Method for processing meteorological data and constructing environment, computer device, medium and product

Abstract

Embodiments of the present disclosure provide a method for processing meteorological data and constructing an environment, a computer device, a medium, and a product. In a specific embodiment, the meteorological data processing method includes: obtaining a plurality of meteorological cloud images within a set time period of a target area, and establishing a first index associating the position of the target area and the acquisition time of the meteorological cloud images of the target area; converting the meteorological cloud images into grayscale images, dividing the target area into rectangular grid areas, determining the grayscale value of each rectangular grid area, and normalizing the grayscale value of each rectangular grid area, and using the normalization result as the meteorological value of each rectangular grid area; dividing the target area into hexagonal grid areas, and determining the meteorological value of the hexagonal grid area according to the positional relationship between the hexagonal grid area and the rectangular grid area and the meteorological value of the rectangular grid area; and establishing a second index associating the position of the hexagonal grid area and the meteorological value.

Description

Technical Field

[0001] The present disclosure relates to the field of simulation technology. More specifically, it relates to a method for processing meteorological data and constructing an environment, a computer device, a medium, and a product. Background Art

[0002] Currently, when conducting tests such as equipment system tests, problems such as high organizational costs and great coordination difficulties of actual equipment tests are usually faced, and it is difficult to comprehensively and effectively test the effectiveness of the equipment system. Therefore, simulation tests are often used to supplement test samples. In the construction of the natural environment of simulation tests, since real meteorological cloud map data is expressed in a relevant format, it is difficult to perform two-dimensional and three-dimensional visual displays for the optical remote sensing satellite earth observation calculation and simulation system, and it is impossible to directly import the externally obtained meteorological cloud map data into the simulation system to realize the construction of the meteorological environment of the simulation test involving optical remote sensing satellites. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method for processing meteorological data and constructing an environment, a computer device, a medium, and a product to solve at least one of the problems existing in the prior art.

[0004] To achieve the above object, the present disclosure adopts the following technical solutions:

[0005] The first aspect of the present disclosure provides a method for processing meteorological data, including:

[0006] Obtaining evaluation index data of multiple targets to be evaluated;

[0007] Obtaining multiple meteorological cloud maps within a set time period of a target area, and establishing a first index associating the position of the target area and the acquisition time of the meteorological cloud maps of the target area;

[0008] Converting the meteorological cloud maps into grayscale maps, dividing the target area into rectangular grid areas, determining the grayscale value of each rectangular grid area, and normalizing the grayscale value of each rectangular grid area, and taking the normalization result as the meteorological value of each rectangular grid area;

[0009] Dividing the target area into hexagonal grid areas, and determining the meteorological value of the hexagonal grid areas according to the positional relationship between the hexagonal grid areas and the rectangular grid areas and the meteorological values of the rectangular grid areas;

[0010] Establishing a second index associating the position and meteorological value of the hexagonal grid areas.

[0011] Optionally, the determining the grayscale value of each rectangular grid area includes:

[0012] Map the center point of the rectangular grid area to the grayscale image, and interpolate the grayscale values of N pixel points in the grayscale image to obtain the grayscale value of the center point of the rectangular grid area, which is used as the grayscale value of the rectangular grid area. The N pixel points are the first N pixel points arranged in ascending order of the distance from the mapping point of the center point of the rectangular grid area in the grayscale image.

[0013] Optionally, the value range of N is 1 to 4.

[0014] Optionally, the principle for setting the size of the hexagonal grid for dividing the target area into hexagonal grids is to be as close as possible to the size of the rectangular grid for dividing the target area into rectangular grids.

[0015] Optionally, determining the meteorological value of the hexagonal grid area according to the positional relationship between the hexagonal grid area and the rectangular grid area and the meteorological value of the rectangular grid area includes: interpolating the meteorological values of the rectangular grid areas having overlapping areas with the hexagonal grid area to obtain the meteorological value of the hexagonal grid area.

[0016] The second aspect of the present disclosure provides a method for constructing a meteorological environment for a simulation experiment, including:

[0017] Process a plurality of meteorological cloud images within a set time period of a target area according to the meteorological data processing method provided in the first aspect of the present disclosure, and store the processing results;

[0018] Based on the time information of the simulation experiment and the target area, query the meteorological data related to the simulation experiment based on the first index and the second index, and construct the meteorological environment of the simulation experiment.

