Meteorological data visualization rendering system

Abstract

The invention relates to the technical field of computers, and discloses a meteorological data visual rendering system, which comprises an acquisition module used for acquiring meteorological data of a first spatial scale; the data rasterization module is used for mapping the meteorological data into a target grid structure according to a geographic range of a second spatial scale and a second resolution; the meteorological interpolation module is used for performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points in the target grid structure to obtain grid numerical values corresponding to grid pixels in the target grid structure; the region cutting module is used for cutting the target grid structure based on the inclusion relation between each pixel in the target grid structure and the to-be-rendered target region to obtain target grid data corresponding to the to-be-rendered target region; the meteorological data of the to-be-rendered target area can be rendered and displayed.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a meteorological data visualization and rendering system. Background Technology

[0002] With the improvement of the accuracy of meteorological data collection and simulation, how to present large-scale, multi-dimensional meteorological data in an intuitive and vivid way has become an important direction for meteorological visualization research.

[0003] Meteorological visualization systems in related technologies, such as contour maps, color patches, and dynamic cloud maps, still have certain limitations in terms of display effects and interactive experience. Specifically, on the one hand, the meteorological visualization systems in related technologies suffer from simple interpolation algorithms and coarse layer display; on the other hand, the meteorological visualization systems in related technologies lack the ability to control regional masking and boundaries. The above two reasons lead to poor final meteorological visualization effects.

[0004] Therefore, there is an urgent need to propose a meteorological data visualization and rendering system to solve the problem of poor meteorological visualization effects in related technologies. Summary of the Invention

[0005] In view of this, the present invention provides a meteorological data visualization and rendering system to solve the problem of poor meteorological visualization effect.

[0006] This embodiment provides a meteorological data visualization and rendering system, comprising: an acquisition module for acquiring meteorological data at a first spatial scale; a data rasterization module for mapping the meteorological data into a target raster structure according to a second spatial scale geographical range and a second resolution; a meteorological interpolation module for performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points within the target raster structure to obtain raster values ​​corresponding to each raster pixel in the target raster structure; a region clipping module for clipping the target raster structure based on the inclusion relationship between each pixel within the target raster structure and the target region to be rendered to obtain target raster data corresponding to the target region to be rendered; and a rendering module for mapping each pixel in the target raster data into a color value and rendering a meteorological rendering image.

[0007] As an exemplary embodiment, the meteorological interpolation module includes a first acquisition unit, a second acquisition unit, an interpolation unit, and a raster value determination unit; the first acquisition unit is used to acquire the first coordinate values ​​of the pixels to be interpolated corresponding to missing points and / or irregular sampling points; the second acquisition unit is used to acquire at least one set of second coordinate values ​​and values ​​of non-missing points or regular sampling points; the interpolation unit is used to interpolate the values ​​of the pixels to be interpolated based on the values ​​and the difference between the first coordinate values ​​and the second coordinate values ​​to obtain interpolated values; the raster value determination unit is used to obtain the raster values ​​corresponding to each raster pixel in the target raster structure based on the interpolated values ​​and the values.

[0008] As an exemplary embodiment, the second acquisition unit is used to acquire the second coordinate values ​​and numerical values ​​of four target preset coordinates; the interpolation unit is used to perform bilinear interpolation based on the first coordinate values, the second coordinate values ​​and the numerical values ​​to obtain the interpolated numerical values.

[0009] As an exemplary embodiment, the region clipping module includes a position calculation unit, a ray emission unit, an intersection point acquisition unit, and a region clipping unit: the ray emission unit is used to emit horizontal rays in the target direction based on each pixel in the target raster structure; the intersection point acquisition unit is used to acquire the number of intersection points of each horizontal ray with the polygon formed by the target region to be rendered; the region clipping unit retains pixels with an odd number of intersection points as target pixels, and clips pixels with an even number of intersection points to be clipped, generating a rendering mask to obtain the target raster data.

[0010] As an exemplary embodiment, the rendering module includes a mapping unit and a drawing unit;

[0011] The mapping unit is used to map each pixel in the target raster data to a color value based on a set color mark; the drawing unit is used to draw a weather rendering image based on the color value.

[0012] As an exemplary embodiment, the drawing unit includes a Canvas unit.

[0013] As an exemplary embodiment, the rendering module further includes a WebGL unit.

