Mesoscale resource map correction method and device

Abstract

The invention provides a method and device for correcting a mesoscale resource atlas, and the method comprises the steps: obtaining a basic data set in a wind power plant development region according to an existing mesoscale resource atlas, and determining a target anemometer tower in the wind power plant development region; then, for each target anemometer tower, reading actual measurement data of the target anemometer tower; then, a wind shear extrapolation method is adopted to calculate the measured data of the target anemometer tower to a target height layer, and target measured data are obtained; extracting target grid point data in the basic data set; and finally, according to the target grid point data and the target measured data, correcting the target grid point data to obtain corrected grid point data. And updating all the corrected lattice point data in the existing mesoscale resource map to obtain a corrected mesoscale resource map. By correcting the deviation in the mesoscale atlas, it is ensured that the prediction result better conforms to the local actual situation.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a method and device for correcting a mesoscale resource map. Background Art

[0002] As the global demand for renewable energy continues to increase, wind power generation has become an important part of the transformation of the energy structure. In the early development stage of a wind farm, wind resource assessment is one of the key steps to determine the feasibility and economic benefits of the project. In order to obtain accurate wind resource information, developers usually rely on mesoscale meteorological model data, which provides long-term average meteorological level forecasts in the region and has wide applicability. However, when evaluating local wind speeds, mesoscale models often lack the accuracy of wind speed maps due to the complexity of the terrain, changes in meteorological conditions, and limitations in model resolution, thus failing to fully reflect the impact of meteorological changes and complex terrain in local areas. Summary of the invention

[0003] In view of this, the present application provides a method and device for correcting a mesoscale resource map, which ensures that the prediction results are more in line with local actual conditions by correcting the deviations in the mesoscale model prediction, thereby providing more accurate and reliable data support for subsequent wind farm site selection, wind turbine layout optimization, etc., reducing the uncertainty of development and investment, improving economic benefits and reducing risks.

[0004] The first aspect of the present application provides a method for correcting a mesoscale resource map, comprising:

[0005] In the wind farm development area, a basic data set is obtained based on an existing mesoscale resource map; wherein the basic data set includes an original wind speed map, an original elevation map, and an original Weibull map;

[0006] Determine a target wind tower in the wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area;

[0007] For each of the target wind towers, read the measured data of the target wind tower; wherein the measured data of the target wind tower includes: measured wind speed, measured elevation, measured Weibull A value and K value;

[0008] The wind shear extrapolation method is used to extrapolate the measured data of the target wind tower to the target altitude layer to obtain the target measured data; wherein the target measured data is the measured data of the target wind tower at the target altitude layer;

[0009] Extracting target grid point data from the basic data set; wherein the target grid point data is grid point data corresponding to the location of the target wind tower; the target grid point data includes a target wind speed map, a target elevation map and a target Weibull map of the grid point corresponding to the location of the target wind tower;

[0010] According to the target grid point data and the target measured data, the target grid point data is corrected to obtain corrected grid point data;

[0011] All the corrected grid data are updated in the existing mesoscale resource atlas to obtain a corrected mesoscale resource atlas.

[0012] Optionally, the measured data of the target wind tower further includes: a data processing state; the data processing state is divided into raw data and processed data. If the data processing state is raw data, the wind shear extrapolation method is used to extrapolate the measured data of the target wind tower to a target altitude layer, and before obtaining the target measured data, the method further includes:

[0013] Clean the original data according to the preset processing specifications to obtain the cleaned measured data;

[0014] Interpolating the cleaned measured data to obtain interpolated measured data;

[0015] Correcting the interpolated measured data to obtain corrected measured data;

[0016] The method of using the wind shear extrapolation method to extrapolate the measured data of the target wind tower to the target altitude layer to obtain the target measured data includes:

[0017] The corrected measured data is extrapolated to the target altitude layer using the wind shear extrapolation method to obtain the target measured data.

[0018] Optionally, extracting target grid point data from the basic data set includes:

[0019] The data in the basic dataset are aligned to the location of the target wind tower using the spatial interpolation method to obtain the target grid data.

[0020] Optionally, the correcting the target grid point data according to the target grid point data and the target measured data to obtain the corrected grid point data includes:

[0021] Determine first correction information according to the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map, and use the first correction information to correct the predicted wind speed in the target wind speed map;

[0022] Determine second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and use the second correction information to correct the predicted elevation in the target elevation map;

[0023] According to the measured Weibull A value and K value of the target wind tower and the predicted Weibull A value and K value in the target Weibull spectrum, third correction information is determined, and the Weibull A value and K value in the target Weibull spectrum are corrected using the third correction information.

[0024] Optionally, determining first correction information according to the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map, and correcting the predicted wind speed in the target wind speed map using the first correction information includes:

[0025] The quotient of the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map is used as the first correction information;

[0026] The first correction information is multiplied by the predicted wind speed in the target wind speed map to obtain a corrected target wind speed map.

[0027] Optionally, determining second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and correcting the predicted elevation in the target elevation map using the second correction information includes:

[0028] The quotient of the measured elevation of the target wind tower and the predicted elevation in the target elevation map is used as the second correction information;

[0029] The second correction information is multiplied by the predicted elevation in the target elevation map to obtain a corrected target elevation map.

