Meteorological data processing and environment construction method, computer equipment, medium and product

Abstract

The embodiment of the invention provides a meteorological data processing and environment construction method, computer equipment, a medium and a product. In a specific embodiment, the meteorological data processing method comprises the steps of obtaining a plurality of meteorological cloud pictures in a set time period of a target area, and establishing a first index associated with the position of the target area and the acquisition time of the meteorological cloud pictures of the target area; converting the meteorological cloud picture into a grey-scale map, performing rectangular grid division on the target area, determining a grey-scale value of each rectangular grid area, performing normalization processing on the grey-scale value of each rectangular grid area, and taking a normalization result as a meteorological value of each rectangular grid area; performing hexagonal grid division on the target area, and determining the meteorological value of the hexagonal grid area according to the position 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 associated with the position and the meteorological value of the hexagonal grid region.

Description

Technical Field

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

[0002] At present, when conducting tests such as equipment system tests, we usually face problems such as high organization costs and difficulty in coordination for actual installation tests, and it is difficult to fully 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 the simulation test, since the real meteorological cloud map data is expressed in a related format, it is difficult to display the two-dimensional and three-dimensional visualization used in the optical remote sensing satellite earth observation calculation and simulation system. It is impossible to directly import the external meteorological cloud map data into the simulation system to realize the construction of the meteorological environment for the simulation test involving optical remote sensing satellites. Summary of the invention

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

[0004] In order to achieve the above objectives, the present invention adopts the following technical solutions: The first aspect of the present disclosure provides a method for processing meteorological data, comprising: Obtain evaluation indicator data of multiple targets to be evaluated; 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; A second index is established that associates the location of the hexagonal grid area with the meteorological value.

[0005] Optionally, 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.

[0006] Optionally, the value range of N is 1~4.

[0007] Optionally, the size of the hexagonal grid used to divide the target area into hexagonal grids is set to be as close as possible to the size of the rectangular grid used to divide the target area into rectangular grids.

[0008] Optionally, 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.

[0009] A second aspect of the present disclosure provides a method for constructing a meteorological environment for a simulation experiment, comprising: According to the meteorological data processing method provided in the first aspect of the present disclosure, a plurality of meteorological cloud images within a set period of time of a target area are processed, and the processing results are stored; 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.

[0010] Optionally, querying the meteorological data related to the simulation test based on the first index and the second index includes: using a geospatial index system to query the meteorological data related to the simulation test based on the first index and the second index.

[0011] A third aspect of the present disclosure provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect or the second aspect of the present disclosure is implemented.

[0012] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect or the second aspect of the present disclosure.

[0013] A fifth aspect of the present disclosure provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method provided in the first aspect or the second aspect of the present disclosure.

[0014] The beneficial effects of the present disclosure are as follows: The meteorological data processing method provided by the present disclosure discretizes the meteorological cloud map data of the target area in time, and discretizes the meteorological cloud map data of the target area twice in space, which provides a favorable basis for the simulation system to efficiently and accurately query meteorological data when conducting simulation experiments, and can improve the operating efficiency of the simulation system and enhance the confidence of the simulation experiment. Among them, in the two gridded discrete processing, 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 has the advantage of being easy for users to set the grid size or the gridding resolution, and the hexagonal grid division provides a data processing benchmark for tools such as the geographic spatial index system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The specific implementation methods of the present disclosure are further described in detail below with reference to the accompanying drawings.

[0016] Figure 1 A flow chart of a meteorological data processing method provided by an embodiment of the present disclosure is shown.

[0017] Figure 2 A schematic diagram showing the mapping points of the center points of the rectangular grid area in the grayscale image.

[0018] Figure 3 A schematic diagram showing the structure of a computer system for implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0019] In order to more clearly illustrate the present disclosure, the present disclosure is further described below in conjunction with the embodiments and drawings. Similar components in the drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present disclosure.

[0020] like Figure 1 As shown, an embodiment of the present disclosure provides a meteorological data processing method, comprising the following steps: S1. 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 and a collection time of the meteorological cloud image of the target area.