[0014] As an exemplary embodiment, the meteorological data visualization rendering system further includes a map engine adaptation module, which is used to implement functions such as layer alignment, dynamic scaling, and view synchronization.

[0015] As an exemplary embodiment, the map engine adaptation layer includes a Leaflet.js map framework integration unit.

[0016] As an exemplary embodiment, the map engine adaptation layer also includes a Mapbox map framework integration unit.

[0017] This embodiment provides a meteorological data visualization and rendering system, comprising: an acquisition module for acquiring meteorological data at a first spatial scale; a data rasterization module for mapping the meteorological data into a target raster structure according to a second spatial scale geographical range and a second resolution; a meteorological interpolation module for performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points within the target raster structure to obtain raster values ​​corresponding to each raster pixel in the target raster structure; a region clipping module for clipping the target raster structure based on the inclusion relationship between each pixel within the target raster structure and the target region to be rendered to obtain target raster data corresponding to the target region to be rendered; and a rendering module for mapping each pixel in the target raster data into a color value and rendering a meteorological rendering image. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a structural block diagram of a meteorological data visualization and rendering system according to an embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating a meteorological data visualization and rendering method according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] According to embodiments of the present invention, a meteorological data visualization and rendering system is provided, such as... Figure 1 As shown, it includes:

[0023] Acquisition module 501 is used to acquire meteorological data at a first spatial scale.

[0024] In this embodiment, the acquisition module 501 is used to acquire meteorological data at a first spatial scale, which may include temperature, humidity, wind speed, etc.

[0025] For example, the first spatial scale is greater than or equal to the spatial scale of the target area to be rendered.

[0026] The data rasterization module 502 is used to map the meteorological data into a target raster structure according to the geographical range and second resolution of the second spatial scale; wherein the second spatial scale is smaller than the first spatial scale.

[0027] In this embodiment, the data rasterization module 502 is used to map the meteorological data obtained by the acquisition module 501 into a target raster structure according to the geographical range and resolution.

[0028] For example, the target grid structure consists of multiple cells (pixels), each cell (pixel) corresponds to a region on the map, and each cell (pixel) corresponds to one or more meteorological values.

[0029] For example, the data rasterization module 502 can map the meteorological data into a two-dimensional array according to the geographical range of the second spatial scale and the second resolution; in the two-dimensional array, data in one dimension is used to represent cells (pixels), and data in another dimension is used to represent meteorological values.

[0030] The meteorological interpolation module 503 is used to perform bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points in the target grid structure to obtain the grid value corresponding to each grid pixel in the target grid structure.

[0031] In this embodiment, the meteorological interpolation module 503 is used to perform bilinear interpolation and / or inverse distance weighted interpolation on the missing points and / or irregular sampling points in the target grid structure to obtain the corresponding grid values ​​of the target grid structure.

[0032] The meteorological visualization system in the related technology has the problem of simple interpolation algorithm and coarse layer display. In order to solve this problem, in this embodiment, the meteorological interpolation module 503 can interpolate the missing points and / or irregular sampling points in the target grid structure based on bilinear interpolation, so as to improve the problem of coarse layer through more refined bilinear interpolation.

[0033] In this embodiment, the meteorological interpolation module 503 can interpolate missing points and / or irregular sampling points in the target raster structure based on the inverse distance weighting method, so as to improve the problem of layer roughness through more refined inverse distance weighting interpolation.

[0034] In this embodiment, the meteorological interpolation module 503 can simultaneously interpolate missing points and / or irregular sampling points within the target grid structure based on bilinear interpolation and inverse distance weighting. Specifically, the meteorological interpolation module 503 can perform a first interpolation based on bilinear interpolation to obtain a first sub-grid value and a first sub-interpolation error, and simultaneously perform a second interpolation based on inverse distance weighting to obtain a second sub-grid value and a second sub-interpolation error. Furthermore, the first sub-grid value and the second sub-grid value are fused based on the first sub-interpolation error and the second sub-interpolation error to obtain a grid value. The first sub-interpolation error and the second sub-interpolation error can be obtained by interpolating based on historical data to obtain historical interpolation data, and further by comparing the historical interpolation data with historical measured data.