[0030] Optionally, determining third correction information according to the measured Weibull A value and K value of the target wind tower and the predicted Weibull A value and K value in the target Weibull spectrum, and correcting the Weibull A value and K value in the target Weibull spectrum using the third correction information includes:

[0031] According to the measured Weibull A value and K value of the target wind tower, the first average wind speed is calculated using the Gamma function;

[0032] According to the predicted Weibull A value and K value in the target Weibull spectrum, the second average wind speed is calculated using the Gamma function;

[0033] Determining an average wind speed proportionality coefficient according to the first average wind speed and the second average wind speed;

[0034] Generate a virtual wind speed time series according to the average wind speed proportional coefficient, the random probability distribution value, the measured Weibull A value and the K value of the target wind tower;

[0035] For each wind speed value in the virtual wind speed time series, correction is performed according to the wind speed value and the average wind speed proportional coefficient to obtain a corrected wind speed value;

[0036] When all wind speed values ​​in the virtual wind speed time series are corrected, a corrected virtual wind speed time series is obtained;

[0037] According to the modified virtual time series, the Weibull A value and K value in the latest Weibull spectrum are obtained by least square fitting, and updated to the target Weibull spectrum.

[0038] Optionally, the method for correcting the mesoscale resource map further includes:

[0039] For each wind tower, the correction weight of each grid point around the wind tower is calculated using the inverse square distance weighting method;

[0040] For each of the grid points, the correction value of the grid point is weighted using the correction weight of the grid point.

[0041] Optionally, after updating all the corrected grid data in the existing mesoscale resource atlas to obtain the corrected mesoscale resource atlas, the method further includes:

[0042] The optimal wind turbine layout position is determined according to the corrected mesoscale resource map.

[0043] The second aspect of the present application provides a device for correcting a mesoscale resource map, comprising:

[0044] An acquisition unit is used to acquire a basic data set in the wind farm development area according to an existing mesoscale resource map; wherein the basic data set includes an original wind speed map, an original elevation map and an original Weibull map;

[0045] A first determining unit is used to determine a target wind tower in a wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area;

[0046] A reading unit, used for reading the measured data of each target wind tower, wherein the measured data of the target wind tower includes: measured wind speed, measured elevation, measured Weibull A value and K value;

[0047] A calculation unit, used to use wind shear extrapolation method to calculate the measured data of the target wind tower to the target altitude layer, so as to obtain target measured data; wherein the target measured data is the measured data of the target wind tower at the target altitude layer;

[0048] An extraction unit is used to extract target grid point data from a basic data set; wherein the target grid point data is grid point data corresponding to the location of a target wind tower; the target grid point data includes a target wind speed map, a target elevation map and a target Weibull map of the grid point corresponding to the location of the target wind tower;

[0049] A correction unit, used for correcting the target grid point data according to the target grid point data and the target measured data to obtain corrected grid point data;

[0050] An updating unit is used to update all the corrected grid data in the existing mesoscale resource map to obtain a corrected mesoscale resource map.

[0051] Optionally, the measured data of the target wind tower further includes: a processing state of the data; the processing state of the data is divided into raw data and processed data. If the processing state of the data is raw data, the correction device of the mesoscale resource map further includes:

[0052] A cleaning unit is used to clean the raw data according to a preset processing specification to obtain cleaned measured data;

[0053] An interpolation unit, used for interpolating the cleaned measured data to obtain interpolated measured data;

[0054] A correction unit, used for correcting the interpolated measured data to obtain corrected measured data;

[0055] Wherein, the estimation unit is further used for:

[0056] The corrected measured data is extrapolated to the target altitude layer using the wind shear extrapolation method to obtain the target measured data.

[0057] Optionally, the extraction unit includes:

[0058] The alignment unit is used to align the data in the basic data set to the position of the target wind tower using the spatial interpolation method to obtain the target grid data.

[0059] Optionally, the correction unit includes:

[0060] A first correction subunit, configured to determine first correction information according to the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map, and to correct the predicted wind speed in the target wind speed map using the first correction information;

[0061] A second correction subunit is used to determine second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and to correct the predicted elevation in the target elevation map using the second correction information;

[0062] The third correction subunit is used to determine third correction information according to the measured Weibull A value and K value of the target wind measurement tower and the predicted Weibull A value and K value in the target Weibull spectrum, and use the third correction information to correct the Weibull A value and K value in the target Weibull spectrum.

[0063] Optionally, the first correction subunit includes:

[0064] a first calculation unit, configured to use a quotient of a measured wind speed of a target wind tower and a predicted wind speed in a target wind speed map as first correction information;

[0065] The second calculation unit is used to multiply the first correction information by the predicted wind speed in the target wind speed map to obtain a corrected target wind speed map.

[0066] Optionally, the second correction subunit includes:

[0067] a third calculation unit, configured to use the quotient of the measured elevation of the target wind tower and the predicted elevation in the target elevation map as second correction information;

[0068] The fourth calculation unit is used to multiply the second correction information by the predicted elevation in the target elevation map to obtain a corrected target elevation map.

[0069] Optionally, the third correction subunit includes:

[0070] A fifth calculation unit, configured to calculate a first average wind speed using a Gamma function according to the measured Weibull A value and K value of the target wind tower;

[0071] A sixth calculation unit, configured to calculate a second average wind speed using a Gamma function according to the predicted Weibull A value and K value in the target Weibull spectrum;

[0072] a second determining unit, configured to determine an average wind speed proportionality coefficient according to the first average wind speed and the second average wind speed;

[0073] A generating unit, used for generating a virtual wind speed time series according to the average wind speed proportional coefficient, the random probability distribution value, the measured Weibull A value and the K value of the target wind tower;

[0074] A wind speed correction unit, configured to correct each wind speed value in the virtual wind speed time series according to the wind speed value and the average wind speed proportional coefficient to obtain a corrected wind speed value;

[0075] A third determining unit is used to obtain a corrected virtual wind speed time series after all wind speed values ​​in the virtual wind speed time series have been corrected;

[0076] The seventh calculation unit is used to obtain the Weibull A value and K value in the latest Weibull spectrum by least square fitting according to the corrected virtual time series, and update the target Weibull spectrum.

