Method and device for calculating spatio-temporal representation of meteorological elements

Abstract

The application provides a kind of meteorological element space-time representative calculation method and device, it is related to the technical field of meteorological element processing, including: obtaining meteorological element data of meteorological element guarantee target area preset period, and based on meteorological element data and preset clustering algorithm, meteorological element guarantee target area is divided into air mass;Based on meteorological element data, the spatial heterogeneity coefficient of each grid point in air mass is calculated;Based on the spatial heterogeneity coefficient of each grid point in air mass and preset meteorological guarantee precision, the spatial representative range of meteorological element data in air mass is calculated;Based on meteorological element data and EOF empirical orthogonal function, the time representative range of meteorological element data in air mass is calculated, the technical problems that the accuracy of existing meteorological element space-time representative calculation method is poor and space-time dimension is missing are solved.

Description

Technical Field

[0001] This invention relates to the technical field of meteorological element processing, and in particular to a method and apparatus for calculating the spatiotemporal representativeness of meteorological elements. Background Technology

[0002] For large-scale events, such as aerospace, disaster relief, large-scale activities, or military operations, meteorological support is indispensable. The ability to obtain timely and accurate meteorological data is crucial to the successful completion of many events.

[0003] When providing meteorological support for large-scale events, relying solely on existing upper-air meteorological networks is insufficient. The 12-hour temporal resolution and spatial resolution exceeding 70km make it difficult to guarantee the accuracy of meteorological data for specific missions. If the meteorological support area is located around a city with sufficient resources and convenient transportation, deploying more meteorological equipment and conducting multi-point, multi-time measurements can address the representativeness and accuracy of meteorological data for the target area. However, in remote areas such as plateaus and deserts, or in foreign countries or on battlefields, the deployment of these upper-air equipment must be planned based on the spatiotemporal representativeness of meteorological elements in the target area, ensuring that limited resources are used to accurately complete the meteorological support mission.

[0004] Currently, meteorological element support tasks typically rely on accumulated experience: using 6 hours as the temporal representative range of meteorological elements and a circle with a radius of 50 km as the spatial representative range. However, this experience-based approach to meteorological element spatiotemporal representativeness lacks data support. Determining the true extent and duration that meteorological elements acquired by meteorological observation equipment can represent—that is, their spatiotemporal representativeness—is a key issue in the deployment of meteorological observation equipment.

[0005] In most cases, the spatiotemporal representativeness of each type of meteorological element is different, and the spatiotemporal representativeness of the same type of meteorological element also varies at different altitudes. Therefore, the temporal representation range of meteorological elements changes with altitude and region, and is not a fixed value. Similarly, the spatial representation range of meteorological elements is not a fixed geometric shape. When providing meteorological element support in unfamiliar areas, it is necessary to obtain the spatiotemporal representativeness of meteorological elements by statistically calculating historical meteorological data for that area, and then deploy meteorological observation stations based on the spatiotemporal representativeness of meteorological elements in order to ensure the representativeness and accuracy of meteorological elements. This results in the relatively poor computational efficiency and accuracy of existing methods for calculating the spatiotemporal representativeness of meteorological elements.

[0006] No effective solution has yet been proposed to address the above problems. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method and apparatus for calculating the spatiotemporal representativeness of meteorological elements, so as to alleviate the technical problems of poor accuracy and lack of spatiotemporal dimension in existing methods for calculating the spatiotemporal representativeness of meteorological elements.

[0008] In a first aspect, embodiments of the present invention provide a method for calculating the spatiotemporal representativeness of meteorological elements, comprising: acquiring meteorological element data for a preset time period of a meteorological element protection target area, and dividing the meteorological element protection target area into air masses based on the meteorological element data and a preset clustering algorithm; calculating the spatial variation coefficient of each grid point within the air mass based on the meteorological element data; calculating the spatial representative range of the meteorological element data within the air mass based on the spatial variation coefficient of each grid point within the air mass and a preset meteorological protection accuracy; and calculating the temporal representative range of the meteorological element data within the air mass based on the meteorological element data and the EOF empirical orthogonal function; wherein the meteorological element data includes: air temperature, air pressure, relative humidity, radial wind, and zonal wind data.

