the interpolated sampling points based on the fourth-order Runge-Kutta method, and the integration step size is dynamically adjusted based on wind speed change trends, reducing computational load and improving computational efficiency while maintaining accuracy. Compared with traditional wind vector visualization methods, this invention can more accurately simulate the dynamic changes of wind vectors, making regional characteristics clearer, while ensuring the continuity and stability of streamlines. Attached Figure Description

[0015] Figure 1 This is a flowchart of a wind element visualization method based on hybrid interpolation, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of a regular grid according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a segmented mesh according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a local vortex involved in an embodiment of the present invention; Figure 5This is a schematic diagram of the drawing result when the layer is 4 in the embodiment of the present invention; Figure 6 This is a schematic diagram of the drawing result when the layer is 7 in the embodiment of the present invention. Detailed Implementation

[0016] The preferred mechanisms and implementation methods of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0017] like Figures 1 to 6 As shown in the figure, this invention discloses a technical solution for a wind element visualization method based on hybrid interpolation.

[0018] Example 1: A wind element visualization method based on hybrid interpolation, comprising the following steps: Step 1: Data Grid Processing: Define the wind vector data on a two-dimensional discrete data grid, and parse the wind vector data into ordered point data. Each grid point contains the grid point's position coordinates, wind speed, and wind direction information. Perform orthogonal decomposition on the wind speed and wind direction to obtain the X-axis velocity component. and y-axis velocity components The formula for orthogonal decomposition is expressed as follows: ,in, This is the wind speed value. This represents the wind direction value.

[0019] Step 2: Perform multi-level control: Perform local streamline control and map hierarchy control on the processed grid data to determine the display view range and data display density rules; Step 2.1: Define the data rendering range based on the data range and view range. Only render the data within the view range. When the view range changes, update the data within the view range in real time to improve data rendering efficiency. Step 2.2: Control the density and number of streamlines rendered by adjusting the map layer. There is a positive correlation between the map layer and the number of streamlines. That is, when the map layer is smaller (i.e., the view area is larger), there are fewer streamlines to maintain overall clarity; when the map layer is larger (i.e., the view area is smaller), the number of streamlines increases to more finely display the characteristics of the local wind field. Users can dynamically observe the different scale characteristics of the wind field by adjusting the map layer, thereby gaining a more comprehensive understanding of the wind field's distribution and local changing trends.

[0020] Step 3: Divide the grid data within the view area into m×m grids, determine the center position of each grid, and starting from the center position of the grid, set the number of sampling points to n along the wind direction and the counter-wind direction. The number of sampling points is user-defined and related to the feature intensity value. Perform wind speed interpolation calculation for each sampling point. For wind vector data within the segmented grid, the characteristic intensity value is calculated. When the characteristic intensity value is less than the set threshold, the bicubic interpolation algorithm is used to interpolate the wind speed of the sampling point data. When the feature intensity value is greater than or equal to the set threshold, the radial basis interpolation algorithm is used to calculate the wind speed interpolation of the sampling point data.

[0021] In this embodiment, the threshold value for feature intensity is set to 0.7.

[0022] Principle: When solving local streamline integrals, velocity vector information for each integration point is required. However, some integration points are not located on the grid. Therefore, velocity interpolation is needed to calculate the velocity vector information for each integration point from the discrete grid data. Based on the distribution characteristics of wind vector data in a regular grid and the significant local variations, bicubic interpolation can directly use nearest neighbor data for interpolation without complex searches and transformations. For complex local features, radial basis interpolation can provide more accurate interpolation results, especially when dealing with complex wind field structures.

[0023] Bicubic interpolation is an orthogonal grid-based interpolation method that selects 16 nearest sampling points around the target point and uses a cubic polynomial for interpolation. This method is suitable for two-dimensional interpolation and provides relatively smooth approximation results. Radial basis interpolation uses the distance between the target point and known data points as input and utilizes radial basis functions to construct an interpolation function that accurately matches all known data points. This method can better capture local variations in the wind field and improve interpolation accuracy. The specific steps of the hybrid interpolation algorithm are as follows: Step 3.1: Data preparation: First, obtain wind vector data within a 3×3 grid area. Each grid point contains location coordinates, wind speed, and wind direction information. Step 3.2: Determining the center point of the patch grid: Calculate the position of the center point of the patch grid as the initial point for integration, and acquire the position data of n sampling points along the wind direction and against the wind direction; Step 3.3: Feature intensity analysis: Extract the wind direction angle and wind speed of 9 sampling points in the piecewise grid, calculate the variance values, and normalize them according to the variance weights of wind speed and wind direction to obtain feature intensity values ​​in the range of 0-1; Step 3.4: When the feature intensity value is less than 0.7, the bicubic interpolation algorithm is used to calculate the wind speed interpolation of the sampling point data; The function is calculated by weighting the 16 nearest sampling points in the rectangular grid. The value at point (x, y) is expressed by the bicubic interpolation formula as follows: ,in, and It is the coordinate offset relative to the target pixel position. It is a coefficient calculated by interpolation based on the values ​​of neighboring pixels.