[0019] Optionally, querying the meteorological data related to the simulation experiment based on the first index and the second index includes: using a geospatial indexing system to query the meteorological data related to the simulation experiment based on the first index and the second index.

[0020] The third aspect of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the methods provided in the first aspect or the second aspect of the present disclosure are implemented.

[0021] The fourth aspect of the present disclosure is a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the methods provided in the first aspect or the second aspect of the present disclosure are implemented.

[0022] The fifth aspect of the present disclosure is a computer program product, including a computer program. When the computer program is executed by a processor, the methods provided in the first aspect or the second aspect of the present disclosure are implemented.

[0023] The beneficial effects of the present disclosure are as follows:

[0024] The meteorological data processing method provided by the present disclosure discretizes the meteorological cloud map data of the target area in terms of time and performs two grid-based discretizations on the meteorological cloud map data of the target area in terms of space, providing a favorable basis for the simulation system to efficiently and accurately query meteorological data during simulation tests, improving the operation efficiency of the simulation system and enhancing the confidence level of simulation tests. Among them, in the two grid-based discretizations, the rectangular grid division can simplify the complex meteorological cloud map into a numerical model that can be processed by the simulation system, providing a benchmark for data processing within the simulation system, and having the advantage of facilitating users to set the grid size or the resolution of the grid. The hexagonal grid division provides a benchmark for data processing of tools such as the geospatial indexing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The following further describes in detail the specific embodiments of the present disclosure with reference to the drawings.

[0026] Figure 1 The flowchart showing the meteorological data processing method provided by the embodiment of the present disclosure is shown.

[0027] Figure 2 The schematic diagram showing the mapping points of the center points of the rectangular grid areas in the grayscale image is shown.

[0028] Figure 3 The schematic diagram showing the structure of the computer system for implementing the embodiment of the present disclosure is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] To more clearly illustrate the present disclosure, the following further describes the present disclosure with reference to the embodiments and the drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present disclosure.

[0030] As Figure 1 shown, an embodiment of the present disclosure provides a meteorological data processing method, including the following steps:

[0031] S1. Obtain a plurality of meteorological cloud maps within a set time period of the target area, and establish a first index associating the location of the target area and the acquisition time of the meteorological cloud maps of the target area.

[0032] In a specific example, in step S1, for example, according to the construction requirements of the meteorological environment for the equipment system simulation test, real meteorological cloud map data that is temporally continuous within a set time period in the target area is collected. The collected meteorological cloud map data is discretized into multiple discrete meteorological cloud maps, and a first index associated with the location of the target area (or coordinates, longitude and latitude range) and the acquisition time of the meteorological cloud map of the target area is established. In this way, when constructing the meteorological environment of the simulation test and querying and loading the meteorological data in this area within the specified time period, only multiple meteorological cloud maps of this area within the set time period need to be queried and extracted according to the first index, which can effectively reduce the amount of meteorological data and improve the operation efficiency of the simulation system.

[0033] Exemplarily, since the meteorological cloud map data often contains interference elements such as noise and impurities, which will affect the accuracy of constructing the meteorological environment of the simulation test. Therefore, after obtaining multiple meteorological cloud maps within the set time period of the target area, noise reduction processing such as median filtering can be performed on the obtained meteorological cloud maps to ensure the accuracy of the obtained meteorological information.

[0034] S2. Convert the meteorological cloud map into a grayscale map, divide the target area into rectangular grid, determine the grayscale value of each rectangular grid area, and perform normalization processing on the grayscale value of each rectangular grid area, and use the normalization result as the meteorological value of each rectangular grid area.

[0035] Continuing the previous example, in step S2, an exemplary process for converting the meteorological cloud map into a grayscale map is as follows: Through the cross-platform computer vision library OpenCV, the meteorological cloud map obtained in step S1 can be subjected to grayscale processing to convert the meteorological cloud map into a grayscale map. Among them, the grayscale map can also be called a two-dimensional matrix map, and each matrix element represents the grayscale value of the corresponding pixel. The grayscale value of the pixel can be directly accessed by specifying the coordinates Q(x, y) of the pixel in the grayscale map.