[0009] Furthermore, based on the meteorological element data, the spatial variation coefficient of each grid point within the air mass is calculated, including: constructing a spatial vector of meteorological elements for each grid point within the air mass based on the meteorological element data; and calculating the spatial variation coefficient of each grid point within the air mass based on the spatial vector of meteorological elements for each grid point within the air mass.

[0010] Further, based on the spatial variation coefficient of each grid point within the air mass and the preset meteorological support accuracy, the spatial representative range of the meteorological element data within the air mass is calculated, including: adding the preset meteorological support accuracy to the meteorological element spatial vector of each grid point within the air mass to obtain the target meteorological element spatial vector of each grid point within the air mass; calculating the maximum threshold of the spatial variation coefficient of each grid point within the air mass based on the target meteorological element spatial vector and the meteorological element spatial vector of each grid point within the air mass; determining the spatial representative range of the meteorological element data at each altitude level within the air mass based on the maximum threshold of the spatial variation coefficient of each grid point within the air mass; and determining the minimum value among the spatial representative ranges of the meteorological element data at each altitude level within the air mass as the spatial representative range of the meteorological element data within the air mass.

[0011] Furthermore, based on the meteorological element data and the EOF empirical orthogonal function, the time representative range of the meteorological element data within the air mass is calculated, including: constructing a meteorological element data field of the air mass based on the meteorological element data; constructing a covariance matrix of the meteorological element data field of the air mass based on the meteorological element data field of the air mass; calculating the eigenvector of the covariance matrix based on the Jacobi algorithm; and calculating the time representative range of the meteorological element data within the air mass based on the eigenvector.

[0012] Further, based on the feature vector, the time representative range of meteorological element data within the air mass is calculated, including: calculating the time coefficient matrix of the air mass based on the feature vector; calculating the time representative range of meteorological element data at each altitude level within the air mass based on the time coefficient matrix of the air mass and the preset meteorological support accuracy; and determining the minimum value among the time representative ranges of meteorological element data at each altitude level within the air mass as the time representative range of meteorological element data within the air mass.

[0013] Secondly, embodiments of the present invention also provide a calculation device for the spatiotemporal representativeness of meteorological elements, comprising: an acquisition unit, configured to acquire meteorological element data for a preset time period of a meteorological element protection target area, and to divide the meteorological element protection target area into air masses based on the meteorological element data and a preset clustering algorithm; a first calculation unit, configured to calculate the spatial variation coefficient of each grid point within the air mass based on the meteorological element data; a second calculation unit, configured to calculate the spatial representative range of the meteorological element data within the air mass based on the spatial variation coefficient of each grid point within the air mass and a preset meteorological protection accuracy; and a third calculation unit, configured to calculate the temporal representative range of the meteorological element data within the air mass based on the meteorological element data and the EOF empirical orthogonal function; wherein the meteorological element data includes: air temperature, air pressure, relative humidity, radial wind, and zonal wind data.

[0014] Furthermore, the first calculation unit is configured to: construct a spatial vector of meteorological elements for each grid point within the air mass based on the meteorological element data; and calculate the spatial variation coefficient of each grid point within the air mass based on the spatial vector of meteorological elements for each grid point within the air mass.

[0015] Further, the second calculation unit is configured to: add the preset meteorological support accuracy to the meteorological element spatial vectors of each grid point within the air mass to obtain the target meteorological element spatial vectors of each grid point within the air mass; calculate the maximum threshold of the spatial variation coefficient of each grid point within the air mass based on the target meteorological element spatial vectors and the meteorological element spatial vectors of each grid point within the air mass; determine the spatial representative range of the meteorological element data at each altitude level within the air mass based on the maximum threshold of the spatial variation coefficient of each grid point within the air mass; and determine the minimum value among the spatial representative ranges of the meteorological element data at each altitude level within the air mass as the spatial representative range of the meteorological element data within the air mass.

[0016] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the method described in the first aspect above, and the processor is configured to execute the program stored in the memory.

[0017] Fourthly, embodiments of the present invention also provide a computer-readable storage medium on which a computer program is stored.