[0021] In a specific example, in step S1, for example, in response to the construction requirements of the meteorological environment of the equipment system simulation test, the temporally continuous real meteorological cloud map data within the set time period of the target area can be collected, the collected meteorological cloud map data can be discretized into multiple discrete meteorological cloud maps, and a first index associated with the location (or coordinates, longitude and latitude range) of the target area 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, it is necessary to query and load the meteorological data of the area within the specified time period, it is only necessary to query and extract multiple meteorological cloud maps of the area within the set time period according to the first index, which can effectively reduce the amount of meteorological data and improve the operating efficiency of the simulation system.

[0022] For example, since meteorological cloud map data often contain interference elements such as noise and impurities, which may affect the accuracy of the meteorological environment construction of the simulation experiment, after obtaining multiple meteorological cloud maps within a set time period of the target area, the obtained meteorological cloud maps may be subjected to denoising processing such as median filtering to ensure the accuracy of the acquired meteorological information.

[0023] S2. 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, normalize the grayscale value of each rectangular grid area, and use the normalized result as the meteorological value of each rectangular grid area.

[0024] Continuing with the above example, in step S2, an exemplary process for converting the weather cloud map into a grayscale map is as follows: the weather cloud map obtained in step S1 can be gray-scaled through the cross-platform computer vision library OpenCV to convert the weather cloud map into a grayscale map, wherein the grayscale map can also be called a two-dimensional matrix map, each matrix element represents the grayscale value of the corresponding pixel, and the grayscale value of the pixel can be directly accessed by specifying the coordinates Q(x, y) of the pixel in the grayscale map.

[0025] Continuing with the above example, in step S2, an exemplary process of dividing the target area into rectangular grids is as follows: By discretizing the target area into grids, the meteorological cloud map data of the target area can be discretized into grid data, thereby simplifying the complex meteorological cloud map into a numerical model that can be processed by the simulation system.

[0026] In the process of rectangular gridding the target area, the rectangular grid size is first set according to the accuracy requirements of the meteorological environment for the simulation test to ensure that the grayscale value of the rectangular grid area can accurately reflect the required meteorological cloud map details. The shape of the rectangular grid can be square or rectangular, which is convenient for subsequent processing.

[0027] For example, the weather cloud map can be loaded into the corresponding geographical location range through the open source map library OpenLayers. When the geographical locations of the four vertices of the weather cloud map are known, the longitude and latitude range of the target area corresponding to the weather cloud map can be obtained according to the geographical locations of any two relative vertices of the weather cloud map. For example, the longitude and latitude range of the target area is: 30.0-40.0 north latitude and 110.0-120.0 east longitude. Then, the rectangular grid size is determined and the rectangular grid division is performed: according to the accuracy requirements of the meteorological environment for the simulation test, the rectangular grid size, that is, the longitude and latitude span of the rectangular grid, is determined, which represents 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, the rectangular grid size is set to 0.072°×0.072°, and the target area at 30.0-40.0 north latitude and 110.0-120.0 east longitude is divided into rectangular grids, and 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 recorded as P(i,j), where i and j represent the row and column numbers of the rectangular grid area in the array, respectively.

[0028] In a possible implementation, in step S2, 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.

[0029] In a possible implementation, the value range of N is 1-4.

[0030] Continuing with the above example, in step S2, an exemplary process of determining the grayscale value of each rectangular grid area is as follows: After dividing the target area into rectangular grids, the rectangular grid area arranged in an array is obtained. The coordinates in the array are P(i,j), and the position is expressed as P(i,j)=W(lon, lat), where i represents the row number of the rectangular grid area in the array (or the number of the horizontal grid discretization), j represents the column number of the rectangular grid area in the array (or the number of the vertical grid discretization), and 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.

[0031] By mapping the actual pixel resolution of the meteorological cloud image (e.g., 300×300) and the coordinates of the rectangular grid area in the array, the center point of each rectangular grid area can be mapped to the pixel coordinate Q(x,y) in the grayscale image.

