Gridding wind speed data set generation method and device, terminal equipment and storage medium

Abstract

The invention discloses a gridding wind speed data set generation method and device, terminal equipment and a storage medium. The method comprises the following steps: determining a local field corresponding to each point to be interpolated according to grid information of each mapping observation station point and each point to be interpolated in an initial gridding wind speed data set; for each point to be interpolated, an average neighbor distance and an experience neighbor distance of the point to be interpolated are calculated, then a neighbor index is calculated according to the experience neighbor distance and the average neighbor distance, and finally a distance attenuation parameter is determined according to a preset triangular membership function and the neighbor index. Calculating an interpolation wind speed value of the to-be-interpolated point according to the distance attenuation parameter and an actual wind speed value and a neighbor distance corresponding to each mapping observation station in the local field; and finally, mapping the interpolation wind speed value of each point to be interpolated into corresponding grid information to generate a final gridding wind speed data set. By implementing the method and the device, the wind energy resource distribution condition can be evaluated.

Description

Technical Field

[0001] The present invention relates to the field of new energy planning, and in particular to a method, device, terminal equipment and storage medium for generating a gridded wind speed data set. Background Art

[0002] The proportion of wind power generation in the current energy structure is increasing year by year. An accurate and comprehensive assessment of wind energy resources is a prerequisite for improving wind power output and absorption capacity.

[0003] In the existing technology, wind speed observation data is not distributed continuously in space. In actual work, data can only be obtained through limited measurement sites, resulting in a lack of sufficiently accurate wind speed information in the vast area between two sites. For example, in a vast area of ​​wind energy resources to be evaluated, there may be only a few measurement stations located at the edge or in specific locations. The data from these stations are difficult to reflect the detailed wind speed conditions in the entire area, and the evaluation of wind energy resources in the area can only be a very rough estimate. Summary of the invention

[0004] The present invention provides a method, an apparatus, a terminal device and a storage medium for generating a gridded wind speed data set. The method generates a more accurate gridded wind speed data set, which is helpful for evaluating the distribution of wind energy resources.

[0005] An embodiment of the present invention provides a method for generating a gridded wind speed data set, comprising:

[0006] Acquire an initial gridded wind speed data set; wherein the initial gridded wind speed data set includes a number of mapped observation sites, a number of points to be interpolated, grid information of each mapped observation site, and grid information of each point to be interpolated;

[0007] Determine the local area corresponding to each point to be interpolated according to the grid information of each mapping observation site and the grid information of each point to be interpolated;

[0008] For each of the points to be interpolated, the average neighbor distance of the point to be interpolated is calculated according to the neighbor distances between each mapped observation station in the local area corresponding to the point to be interpolated and the point to be interpolated; the empirical neighbor distance of the point to be interpolated is calculated according to the number of mapped observation stations in the local area and the area of ​​the local area; the neighbor index is calculated according to the empirical neighbor distance and the average neighbor distance, and the distance attenuation parameter is determined according to the preset triangle membership function and the neighbor index; the interpolation wind speed value of the point to be interpolated is calculated according to the actual wind speed value in the grid information of each mapped observation station, the neighbor distance and the distance attenuation parameter;

[0009] The interpolated wind speed value of each of the interpolation points is mapped to the corresponding grid information to generate a final gridded wind speed data set.

[0010] Furthermore, the process of determining the initial gridded wind speed data set includes:

[0011] Obtaining attribute data and wind speed values ​​of each actual observation site within the target planning area;

[0012] Build an initial visualization grid;

[0013] Mapping the attribute data and wind speed value of each actual observation site to the grid coordinates corresponding to the initial visualization grid, generating a plurality of mapped observation sites and grid information corresponding to each mapped observation site;

[0014] An initial visualization grid including a plurality of mapping observation sites and grid information corresponding to each mapping observation site is used as a first visualization grid; wherein each of the mapping observation sites corresponds to a unique actual observation site;

[0015] Determine the grid coordinates corresponding to each to-be-interpolated point according to the first visualization grid, and obtain a second visualization grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point;

[0016] The second visualization grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point is used as the initial gridded wind speed data set.

[0017] Further, the attribute data includes longitude data, latitude data and elevation data;

[0018] The mapping of the attribute data and wind speed value of each actual observation site to the grid coordinates corresponding to the initial visualization grid, generating a plurality of mapped observation sites and grid information corresponding to each mapped observation site, includes:

[0019] Determine the grid coordinates corresponding to each actual observation site on the initial visualization grid according to the longitude data, latitude data and elevation data corresponding to each actual observation site;

[0020] generating a number of mapping observation sites according to the grid coordinates;

[0021] The wind speed value of each actual observation station is mapped to the grid coordinates of the corresponding mapped observation station to obtain the grid information of each mapped observation station.