[0018] In this embodiment of the invention, meteorological element data for a preset time period of the meteorological element support target area is acquired, and the target area is divided into air masses based on the meteorological element data and a preset clustering algorithm. Based on the meteorological element data, the spatial variation coefficient of each grid point within the air mass is calculated. Based on the spatial variation coefficient of each grid point within the air mass and a preset meteorological support accuracy, the spatial representative range of the meteorological element data within the air mass is calculated. Based on the meteorological element data and the EOF empirical orthogonal function, the temporal representative range of the meteorological element data within the air mass is calculated. The meteorological element data includes: temperature, air pressure, relative humidity, radial wind, and zonal wind data. This achieves the goal of efficiently and accurately calculating the spatiotemporal representativeness of meteorological elements, thereby solving the technical problem of poor calculation efficiency and accuracy in existing methods for calculating the spatiotemporal representativeness of meteorological elements, and thus realizing the technical effect of providing data support for meteorological detection.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] 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.

[0022] Figure 1 A flowchart illustrating a method for calculating the spatiotemporal representativeness of meteorological elements, provided as an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of a calculation device for the spatiotemporal representativeness of meteorological elements provided in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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.

[0026] Example 1:

[0027] According to an embodiment of the present invention, an embodiment of a method for calculating the spatiotemporal representativeness of meteorological elements is provided. 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.

[0028] Figure 1 This is a flowchart of a method for calculating the spatiotemporal representativeness of meteorological elements according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0029] Step S102: Obtain meteorological element data for a preset time period in the meteorological element protection target area, and divide the meteorological element protection target area into air masses based on the meteorological element data and a preset clustering algorithm;

[0030] Specifically, meteorological data include: air temperature, air pressure, relative humidity, radial wind and zonal wind data, and the number of the above air masses is one or more.

[0031] In this embodiment of the invention, meteorological elements are used to obtain hourly ERA5 reanalysis data from the European Centre for Meteorological Research (ECRS) for the target area over the past 30 days, with a horizontal resolution of 0.25° and a vertical stratification of 37 layers.

[0032] Meteorological element data are obtained by extracting temperature, air pressure, relative humidity, radial wind, and zonal wind data from reanalysis data.

[0033] Specifically, in this embodiment of the invention, according to the definition of an air mass, a large air mass with relatively uniform horizontal distribution of meteorological elements (mainly temperature and humidity) and a certain degree of vertical stability is divided into one or more air mass regions by using the Kmeans clustering partitioning algorithm to partition the cyclone protection area.

[0034] The gridded data of meteorological elements in the air mass region is X = {X(i,j)|X(i,j)∈R}, where R is the set of meteorological elements in the air mass region. After the meteorological support area is divided into k zones, the center of each zone is {C1, C2, C3, ..., Ck}. Then, the Euclidean distance from each pixel to each cluster center is calculated, as shown in the following formula:

[0035]

[0036] In the above formula, X in Let C represent the meteorological element data value of the i-th grid point, where m ≥ i ≥ 1. kn Let $k$ represent the nth cluster center, where $k \ge n \ge 1$. The distance between the meteorological element data value of each grid point and the meteorological element value of each cluster center is compared sequentially. Objects are assigned to the cluster with the nearest cluster center, resulting in k clusters {S1, S2, S3, ..., Sk}. The K-means algorithm defines the prototype of a cluster using centers. The cluster center is the mean of all objects within the cluster across all dimensions, calculated as follows:

[0037]

[0038] In the formula, C t Let |S| represent the t-th cluster center. n | represents the number of grid points in the nth cluster. The air mass region segmentation is completed through iterative looping.

[0039] Step S104: Based on the meteorological element data, calculate the spatial variation coefficient of each grid point within the air mass;

[0040] Step S106: Based on the spatial variation coefficient of each grid point within the air mass and the preset meteorological support accuracy, calculate the spatial representative range of the meteorological element data within the air mass.

[0041] Step S108: Based on the meteorological element data and the EOF empirical orthogonal function, calculate the time representative range of the meteorological element data within the air mass.