[0036] Continuing the previous example, in step S2, an exemplary process for dividing the target area into rectangular grid is as follows:

[0037] Discretizing the target area into grids can discretize the meteorological cloud map data of the target area into grid data, thereby realizing the simplification of the complex meteorological cloud map into a numerical model that can be processed by the simulation system.

[0038] In the process of performing rectangular grid processing on the target area, first, the rectangular grid size needs to be set according to the accuracy requirements for constructing the meteorological environment of the simulation test to ensure that the grayscale value of the rectangular grid area can accurately reflect the required meteorological cloud map detail information. The shape of the rectangular grid can be a square or a rectangle, as long as it is convenient for subsequent processing.

[0039] For example, the meteorological satellite cloud image can be loaded into the corresponding geographical location range through the open-source map library OpenLayers first. When the geographical locations of the four vertices of the meteorological satellite cloud image are known, the longitude and latitude range of the target area corresponding to the meteorological satellite cloud image can be obtained according to the geographical locations of any two opposite vertices of the meteorological satellite cloud image. For example, the longitude and latitude range of the target area is: 30.0 to 40.0 degrees north latitude, 110.0 to 120.0 degrees east longitude. Then, after determining the rectangular grid size, rectangular grid division is performed: according to the accuracy requirements of the meteorological environment for constructing the simulation experiment, the rectangular grid size is determined, that is, the longitude and latitude span of the rectangular grid, which characterizes the resolution of the rectangular grid. The larger the rectangular grid size, the higher the calculation efficiency, but the lower the accuracy of reflecting the actual situation. For example, when the rectangular grid size is set to 0.072°×0.072°, after performing rectangular grid division on the target area of 30.0 to 40.0 degrees north latitude, 110.0 to 120.0 degrees east longitude, 138×138 rectangular grid areas arranged in an array are obtained. The coordinates of each rectangular grid area in the array (two-dimensional matrix) can be denoted as P(i,j), where i and j respectively represent the row and column numbers of the rectangular grid area in the array.

[0040] In a possible implementation manner, in step S2, the determining the gray value of each rectangular grid area includes:

[0041] Mapping the center point of the rectangular grid area to the gray-scale image, and interpolating the gray values of N pixel points in the gray-scale image to obtain the gray value of the center point of the rectangular grid area, which is used as the gray value of the rectangular grid area. The N pixel points are the first N pixel points arranged in ascending order of the distance from the mapping point of the center point of the rectangular grid area in the gray-scale image.

[0042] In a possible implementation manner, the value range of N is 1 to 4.

[0043] Continuing with the foregoing example, in step S2, an exemplary process for determining the gray value of each rectangular grid area is as follows:

[0044] After performing rectangular grid division on the target area, rectangular grid areas arranged in an array are obtained. The coordinates in the array are P(i,j), and the position is represented as P(i,j)=W(lon, lat), where i represents the row number of the rectangular grid area in the array (or the serial number of the horizontal grid discretization), j represents the column number of the rectangular grid area in the array (or the serial number of the vertical grid discretization), W(lon, lat) is the actual position coordinate of the center point of the rectangular grid area, lon is the longitude, and lat is the latitude. For example, P(3,5)=W(112,35) means that the position coordinate of the center point of the rectangular grid area in the 3rd row and 5th column in the array is longitude 112 and latitude 35.

[0045] By mapping the actual pixel resolution of the meteorological cloud image (e.g., 300×300) to the coordinates of the rectangular grid area in the array, the pixel coordinates Q(x, y) in the grayscale image corresponding to the center point of each rectangular grid area can be obtained.

[0046] For the pixel coordinates Q(x, y) in the grayscale image corresponding to the center point of a rectangular grid area:

[0047] If there is a pixel point corresponding to the pixel coordinates Q(x, y), the gray value gray_value_i of this pixel point is assigned to the center point of this rectangular grid area and used as the gray value of this rectangular grid area.