[0022] Furthermore, the determining, based on the first visualization grid, grid coordinates corresponding to each to-be-interpolated point, and obtaining a second visualization grid including each to-be-interpolated point and grid coordinates corresponding to each to-be-interpolated point includes:

[0023] According to the grid coordinates corresponding to each mapped observation site in the first visualization grid, the clustering operation is repeatedly performed until each pre-selected cluster center no longer changes, the clustering operation is stopped, and each pre-selected cluster center that no longer moves is used as the final cluster center;

[0024] For each cluster center, the cluster center and each mapping observation site assigned to the cluster center are regarded as a cluster cluster; the cluster center is regarded as a point to be interpolated, and the grid coordinates of the cluster center are regarded as the grid coordinates of the point to be interpolated;

[0025] A second visualization grid including a plurality of to-be-interpolated points and grid coordinates corresponding to each to-be-interpolated point is obtained.

[0026] Furthermore, the determining of the local area corresponding to each to-be-interpolated point according to the grid information of each mapped observation site and the grid information of each to-be-interpolated point includes:

[0027] For each cluster, the mapping observation site farthest from the point to be interpolated in the cluster is taken as the target mapping observation site, the target distance between the target mapping observation site and the point to be interpolated is determined, and a circle is drawn with the target distance as the radius and the point to be interpolated as the center, and the area falling into the circle is taken as the local area corresponding to the point to be interpolated in the cluster.

[0028] Furthermore, the calculating of the average nearest neighbor distance of the point to be interpolated based on the nearest neighbor distances between each mapping observation station in the local area corresponding to the point to be interpolated and the point to be interpolated includes:

[0029] Calculating the nearest neighbor distance between each mapping observation station in the local area corresponding to the point to be interpolated and the point to be interpolated, to obtain a nearest neighbor distance data set;

[0030] A mean solution operation is performed on the neighbor distance data set to obtain the average neighbor distance of the point to be interpolated.

[0031] Furthermore, the calculating of the neighbor index according to the empirical neighbor distance and the average neighbor distance, and determining the distance attenuation parameter according to the preset triangle membership function and the neighbor index include:

[0032] Calculating the neighbor index of the point to be interpolated according to the empirical neighbor distance and the average neighbor distance;

[0033] Normalizing the neighbor index to obtain a normalized neighbor index;

[0034] Using the normalized neighbor index as the input of the triangle membership function to obtain the candidate membership degree output by the triangle membership function;

[0035] Selecting the candidate membership with the largest value from the candidate memberships, and taking the candidate membership with the largest value as the target membership;

[0036] According to the target membership, a distance attenuation parameter of the point to be interpolated is determined.

[0037] An embodiment of the present invention further provides a gridded wind speed data set generation device, comprising: a data set acquisition module, a local area determination module, an interpolation wind speed value calculation module, and a gridded wind speed data set generation module;

[0038] The data set acquisition module is used to acquire an initial gridded wind speed data set; wherein the initial gridded wind speed data set includes a number of mapped observation sites, a number of points to be interpolated, grid information of each mapped observation site, and grid information of each point to be interpolated;

[0039] The local area determination module is used to determine the local area corresponding to each to-be-interpolated point according to the grid information of each mapping observation site and the grid information of each to-be-interpolated point;

[0040] The interpolation wind speed value calculation module is used to calculate, for each of the to-be-interpolated points, the average neighbor distance of the to-be-interpolated point according to the neighbor distances between each mapped observation station in the local area corresponding to the to-be-interpolated point and the to-be-interpolated point; calculate the empirical neighbor distance of the to-be-interpolated point according to the number of mapped observation stations in the local area and the area of ​​the local area; calculate the neighbor index according to the empirical neighbor distance and the average neighbor distance, and determine the distance attenuation parameter according to the preset triangle membership function and the neighbor index; calculate the interpolation wind speed value of the to-be-interpolated point according to the actual wind speed value in the grid information of each mapped observation station, the neighbor distance and the distance attenuation parameter;

[0041] The gridded wind speed data set generation module is used to map the interpolated wind speed value of each of the to-be-interpolated points to the corresponding grid information to generate a final gridded wind speed data set.

[0042] The present application also provides a terminal device, including:

[0043] one or more processors;

[0044] A memory, coupled to the processor, for storing one or more programs;

[0045] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating a gridded wind speed data set as described in the above-mentioned embodiment of the invention.

[0046] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for generating a gridded wind speed data set as described in the above-mentioned embodiment of the invention is implemented.