[0077] Optionally, the device for correcting the mesoscale resource map further includes:

[0078] A weight calculation unit, used for calculating, for each wind tower, a correction weight of each grid point around the wind tower using an inverse square distance weighting method;

[0079] A weighting unit is used to weight the correction value of each grid point by using the correction weight of the grid point.

[0080] Optionally, the device for correcting the mesoscale resource map further includes:

[0081] The fourth determination unit is used to determine the optimal wind turbine layout position according to the corrected mesoscale resource map.

[0082] A third aspect of the present application provides an electronic device, including:

[0083] one or more processors;

[0084] a storage device having one or more programs stored thereon;

[0085] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for correcting the mesoscale resource map as described in any one of the first aspects.

[0086] A fourth aspect of the present application provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for correcting a mesoscale resource map as described in any one of the first aspects is implemented.

[0087] It can be seen from the above scheme that the present application provides a method and device for correcting a mesoscale resource map, which obtains a basic data set according to an existing mesoscale resource map in a wind farm development area and determines a target wind tower in the wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area; then, for each target wind tower, read the measured data of the target wind tower; then, use the wind shear extrapolation method to extrapolate the measured data of the target wind tower to the target height layer to obtain the target measured data; extract target grid data from the basic data set; wherein the target grid data is the grid data corresponding to the location of the target wind tower; finally, according to the target grid data and the target measured data, correct the target grid data to obtain the corrected grid data; update all the corrected grid data in the existing mesoscale resource map to obtain the corrected mesoscale resource map. By correcting the deviations in the mesoscale map, we ensure that the prediction results are more in line with local actual conditions, thereby providing more accurate and reliable data support for subsequent wind farm site selection, wind turbine layout optimization, etc., reducing the uncertainty of development and investment, improving economic benefits and reducing risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0089] Figure 1 A specific flow chart of a method for correcting a mesoscale resource map provided in an embodiment of the present application;

[0090] Figure 2 A specific flow chart of a method for quality control of data provided in another embodiment of the present application;

[0091] Figure 3 A specific flow chart of a method for correcting target grid point data provided by another embodiment of the present application;

[0092] Figure 4 A specific flow chart of a method for correcting the Weibull A value and K value in a target Weibull spectrum provided in another embodiment of the present application;

[0093] Figure 5 A flowchart of a method for correcting a mesoscale resource map provided in another embodiment of the present application;

[0094] Figure 6 A comparison diagram of the arrangement of points of an XX project provided in another embodiment of the present application;

[0095] Figure 7 A layout diagram of machine positions near a wind tower provided in another embodiment of the present application;

[0096] Figure 8 A schematic diagram of a device for correcting a mesoscale resource map provided in another embodiment of the present application. DETAILED DESCRIPTION

[0097] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0098] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0099] It should be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0100] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0101] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0102] The present application embodiment provides a method for correcting a mesoscale resource map, such as Figure 1 As shown, the specific steps include:

[0103] S101. In the wind farm development area, obtain a basic data set based on the existing mesoscale resource map.

[0104] Among them, the basic data set includes the original wind speed map, the original elevation map and the original Weibull map. The wind speed map includes the wind speed distribution in a specific area; the elevation map includes the description of the terrain undulations in the area; the Weibull map: contains the Weibull A value and K value in the area; these mesoscale data are the original data generated by the mesoscale model and can be directly used for early development planning.

[0105] S102. Determine a target wind measurement tower within the wind farm development area.

[0106] The target wind tower is an available wind tower in the wind farm development area.

[0107] S103 . For each target wind tower, read the measured data of the target wind tower.

[0108] Among them, the measured data of the target wind tower include: measured wind speed, measured elevation, measured Weibull A value and K value.

[0109] In the actual application process of the present application, the measured data of the target wind tower obtained by reading also includes: the processing status of the data; the processing status of the data is divided into original data and processed data.

[0110] Specifically, in the process of finding all available wind towers, the underlying data is based on the already sorted "wind tower database", in which there will be processed data and unprocessed data, that is, processed data and raw data. Therefore, the processing status of each available wind tower can be directly obtained.

[0111] It should be noted that if the data processing state is raw data, the data quality control needs to be performed first. Therefore, in another embodiment of the present application, an implementation method of the correction method of the mesoscale resource map is as follows: Figure 2 As shown, it also includes:

[0112] S201. Clean the original data according to the preset processing specification to obtain cleaned measured data.

[0113] Among them, the preset processing specifications include but are not limited to national standards, processing specification manuals, etc., which are not limited here.

[0114] Specifically, the raw data is cleaned according to the preset processing specifications to remove outliers and noise, and the cleaned measured data is obtained.

[0115] S202 , interpolating the cleaned measured data to obtain interpolated measured data.

[0116] Among them, the interpolation method includes but is not limited to correlation interpolation, wind shear interpolation, ratio method interpolation, etc., which are not limited here.

[0117] Correlation interpolation: By establishing a correlation equation between two altitude layers, the correlation coefficient is obtained, and then interpolation is performed based on the coefficient. Wind shear interpolation: The shear coefficient is obtained through multiple altitude layers, and then the target altitude layer is calculated from the interpolation altitude layer according to the shear fitting formula. Ratio method interpolation, through the ratio coefficient of the average wind speed between two altitude layers, the target altitude layer is calculated from the interpolation altitude layer.