[0048] If the pixel coordinates Q(x, y) cannot correspond to a pixel point, the gray values of N adjacent pixel points around the pixel coordinates Q(x, y) can be interpolated to obtain the gray value of the pixel coordinates Q(x, y), which is used as the gray value of the center point of this rectangular grid area and further used as the gray value of this rectangular grid area. For example, let N = 1, then the gray value of the pixel coordinates Q(x, y) is obtained by the zero-order interpolation method (nearest neighbor interpolation). For example, Figure 2 As shown, the pixel coordinates Q(x, y) in the grayscale image corresponding to the center point of the rectangular grid area correspond to point P. The pixel point closest to point P is Q11, then f(P) = f(Q11), that is, the gray value of the pixel coordinates Q(x, y) takes the gray value of the pixel point Q11. In addition, other interpolation methods such as bilinear interpolation can also be used to obtain the gray value of the pixel coordinates Q(x, y). For example, let N = 4, then for Figure 2 the situation shown, the gray values of pixel point Q11, pixel point Q12, pixel point Q21, and pixel point Q22 can be weighted and summed to obtain the gray value of the pixel coordinates Q(x, y). The gray value weights are set to be inversely proportional to the distances between pixel point Q11, pixel point Q12, pixel point Q21, and pixel point Q22 and point P respectively.

[0049] Continuing the foregoing example, in step S2, an exemplary process of normalizing the gray value of each rectangular grid area and using the normalization result as the meteorological value of each rectangular grid area is as follows:

[0050] By normalizing the gray value of each rectangular grid area, the meteorological value of the rectangular grid area can be approximately given, and thus a two-dimensional meteorological value matrix can be generated.

[0051] After the meteorological cloud image is converted into a grayscale image, the grayscale value can reflect the density of the cloud layer in engineering applications, so it can characterize meteorological features. For example, when conducting a simulation experiment and constructing a meteorological environment, if the meteorological value (such as the cloud layer density) is divided into levels from 0 to 10, then the normalization process of the grayscale value of the rectangular grid area can be to map the grayscale value of the rectangular grid area into the interval [0, 1], then multiply by 10 and round to obtain the meteorological value representing the cloud layer density level of the rectangular grid area, for example.

[0052] S3. Perform hexagonal grid division on the target area. According to the positional relationship between the hexagonal grid area and the rectangular grid area and the meteorological value of the rectangular grid area, determine the meteorological value of the hexagonal grid area, and establish a second index associating the position and meteorological value of the hexagonal grid area.

[0053] In a possible implementation manner, in step S3, the principle for setting the size of the hexagonal grid for performing hexagonal grid division on the target area is to be as close as possible to the size of the rectangular grid for performing rectangular grid division on the target area.

[0054] The design that the size of the hexagonal grid is as close as possible to the size of the rectangular grid can reduce information loss when determining the meteorological value of the hexagonal grid area, so as to ensure that the grayscale value of the hexagonal grid area can accurately reflect the required meteorological cloud image detail information.

[0055] In a possible implementation manner, in step S3, the determining the meteorological value of the hexagonal grid area according to the positional relationship between the hexagonal grid area and the rectangular grid area and the meteorological value of the rectangular grid area includes: interpolating the meteorological values of the rectangular grid areas having overlapping areas with the hexagonal grid area to obtain the meteorological value of the hexagonal grid area.

[0056] Continuing with the foregoing example, the exemplary process of step S3 is as follows: According to the requirements of the actual simulation geographical environment area for discretization calculation efficiency and accuracy, when constructing the meteorological environment of the simulation experiment, a geospatial indexing system H3 can be used to simulate the cloud meteorological environment. In step S2, the rectangular grid size is set to 0.072°×0.072°. Since a 0.072° span is approximately equal to 8 km, hexagonal grids of level 5 in the geospatial indexing system H3 (when the level is 5, the side length of the H3 grid is approximately 8.544 km) can be used to perform hexagonal grid division on the target area, obtaining multiple hexagonal grid areas. Then, the meteorological values of the rectangular grid areas that overlap with the hexagonal grid areas are interpolated to obtain the meteorological values of the hexagonal grid areas. For example, the meteorological value of the rectangular grid with the largest overlapping range with a certain hexagonal grid area can be assigned to this hexagonal grid, or the meteorological values of multiple rectangular grid areas that overlap with a certain hexagonal grid area can be weighted and summed to obtain the meteorological value of this hexagonal grid. The meteorological value weights are set to be proportional to the overlapping area between each rectangular grid area and this hexagonal grid area. Finally, a second index associating the positions and meteorological values of the hexagonal grid areas is established. The position of the hexagonal grid area is, for example, the center point coordinates center(lon, lat) of the hexagonal grid area.