[0047] The following beneficial effects are achieved by implementing the present invention:

[0048] The present invention provides a gridded wind speed data set generation method, device, terminal device and storage medium. The method first determines the local area corresponding to each to-be-interpolated point based on the grid information of each mapped observation site and each to-be-interpolated point. By determining the local area of ​​each to-be-interpolated point, the relevant mapped observation site conditions around the to-be-interpolated point are focused, which is more in line with the wind speed variation characteristics in the local space, can better reflect the local wind speed difference, avoid the error caused by large-scale unified processing, make the interpolation result more consistent with the actual wind speed distribution, and thus improve the accuracy of the data set;

[0049] For each point to be interpolated, the average neighbor distance is calculated first, and then the empirical neighbor distance is calculated. Then, the neighbor index is calculated by combining the two. Then, the distance attenuation parameter is determined according to the preset triangle membership function and the neighbor index. Finally, the interpolation wind speed value of the point to be interpolated is calculated using the actual wind speed value, the neighbor distance and the distance attenuation parameter. Therefore, by calculating the average neighbor distance, the empirical neighbor distance and further obtaining the neighbor index and the distance attenuation parameter, the number of surrounding mapped observation sites, the distribution range and the distance between them are comprehensively considered. It is not a single dimension to measure the interpolation weight. In this way, when calculating the interpolation wind speed value, the weight can be allocated more scientifically and reasonably according to the influence of each observation site, so that the interpolation wind speed value obtained is closer to the true value, thereby improving the accuracy of the entire gridded wind speed data set. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 It is a flowchart of a method for generating a gridded wind speed data set provided in a certain embodiment of the present application;

[0052] Figure 2 is a schematic diagram of points to be interpolated provided in a certain embodiment of the present application;

[0053] Figure 3 is a schematic diagram of a triangular membership function provided by an embodiment of the present application;

[0054] Figure 4 This is a schematic diagram comparing the evaluation indicators of three interpolation methods provided in a certain embodiment of the present application at six representative sites;

[0055] Figure 5 It is a comparison chart of 24-hour wind speed observation, grid interpolation, and back-calculation interpolation at 6 representative sites provided in a certain embodiment of the present application;

[0056] Figure 6 It is a schematic diagram of the back calculation and grid interpolation errors and correlations of 6 representative sites provided in a certain embodiment of the present application;

[0057] Figure 7 is a schematic diagram of the intercorrelation of interpolation data of six representative sites provided in an embodiment of the present application;

[0058] Figure 8 It is a structural schematic diagram of a gridded wind speed data set generating device provided in a certain embodiment of the present application;

[0059] Fig. 9 It is a structural diagram of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0062] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0063] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0064] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0065] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0066] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like 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 an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0067] See also Figure 1 , is a flow chart of a method for generating a gridded wind speed data set provided by an embodiment of the present invention, comprising:

[0068] S1. Obtain an initial gridded wind speed data set; wherein the initial gridded wind speed data set includes a number of mapped observation sites, a number of points to be interpolated, grid information of each mapped observation site, and grid information of each point to be interpolated;

[0069] In a preferred embodiment, the process of determining the initial gridded wind speed data set includes:

[0070] Obtaining attribute data and wind speed values ​​of each actual observation site within the target planning area;

[0071] Build an initial visualization grid;

[0072] Mapping the attribute data and wind speed value of each actual observation site to the grid coordinates corresponding to the initial visualization grid, generating a plurality of mapped observation sites and grid information corresponding to each mapped observation site;

[0073] An initial visualization grid including a plurality of mapping observation sites and grid information corresponding to each mapping observation site is used as a first visualization grid; wherein each of the mapping observation sites corresponds to a unique actual observation site;

[0074] Determine the grid coordinates corresponding to each to-be-interpolated point according to the first visualization grid, and obtain a second visualization grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point;

[0075] The second visualization grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point is used as the initial gridded wind speed data set;

[0076] In a preferred embodiment, the attribute data includes longitude data, latitude data and elevation data;

[0077] The mapping of the attribute data and wind speed value of each actual observation site to the grid coordinates corresponding to the initial visualization grid, generating a plurality of mapped observation sites and grid information corresponding to each mapped observation site, includes:

[0078] Determine the grid coordinates corresponding to each actual observation site on the initial visualization grid according to the longitude data, latitude data and elevation data corresponding to each actual observation site;

[0079] generating a number of mapping observation sites according to the grid coordinates;

[0080] Map the wind speed value of each actual observation station to the grid coordinates of the corresponding mapped observation station to obtain the grid information of each mapped observation station;

[0081] Specifically, an initial visualization grid with a spatial resolution of 9 km × 9 km and a temporal resolution of 1 hour was constructed;

[0082] Then, for each actual observation site, according to the longitude data, latitude data and elevation data of the actual observation site, the actual observation site is mapped to the corresponding grid coordinates in the initial visualization grid to generate a mapped observation site corresponding to the actual observation site;

[0083] Next, for each mapped observation site, the wind speed value of the corresponding actual observation site at a certain time point is mapped to the grid coordinates of the mapped observation site, and the latitude and longitude data, elevation data and wind speed value of the mapped observation site on the grid coordinates are used as the grid information of the mapped observation site;

[0084] Thus, the grid information of each mapping observation site is obtained, and the initial visualization grid including the plurality of mapping observation sites and the grid information corresponding to each mapping observation site is used as the first visualization grid.