[0042] In this embodiment of the invention, meteorological element data for a preset time period of the meteorological element support target area is acquired, and the target area is divided into air masses based on the meteorological element data and a preset clustering algorithm. Based on the meteorological element data, the spatial variation coefficient of each grid point within the air mass is calculated. Based on the spatial variation coefficient of each grid point within the air mass and a preset meteorological support accuracy, the spatial representative range of the meteorological element data within the air mass is calculated. Based on the meteorological element data and the EOF empirical orthogonal function, the temporal representative range of the meteorological element data within the air mass is calculated. The meteorological element data includes: temperature, air pressure, relative humidity, radial wind, and zonal wind data. This achieves the goal of efficiently and accurately calculating the spatiotemporal representativeness of meteorological elements, thereby solving the technical problems of poor accuracy and missing spatiotemporal dimensions in existing methods for calculating the spatiotemporal representativeness of meteorological elements, and thus realizing the technical effect of providing data support for meteorological detection.

[0043] In this embodiment, step S104 includes the following steps:

[0044] Based on the meteorological element data, construct the meteorological element spatial vector of each grid point within the air mass;

[0045] Based on the spatial vectors of meteorological elements at each grid point within the air mass, the spatial variation coefficients of each grid point within the air mass are calculated.

[0046] In this embodiment of the invention, after completing the air mass region segmentation, meteorological element spatial vectors for each grid point within the air mass are constructed using meteorological element data, wherein the meteorological element spatial vectors are D. (i,j) (n), (i,j) are the coordinates of the grid point within the air mass, and n is the time series dimension of the meteorological element data at grid point (i,j).

[0047] Let the total number of grid points be p × q. Let the representative grid points for meteorological elements be... After time alignment of the data, the following formula is used to calculate and represent the grid data. Spatial variation coefficient:

[0048] The formula for calculating the spatial variation coefficient is as follows: W (i,j) Here, F(i,j) represents the variation weight coefficients, and F(i,j) represents the variation function.

[0049] D (i,j) (n) and The average distance between the meteorological element values ​​of the two spatial vectors, where p and q represent the total number of rows and columns of the grid points within the air mass, respectively.

[0050] for variance D (i,j) (n) and The covariance between two spatial vectors D (i,j) (n) and The cosine of the angle between two spatial vectors.

[0051] Traverse all grid points and obtain the spatial variation coefficients of all grid points within a cluster relative to the representative grid points of meteorological elements.

[0052] In this embodiment of the invention, step S106 includes the following steps:

[0053] The preset meteorological support accuracy is added to the meteorological element spatial vector of each grid point in the air mass to obtain the target meteorological element spatial vector of each grid point in the air mass.

[0054] Based on the spatial vectors of target meteorological elements at each grid point within the air mass and the spatial vectors of meteorological elements at each grid point within the air mass, the maximum threshold of the spatial variation coefficient at each grid point within the air mass is calculated.

[0055] Based on the maximum threshold of the spatial variation coefficient of each grid point within the air mass, the spatial representative range of meteorological element data at each altitude layer within the air mass is determined.

[0056] The minimum value among the spatial representative ranges of meteorological element data at each altitude level within the air mass is determined as the spatial representative range of the meteorological element data within the air mass.

[0057] In this embodiment of the invention, based on the maximum permissible error e of meteorological support accuracy... m Calculate the maximum threshold S for the spatial variation coefficient. th The maximum permissible error e m Integrate meteorological elements into the grid-based spatial vector:

[0058]

[0059] Next, the maximum threshold S of the spatial variation coefficient is calculated. th :

[0060]

[0061] Spatial representation range φ of meteorological elements at various altitudes within an air mass H for The gridded area was calculated. The spatial representation range of meteorological elements at heights of 1 to 37 layers [φ1, φ2, φ3, ... φ] was calculated respectively. 37 The minimum value is taken as the spatial representative range φ of meteorological elements within the air mass: φ = min([φ1, φ2, φ3, ... φ) 37 ]).

[0062] In this embodiment of the invention, step S108 includes the following steps:

[0063] Based on the meteorological element data, a meteorological element data field for the air mass is constructed;

[0064] Based on the meteorological element data field of the air mass, construct the covariance matrix of the meteorological element data field of the air mass;

[0065] The eigenvectors of the covariance matrix are calculated based on the Jacobi algorithm.

[0066] Based on the feature vector, the time range of meteorological element data within the air mass is calculated.

[0067] Specifically, based on the feature vector, the time representative range of the meteorological element data within the air mass is calculated, including:

[0068] Based on the feature vector, the time coefficient matrix of the air mass is calculated;

[0069] Based on the time coefficient matrix of the air mass and the preset meteorological support accuracy, the time representative range of meteorological element data at each altitude layer within the air mass is calculated.