[0118] It is understandable that any method may be used to perform the interpolation operation according to the scenario or personal experience.

[0119] Specifically, reanalysis such as other height layers of the same tower or ERA5 / MERRA2 can be used but is not limited to perform correlation interpolation on the data of the actual wind measurement period (mainly wind speed and wind direction), which is not limited here.

[0120] S203, correcting the interpolated measured data to obtain corrected measured data.

[0121] Specifically, the representative year correction of the short-term wind measurement data can be performed by combining the interpolated data with the MCP (Measure Correlate Predict) method, but is not limited to the method.

[0122] In the specific implementation process of this application, the correction method includes but is not limited to: 1) based on the national standard, according to the relevant equations between the wind tower and the mesoscale data and the difference between the long-term mesoscale average wind speed and the corresponding annual average wind speed of the wind tower in the same period, correction is performed in 16 sectors (k1*Δx, k2*Δx, ... k16*Δx); 2) according to the relevant equations between the wind tower and the mesoscale data and the difference between the long-term mesoscale average wind speed and the corresponding annual average wind speed of the wind tower in the same period, correction is performed in the entire sector (k*Δx); 3) according to the difference between the long-term mesoscale average wind speed and the corresponding annual average wind speed of the wind tower in the same period, correction (Δx) is performed; 4) according to the ratio of the long-term mesoscale average wind speed to the annual average wind speed of the wind tower in the same period, correction (a*Δx) is performed.

[0123] It can be understood that if the processing state of the data is raw data, after performing the above processing to obtain the corrected measured data, step S104 uses the wind shear extrapolation method to extrapolate the measured data of the target wind tower to the target altitude layer to obtain the target measured data. The corresponding change is to use the wind shear extrapolation method to extrapolate the corrected measured data to the target altitude layer to obtain the target measured data.

[0124] S104, using the wind shear extrapolation method to extrapolate the measured data of the target wind tower to the target altitude layer to obtain the target measured data.

[0125] Among them, the target measured data is the measured data of the target wind tower at the target height layer. The target height layer is the height layer that the user wants to calculate. For example, the wind tower data is at an altitude of 80m, the mesoscale atlas data is at an altitude of 100m, and the user's target development altitude is 120m. That is, the data at altitudes of 80m and 100m are needed to comprehensively calculate the wind resource situation at an altitude of 120m.

[0126] Specifically, the following formula can be used for calculation:

[0127] Among them, the wind speed v1 and wind speed v2 of the two altitude layers, the altitude layer z1 and altitude layer z2, and the wind shear index α. If you know any four of these parameters, you can calculate the other parameter.

[0128] S105. Extract target grid data from the basic data set.

[0129] The target grid point data are grid point data corresponding to the location of the target wind tower; the target grid point data include a target wind speed map, a target elevation map and a target Weibull map of the grid points corresponding to the location of the target wind tower.

[0130] Optionally, in another embodiment of the present application, an implementation of step S105 includes:

[0131] The data in the basic dataset are aligned to the location of the target wind tower using the spatial interpolation method to obtain the target grid data.

[0132] It is understandable that the mesoscale wind resource map data can be understood as N*N regular grid points, and the wind tower position is an arbitrary point. Now we need to know the value corresponding to the wind tower position on the mesoscale map. We only need to obtain the 4 mesoscale grid points closest to the wind tower point, perform bilinear interpolation, and obtain the "mesoscale value" corresponding to the wind tower position.

[0133] S106. Correct the target grid point data according to the target grid point data and the target measured data to obtain corrected grid point data.

[0134] It is understandable that by combining the measured data of the wind tower and correcting the existing mesoscale map, the error problem of the mesoscale model in complex terrain and different altitude layers has been overcome, and the accuracy of wind resource parameter prediction has been greatly improved. With the increase of historical data in the regional scope, the accuracy of the mesoscale wind speed map will be greatly improved and eventually tend to the actual level.

[0135] Optionally, in the specific implementation process of the present application, an implementation method of step S106 is as follows: Figure 3 As shown, including:

[0136] S301. Determine first correction information according to the measured wind speed of a target wind measuring tower and the predicted wind speed in a target wind speed map, and use the first correction information to correct the predicted wind speed in the target wind speed map.

[0137] Specifically, the quotient of the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map is used as the first correction information; then, the first correction information is multiplied by the predicted wind speed in the target wind speed map to obtain a corrected target wind speed map.

[0138] In the implementation process of this application, the first correction information can be calculated according to the following formula:

[0139] Among them, P is the proportionality coefficient, S real is the measured wind speed after being processed by the wind tower, S meso Mesoscale wind speed.

[0140] S302: Determine second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and use the second correction information to correct the predicted elevation in the target elevation map.

[0141] Compare the actual elevation of the wind tower with the elevation data in the mesoscale elevation map and calculate their deviation. Use a method similar to wind speed correction to adjust the mesoscale elevation map based on the actual elevation data.

[0142] Specifically, the quotient of the measured elevation of the target wind tower and the predicted elevation in the target elevation map is used as the second correction information; then, the second correction information is multiplied by the predicted elevation in the target elevation map to obtain a corrected target elevation map.

[0143] S303: Determine third correction information according to the measured Weibull A value and K value of the target wind tower and the predicted Weibull A value and K value in the target Weibull spectrum, and use the third correction information to correct the Weibull A value and K value in the target Weibull spectrum.

[0144] Optionally, in another embodiment of the present application, an implementation of step S303 is as follows: Figure 4 As shown, including:

[0145] S401. According to the measured Weibull A value and K value of the target wind tower, a first average wind speed is calculated using a Gamma function.