[0057] In summary, the meteorological data processing method provided in this embodiment discretizes the meteorological cloud map data of the target area in terms of time and performs two grid-based discretizations of the meteorological cloud map data of the target area in terms of space. The rectangular grid division provides a data processing benchmark within the simulation system, and the hexagonal grid division provides a data processing benchmark for tools such as the geospatial indexing system. The meteorological data processing method provided in this embodiment provides a favorable basis for the simulation system to efficiently and accurately query meteorological data during the simulation experiment, which can improve the operation efficiency of the simulation system and enhance the confidence level of the simulation experiment.

[0058] Another embodiment of the present disclosure provides a method for constructing a meteorological environment, including the following steps:

[0059] Process multiple meteorological cloud maps within a set time period of the target area according to the meteorological data processing method provided in the above embodiment, and store the processing results;

[0060] Based on the time information and the target area of the simulation experiment, query the meteorological data related to the simulation experiment based on the first index and the second index, and construct the meteorological environment of the simulation experiment.

[0061] Among them, the time information of the simulation experiment includes the start and end times of the simulation experiment, and the target area of the simulation experiment is the spatial information of the simulation experiment.

[0062] In a possible implementation, querying meteorological data related to a simulation experiment based on the first index and the second index includes: using a geospatial indexing system to query meteorological data related to the simulation experiment based on the first index and the second index.

[0063] Continuing with the foregoing example, after processing multiple meteorological cloud images within a set time period of a target area using the meteorological data processing method provided in the above embodiment, an exemplary process for storing the processing results is as follows:

[0064] Considering the efficiency of querying and loading meteorological data during actual simulation and the effect of visual display, for the processing results obtained by the meteorological data processing method provided in the above embodiment, the meteorological values of all hexagonal grid areas corresponding to each acquisition time are uniformly formatted and stored in separate tables. Uniform formatting or standardization means: uniformly discretizing the positions and meteorological values of all hexagonal grid areas according to the geospatial indexing system standard into a single unit. One acquisition time corresponds to a set of units, and the meteorological data for a time period of a target area consists of multiple sets of units corresponding to multiple acquisition times. The second index in each unit can quickly resolve the position of the hexagonal grid area through the geospatial indexing system for meteorological visualization rendering; storing in separate tables means storing the unit data for each period in a separate table according to a fixed time period cycle. This can effectively reduce the amount of redundant data and improve the interaction speed of the simulation model in the simulation system.

[0065] Continuing with the foregoing example, an exemplary process for querying meteorological data related to a simulation experiment, constructing a meteorological environment for the simulation experiment, and visually displaying it is as follows:

[0066] Visual display is an important part of a simulation experiment. Through visualization, the constructed meteorological environment information can be visually presented, the characteristics and change trends of clouds can be intuitively displayed, and strong support can be provided for simulation deduction and analysis. The specific process of meteorological environment construction and visual display includes:

[0067] First, the front end sets the start and end times to obtain the time range parameters of the meteorological environment, and sets a regional range on a two-dimensional map or a three-dimensional map to obtain the spatial range parameters of the meteorological environment;

[0068] Then, the server receives the time range parameters and the spatial range parameters, splits the time period according to the meteorological data storage cycle, parses out all sets of meteorological tables mapped, obtains the second indexes of all hexagonal grid areas within the regional range, and generates an index set;

[0069] Finally, the server quickly retrieves the meteorological values of all index sets from the meteorological table set in parallel and returns them to the front end. The front end quickly parses the positions of all hexagonal grid regions according to the geospatial index system and completes the visualization rendering according to the meteorological values.

[0070] As Figure 3 shown, a computer system suitable for implementing the meteorological data processing method provided in the above embodiment or the meteorological environment construction method for the simulation experiment provided in the above embodiment includes a central processing module (CPU), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) or the program loaded from the storage part into the random access memory (RAM). In the RAM, various programs and data required for the operation of the computer system are also stored. The CPU, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0071] The following components are connected to the I / O interface: an input part including a keyboard, a mouse, etc.; an output part including a liquid crystal display (LCD), a speaker, etc.; a storage part including a hard disk, etc.; and a communication part including a network interface card such as a LAN card, a modem, etc. The communication part performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. A removable medium, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive as needed so that the computer program read from it can be installed into the storage part as needed.