[0035] The region clipping module 504 is used to clip the target raster structure based on the inclusion relationship between each pixel in the target raster structure and the target region to be rendered, so as to obtain the target raster data corresponding to the target region to be rendered.

[0036] In this embodiment, the region clipping module is used to clip the target raster structure based on the inclusion relationship between each pixel within the target raster structure and the target region to be rendered, so as to solve the problem of lack of region masking and boundary control capabilities in meteorological visualization technology in related technologies.

[0037] In one embodiment, for regular boundary regions such as rectangles or circles, the target raster structure can be filtered by considering the inclusion relationship between the boundary coordinates of the region to be rendered and the pixel coordinates of each pixel within the target raster structure, thereby achieving the cropping of the target raster structure and finally obtaining the target raster data corresponding to the target region to be rendered.

[0038] The rendering module 505 is used to map each pixel in the target raster data to a color value and draw a weather rendering image.

[0039] In this embodiment, the correspondence between pixel values ​​and color codes corresponding to each meteorological data can be pre-calibrated in the laboratory as a pre-set color code, and the color code is stored in the rendering module. This allows the rendering module to map each grid value to a color value based on each pixel in the target grid data and the color code, and finally use the existing rendering module to draw the meteorological rendering image.

[0040] A meteorological data visualization and rendering system of the present invention includes: an acquisition module 501 for acquiring meteorological data at a first spatial scale; a data rasterization module 502 for mapping the meteorological data into a target raster structure according to a geographical range and a second resolution at a second spatial scale; wherein the second spatial scale is smaller than the first spatial scale; a meteorological interpolation module 503 for performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points in the target raster structure to obtain the raster values ​​corresponding to each raster pixel in the target raster structure; and a region clipping module 504 for... The target raster structure is cropped based on the inclusion relationship between each pixel in the target raster structure and the target area to be rendered, to obtain target raster data corresponding to the target area to be rendered; the rendering module 505 is used to map each pixel in the target raster data to a color value and draw a meteorological rendering image; the above-mentioned meteorological data visualization rendering system can interpolate the missing points and irregular sampling points in the target raster structure based on large-scale meteorological data to obtain the raster values ​​corresponding to each raster pixel in the target raster structure, further crop the target raster structure based on the inclusion relationship between each pixel in the target raster structure and the target area to be rendered, and finally render and display the cropped target raster data.

[0041] As an exemplary embodiment, the meteorological interpolation module 503 includes a first acquisition unit, a second acquisition unit, an interpolation unit, and a raster value determination unit; the first acquisition unit is used to acquire the first coordinate values ​​of the pixels to be interpolated corresponding to missing points and / or irregular sampling points; the second acquisition unit is used to acquire at least one set of second coordinate values ​​and values ​​of non-missing points or regular sampling points; the interpolation unit is used to interpolate the values ​​of the pixels to be interpolated based on the values ​​and the difference between the first coordinate values ​​and the second coordinate values ​​to obtain interpolated values; the raster value determination unit is used to obtain the raster values ​​corresponding to each raster pixel in the target raster structure based on the interpolated values ​​and the values.

[0042] In this embodiment, the interpolation unit is used to interpolate the value of the pixel to be interpolated based on the numerical value and the difference between the first coordinate value and the second coordinate value, so as to obtain the interpolated value.

[0043] For example, the interpolation unit can be constructed based on the inverse distance weighting method and / or the bilinear interpolation method; the function of the interpolation unit can be referred to the content in the above embodiments, and will not be repeated here.

[0044] As an exemplary embodiment, the second acquisition unit is used to acquire the second coordinate values ​​and numerical values ​​at four target preset coordinates; the interpolation unit is used to perform bilinear interpolation based on the first coordinate values, the second coordinate values ​​and the numerical values ​​to obtain the interpolated numerical values.

[0045] In this embodiment, the first coordinate value of the pixel p to be interpolated corresponding to the missing point and / or irregular sampling point acquired by the first acquisition unit is denoted as (x,y), and the grid value is denoted as f(x,y).

[0046] In this embodiment, the target preset coordinates are the lower left, lower right, upper left, and upper right corners of the target grid structure where the pixel to be interpolated is located. Specifically, the second acquisition unit acquires the coordinates of the lower left corner point Q11 of the target grid structure where the pixel to be interpolated is located, denoted as... The numerical value is denoted as The coordinates of the lower right corner point Q21 are denoted as The numerical value is denoted as The coordinates of the top-left corner point Q12 are denoted as The numerical value is denoted as The coordinates of the upper right corner point Q22 are denoted as The numerical value is denoted as .