[0146] In the specific implementation process of this application, the first average wind speed can be calculated using the following formula:

[0147]

[0148] Among them, A real and K real are the measured Weibull A and K values ​​of the target wind tower, and Γ is the Gamma function.

[0149] S402: According to the predicted Weibull A value and K value in the target Weibull spectrum, a second average wind speed is calculated using a Gamma function.

[0150] In the specific implementation process of this application, the first average wind speed can be calculated using the following formula:

[0151]

[0152] Among them, A meso and K meso are the predicted Weibull A value and K value in the target Weibull spectrum, and Γ is the Gamma function.

[0153] S403: Determine an average wind speed proportionality coefficient according to the first average wind speed and the second average wind speed.

[0154] In the specific implementation process of the present application, after the wind speed in the target wind speed map and the measured wind speed of the target wind tower are extrapolated to the same target height, the average wind speed proportionality coefficient at the extrapolated target wind tower position is calculated.

[0155] The measured wind speed of the target wind tower is pushed to the target height, and the following calculation formula can be used for calculation:

[0156] Among them, H target is the target altitude, H source is the known altitude layer height, α is the wind shear coefficient, V real It is the wind speed after the measured wind speed of the target wind measurement tower is pushed to the target height.

[0157] The wind speed in the target wind speed map is pushed to the target height, and the following calculation formula can be used for calculation:

[0158] Among them, H target is the target altitude, H source is the known altitude layer height, α is the wind shear coefficient, V meso It is the wind speed after the wind speed in the target wind speed map is pushed to the target height.

[0159] V real With V meso The quotient of is taken as the average wind speed proportional coefficient R, that is,

[0160] S404: Generate a virtual wind speed time series according to the average wind speed proportional coefficient, the random probability distribution value, and the measured Weibull A value and K value of the target wind tower.

[0161] In the specific implementation process of this application, a virtual wind speed time series is constructed by measuring the AK value. Assuming that the wind speed data points in the time series follow the Weibull distribution, a series of wind speed data can be generated by the Weibull distribution formula:

[0162] Among them, P(t) is a random probability distribution value, a time series generated based on the measured A and K values, and V(t) is a virtual wind speed time series.

[0163] S405 , for each wind speed value in the virtual wind speed time series, correction is performed according to the wind speed value and the average wind speed proportional coefficient to obtain a corrected wind speed value.

[0164] S406: When all wind speed values ​​in the virtual wind speed time series have been corrected, a corrected virtual wind speed time series is obtained.

[0165] Specifically, the virtual wind speed time series is corrected according to the aforementioned average wind speed ratio coefficient R. Each wind speed value in the virtual wind speed time series is multiplied by the average wind speed ratio coefficient R to obtain a corrected virtual wind speed time series:

[0166] V modifie (t)=V(t)×R; where V modifie (t) is the corrected virtual wind speed time series.

[0167] S407 , according to the corrected virtual time series, obtain the latest Weibull A value and K value in the Weibull spectrum by least square fitting, and update to the target Weibull spectrum.

[0168] S107. Update all corrected grid data in the existing mesoscale resource map to obtain a corrected mesoscale resource map.

[0169] In the actual application process of the present application, after obtaining the corrected mesoscale resource map, the optimal wind turbine layout position can be determined according to the corrected mesoscale resource map, thereby ensuring that the power generation efficiency of the wind farm is maximized.

[0170] Wind turbine site selection methods include but are not limited to: 1) Traditional manual methods, manually selecting points and arranging points according to the revised resource map. 2) Automatically obtaining a set of relatively high-quality wind turbine layout points through optimization algorithms. 3) Automatically selecting a set of relatively high-quality wind turbine layout points through genetic algorithms, which are not limited here.

[0171] Optionally, in another embodiment of the present application, an implementation method of a method for correcting a mesoscale resource map is as follows: Figure 5 As shown, it also includes:

[0172] S501 . For each wind tower, use the inverse square distance weighting method to calculate the correction weight of each grid point around the wind tower.

[0173] It can be understood that the grid points in the area near the wind tower can be weighted by the inverse square of the distance. The closer the distance, the greater the correction; the farther the distance, the smaller the correction.

[0174] For the correction of a single wind tower, the inverse square distance weighting method is used, that is:

[0175] Where w is the weight of the grid point and d is the distance from the grid point to the wind tower.

[0176] When there are multiple wind towers, the inverse square distance weighting method is still used for each grid point, and the correction weight of each tower to the grid point is weighted by the following formula:

[0177] Among them, w i is the weight of the ith grid point, d i is the distance from the ith grid point to the wind tower.

[0178] After obtaining the weight of each wind tower for the grid point, the weighted sum of the corrected data of each tower is performed:

[0179] Among them, V i is the correction value of the i-th wind tower, w i is its corresponding weight, and N is the total number of wind towers.

[0180] S502 . For each grid point, weight the correction value of the grid point using the correction weight of the grid point.

[0181] like Figure 6 As shown in the figure, it is a comparison diagram of the point arrangement of the XX project, where circles are actual project points, triangles are preliminary selected points, squares are revised points, and diamonds are wind tower points. The actual project points are the final construction points obtained by comprehensive consideration of on-site sensitivity factors, construction conditions, surrounding obstacles, etc. The preliminary selected points are points preliminarily selected based on mesoscale resource data using ready-made software tools (without considering sensitivity factors and construction conditions), squares are points selected after correction by adding actual wind tower data, and diamonds are actual wind tower points.