[0085] In a preferred embodiment, determining the grid coordinates corresponding to each to-be-interpolated point according to the first visual grid to obtain a second visual grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point includes:

[0086] According to the grid coordinates corresponding to each mapped observation site in the first visualization grid, the clustering operation is repeatedly performed until each pre-selected cluster center no longer changes, the clustering operation is stopped, and each pre-selected cluster center that no longer moves is used as the final cluster center;

[0087] For each cluster center, the cluster center and each mapping observation site assigned to the cluster center are regarded as a cluster cluster; the cluster center is regarded as a point to be interpolated, and the grid coordinates of the cluster center are regarded as the grid coordinates of the point to be interpolated;

[0088] Obtaining a second visualization grid including a plurality of to-be-interpolated points and grid coordinates corresponding to each to-be-interpolated point;

[0089] Illustratively, the present application uses a preset K-means++ clustering algorithm to determine the points to be interpolated and the grid coordinates corresponding to each of the points to be interpolated;

[0090] Specifically, the initial pre-selected cluster centers are selected, and then the grid coordinates corresponding to each mapping observation site are assigned to the pre-selected cluster centers closest to them according to a certain distance metric;

[0091] Then, for each pre-selected cluster center, the clustering operation is repeated until the pre-selected cluster centers no longer change. At this time, the clustering converges, the clustering operation is stopped, and the pre-selected cluster centers that no longer move are used as the final cluster centers.

[0092] Next, for each cluster center, the cluster center and each mapping observation site assigned to the cluster center are regarded as a cluster cluster, the cluster center is regarded as a point to be interpolated, and the grid coordinates of the cluster center are regarded as the grid coordinates of the point to be interpolated;

[0093] Thus, a second visualization grid including a plurality of to-be-interpolated points and grid coordinates corresponding to each to-be-interpolated point is obtained.

[0094] S2, determining the local area corresponding to each point to be interpolated according to the grid information of each mapping observation site and the grid information of each point to be interpolated;

[0095] In a preferred embodiment, the determining the local area corresponding to each to-be-interpolated point according to the grid information of each mapped observation site and the grid information of each to-be-interpolated point includes:

[0096] For each cluster, the mapping observation site farthest from the point to be interpolated in the cluster is used as the target mapping observation site, the target distance between the target mapping observation site and the point to be interpolated is determined, and a circle is drawn with the target distance as the radius and the point to be interpolated as the center, and the area falling within the circle is taken as the local area corresponding to the point to be interpolated in the cluster;

[0097] Indicatively, see Figure 2 , for point A, the mapping observation sites in its neighborhood are relatively concentrated, and there is no need to use a relatively large power exponent p value. Since the mapping observation sites in its neighborhood are relatively close to it, these observation points are assigned relatively large weights. Otherwise, if a relatively large p value is used, the error of the interpolation result will increase. Similarly, for the point B to be interpolated, the mapping observation sites in its neighborhood are relatively scattered, and the more reliable observation points are the sites closer to it. At this time, if a relatively small power exponent p value is used, the error of the interpolation result will increase.

[0098] Therefore, it is necessary to first determine the local area corresponding to each point to be interpolated, and determine the distance attenuation parameter of each point to be interpolated based on the mapped observation station in the local area corresponding to each point to be interpolated, so as to obtain a more accurate interpolated wind speed value;

[0099] Specifically, for each cluster, the mapping observation site farthest from the point to be interpolated in the cluster is taken as the target mapping observation site, the target distance between the target mapping observation site and the point to be interpolated is determined, and a circle is drawn with the target distance as the radius and the point to be interpolated as the center, and the area falling into the circle is taken as the local area corresponding to the point to be interpolated in the cluster.