[0047] Furthermore, the interpolation unit is used to perform bilinear interpolation based on the first coordinate value, the second coordinate value, and the numerical value to obtain the interpolated numerical value.

[0048] Specifically, the interpolation unit can perform interpolation using the following formula to obtain the interpolated value:

[0049] (1)

[0050] In the formula, x is the x-coordinate of the missing point and the irregular sampling point, and y is the y-coordinate of the missing point and the irregular sampling point. These are interpolated values; x1 is the x-coordinate of the lower left corner point Q11, and y1 is the y-coordinate of the lower left corner point Q11. x1 is the value of the bottom left corner point Q11; x2 is the x-coordinate of the bottom right corner point Q21, and y1 is the y-coordinate of the bottom right corner point Q21. x1 is the value of the bottom right corner point Q21; x2 is the x-coordinate of the top left corner point Q12, and y2 is the y-coordinate of the top left corner point Q12. x1 is the value of the top-left corner point Q12; x2 is the x-coordinate of the top-right corner point Q22, and y2 is the y-coordinate of the top-right corner point Q22. The value of Q22 is the top right corner point.

[0051] The bilinear interpolation unit described above is suitable for efficient rendering, hardware acceleration, and algorithm implementation in GPU shaders, and is used for real-time rendering of high-resolution weather raster layers.

[0052] Meteorological visualization systems in related technologies lack the ability to mask and control boundaries. To solve this problem, this invention uses the ray casting method to filter the raster pixels in the raster structure and obtain the target raster data corresponding to the target area to be rendered.

[0053] As an exemplary embodiment, the region clipping module 504 includes a position calculation unit, a ray emission unit, an intersection point acquisition unit, and a region clipping unit: the ray emission unit is used to emit horizontal rays in the target direction based on each pixel in the target raster structure; the intersection point acquisition unit is used to acquire the number of intersection points of each horizontal ray with the polygon formed by the target region to be rendered; the region clipping unit retains pixels with an odd number of intersection points as target pixels, and clips pixels with an even number of intersection points to be clipped, generating a rendering mask to obtain the target raster data.

[0054] For example, the target direction can be left or right; specifically, taking the target direction as right as an example, the technical solution of this embodiment is explained; specifically, the ray emitting unit uses each pixel in the target grid structure as a reference and emits horizontal rays from each pixel to the right.

[0055] Furthermore, the intersection point acquisition unit is used to acquire the number of intersection points of each of the horizontal rays and the polygon formed by the target area to be rendered.

[0056] Specifically, the intersection point acquisition unit first determines the polygon boundary based on the polygon formed by the target area to be rendered. Then, for each ray emitted by the ray emission unit, it traverses all line segments in the polygon boundary and counts the intersection points for each line segment to obtain the valid intersection points.

[0057] For example, a polygon can be denoted as .

[0058] For example, the intersection points of each ray emitted by the ray emitting unit are counted using equations (2) and (3):

[0059] (2)

[0060] (3)

[0061] Online segment When both equations (2) and (3) are satisfied, it is counted as a valid intersection point, and the final number of intersection points is obtained.

[0062] Furthermore, the region clipping unit retains pixels with an odd number of intersection points as target pixels, and clips pixels with an even number of intersection points to be clipped, generating a rendering mask to obtain the target raster data.

[0063] As an exemplary embodiment, the rendering module 505 includes a mapping unit and a drawing unit; the mapping unit is used to map each pixel in the target raster data to a color value based on a set color scale; the drawing unit is used to draw a weather rendering image based on the color values.

[0064] For example, the drawing unit includes a Canvas unit or a WebGL unit. The rendering module 505 maps each grid value to a color value according to the set color swatches and draws it through Canvas or WebGL to support effects such as transparency adjustment and layer overlay.

[0065] As an exemplary embodiment, the meteorological data visualization rendering system further includes a map engine adaptation module, which is used to implement functions such as layer alignment, dynamic scaling, and view synchronization.

[0066] In this embodiment, the meteorological data visualization and rendering system also includes a map engine adaptation module to enable functions such as layer alignment, dynamic scaling, and view synchronization.