[0182] like Figure 7As shown in the figure, it is a layout diagram of the positions near the wind tower. It can be seen that the closer to the wind tower, the greater the adjustment of the point position, and the distribution trend is closer to the actual point position of the project. It is obvious that the addition of the wind tower correction has a positive impact on the point selection.

[0183] As shown in Table 1, it is a comparison of power generation at different points. When the model, wind resource data, power generation calculation method and other conditions are the same, only the point arrangement method changes. The power generation of each version of the point is calculated. The actual point power generation is 1316h, the selected point is 1364h, and the corrected point is 1338h. The errors are 0%, 3.6%, and 1.7% respectively. It can be seen that after adding the wind tower to correct the point, the accuracy of the power generation results is also significantly improved.

[0184]

[0185]

[0186] Table 1

[0187] It can be seen from the above scheme that the present application provides a method for correcting a mesoscale resource map, by obtaining a basic data set according to an existing mesoscale resource map in a wind farm development area and determining a target wind tower in the wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area; then, for each target wind tower, the measured data of the target wind tower is read; thereafter, the measured data of the target wind tower is extrapolated to a target height layer using a wind shear extrapolation method to obtain target measured data; target grid data is extracted from the basic data set; wherein the target grid data is grid data corresponding to the location of the target wind tower; finally, the target grid data is corrected according to the target grid data and the target measured data to obtain corrected grid data; all corrected grid data are updated in the existing mesoscale resource map to obtain a corrected mesoscale resource map. By correcting the deviations in the mesoscale map, we ensure that the prediction results are more in line with local actual conditions, thereby providing more accurate and reliable data support for subsequent wind farm site selection, wind turbine layout optimization, etc., reducing the uncertainty of development and investment, improving economic benefits and reducing risks.

[0188] The present application embodiment provides a device for correcting a mesoscale resource map, such as Figure 8 As shown, specifically including:

[0189] The acquisition unit 801 is used to acquire a basic data set in the wind farm development area according to an existing mesoscale resource map.

[0190] Among them, the basic data sets include original wind speed maps, original elevation maps and original Weibull maps.

[0191] The first determining unit 802 is configured to determine a target wind tower in a wind farm development area.

[0192] The target wind tower is an available wind tower in the wind farm development area.

[0193] The reading unit 803 is used to read the measured data of each target wind tower.

[0194] Among them, the measured data of the target wind tower include: measured wind speed, measured elevation, measured Weibull A value and K value.

[0195] The calculation unit 804 is used to calculate the measured data of the target wind tower to the target altitude layer by using the wind shear extrapolation method to obtain the target measured data.

[0196] The target measured data are the measured data of the target wind tower at the target altitude layer.

[0197] Optionally, in another embodiment of the present application, the measured data of the target wind tower further includes: a processing state of the data; the processing state of the data is divided into raw data and processed data. If the processing state of the data is raw data, an implementation of the correction device for the mesoscale resource map further includes:

[0198] The cleaning unit is used to clean the original data according to the preset processing specification to obtain the cleaned measured data.

[0199] The interpolation unit is used to interpolate the cleaned measured data to obtain interpolated measured data.

[0200] The correction unit is used to correct the interpolated measured data to obtain the corrected measured data.

[0201] The calculation unit 804 is further used for:

[0202] The wind shear extrapolation method is used to extrapolate the corrected measured data to the target altitude layer to obtain the target measured data.

[0203] For the specific working process of the units disclosed in the above embodiments of the present application, please refer to the corresponding method embodiments, such as Figure 2 As shown, no further description is given here.

[0204] The extraction unit 805 is used to extract target grid point data from the basic data set.

[0205] The target grid point data are grid point data corresponding to the location of the target wind tower; the target grid point data include a target wind speed map, a target elevation map and a target Weibull map of the grid points corresponding to the location of the target wind tower.

[0206] Optionally, in another embodiment of the present application, an implementation of the extraction unit 805 includes:

[0207] The alignment unit is used to align the data in the basic data set to the position of the target wind tower using the spatial interpolation method to obtain the target grid data.

[0208] The specific working process of the units disclosed in the above embodiments of the present application can be found in the corresponding method embodiments, which will not be repeated here.

[0209] The correction unit 806 is used to correct the target grid point data according to the target grid point data and the target measured data to obtain corrected grid point data.

[0210] Optionally, in another embodiment of the present application, an implementation of the correction unit 806 includes:

[0211] The first correction subunit is used to determine first correction information according to the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map, and to correct the predicted wind speed in the target wind speed map using the first correction information.

[0212] The second correction subunit is used to determine second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and to correct the predicted elevation in the target elevation map using the second correction information.

[0213] The third correction subunit is used to determine third correction information according to the measured Weibull A value and K value of the target wind measurement tower and the predicted Weibull A value and K value in the target Weibull spectrum, and use the third correction information to correct the Weibull A value and K value in the target Weibull spectrum.

[0214] For the specific working process of the units disclosed in the above embodiments of the present application, please refer to the corresponding method embodiments, such as Figure 3 As shown, no further description is given here.

[0215] Optionally, in another embodiment of the present application, an implementation of the first correction subunit includes:

[0216] The first calculation unit is used to use the quotient of the measured wind speed of the target wind measurement tower and the predicted wind speed in the target wind speed map as the first correction information.

[0217] The second calculation unit is used to multiply the first correction information by the predicted wind speed in the target wind speed map to obtain a corrected target wind speed map.

[0218] The specific working process of the units disclosed in the above embodiments of the present application can be found in the corresponding method embodiments, which will not be repeated here.