[0070] The minimum value among the time-representative ranges of meteorological element data at various altitudes within the air mass is determined as the time-representative range of meteorological element data within the air mass.

[0071] In this embodiment of the invention, the EOF empirical orthogonal function analysis method is employed. By utilizing the values ​​of representative grid points of meteorological elements at various altitude levels within an air mass, useful information from the data is concentrated onto spatial distribution functions and time series functions, thereby reflecting the spatiotemporal variations of the element field. The specific calculation steps are as follows:

[0072] (1) Within the air mass, obtain air temperature, air pressure, relative humidity, radial wind and zonal wind data from meteorological data.

[0073] (2) Construct a meteorological element field X, standardize it, and then calculate its covariance matrix S = XX. ′ .

[0074]

[0075] In the formula, m is the spatial grid point of the meteorological element; n is the number of observations of the meteorological element in time; x ij This represents the observation value of a meteorological element at the i-th grid point at that altitude level, for the j-th time.

[0076]

[0077] S is an m×m real symmetric matrix, where each element of S is the covariance between the vector elements of X.

[0078] (3) Use the Jacobi method to find the eigenvalues ​​μ and eigenvectors V of S.

[0079] The Jacobi method is a common method for finding eigenvalues ​​and eigenvectors. It approximates the diagonalization of a symmetric matrix by using a series of orthogonal similar matrices, thereby obtaining all the eigenvalues ​​of the symmetric matrix. This is implemented by a computer program, which calculates the eigenvalues ​​μ and eigenvectors V. The obtained eigenvectors V are the space function matrix V.

[0080] (4) Find the time coefficient matrix T.

[0081] Using the spatial function matrix V, based on T = V′X H Find the time coefficient matrix T.

[0082]

[0083] In the formula, m represents the spatial grid points of the meteorological element, and n represents the observation times of the meteorological element. [t] i1 t i2 … t in ] is the time coefficient vector at the i-th grid point.

[0084] (5) Utilizing the maximum permissible error e m The time range τ for calculating meteorological elements.

[0085] In the spatial error field of EOF, matching e m Corresponding grid point V i The corresponding time coefficient vector is [t i1 t i2 … t in The maximum time is τ. i For all calculated τ i The average value is taken as the time representative range τ of the meteorological elements at that altitude. H .

[0086] Calculate the time range representing the height of floors 1 to 37 [τ1, τ2, τ3, ... τ] respectively. 37The minimum value is taken as the time representative range τ of the meteorological element data of the air mass: τ=min([τ1,τ2,τ3,…τ…) 37 ]).

[0087] After determining the spatial and temporal representative ranges of meteorological element data within an air mass, since the spatial representative range of meteorological elements is also the spatial coverage range of meteorological detection equipment, the target area is covered by the coverage range of the meteorological detection equipment to achieve highly accurate meteorological support.

[0088] Under this premise, meteorological detection equipment can be flexibly deployed according to the actual situation. The frequency of meteorological detection equipment deployment can be determined by utilizing the time representative range of meteorological elements.

[0089] In this embodiment of the invention, the spatial representative range of meteorological elements is calculated based on the spatial variation coefficient. This is achieved by constructing spatial vectors of meteorological elements at grid points, weighting the anomalies of the meteorological element values, and considering the directional and consistency factors of the spatial vectors. Compared to traditional experience-based meteorological support methods, this method offers higher accuracy.

[0090] Example 2:

[0091] This invention also provides a calculation device for the spatiotemporal representativeness of meteorological elements. This device is used to execute the calculation method for the spatiotemporal representativeness of meteorological elements provided in the above-described embodiments of this invention. The following is a detailed description of the device provided in this invention.

[0092] like Figure 2 As shown, Figure 2 This is a schematic diagram of a calculation device for the spatiotemporal representativeness of the aforementioned meteorological elements. The calculation device for the spatiotemporal representativeness of these meteorological elements includes:

[0093] The acquisition unit 10 is used to acquire meteorological element data of the meteorological element protection target area for a preset period, and divide the meteorological element protection target area into air masses based on the meteorological element data and a preset clustering algorithm.