[0100] S3. For each of the points to be interpolated, the average neighbor distance of the point to be interpolated is calculated according to the neighbor distances between each mapped observation station in the local area corresponding to the point to be interpolated and the point to be interpolated; the empirical neighbor distance of the point to be interpolated is calculated according to the number of mapped observation stations in the local area and the area of ​​the local area; the neighbor index is calculated according to the empirical neighbor distance and the average neighbor distance, and the distance attenuation parameter is determined according to the preset triangle membership function and the neighbor index; the interpolation wind speed value of the point to be interpolated is calculated according to the actual wind speed value in the grid information of each mapped observation station, the neighbor distance and the distance attenuation parameter;

[0101] In a preferred embodiment, the step of calculating the average neighbor distance of the point to be interpolated based on the neighbor distances between each mapping observation station in the local area corresponding to the point to be interpolated and the point to be interpolated comprises:

[0102] Calculating the nearest neighbor distance between each mapping observation station in the local area corresponding to the point to be interpolated and the point to be interpolated, to obtain a nearest neighbor distance data set;

[0103] Performing a mean solution operation on the neighbor distance data set to obtain an average neighbor distance of the point to be interpolated;

[0104] Schematically, after determining the local area corresponding to each point to be interpolated, it is necessary to determine the distance attenuation parameter of each point to be interpolated;

[0105] Specifically, assuming that there is currently a point A to be interpolated, the specific steps for determining the distance attenuation parameter of the point A to be interpolated are as follows:

[0106] First, determine the grid coordinates of each mapping observation station in the local area of ​​the interpolation point A, and calculate the nearest distance d between the n mapping observation stations located in the local area and the interpolation point A. i , i∈[1,n], and obtain a nearest neighbor distance data set; then perform a mean solution operation on the nearest neighbor distance data set to obtain the average nearest neighbor distance D of the point to be interpolated avg , the specific solution formula is as follows:

[0107]

[0108] Specifically, according to the number n of mapped observation sites in the local area corresponding to the interpolation point A and the area S of the local area, the empirical nearest neighbor distance D of the interpolation point A is calculated: exp , the specific solution formula is as follows:

[0109]

[0110] Indicatively, after calculating the empirical nearest neighbor distance D of the interpolation point A, exp And the average neighbor distance D of the interpolation point avg Then, the neighbor index R(A) of the interpolation point A is calculated according to the empirical neighbor distance and the average neighbor distance, and the distance attenuation parameter is determined according to the preset triangle membership function and the neighbor index R(A);

[0111] In a preferred embodiment, the neighbor index is calculated based on the empirical neighbor distance and the average neighbor distance, and the distance attenuation parameter is determined based on a preset triangle membership function and the neighbor index, including:

[0112] Calculating the neighbor index of the point to be interpolated according to the empirical neighbor distance and the average neighbor distance;

[0113] Normalizing the neighbor index to obtain a normalized neighbor index;

[0114] Using the normalized neighbor index as the input of the triangle membership function to obtain the candidate membership degree output by the triangle membership function;

[0115] Selecting the candidate membership with the largest value from the candidate memberships, and taking the candidate membership with the largest value as the target membership;

[0116] Determining a distance attenuation parameter of the point to be interpolated according to the target membership;

[0117] Specifically, after calculating the empirical nearest neighbor distance D of the interpolation point A, exp And the average neighbor distance D of the interpolation point avg , the neighbor index R(A) of the interpolation point A is calculated by the following formula:

[0118] R(A)=D avg / D exp ;

[0119] It should be noted that the smaller the R(A) value is, the more concentrated the distribution pattern of the mapping observation sites in the local neighborhood of the interpolation point A is, and vice versa, the more dispersed the distribution pattern of the observation points in the local neighborhood of the interpolation point A is.

[0120] Specifically, the neighbor index is normalized by the following formula to obtain the normalized neighbor index μ R(A) :

[0121]

[0122] In the formula, R min is the empirical minimum value, R max is the maximum value of experience;

[0123] Indicatively, see Figure 3 , according to the normalized neighbor index μ R(A) , determine the neighbor index μ R(A) A number of candidate memberships corresponding to the triangular membership function, and selecting the candidate membership with the largest value from the candidate memberships as the target membership;

[0124] Specifically, the triangle membership function includes five levels (such as Figure 3 Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ), the neighbor index μR(A) Corresponding to one / two levels, the membership associated with the corresponding level is used as the candidate membership, and the candidate membership with the largest value is selected from the candidate membership as the target membership, so as to determine the power exponent p(A) corresponding to the target membership, and finally, the power exponent p(A) is used as the distance attenuation parameter of the interpolation point A;

[0125] In schematic form, the interpolation wind speed value of the point to be interpolated is calculated based on the actual wind speed value of each mapped observation station in the point to be interpolated A, the neighbor distance and the distance attenuation parameter;

[0126] Specifically, the interpolation wind speed value z of the point to be interpolated is calculated by the following formula: j :

[0127]

[0128] In the formula, w i is the weight value of the i-th mapping observation station; z i is the actual wind speed value of the i-th mapped observation station.