[0219] Optionally, in another embodiment of the present application, an implementation of the second correction subunit includes:

[0220] The third calculation unit is used to use the quotient of the measured elevation of the target wind tower and the predicted elevation in the target elevation map as the second correction information.

[0221] The fourth calculation unit is used to multiply the second correction information by the predicted elevation in the target elevation map to obtain a corrected target elevation map.

[0222] The specific working process of the units disclosed in the above embodiments of the present application can be found in the corresponding method embodiments, which will not be repeated here.

[0223] Optionally, in another embodiment of the present application, an implementation of the third correction subunit includes:

[0224] The fifth calculation unit is used to calculate the first average wind speed by using the Gamma function according to the actually measured Weibull A value and K value of the target wind measurement tower.

[0225] The sixth calculation unit is used to calculate the second average wind speed using the Gamma function according to the predicted Weibull A value and K value in the target Weibull spectrum.

[0226] The second determining unit is used to determine an average wind speed proportional coefficient according to the first average wind speed and the second average wind speed.

[0227] The generating unit is used to generate a virtual wind speed time series according to the average wind speed proportional coefficient, the random probability distribution value, and the measured Weibull A value and K value of the target wind tower.

[0228] The wind speed correction unit is used to correct each wind speed value in the virtual wind speed time series according to the wind speed value and the average wind speed proportional coefficient to obtain a corrected wind speed value.

[0229] The third determining unit is used to obtain a corrected virtual wind speed time series after all wind speed values ​​in the virtual wind speed time series have been corrected.

[0230] The seventh calculation unit is used to obtain the Weibull A value and K value in the latest Weibull spectrum through least square fitting according to the corrected virtual time series, and update it to the target Weibull spectrum.

[0231] For the specific working process of the units disclosed in the above embodiments of the present application, please refer to the corresponding method embodiments, such as Figure 4 As shown, no further description is given here.

[0232] The updating unit 807 is used to update all the corrected grid data in the existing mesoscale resource map to obtain a corrected mesoscale resource map.

[0233] For the specific working process of the units disclosed in the above embodiments of the present application, please refer to the corresponding method embodiments, such as Figure 1 As shown, no further description is given here.

[0234] Optionally, in another embodiment of the present application, an implementation of the device for correcting the mesoscale resource map further includes:

[0235] The weight calculation unit is used to calculate the correction weight of each grid point around each wind tower by using the inverse square distance weighting method.

[0236] The weighting unit is used to weight the correction value of each grid point by using the correction weight of the grid point.

[0237] For the specific working process of the units disclosed in the above embodiments of the present application, please refer to the corresponding method embodiments, such as Figure 5 As shown, no further description is given here.

[0238] Optionally, in another embodiment of the present application, an implementation of the device for correcting the mesoscale resource map further includes:

[0239] The fourth determination unit is used to determine the optimal wind turbine layout position according to the corrected mesoscale resource map.

[0240] The specific working process of the units disclosed in the above embodiments of the present application can be found in the corresponding method embodiments, which will not be repeated here.

[0241] As can be seen from the above scheme, the present application provides a correction device for a mesoscale resource map, wherein an acquisition unit 801 acquires a basic data set according to an existing mesoscale resource map in a wind farm development area, and a first determination unit 802 determines a target wind tower in the wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area; then, a reading unit 803 reads the measured data of the target wind tower for each target wind tower; thereafter, a calculation unit 804 uses a wind shear extrapolation method to calculate the measured data of the target wind tower to a target height layer to obtain target measured data; an extraction unit 805 extracts target grid data from the basic data set; wherein the target grid data is grid data corresponding to the location of the target wind tower; finally, a correction unit 806 corrects the target grid data according to the target grid data and the target measured data to obtain corrected grid data; an updating unit 807 updates all corrected grid data in the existing mesoscale resource map to obtain a corrected mesoscale resource map. By correcting the deviations in the mesoscale map, we ensure that the prediction results are more in line with local actual conditions, thereby providing more accurate and reliable data support for subsequent wind farm site selection, wind turbine layout optimization, etc., reducing the uncertainty of development and investment, improving economic benefits and reducing risks.

[0242] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0243] Another embodiment of the present application provides an electronic device, including:

[0244] One or more processors.

[0245] A storage device having one or more programs stored thereon.

[0246] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for correcting the mesoscale resource map as described in any one of the above embodiments.

[0247] Another embodiment of the present application provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for correcting a mesoscale resource map as described in any one of the above embodiments is implemented.

[0248] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0249] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0250] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0251] Another embodiment of the present application provides a computer program product, which, when executed, is used to execute any of the above-mentioned methods for correcting a mesoscale resource map.

[0252] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0253] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined in this application is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing this application.

[0254] Although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present application. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0255] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for correcting a mesoscale resource map, characterized in that: include: In the wind farm development area, a basic data set is obtained based on an existing mesoscale resource map; wherein the basic data set includes an original wind speed map, an original elevation map, and an original Weibull map; Determine a target wind tower in the wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area; For each of the target wind towers, read the measured data of the target wind tower; wherein the measured data of the target wind tower includes: measured wind speed, measured elevation, measured Weibull A value and K value; The wind shear extrapolation method is used to extrapolate the measured data of the target wind tower to the target altitude layer to obtain the target measured data; wherein the target measured data is the measured data of the target wind tower at the target altitude layer; Extracting target grid point data from the basic data set; wherein the target grid point data is grid point data corresponding to the location of the target wind tower; the target grid point data includes a target wind speed map, a target elevation map and a target Weibull map of the grid point corresponding to the location of the target wind tower; According to the target grid point data and the target measured data, the target grid point data is corrected to obtain corrected grid point data; All the corrected grid data are updated in the existing mesoscale resource atlas to obtain a corrected mesoscale resource atlas.

