Wind resource intelligent analysis processing system and method

Abstract

The invention belongs to the field of wind resource data processing, and provides a wind resource intelligent analysis processing system and method. The system comprises a wind resource data import module, a wind resource data processing module and a wind resource report generation module, the wind resource data importing module is used for importing the wind resource data into a pre-created item set; the wind resource data processing module is used for calling wind resource data in a to-be-processed item set, performing primary detection on the wind resource data in the called item set by using a set prior rule, and marking abnormal data; performing secondary quality evaluation on the marked abnormal data by using the trained data evaluation model to determine the type of the abnormal data; performing matching interpolation and correction on the wind resource data according to the type of the abnormal data until the interpolated and corrected wind resource data meets a preset quality requirement; and the wind resource report generation module is used for calling the corresponding wind resource data and filling the corresponding wind resource data into a set report template, and generating a matched wind resource report by one key.

Description

Technical Field

[0001] The present invention belongs to the field of wind resource data processing, and in particular relates to a wind resource intelligent analysis and processing system and method. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] After wind resource data is measured by wind towers at various observation sites, there may be incomplete or inaccurate data in the measurement link and data transmission link. Most existing technologies use manual data calibration and correction, and cannot match interpolation and correction for different abnormal problems, resulting in the inability of wind resource data to provide accurate data for subsequent wind farm site selection. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a wind resource intelligent analysis and processing system and method, which can improve the speed and accuracy of wind resource processing.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a wind resource intelligent analysis and processing system.

[0006] In one or more embodiments, a wind resource intelligent analysis and processing system is provided, comprising: Wind resource data import module, wind resource data processing module and wind resource report generation module; The wind resource data import module is used to import the wind resource data into a pre-created project set, and store the wind resource data in the project set using the observation site, wind tower and observation time as key values; The wind resource data processing module is used to retrieve the wind resource data in the project set to be processed, perform a primary detection on the retrieved wind resource data in the project set using the set prior rules, and mark abnormal data; perform a secondary quality assessment on the marked abnormal data using the trained data assessment model to determine the type of abnormal data; match, interpolate and correct the wind resource data according to the type of abnormal data until the interpolated and corrected wind resource data meets the preset quality requirements; The wind resource report generation module is used to use any key value of the observation site, wind tower and observation time as an index condition, retrieve the corresponding wind resource data and fill it into the set report template, and generate a matching wind resource report with one click.

[0007] As an implementation mode, in the wind resource report generation module, a report template is set to consist of a plurality of sections, each section has two states: hidden and displayed, and the position of each section in the set report template is adjustable.

[0008] As an implementation manner, the wind resource data includes wind tower data, wind radar data, mesoscale weather data and meteorological station data.

[0009] As an implementation manner, the types of abnormal data include missing wind resource data of the same tower at different heights and missing wind direction and / or wind speed in the wind resource data of the target wind measurement tower.

[0010] As an implementation method, based on the wind resource data of the available height at the target wind tower setting time, the wind resource data of different heights at the current time are interpolated on the same tower; the wind resource data of different heights interpolated on the same tower include wind speed data, wind direction data and temperature data.

[0011] As an implementation method, the process of interpolating wind speed data at different heights at the current moment on the same tower is as follows: Determine whether there is at least one valid wind speed at the target height at a certain moment. If so, calculate the average of these valid wind speeds as the wind speed value of the target height at that moment; If there is no effective wind speed at the target height at a certain moment, determine whether there is an effective wind speed other than the target height at that moment; When there are effective wind speeds at at least two heights other than the target height at that moment, the Pearson correlation coefficient between the target height and the channels with effective wind speeds at that moment is calculated respectively, and the effective wind speeds of a set number of channels with the highest correlation are selected for same-tower interpolation of the wind speed data of the target height at that moment; when interpolating at the same tower, the wind shear coefficient at that moment is calculated using the selected effective wind speed, and the wind shear profile is calculated based on the wind shear coefficient at that moment, thereby inferring the wind speed data of the target height at that moment; When there is an effective wind speed at a height other than the target height at that moment, the wind shear profile is calculated using the average wind shear coefficient observed in the known wind resource data set at similar times and dates in the day, and then the wind speed data at the target height at that moment is extrapolated.