[0067] For example, the map engine adaptation module integrates with map frameworks such as Leaflet.js or Mapbox to achieve functions such as layer alignment, dynamic scaling, and view synchronization.

[0068] Based on this, as an exemplary embodiment, the map engine adaptation layer includes a Leaflet.js map framework integration unit.

[0069] Based on this, as an exemplary embodiment, the map engine adaptation layer also includes a Mapbox map framework integration unit.

[0070] Secondly, the present invention provides an embodiment of a meteorological data visualization rendering method. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0071] This embodiment provides a method for visualizing and rendering meteorological data. Figure 2 This is a flowchart illustrating a meteorological data visualization and rendering method according to an embodiment of the present invention, as shown below. Figure 2 As shown:

[0072] Step S101: Obtain meteorological data at the first spatial scale.

[0073] For example, meteorological data may include temperature, humidity, wind speed, wind direction, etc.

[0074] Step S102: Map the meteorological data into a target raster structure based on a preset geographical range and preset resolution at the second spatial scale.

[0075] For example, the target grid structure consists of multiple cells (pixels), each cell (pixel) corresponds to a region on the map, and each cell (pixel) corresponds to one or more meteorological values.

[0076] For example, when mapping the meteorological data into a target raster structure based on a preset geographical range and a preset resolution of a second spatial scale, the meteorological data can be mapped into a two-dimensional array according to the geographical range and the second resolution of the second spatial scale; in the two-dimensional array, data in one dimension is used to represent cells (pixels), and data in the other dimension is used to represent meteorological values.

[0077] Step S103: Perform bilinear interpolation and / or inverse distance weighted interpolation on the missing points and / or irregular sampling points in the target grid structure to obtain the grid value corresponding to each grid pixel in the target grid structure.

[0078] In this embodiment, missing points and / or irregular sampling points within the target grid structure are interpolated to obtain the grid values ​​corresponding to the target grid structure.

[0079] The meteorological visualization system in related technologies suffers from simple interpolation algorithms and coarse layer display. To solve this problem, in this embodiment, bilinear interpolation can be used to interpolate missing points and / or irregular sampling points in the target raster structure, so as to improve the coarse layer problem through more refined bilinear interpolation.

[0080] In this embodiment, inverse distance weighting can be used to interpolate missing points and / or irregular sampling points within the target raster structure, thereby improving the layer coarseness problem through more refined inverse distance weighted interpolation.

[0081] In this embodiment, interpolation of missing points and / or irregular sampling points within the target grid structure can be achieved simultaneously using bilinear interpolation and inverse distance weighting. Specifically, a first interpolation can be performed using bilinear interpolation to obtain a first sub-grid value and a first sub-interpolation error, while a second interpolation can be performed using inverse distance weighting to obtain a second sub-grid value and a second sub-interpolation error. Furthermore, the first sub-grid value and the second sub-grid value are fused based on the first sub-interpolation error and the second sub-interpolation error to obtain the grid value. The first sub-interpolation error and the second sub-interpolation error can be obtained by interpolating based on historical data to obtain historical interpolation data, and further by comparing the historical interpolation data with historical measured data.

[0082] Step S104: Based on the inclusion relationship between each pixel in the target raster structure and the target area to be rendered, the target raster structure is cropped to obtain the target raster data corresponding to the target area to be rendered.

[0083] In this embodiment, the target grid structure is cropped based on the inclusion relationship between each pixel within the target grid structure and the target area to be rendered, in order to solve the problem of lack of area masking and boundary control capabilities in meteorological visualization technology in related technologies.

[0084] In one embodiment, for regular boundary regions such as rectangles or circles, the target raster structure can be filtered by considering the inclusion relationship between the boundary coordinates of the region to be rendered and the pixel coordinates of each pixel within the target raster structure, thereby achieving the cropping of the target raster structure and finally obtaining the target raster data corresponding to the target region to be rendered.

[0085] Step S105: Map each pixel in the target raster data to a color value and draw the weather rendering image.

[0086] In this embodiment, the correspondence between pixel values ​​and color scales corresponding to each meteorological data can be pre-calibrated in the laboratory as a pre-set color scale. Furthermore, each grid value is mapped to a color value according to each pixel in the target grid data and the set color scale. Finally, the existing rendering module is used to draw the meteorological rendering image.