2. The method for correcting a mesoscale resource map according to claim 1, characterized in that: The measured data of the target wind tower also includes: data processing status; the data processing status is divided into raw data and processed data. If the data processing status is raw data, the wind shear extrapolation method is used to extrapolate the measured data of the target wind tower to the target altitude layer, and before obtaining the target measured data, it also includes: Clean the original data according to the preset processing specifications to obtain the cleaned measured data; Interpolating the cleaned measured data to obtain interpolated measured data; Correcting the interpolated measured data to obtain corrected measured data; The method of using the wind shear extrapolation method to extrapolate the measured data of the target wind tower to the target altitude layer to obtain the target measured data includes: The corrected measured data is extrapolated to the target altitude layer using the wind shear extrapolation method to obtain the target measured data.

3. The method for correcting a mesoscale resource map according to claim 1, characterized in that: The step of extracting target grid point data from the basic data set includes: The data in the basic dataset are aligned to the location of the target wind tower using the spatial interpolation method to obtain the target grid data.

4. The method for correcting a mesoscale resource map according to claim 1, characterized in that: The step of correcting the target grid point data according to the target grid point data and the target measured data to obtain corrected grid point data includes: Determine first correction information according to the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map, and use the first correction information to correct the predicted wind speed in the target wind speed map; Determine second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and use the second correction information to correct the predicted elevation in the target elevation map; According to the measured Weibull A value and K value of the target wind tower and the predicted Weibull A value and K value in the target Weibull spectrum, third correction information is determined, and the Weibull A value and K value in the target Weibull spectrum are corrected using the third correction information.

5. The method for correcting a mesoscale resource map according to claim 4, characterized in that: The method of determining first correction information according to the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map, and correcting the predicted wind speed in the target wind speed map using the first correction information, comprises: The quotient of the measured wind speed of the target wind tower and the predicted wind speed in the target wind speed map is used as the first correction information; The first correction information is multiplied by the predicted wind speed in the target wind speed map to obtain a corrected target wind speed map.

6. The method for correcting a mesoscale resource map according to claim 4, characterized in that: The method of determining second correction information according to the measured elevation of the target wind tower and the predicted elevation in the target elevation map, and correcting the predicted elevation in the target elevation map using the second correction information, comprises: The quotient of the measured elevation of the target wind tower and the predicted elevation in the target elevation map is used as the second correction information; The second correction information is multiplied by the predicted elevation in the target elevation map to obtain a corrected target elevation map.

7. The method for correcting a mesoscale resource map according to claim 4, characterized in that: The method of determining third correction information according to the measured Weibull A value and K value of the target wind tower and the predicted Weibull A value and K value in the target Weibull spectrum, and using the third correction information to correct the Weibull A value and K value in the target Weibull spectrum includes: According to the measured Weibull A value and K value of the target wind tower, the first average wind speed is calculated using the Gamma function; According to the predicted Weibull A value and K value in the target Weibull spectrum, the second average wind speed is calculated using the Gamma function; Determining an average wind speed proportionality coefficient according to the first average wind speed and the second average wind speed; Generate a virtual wind speed time series according to the average wind speed proportional coefficient, the random probability distribution value, the measured Weibull A value and the K value of the target wind tower; For each wind speed value in the virtual wind speed time series, correction is performed according to the wind speed value and the average wind speed proportional coefficient to obtain a corrected wind speed value; When all wind speed values ​​in the virtual wind speed time series are corrected, a corrected virtual wind speed time series is obtained; According to the corrected virtual time series, the Weibull A value and K value in the latest Weibull spectrum are obtained by least square fitting, and updated to the target Weibull spectrum.

8. The method for correcting a mesoscale resource map according to claim 1, characterized in that: Also includes: For each wind tower, the correction weight of each grid point around the wind tower is calculated using the inverse square distance weighting method; For each of the grid points, the correction value of the grid point is weighted using the correction weight of the grid point.

9. The method for correcting a mesoscale resource map according to claim 1, characterized in that: After updating all the modified grid point data in the existing mesoscale resource map to obtain the modified mesoscale resource map, the method further includes: The optimal wind turbine layout position is determined according to the modified mesoscale resource map.

10. A device for correcting a mesoscale resource map, characterized in that: include: An acquisition unit is used to acquire a basic data set in the wind farm development area according to an existing mesoscale resource map; wherein the basic data set includes an original wind speed map, an original elevation map and an original Weibull map; A first determining unit is used to determine a target wind tower in a wind farm development area; wherein the target wind tower is an available wind tower in the wind farm development area; A reading unit, used for reading the measured data of each target wind tower; wherein the measured data of the target wind tower includes: measured wind speed, measured elevation, measured Weibull A value and K value; A calculation unit, used to use wind shear extrapolation method to calculate the measured data of the target wind tower to the target altitude layer, so as to obtain target measured data; wherein the target measured data is the measured data of the target wind tower at the target altitude layer; An extraction unit is used to extract target grid point data from a basic data set; wherein the target grid point data is grid point data corresponding to the location of a target wind tower; the target grid point data includes a target wind speed map, a target elevation map and a target Weibull map of the grid point corresponding to the location of the target wind tower; A correction unit, used for correcting the target grid point data according to the target grid point data and the target measured data to obtain corrected grid point data; An updating unit is used to update all the corrected grid data in the existing mesoscale resource map to obtain a corrected mesoscale resource map.

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