[0024] When the feature intensity value is greater than or equal to 0.7, the radial basis function (RBF) interpolation algorithm is used to calculate the wind speed interpolation for the sampled data, further improving the accuracy of the wind speed interpolation. The RBF interpolation algorithm uses the distance between the target point and known data points as input, and constructs an interpolation function using radial basis functions, ensuring that the function accurately matches all known data points. The target point is the "sampled point" calculated in the piecewise grid, and the known data points are the individual grid points in the piecewise grid; for example, in a 3×3 piecewise grid, these are 9 vector data points. The interpolation function is expressed as follows: ,in, Represents the target point. Represents known data points. Represents the weighting coefficient. It is a radial basis function, where P is the number of grids contained in the piecewise mesh, and the value of P is m×m.

[0025] Step 4: Perform velocity integration: For the multiple sampling point data obtained from wind speed interpolation calculation, perform velocity integration using the fourth-order Runge-Kutta method to generate streamline position points; The speed integral step size is adaptively adjusted based on the wind speed angle.

[0026] Its core idea is to approximate the solution of the differential equation by selecting multiple points within the integration interval, and to improve the calculation accuracy by using a weighted average. The fourth-order Runge-Kutta method calculates four intermediate values ​​(k1, k2, k3, k4) and then takes a weighted average. The formula for the fourth-order Runge-Kutta method is as follows: ,in, , , and The calculation formulas are respectively ; ; ; ; Indicates the current trajectory point, Indicates the next trajectory point, The integration step size is... The original slope at step n. To predict the slope using a half-step, Based on The half-step predicted slope, To estimate the slope for the complete step size, The "virtual time step" for step n is the associated marker of the "position-wind vector".

[0027] Adaptive Adjustment of Velocity Integration Step Size: In velocity integration algorithms, the integration step size affects the speed and accuracy of streamline generation. To balance integration accuracy and speed, this invention employs an adaptive integration step size strategy. After each integration step, the velocity change is calculated. For drastic velocity changes, the step size is decreased; for gradual velocity changes, the step size is increased. In this embodiment, the angle between the lines connecting a point on the streamline to the preceding and following points is calculated as the basis for dynamic step size adjustment. The wind speed angle is expressed as... ,in, , , These represent the velocity vectors at the (i-1)th, ith, and (i+1)th positions on the streamline, respectively. The adjustment rule for the velocity integral step size is expressed as follows: ,in, It is the velocity integration step size at the (k+1)th position on the streamline. Let be the velocity integration step size at the k-th position on the streamline. In this embodiment, The value is 0.8°. The value is 5°.

[0028] Step 5: Complete streamline control: Smoothly draw a series of streamline position points generated by velocity integration using the cubic Bézier curve method to generate airflow streamlines.

[0029] It also includes step 6: if the view range or map level changes, the data within the view range is redrawn according to steps 1 to 5 to achieve dynamic drawing and updating of wind vector data.

[0030] Example 2: This example is basically the same as Example 1, except that it takes the visualization of the streamlines of Typhoon Lekima on August 10, 2019 as an example.

[0031] Step 1: Data processing into regular grid data The test data's latitude and longitude range is the minimum longitude. It is 60, and the minimum latitude is =0, maximum longitude 150, maximum latitude The value is 60, and the resolution is 0.125. Based on the data range information, the data is processed into a regular grid of 481 rows and 721 columns, as follows: Figure 2 As shown.

[0032] Step 2: Patch Mesh Construction Based on the map hierarchy and view extent, determine the data display range. Divide the data within this range into n 3×3 tiled grids according to the set 3×3 grid size, and determine the grid center. The tiled grids are as follows: Figure 3 As shown.

[0033] Step 3: Streamline Calculation like Figure 3 As shown, the location information of each grid data in each segmented grid, as well as the orthogonal components u and v of the wind vector at each point, are known. For each sampling point in the grid, combined with bicubic interpolation and radial basis interpolation algorithms, Q-step velocity interpolation calculations are performed along the wind direction and counter-wind direction, with the grid center as the origin, to obtain the wind speed interpolation values ​​for 2Q sampling points. Then, fourth-order Runge-Kutta velocity integration is performed on the interpolation points to obtain the streamline drawing position. The integration step size is dynamically adjusted according to the characteristics of the wind vector region, and finally, the streamline is smoothly drawn. Figure 4 The streamlines drawn from the piecewise mesh, which are the results of the local vortex rendering, can clearly reflect the current local wind trend characteristics.

[0034] Step 4: Streamline Visualization Results Figure 5 This invention provides the visualization results of wind vector data at map level 4, achieved through the method of this invention. Figure 6 The image shows the wind vector rendering result at map level 7. As can be seen from the figure, the multi-level control and view control implemented in this invention only renders data within the view area, and the streamline density varies at different levels. At lower levels, the streamlines are drawn more sparsely to ensure the clarity of the overall trend. At higher levels, the number of streamlines increases to more precisely display local wind field characteristics.