[0129] S4, mapping the interpolated wind speed value of each of the to-be-interpolated points to the corresponding grid information to generate a final gridded wind speed data set;

[0130] Specifically, after obtaining the interpolated wind speed value of each to-be-interpolated point, the interpolated wind speed value of each to-be-interpolated point is mapped to the corresponding grid information to generate a final gridded wind speed data set;

[0131] Specifically, in order to better illustrate the present application, six representative meteorological observation stations are selected as interpolation points according to different geographical locations and altitude distributions. The gridded wind speed data set generation method of the present application, the traditional IDW interpolation method and the Kriging interpolation method are used to interpolate the six representative meteorological observation stations. The interpolated results are compared with the real observation data. The specific data analysis results are shown in Table 1. The image analysis results are shown in Table 1. Figure 4 shown.

[0132]

[0133] Table 1

[0134] It can be concluded from Table 1 that the gridded wind speed dataset generation method of the present application shows significant accuracy improvement in multiple key indicators compared with the traditional IDW interpolation method and Kriging interpolation method.

[0135] First, in terms of R, the average value of the gridded wind speed data set generation method of the present application is 0.7799, which is significantly higher than 0.7551 of IDW and 0.7229 of Kriging, indicating that the present application can more accurately reflect the trend of wind speed changes. Secondly, for MAE, the average value of the gridded wind speed data set generation method of the present application is 0.8772, which is 5.4% lower than the IDW method and 7.8% lower than the Kriging method, showing the advantage of the present application in overall error control. In addition, in terms of RMSE, the gridded wind speed data set generation method of the present application is 5.9% lower than the IDW method and 8.1% lower than the Kriging method, indicating that the present application can reduce the impact of errors and show that it has higher accuracy in data fitting. In summary, the present application has shown significant advantages in improving prediction relevance, reducing errors, improving stability and reducing deviations, effectively improving the accuracy of wind speed interpolation, and providing a more reliable method for the refined management and application of wind speed data.

[0136] Also using the above-mentioned six meteorological observation stations, a gridded wind speed dataset with a spatial resolution of 9 km × 9 km was constructed using the gridded wind speed dataset generation method of this application, and compared with the wind resource elements in the ERA5 back-calculated dataset.

[0137] It should be noted that ERA5 is the fifth generation of atmospheric reanalysis data set of the European Centre for Medium-Range Weather Forecasts for the global climate from January 1950 to the present, providing estimates of a large number of atmospheric, land and ocean climate variables. ERA5 has a horizontal resolution of 0.25°×0.25° (about 27.75km×27.75km in mid-latitudes), an hourly temporal resolution, and vertically resolves the atmosphere in 137 layers from the surface to an altitude of 80 kilometers, including uncertainty information of all variables when reducing spatial and temporal resolution, combining physical model data with observational data from all over the world to form a global complete and consistent data set, which is widely used in climate research, weather forecasting, resource assessment and other fields.

[0138] For each observation site, the four grid point data closest to the observation site are selected from the back-calculated dataset (ERA5) and the gridded wind speed dataset constructed based on the method of this application, and the distances from these four grid points to the observation site are calculated. The distances are weighted averaged as the weight to calculate the interpolation result of the observation site.

[0139] The results are as follows Figure 5 , 6 , 7, Figure 5 The wind speed data for 24 moments of any day are displayed from the interpolation results of each station. Figure 5The correlation and various error indicators between the back-calculated data set and the gridded wind speed data set and the original meteorological observation station data were compared and analyzed. Figure 7 The cross-correlations between the back-calculated datasets and gridded wind speed datasets at each station and the observations were compared.

[0140] pass Figure 5 , 6 , 7, it can be seen that the gridded wind speed dataset with a spatial resolution of 9km×9km designed by the present invention shows a significant improvement in accuracy in multiple key indicators compared to the ERA5 back-calculated wind resource dataset.

[0141] First, from the perspective of mean square error, the MSE of the 9km×9km gridded wind speed dataset at all sites is significantly lower than that of the back-calculated dataset, with an average reduction of about 6.97%. Second, the average RMSE of the 9km×9km gridded wind speed dataset is 38.60%, which is 10.07% lower than that of the back-calculated dataset. In addition, the average MAE of the 9km×9km gridded wind speed dataset is 27.17%, which is significantly lower than the 39.13% of the back-calculated dataset, a reduction of 11.95%. In addition, the R 2 It is significantly higher than the back-calculated dataset, indicating that the former can better explain the changing trend of wind speed and has higher simulation accuracy. In summary, the gridded wind speed dataset with a spatial resolution of 9km×9km is superior to the gridded wind speed dataset with a spatial resolution of 0.25°×0.25° in multiple dimensions, and is also closer to the actual observation results.