[0087] In this embodiment, the correspondence between pixel values ​​and color scales corresponding to each meteorological data can be pre-calibrated in the laboratory as a pre-set color scale. Then, each grid value is mapped to a color value according to each pixel in the target grid data and the set color scale. Finally, the existing rendering module is used to draw the meteorological rendering image.

[0088] This invention provides a meteorological data visualization rendering method, which includes: acquiring meteorological data at a first spatial scale; mapping the meteorological data to a target raster structure according to a second spatial scale geographical range and a second resolution; performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points within the target raster structure to obtain raster values ​​corresponding to each raster pixel in the target raster structure; cropping the target raster structure based on the inclusion relationship between each pixel within the target raster structure and the target area to be rendered to obtain target raster data corresponding to the target area to be rendered; mapping each pixel in the target raster data to a color value and drawing a meteorological rendering image; the above method can use meteorological data at a large spatial scale as a reference to interpolate missing points and irregular sampling points within the target raster structure to obtain raster values ​​corresponding to each raster pixel in the target raster structure, further cropping the target raster structure based on the inclusion relationship between each pixel within the target raster structure and the target area to be rendered, and finally rendering and displaying the cropped target raster data.

[0089] As an exemplary embodiment, the step of performing bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregular sampling points within the target grid structure to obtain the grid value corresponding to each grid pixel in the target grid structure includes: obtaining the first coordinate value of the pixel to be interpolated corresponding to the missing points and / or irregular sampling points; obtaining at least one set of second coordinate values ​​and values ​​of non-missing points or regular sampling points; interpolating the value of the pixel to be interpolated based on the value and the difference between the first coordinate value and the second coordinate value to obtain the interpolated value; and obtaining the grid value corresponding to each grid pixel in the target grid structure based on the interpolated value and the value.

[0090] In this embodiment, the value of the pixel to be interpolated is interpolated based on the numerical value and the difference between the first coordinate value and the second coordinate value to obtain the interpolated value.

[0091] For example, interpolation of missing points and / or irregular sampling points within the target grid structure can be achieved based on the inverse distance weighting method and / or bilinear interpolation method; the specific interpolation method can be referred to the content disclosed in the above embodiments, and will not be repeated here.

[0092] As an exemplary embodiment, the second coordinate values ​​and numerical values ​​at four preset coordinate locations of the target are obtained; bilinear interpolation is performed based on the first coordinate values, the second coordinate values, and the numerical values ​​to obtain the interpolated numerical values.

[0093] In this embodiment, the first coordinate value of the pixel p to be interpolated corresponding to the missing point and / or irregular sampling point acquired by the first acquisition unit is denoted as (x,y), and the grid value is denoted as f(x,y).

[0094] In this embodiment, the target preset coordinates are the lower left, lower right, upper left, and upper right corners of the target grid structure where the pixel to be interpolated is located. Specifically, the coordinates of the lower left corner Q11 of the target grid structure where the pixel to be interpolated is located are obtained and denoted as... The numerical value is denoted as The coordinates of the lower right corner point Q21 are denoted as The numerical value is denoted as The coordinates of the top-left corner point Q12 are denoted as The numerical value is denoted as The coordinates of the upper right corner point Q22 are denoted as The numerical value is denoted as .

[0095] Further, bilinear interpolation is performed based on the first coordinate value, the second coordinate value, and the numerical value to obtain the interpolated numerical value.

[0096] Specifically, the bilinear interpolation method can be referred to the content disclosed in the above embodiments, and will not be repeated here.

[0097] The bilinear interpolation method described above is suitable for efficient rendering, hardware acceleration, and algorithm implementation in GPU shaders, and can be used for real-time rendering of high-resolution weather raster layers.

[0098] Meteorological visualization systems in related technologies lack the ability to mask and control boundaries. To solve this problem, this invention uses the ray casting method to filter the raster pixels in the raster structure and obtain the target raster data corresponding to the target area to be rendered.

[0099] As an exemplary embodiment, the step of cropping the target grid structure based on the inclusion relationship between each pixel in the target grid structure and the target area to be rendered to obtain target grid data corresponding to the target area to be rendered includes: emitting horizontal rays in the target direction based on each pixel in the target grid structure; obtaining the number of intersection points of the polygons formed by each horizontal ray and the target area to be rendered; retaining pixels with an odd number of intersection points as target pixels, cropping pixels with an even number of intersection points to be cropped, generating a rendering mask, and obtaining the target grid data.