[0094] The first calculation unit 20 is used to calculate the spatial variation coefficient of each grid point within the air mass based on the meteorological element data.

[0095] The second calculation unit 30 is used to calculate the spatial representative range of meteorological element data within the air mass based on the spatial variation coefficient of each grid point within the air mass and the preset meteorological support accuracy.

[0096] The third calculation unit 40 is used to calculate the time representative range of the meteorological element data within the air mass based on the meteorological element data and the EOF empirical orthogonal function.

[0097] The meteorological data include: air temperature, air pressure, relative humidity, radial wind, and zonal wind data.

[0098] In this embodiment of the invention, meteorological element data for a preset time period of the meteorological element support target area is acquired, and the target area is divided into air masses based on the meteorological element data and a preset clustering algorithm. Based on the meteorological element data, the spatial variation coefficient of each grid point within the air mass is calculated. Based on the spatial variation coefficient of each grid point within the air mass and a preset meteorological support accuracy, the spatial representative range of the meteorological element data within the air mass is calculated. Based on the meteorological element data and the EOF empirical orthogonal function, the temporal representative range of the meteorological element data within the air mass is calculated. The meteorological element data includes: temperature, air pressure, relative humidity, radial wind, and zonal wind data. This achieves the goal of efficiently and accurately calculating the spatiotemporal representativeness of meteorological elements, thereby solving the technical problem of poor calculation efficiency and accuracy in existing methods for calculating the spatiotemporal representativeness of meteorological elements, and thus realizing the technical effect of providing data support for meteorological detection.

[0099] Example 3:

[0100] This invention also provides an electronic device, including a memory and a processor. The memory is used to store a program that supports the processor in executing the method described in Embodiment 1 above, and the processor is configured to execute the program stored in the memory.

[0101] See Figure 3 The present invention also provides an electronic device 100, including: a processor 50, a memory 51, a bus 52 and a communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected through the bus 52; the processor 50 is used to execute executable modules, such as computer programs, stored in the memory 51.

[0102] The memory 51 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 53 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0103] Bus 52 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0104] The memory 51 is used to store programs. After receiving an execution instruction, the processor 50 executes the programs. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 50 or implemented by the processor 50.

[0105] Processor 50 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 50 or by instructions in software form. Processor 50 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 51. The processor 50 reads the information in memory 51 and, in conjunction with its hardware, completes the steps of the above method.

[0106] Example 4:

[0107] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in Embodiment 1 above.

[0108] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0109] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus 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. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0111] 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 this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of the present invention 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.

[0113] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for calculating the spatiotemporal representativeness of meteorological elements, characterized in that, include: Meteorological element data for a preset time period is obtained for the meteorological element protection target area, and based on the meteorological element data and a preset clustering algorithm, the meteorological element protection target area is divided into air masses; Based on the meteorological data, the spatial variation coefficient of each grid point within the air mass is calculated; wherein, the formula for calculating the spatial variation coefficient is: , These are the variation weighting coefficients. It is a variable function; , for and The average distance between meteorological element values ​​between two spatial vectors , These represent the total number of rows and columns of the grid points within the air mass, respectively. Grid points representing meteorological elements; , for variance for and The covariance between two spatial vectors for and The cosine of the angle between two spatial vectors; The coordinates of the grid points within the air mass are given. Meteorological element data in grids Time series dimension; Based on the spatial variation coefficient of each grid point within the air mass and the preset meteorological support accuracy, the spatial representative range of meteorological element data within the air mass is calculated. Based on the meteorological element data and the empirical orthogonal function, the time representative range of the meteorological element data within the air mass is calculated; The meteorological data include: air temperature, air pressure, relative humidity, radial wind, and zonal wind data.

2. The method according to claim 1, characterized in that, Based on the meteorological data, the spatial variation coefficients of each grid point within the air mass are calculated, including: Based on the meteorological element data, construct the meteorological element spatial vector of each grid point within the air mass; Based on the formula for calculating the spatial vectors of meteorological elements and the spatial variation coefficients of each grid point within the air mass, the spatial variation coefficients of each grid point within the air mass are calculated.