[0142] See also Figure 8 , is a gridded wind speed data set generation device provided by an embodiment of the present invention, comprising: a data set acquisition module, a local area determination module, an interpolation wind speed value calculation module and a gridded wind speed data set generation module;

[0143] The data set acquisition module is used to acquire an initial gridded wind speed data set; wherein the initial gridded wind speed data set includes a number of mapped observation sites, a number of points to be interpolated, grid information of each mapped observation site, and grid information of each point to be interpolated;

[0144] The local area determination module is used to determine the local area corresponding to each to-be-interpolated point according to the grid information of each mapping observation site and the grid information of each to-be-interpolated point;

[0145] The interpolation wind speed value calculation module is used to calculate, for each of the to-be-interpolated points, the average neighbor distance of the to-be-interpolated point according to the neighbor distances between each mapped observation station in the local area corresponding to the to-be-interpolated point and the to-be-interpolated point; calculate the empirical neighbor distance of the to-be-interpolated point according to the number of mapped observation stations in the local area and the area of ​​the local area; calculate the neighbor index according to the empirical neighbor distance and the average neighbor distance, and determine the distance attenuation parameter according to the preset triangle membership function and the neighbor index; calculate the interpolation wind speed value of the to-be-interpolated point according to the actual wind speed value in the grid information of each mapped observation station, the neighbor distance and the distance attenuation parameter;

[0146] The gridded wind speed data set generation module is used to map the interpolated wind speed value of each of the to-be-interpolated points to the corresponding grid information to generate a final gridded wind speed data set.

[0147] See also Fig. 9 , a certain embodiment of the present application further provides a terminal device, including:

[0148] one or more processors;

[0149] A memory, coupled to the processor, for storing one or more programs;

[0150] When the one or more programs are executed by the one or more processors, the one or more processors implement the gridded wind speed data set generation method as described above.

[0151] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned gridded wind speed data set generation method. The memory is used to store various types of data to support the operation of the terminal device, and these data may include, for example, instructions for any application or method used to operate on the terminal device, and application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0152] In an exemplary embodiment, the terminal device can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, ASIC), digital signal processors (Digital Signal Processor, DSP), digital signal processing devices (Digital Signal Processing Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the gridded wind speed data set generation method described in any of the above embodiments, and achieve the same technical effect as the above method.

[0153] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided, and when the computer program is executed by a processor, the steps of the method for generating a gridded wind speed data set as described in any of the above embodiments are implemented. For example, the computer-readable storage medium may be the above-mentioned memory including the computer program, and the above-mentioned computer program may be executed by a processor of a terminal device to complete the method for generating a gridded wind speed data set as described in any of the above embodiments, and achieve the technical effect consistent with the above-mentioned method.

[0154] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for generating a gridded wind speed data set, characterized in that: include: Acquire an initial gridded wind speed data set; wherein the initial gridded wind speed data set includes a number of mapped observation sites, a number of points to be interpolated, grid information of each mapped observation site, and grid information of each point to be interpolated; Determine the local area corresponding to each point to be interpolated according to the grid information of each mapping observation site and the grid information of each point to be interpolated; For each of the points to be interpolated, the average neighbor distance of the point to be interpolated is calculated according to the neighbor distances between each mapped observation station in the local area corresponding to the point to be interpolated and the point to be interpolated; the empirical neighbor distance of the point to be interpolated is calculated according to the number of mapped observation stations in the local area and the area of ​​the local area; the neighbor index is calculated according to the empirical neighbor distance and the average neighbor distance, and the distance attenuation parameter is determined according to the preset triangle membership function and the neighbor index; the interpolation wind speed value of the point to be interpolated is calculated according to the actual wind speed value in the grid information of each mapped observation station, the neighbor distance and the distance attenuation parameter; The interpolated wind speed value of each of the interpolation points is mapped to the corresponding grid information to generate a final gridded wind speed data set.

2. The method for generating a gridded wind speed data set according to claim 1, characterized in that: The process of determining the initial gridded wind speed data set includes: Obtaining attribute data and wind speed values ​​of each actual observation site within the target planning area; Build an initial visualization grid; Mapping the attribute data and wind speed value of each actual observation site to the grid coordinates corresponding to the initial visualization grid, generating a plurality of mapped observation sites and grid information corresponding to each mapped observation site; An initial visualization grid including a plurality of mapping observation sites and grid information corresponding to each mapping observation site is used as a first visualization grid; wherein each of the mapping observation sites corresponds to a unique actual observation site; Determine the grid coordinates corresponding to each to-be-interpolated point according to the first visualization grid, and obtain a second visualization grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point; The second visualization grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point is used as the initial gridded wind speed data set.