[0035] This invention provides a wind element visualization method based on hybrid interpolation. Compared with traditional wind vector visualization methods, this invention can more accurately simulate the dynamic changes of wind vectors, making regional features clearer, while ensuring the continuity and stability of streamlines.

[0036] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. However, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wind element visualization method based on hybrid interpolation, characterized in that, Includes the following steps: Step 1: Data Grid Processing: Define the wind vector data on a two-dimensional discrete data grid, and parse the wind vector data into ordered point data. Each grid point contains the position coordinates, wind speed, and wind direction information of the grid point. Perform orthogonal decomposition on the wind speed and wind direction to obtain the X-axis velocity component. and y-axis velocity components The formula for orthogonal decomposition is expressed as follows: ,in, This is the wind speed value. This refers to the wind direction value. Step 2: Perform multi-level control: Perform local streamline control and map hierarchy control on the processed grid data to determine the display view range and data display density rules; Step 3: Divide the grid data within the view area into m×m grids, determine the center position of each grid, and starting from the center position of the grid, set the number of sampling points to n along the wind direction and the counter-wind direction, and perform wind speed interpolation calculation for each sampling point; For wind vector data within the segmented grid, the characteristic intensity value is calculated. When the characteristic intensity value is less than the set threshold, the bicubic interpolation algorithm is used to interpolate the wind speed of the sampling point data. When the feature intensity value is greater than or equal to the set threshold, the radial basis interpolation algorithm is used to calculate the wind speed of the sampling point data; Step 4: Perform velocity integration: For the multiple sampling point data obtained from wind speed interpolation calculation, perform velocity integration using the fourth-order Runge-Kutta method to generate streamline position points; The speed integral step size is adaptively adjusted based on the wind speed angle. Step 5: Complete streamline control: Smoothly draw a series of streamline position points generated by velocity integration using the cubic Bézier curve method to generate airflow streamlines.

2. The wind element visualization method based on hybrid interpolation according to claim 1, characterized in that, It also includes step 6: if the view range or map level changes, the data within the view range is redrawn according to steps 1 to 5 to achieve dynamic drawing and updating of wind vector data.

3. The wind element visualization method based on hybrid interpolation according to claim 1, characterized in that, Step 2 involves performing local streamline control and map hierarchy control on the processed grid data, including the following steps: Step 2.1: Define the data rendering range based on the data range and view range. Only render the data within the view range. When the view range changes, update the data within the view range in real time to improve data rendering efficiency. Step 2.2: Control the density and number of streamline rendering by map layer. Map layer and number of streamline are positively correlated.

4. The wind element visualization method based on hybrid interpolation according to claim 1, characterized in that, In step 3, the threshold for setting the feature intensity value is 0.

7.

5. The wind element visualization method based on hybrid interpolation according to claim 1, characterized in that, Step 3 uses a bicubic interpolation algorithm to calculate wind speed interpolation for the sampled data, including the following steps: Step 3.1: Data preparation: First, obtain wind vector data within a 3×3 grid area. Each grid point contains location coordinates, wind speed, and wind direction information. Step 3.2: Determining the center point of the patch grid: Calculate the position of the center point of the patch grid as the initial point for integration, and acquire the position data of n sampling points along the wind direction and against the wind direction; Step 3.3: Feature intensity analysis: Extract the wind direction angle and wind speed of 9 sampling points in the piecewise grid, calculate the variance values, and normalize them according to the variance weights of wind speed and wind direction to obtain feature intensity values ​​in the range of 0-1; Step 3.4: When the feature intensity value is less than 0.7, the bicubic interpolation algorithm is used to calculate the wind speed interpolation of the sampling point data; The function is calculated by weighting the 16 nearest sampling points in the rectangular grid. The value at point (x, y) is expressed by the bicubic interpolation formula as follows: ,in, and It is the coordinate offset relative to the target pixel position. It is a coefficient calculated by interpolation based on the values ​​of neighboring pixels.

6. The wind element visualization method based on hybrid interpolation according to claim 1, characterized in that, In step 3, the radial basis function (RBF) interpolation algorithm is used to calculate the wind speed from the sampled data. The RBF interpolation algorithm uses the distance between the target point and known data points as input and constructs an interpolation function using radial basis functions. The interpolation function is expressed as follows: ,in, Represents the target point. Represents known data points. Represents the weighting coefficient. It is a radial basis function, where P is the number of grids contained in the piecewise mesh, and the value of P is m×m.

7. The wind element visualization method based on hybrid interpolation according to claim 1, characterized in that, In step 4, the wind speed angle is the angle between the lines connecting the streamline position point to the two points before and after it. The wind speed angle is expressed as... ,in, , , These represent the velocity vectors at the (i-1)th, ith, and (i+1)th positions on the streamline, respectively. The adjustment rule for the velocity integral step size is expressed as follows: ,in, It is the velocity integration step size at the (k+1)th position on the streamline. Let be the velocity integration step size at the k-th position on the streamline.

8. The wind element visualization method based on hybrid interpolation according to claim 7, characterized in that, The value is 0.8°. The value is 5°.

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