[0072] Specifically, according to this embodiment, the process described in the above flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product, which includes a computer program tangibly contained on a computer-readable medium, and the above computer program includes program codes for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part and / or installed from the removable medium.

[0073] The flowcharts and schematic diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the system, method, and computer program product of this embodiment. In this regard, each block in the flowchart or schematic diagram may represent a module, a segment of a program, or a part of code, and the above-mentioned module, segment of a program, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the schematic diagram and / or flowchart, as well as the combination of blocks in the schematic and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0074] As another aspect, this embodiment also provides a non-volatile computer storage medium, which may be the non-volatile computer storage medium included in the above-mentioned device in the above-mentioned embodiment, or may exist separately and be unassembled into the terminal. The above-mentioned non-volatile computer storage medium stores one or more programs, and when the above-mentioned one or more programs are executed by a device, the device implements the meteorological data processing method provided by the above-mentioned embodiment or the method for constructing the meteorological environment of the simulation experiment provided by the above-mentioned embodiment.

[0075] As another aspect, this embodiment also provides a computer program product, including a computer program, which when executed by a processor implements the meteorological data processing method provided by the above-mentioned embodiment or the method for constructing the meteorological environment of the simulation experiment provided by the above-mentioned embodiment.

[0076] In the description of the present disclosure, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present disclosure. Unless otherwise clearly specified and defined, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0077] It should also be noted that in the description of the present disclosure, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0078] Obviously, the above embodiments of the present disclosure are merely examples for clearly illustrating the present disclosure, rather than limitations on the implementation manners of the present disclosure. For those of ordinary skill in the art, other different forms of changes or variations can be made on the basis of the above description. It is impossible to enumerate all the implementation manners here. Any obvious changes or variations derived from the technical solutions of the present disclosure still fall within the protection scope of the present disclosure.

Claims

1. A meteorological data processing method, characterized in that: include: Acquire a plurality of meteorological cloud images of a target area within a set period of time, and establish a first index associating a location of the target area with a collection time of the meteorological cloud image of the target area; Convert the meteorological cloud map into a grayscale map, divide the target area into rectangular grids, determine the grayscale value of each rectangular grid area, and normalize the grayscale value of each rectangular grid area, using the normalized result as the meteorological value of each rectangular grid area; Dividing the target area into hexagonal grids, and determining the meteorological value of the hexagonal grid area according to the positional relationship between the hexagonal grid area and the rectangular grid area and the meteorological value of the rectangular grid area; Establishing a second index associating the location and meteorological value of the hexagonal grid area; The principle for setting the size of the hexagonal grid used to divide the target area into hexagonal grids is to be as close as possible to the size of the rectangular grid used to divide the target area into rectangular grids; Determining the meteorological value of the hexagonal grid area based on the positional relationship between the hexagonal grid area and the rectangular grid area and the meteorological value of the rectangular grid area includes: interpolating the meteorological values ​​of the rectangular grid area that overlaps with the hexagonal grid area to obtain the meteorological value of the hexagonal grid area.

2. The method according to claim 1, characterized in that Determining the grayscale value of each rectangular grid area includes: The center point of the rectangular grid area is mapped to the grayscale image, and the grayscale values ​​of N pixel points in the grayscale image are interpolated to obtain the grayscale value of the center point of the rectangular grid area as the grayscale value of the rectangular grid area, and the N pixel points are the first N pixel points arranged in ascending distance from the mapping point of the center point of the rectangular grid area in the grayscale image.

3. The method according to claim 2, characterized in that The value range of N is 1~4.

4. A method for constructing a meteorological environment for a simulation experiment, characterized in that: include: Processing a plurality of meteorological cloud images within a set period of time of a target area according to the method described in any one of claims 1 to 3, and storing the processing results; According to the time information and the target area of ​​the simulation test, meteorological data related to the simulation test is queried based on the first index and the second index to construct the meteorological environment of the simulation test.

5. The method according to claim 4, characterized in that The querying of meteorological data related to the simulation test based on the first index and the second index includes: using a geospatial index system to query meteorological data related to the simulation test based on the first index and the second index.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented, or the method according to claim 4 or 5 is implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented, or the method according to claim 4 or 5 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented, or the method according to claim 4 or 5 is implemented.

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