[0032] For a rectangular network area, the center point is mapped to the pixel coordinates Q(x,y) in the grayscale image: If the pixel coordinate Q(x,y) corresponds to a pixel point, the gray value gray_value_i of the pixel point is assigned to the center point of the rectangular grid area and used as the gray value of the rectangular grid area; If the pixel coordinate Q(x,y) cannot correspond to a pixel point, the grayscale value of the pixel coordinate Q(x,y) can be obtained by interpolating the grayscale values ​​of the N pixels around the pixel coordinate Q(x,y), and used as the grayscale value of the center point of the rectangular network area, and further as the grayscale value of the rectangular network area. For example, if N=1, the grayscale value of the pixel coordinate Q(x,y) can be obtained by the zero-order interpolation method (nearest neighbor interpolation), for example Figure 2 As shown in the figure, the center point of the rectangular network area is mapped to the pixel coordinate Q(x,y) in the grayscale image, which corresponds to point P. The pixel point closest to point P is Q11, so f(P)=f(Q11), that is, the grayscale value of the pixel coordinate Q(x,y) is the grayscale value of the pixel point Q11. In addition, other interpolation methods such as bilinear interpolation can also be used to obtain the grayscale value of the pixel coordinate Q(x,y). For example, if N=4, then for Figure 2 In the situation shown, the grayscale values ​​of pixel point Q11, pixel point Q12, pixel point Q21, and pixel point Q22 can be weightedly summed to obtain the grayscale value of the pixel coordinate Q(x, y), and the grayscale value weight is set to be inversely proportional to the distance between pixel point Q11, pixel point Q12, pixel point Q21, and pixel point Q22 and point P respectively.

[0033] Continuing with the above example, in step S2, the grayscale value of each rectangular grid area is normalized, and the normalized result is used as the meteorological value of each rectangular grid area. For example, the exemplary process is as follows: By normalizing the grayscale value of each rectangular grid area, the meteorological value of the rectangular grid area can be approximated, thereby generating a two-dimensional meteorological value matrix.

[0034] 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, and thus can characterize meteorological characteristics. For example, when conducting simulation experiments and constructing a meteorological environment, the meteorological value (such as the density of the cloud layer) is divided into levels of 0-10, and the normalization processing of the grayscale value of the rectangular grid area can be to map the grayscale value of the rectangular grid area to the interval [0,1], and then multiply it by 10 and round it off to obtain the meteorological value of the rectangular grid area, such as the cloud density level.

[0035] S3. Divide the target area into hexagonal grids, determine 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 establish a second index associating the position and meteorological value of the hexagonal grid area.

[0036] In a possible implementation, in step S3, the size of the hexagonal grid used to divide the target area into hexagonal grids is set to be as close as possible to the size of the rectangular grid used to divide the target area into rectangular grids.

[0037] The design of the hexagonal grid size being as close as possible to the rectangular grid size can reduce the 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 map detail information.

[0038] In one possible implementation, in step S3, 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 value of the rectangular grid area that overlaps with the hexagonal grid area to obtain the meteorological value of the hexagonal grid area.

[0039] Continuing with the above example, the exemplary process of step S3 is, for example: according to the requirements of the discretization calculation efficiency and accuracy of the actual simulated geographical environment area, when constructing the meteorological environment of the simulation test, the geospatial index 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°, and the span of 0.072° is approximately equal to 8km. Then, the hexagonal grid of level 5 (when the level is 5, the side length of the H3 grid is approximately 8.544km) in the geographic spatial index system H3 can be set to divide the target area into hexagonal grids to obtain multiple hexagonal grid areas, and 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 that overlaps with a certain hexagonal grid area with the largest range can be assigned to the hexagonal grid, and the meteorological values ​​of multiple rectangular grid areas that overlap with a certain hexagonal grid can also be weighted summed to obtain the meteorological value of the hexagonal grid, and the meteorological value weight is set to be proportional to the area of ​​the overlap between each rectangular grid area and the hexagonal grid. Finally, a second index associating the position of the hexagonal grid area and the meteorological value is established. The position of the hexagonal grid area is, for example, the coordinates of the center point of the hexagonal grid area, center (lon, lat).

[0040] In summary, the meteorological data processing method provided in this embodiment discretizes the meteorological cloud map data of the target area in time, and discretizes the meteorological cloud map data of the target area twice in space. The rectangular grid division provides a benchmark for data processing within the simulation system, and the hexagonal grid division provides a benchmark for data processing of tools such as geographic spatial indexing systems. The meteorological data processing method provided in this embodiment provides a favorable basis for the simulation system to efficiently and accurately query meteorological data when conducting simulation experiments, which can improve the operating efficiency of the simulation system and enhance the confidence of the simulation experiment.

[0041] Another embodiment of the present disclosure provides a method for constructing a meteorological environment, comprising the following steps: According to the meteorological data processing method provided in the above embodiment, multiple meteorological cloud images within a set period of time of the target area are processed, and the processing results are stored; 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.