3. The method according to claim 2, characterized in that, Based on the spatial variation coefficients of each grid point within the air mass and the preset meteorological support accuracy, the spatial representative range of meteorological element data within the air mass is calculated, including: The preset meteorological support accuracy is added to the meteorological element spatial vector of each grid point in the air mass to obtain the target meteorological element spatial vector of each grid point in the air mass. Based on the spatial vectors of target meteorological elements at each grid point within the air mass and the spatial vectors of meteorological elements at each grid point within the air mass, the maximum threshold of the spatial variation coefficient at each grid point within the air mass is calculated. Based on the maximum threshold of the spatial variation coefficient of each grid point within the air mass, the spatial representative range of meteorological element data at each altitude layer within the air mass is determined. The minimum value among the spatial representative ranges of meteorological element data at each altitude level within the air mass is determined as the spatial representative range of the meteorological element data within the air mass.

4. The method according to claim 1, characterized in that, Based on the meteorological element data and empirical orthogonal functions, the time representative range of the meteorological element data within the air mass is calculated, including: Based on the meteorological element data, a meteorological element data field for the air mass is constructed; Based on the meteorological element data field of the air mass, construct the covariance matrix of the meteorological element data field of the air mass; The eigenvectors of the covariance matrix are calculated based on the Jacobi algorithm. Based on the feature vector, the time range of meteorological element data within the air mass is calculated.

5. The method according to claim 4, characterized in that, Based on the feature vector, the time representative range of the meteorological element data within the air mass is calculated, including: Based on the feature vector, the time coefficient matrix of the air mass is calculated; Based on the time coefficient matrix of the air mass and the preset meteorological support accuracy, the time representative range of meteorological element data at each altitude layer within the air mass is calculated. The minimum value among the time-representative ranges of meteorological element data at various altitudes within the air mass is determined as the time-representative range of meteorological element data within the air mass.

6. A calculation device for the spatiotemporal representativeness of meteorological elements, characterized in that, include: The acquisition unit is used to acquire meteorological element data for a preset time period in the meteorological element protection target area, and to divide the meteorological element protection target area into air masses based on the meteorological element data and a preset clustering algorithm. The first calculation unit is used to calculate the spatial variation coefficient of each grid point within the air mass based on the meteorological element data; wherein the formula for calculating the spatial variation coefficient is: , These are the variation weighting coefficients. It is a variable function; , for and The average distance between meteorological element values ​​between two spatial vectors , These represent the total number of rows and columns of the grid points within the air mass, respectively. Grid points representing meteorological elements; , for variance for and The covariance between two spatial vectors for and The cosine of the angle between two spatial vectors; The coordinates of the grid points within the air mass are given. Meteorological element data in grids Time series dimension; The second calculation unit is used to calculate the spatial representative range of meteorological element data within the air mass based on the spatial variation coefficient of each grid point within the air mass and the preset meteorological support accuracy. The third calculation unit is used to calculate the time representative range of the meteorological element data within the air mass based on the meteorological element data and the empirical orthogonal function. The meteorological data include: air temperature, air pressure, relative humidity, radial wind, and zonal wind data.

7. The apparatus according to claim 6, characterized in that, The first computing unit is used for: Based on the meteorological element data, construct the meteorological element spatial vector of each grid point within the air mass; Based on the spatial vectors of meteorological elements at each grid point within the air mass, the spatial variation coefficients of each grid point within the air mass are calculated.

8. The apparatus according to claim 7, characterized in that, The second computing unit is used for: The preset meteorological support accuracy is added to the meteorological element spatial vector of each grid point in the air mass to obtain the target meteorological element spatial vector of each grid point in the air mass. Based on the spatial vectors of target meteorological elements at each grid point within the air mass and the spatial vectors of meteorological elements at each grid point within the air mass, the maximum threshold of the spatial variation coefficient at each grid point within the air mass is calculated. Based on the maximum threshold of the spatial variation coefficient of each grid point within the air mass, the spatial representative range of meteorological element data at each altitude layer within the air mass is determined. The minimum value among the spatial representative ranges of meteorological element data at each altitude level within the air mass is determined as the spatial representative range of the meteorological element data within the air mass.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a program that enables the processor to execute the method of any one of claims 1 to 5, and the processor being configured to execute the program stored in the memory.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When a computer program is run by a processor, it performs the steps of the method described in any one of claims 1 to 5.

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