3. The method for generating a gridded wind speed data set according to claim 2, characterized in that: The attribute data includes longitude data, latitude data and elevation data; The mapping of the attribute data and wind speed value of each actual observation site to the grid coordinates corresponding to the initial visualization grid, generating a plurality of mapped observation sites and grid information corresponding to each mapped observation site, includes: Determine the grid coordinates corresponding to each actual observation site on the initial visualization grid according to the longitude data, latitude data and elevation data corresponding to each actual observation site; generating a number of mapping observation sites according to the grid coordinates; The wind speed value of each actual observation station is mapped to the grid coordinates of the corresponding mapped observation station to obtain the grid information of each mapped observation station.

4. The method for generating a gridded wind speed data set according to claim 2, wherein: The step of determining the grid coordinates corresponding to each to-be-interpolated point according to the first visual grid, and obtaining a second visual grid including each to-be-interpolated point and the grid coordinates corresponding to each to-be-interpolated point, comprises: According to the grid coordinates corresponding to each mapped observation site in the first visualization grid, the clustering operation is repeatedly performed until each pre-selected cluster center no longer changes, the clustering operation is stopped, and each pre-selected cluster center that no longer moves is used as the final cluster center; For each cluster center, the cluster center and each mapping observation site assigned to the cluster center are regarded as a cluster cluster; the cluster center is regarded as a point to be interpolated, and the grid coordinates of the cluster center are regarded as the grid coordinates of the point to be interpolated; A second visualization grid including a plurality of to-be-interpolated points and grid coordinates corresponding to each to-be-interpolated point is obtained.

5. The method for generating a gridded wind speed data set according to claim 4, characterized in that: Determining the local area corresponding to each to-be-interpolated point according to the grid information of each mapped observation site and the grid information of each to-be-interpolated point includes: For each cluster, the mapping observation site farthest from the point to be interpolated in the cluster is taken as the target mapping observation site, the target distance between the target mapping observation site and the point to be interpolated is determined, and a circle is drawn with the target distance as the radius and the point to be interpolated as the center, and the area falling into the circle is taken as the local area corresponding to the point to be interpolated in the cluster.

6. The method for generating a gridded wind speed data set according to claim 1, characterized in that: The step of calculating the average neighbor distance of the point to be interpolated based on the neighbor distances between each mapping observation station in the local area corresponding to the point to be interpolated and the point to be interpolated comprises: Calculating the nearest neighbor distance between each mapping observation station in the local area corresponding to the point to be interpolated and the point to be interpolated, to obtain a nearest neighbor distance data set; A mean solution operation is performed on the neighbor distance data set to obtain the average neighbor distance of the point to be interpolated.

7. The method for generating a gridded wind speed data set according to claim 1, characterized in that: The calculating a neighbor index according to the empirical neighbor distance and the average neighbor distance, and determining a distance attenuation parameter according to a preset triangle membership function and the neighbor index, includes: Calculating the neighbor index of the point to be interpolated according to the empirical neighbor distance and the average neighbor distance; Normalizing the neighbor index to obtain a normalized neighbor index; Using the normalized neighbor index as the input of the triangle membership function to obtain the candidate membership degree output by the triangle membership function; Selecting the candidate membership with the largest value from the candidate memberships, and taking the candidate membership with the largest value as the target membership; According to the target membership, a distance attenuation parameter of the point to be interpolated is determined.

8. A gridded wind speed data set generation device, characterized in that: include: Dataset acquisition module, local area determination module, interpolation wind speed value calculation module and gridded wind speed dataset generation module; The data set acquisition module is used to acquire an initial gridded wind speed data set; wherein the initial gridded wind speed data set includes a number of mapped observation sites, a number of points to be interpolated, grid information of each mapped observation site, and grid information of each point to be interpolated; The local area determination module is used to determine the local area corresponding to each to-be-interpolated point according to the grid information of each mapping observation site and the grid information of each to-be-interpolated point; The interpolation wind speed value calculation module is used to calculate, for each of the to-be-interpolated points, the average neighbor distance of the to-be-interpolated point according to the neighbor distances between each mapped observation station in the local area corresponding to the to-be-interpolated point and the to-be-interpolated point; calculate the empirical neighbor distance of the to-be-interpolated point according to the number of mapped observation stations in the local area and the area of ​​the local area; calculate the neighbor index according to the empirical neighbor distance and the average neighbor distance, and determine the distance attenuation parameter according to the preset triangle membership function and the neighbor index; calculate the interpolation wind speed value of the to-be-interpolated point according to the actual wind speed value in the grid information of each mapped observation station, the neighbor distance and the distance attenuation parameter; The gridded wind speed data set generation module is used to map the interpolated wind speed value of each of the to-be-interpolated points to the corresponding grid information to generate a final gridded wind speed data set.

9. A terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the gridded wind speed data set generation method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating a gridded wind speed data set according to any one of claims 1 to 7 is implemented.

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