[0100] For example, the target direction can be left, right, above, or below; specifically, taking the target direction as right as an example, the technical solution of this embodiment is explained; specifically, the ray emitting unit uses each pixel in the target grid structure as a reference and emits horizontal rays from each pixel to the right.

[0101] Furthermore, the number of intersection points of each horizontal ray and the polygon formed by the target area to be rendered is obtained.

[0102] Specifically, the method for obtaining the number of intersections can be referred to the content disclosed in the above embodiments, and will not be repeated here.

[0103] Furthermore, pixels with an odd number of intersection points are retained as target pixels, and pixels with an even number of intersection points are cropped to generate a rendering mask, thereby obtaining the target raster data.

[0104] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0105] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods in the above embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the solution provided in this embodiment, depending on actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0110] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A weather data visualization rendering system, comprising: The meteorological data visualization rendering system comprises: an acquisition module configured to acquire meteorological data of a first spatial scale; a data gridding module configured to map the meteorological data to a target grid structure according to a geographical range and a second resolution of a second spatial scale, wherein the second spatial scale is smaller than the first spatial scale; a meteorological interpolation module configured to perform bilinear interpolation and / or inverse distance weighted interpolation on missing points and / or irregularly sampled points in the target grid structure to obtain grid values corresponding to each grid pixel in the target grid structure; a region clipping module configured to clip the target grid structure based on the inclusion relationship between each pixel in the target grid structure and a target region to be rendered to obtain target grid data corresponding to the target region to be rendered; a rendering module configured to map each pixel in the target grid data to a color value and draw a meteorological rendering image.

2. The weather data visualization rendering system of claim 1, wherein, The meteorological interpolation module comprises a first acquisition unit, a second acquisition unit, an interpolation unit, and a grid value determination unit. The first acquisition unit is configured to acquire first coordinate values of a pixel to be interpolated corresponding to missing points and / or irregularly sampled points. The second acquisition unit is configured to acquire second coordinate values and values of at least one group of non-missing points or regularly sampled points. The interpolation unit is configured to interpolate the value of the pixel to be interpolated based on the values and the difference between the first coordinate values and the second coordinate values to obtain an interpolated value. The grid value determination unit is configured to obtain the grid values corresponding to each grid pixel in the target grid structure based on the interpolated value and the values.

3. The weather data visualization rendering system of claim 2, wherein, The second acquisition unit is configured to acquire second coordinate values and values of four target preset coordinates. The interpolation unit is configured to perform bilinear interpolation based on the first coordinate values, the second coordinate values, and the values to obtain the interpolated value.

4. The weather data visualization rendering system of claim 1, wherein, The region clipping module comprises a position calculation unit, a ray emission unit, an intersection point acquisition unit, and a region clipping unit. The ray emission unit is configured to emit horizontal rays in a target direction based on each pixel in the target grid structure. The intersection point acquisition unit is configured to acquire the number of intersection points of each horizontal ray with a polygon formed by the target region to be rendered. The region clipping unit retains a target pixel whose intersection point number is odd and clips a to-be-clipped pixel whose intersection point number is even to generate a rendering mask and obtain the target grid data.

5. The weather data visualization rendering system of claim 1, wherein, The rendering module comprises a mapping unit and a drawing unit. The mapping unit is configured to map each pixel in the target grid data to a color value based on a set color scale. The drawing unit is configured to draw a meteorological rendering image based on the color value.

6. The weather data visualization rendering system of claim 5, wherein, The drawing unit comprises a Canvas unit.

7. The weather data visualization rendering system of claim 5, wherein, The rendering module further comprises a WebGL unit.

8. The weather data visualization rendering system of claim 1, wherein, The meteorological data visualization rendering system further comprises a map engine adaptation module configured to implement functions such as layer alignment, dynamic scaling, and view synchronization.

9. The weather data visualization rendering system of claim 8, wherein, The map engine adaptation layer comprises a Leaflet.js map framework integration unit.

10. The weather data visualization rendering system of claim 8, wherein, The map engine adaptation layer further comprises a Mapbox map framework integration unit.

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