[0042] The time information of the simulation test includes the start and end time of the simulation test, and the target area of ​​the simulation test is the spatial information of the simulation test.

[0043] In a possible implementation, querying the meteorological data related to the simulation test based on the first index and the second index includes: using a geospatial index system to query the meteorological data related to the simulation test based on the first index and the second index.

[0044] Continuing with the above example, after processing multiple meteorological cloud images within a set period of time of a target area according to the meteorological data processing method provided in the above embodiment, an exemplary process of storing the processing results is as follows: Taking into account the efficiency of querying and loading meteorological data and the effect of visual display during actual simulation, the meteorological values ​​of all hexagonal grid areas corresponding to each collection time in the processing results obtained by the meteorological data processing method provided in the above embodiment are formatted in a unified and separate table for storage. Format unification or standardization means: the locations and meteorological values ​​of all hexagonal grid areas are discretized into a unit according to the standards of the geographic spatial index system, one collection time corresponds to a unit set, and the meteorological data within a time period of a target area is composed of multiple units corresponding to multiple collection times. The second index in each unit can be quickly parsed by the geographic spatial index system to obtain the location of the hexagonal grid area for meteorological visualization rendering; separate table storage means that the unit data of each period is stored in a table corresponding to a fixed time period. This can effectively reduce the amount of redundant data and improve the interaction speed of the simulation model of the simulation system.

[0045] Continuing with the above example, the exemplary process of querying meteorological data related to the simulation test, constructing the meteorological environment of the simulation test, and visually displaying it is as follows: Visualization is an important part of simulation experiments. Through visualization, the constructed meteorological environment information can be visually presented, and the characteristics and changing trends of clouds can be intuitively displayed, providing strong support for simulation deduction and analysis. The specific process of meteorological environment construction and visualization includes: First, the front end sets the start and end time to obtain the time range parameters of the meteorological environment, sets an area range on a two-dimensional map or a three-dimensional map to obtain the spatial range parameters of the meteorological environment; Then, the server receives the time range parameter and the space range parameter, splits the time period according to the meteorological data storage cycle, parses all mapped meteorological table sets, obtains the second index of all hexagonal grid areas within the area, and generates an index set; Finally, the server quickly retrieves the weather values ​​of all index sets from the weather table collection in parallel and returns them to the front end. The front end quickly parses the locations of all hexagonal grid areas based on the geospatial index system and completes the visual rendering based on the weather values.

[0046] like Figure 3As shown, a computer system suitable for implementing the meteorological data processing method provided by the above embodiment or the meteorological environment construction method of the simulation test provided by 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 to 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.

[0047] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a LAN card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive as needed so that a computer program read therefrom is installed into the storage section as needed.

[0048] In particular, according to this embodiment, the process described in the flowchart above 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 computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium.

[0049] The flow chart and schematic diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product of the present embodiment. In this regard, each square box in the flow chart or schematic diagram can represent a part of a module, program segment or code, and a part of the above-mentioned module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the schematic diagram and / or flow chart, and the combination of the square boxes in the schematic diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0050] As another aspect, this embodiment further 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 be a non-volatile computer storage medium that exists independently and is not installed in 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 above-mentioned device performs the meteorological data processing method provided in the above-mentioned embodiment or the meteorological environment construction method for the simulation experiment provided in the above-mentioned embodiment.

[0051] As another aspect, the present embodiment further provides a computer program product, including a computer program, which, when executed by a processor, implements 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.

[0052] In the description of the present disclosure, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", etc. 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 cannot be understood as a limitation on the present disclosure. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of 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 the specific circumstances.

[0053] It should also be noted that, in the description of the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "comprise one..." do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0054] Obviously, the above embodiments of the present disclosure are merely examples for clearly illustrating the present disclosure, and are not limitations on the implementation methods of the present disclosure. For ordinary technicians in this field, other different forms of changes or modifications can be made on the basis of the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present disclosure are still 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; A second index is established that associates the location of the hexagonal grid area with the meteorological value.

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. The method according to claim 1, characterized in that 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.

5. The method according to claim 4, characterized in that 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.

6. 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 5, 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.

7. The method according to claim 6, 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.

8. 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 5 is implemented, or the method according to claim 6 or 7 is implemented.

9. 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 5 is implemented, or the method according to claim 6 or 7 is implemented.

10. 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 5 is implemented, or the method according to claim 6 or 7 is implemented.

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