[0012] As an implementation method, the process of interpolating the wind direction data / temperature data at different heights at the current moment on the same tower is as follows: Determine the amount of wind direction data / temperature data at the target height at a certain moment; When the number of wind direction data / temperature data at the target height at a certain moment is at least two, the average of these wind direction data / temperature data is calculated as the wind direction data / temperature data at the target height at that moment; When there is only one wind direction data / temperature data at the target height at a certain moment, the wind direction data / temperature data at the target height at that moment are inferred by using the average wind direction deflection / temperature gradient observed at similar times and days in a known wind resource data set.

[0013] As an implementation method, wind direction and / or wind speed are interpolated according to wind resource data of adjacent wind towers; Among them, the process of interpolating wind direction from different towers based on the wind resource data of adjacent wind towers is as follows: The neighboring wind tower is used as the reference wind tower, and the wind direction data of the target wind tower is divided into different sectors according to the wind direction data of the reference wind tower. The change from the reference wind direction to the target wind direction at each moment in each sector is calculated respectively, and the average offset of each sector is calculated. The interpolation value at the corresponding moment can be obtained by adding the average sector offset to the reference wind direction.

[0014] As an implementation method, the process of interpolating wind speed from different towers based on wind resource data from adjacent wind towers is as follows: The wind speed data of the target wind tower are divided into several sectors according to the interpolated wind direction data of the target wind tower. The neighboring wind towers are used as reference wind towers, and the relationship between the wind speed data of the target wind tower and the reference wind speed of the reference wind tower is fitted sector by sector. Based on the fitted wind speed relationship of each sector, the wind speed data in each sector of the target wind tower are interpolated.

[0015] As an implementation method, wind resource data is corrected to representative wind resource data; it is determined whether the representative wind resource data has a tower shadow effect, and if not, the final interpolated and corrected target wind tower wind resource data is obtained; otherwise, the representative wind resource data is subjected to tower shadow correction, and the result after tower shadow correction is used as the final interpolated and corrected target wind tower wind resource data.

[0016] A second aspect of the present invention provides a method for intelligent analysis and processing of wind resources.

[0017] In one or more embodiments, a method for intelligent analysis and processing of wind resources is provided, which is based on the wind resource intelligent analysis and processing system as described above; comprising: Import wind resource data into a pre-created project set, and store the wind resource data in the project set using observation sites, wind towers, and observation times as key values; Retrieve the wind resource data in the project set to be processed, perform a primary test on the wind resource data in the retrieved project set using the set of prior rules, and mark the abnormal data; perform a secondary quality assessment on the marked abnormal data using the trained data assessment model to determine the type of abnormal data; match, interpolate and correct the wind resource data according to the type of abnormal data until the interpolated and corrected wind resource data meets the preset quality requirements; Using any key value of the observation site, wind tower and observation time as the index condition, the corresponding wind resource data is retrieved and filled into the set report template, and the matching wind resource report is generated with one click.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) The wind resource intelligent analysis and processing system of the present invention is composed of a wind resource data import module, a wind resource data processing module and a wind resource report generation module, which can realize the intelligent import and classified key-value storage of wind resource data, and can also use the wind resource data processing module to automatically process abnormal data, and use the wind resource report generation module to generate a matching wind resource report with one click, thereby improving the intelligence and analysis efficiency of the entire wind resource analysis.

[0019] (2) The present invention uses a priori rule settings to perform a primary detection on wind resource data, and then uses a data evaluation model to perform a secondary quality evaluation on the initially marked abnormal data. The two-detection mechanism is used to timely discover and solve data quality problems, thereby improving the integrity, accuracy and credibility of the data, thereby improving the quality and reliability of the data.

[0020] (3) The present invention utilizes the effective wind resource data and mesoscale weather data of the target wind tower and its neighboring wind towers, and performs same-tower interpolation based on the effective wind resource data of the available height at the target wind tower at the set time, thereby improving the processing accuracy and reliability of wind resource data at different heights of the same tower.

[0021] (4) The present invention interpolates the wind direction and / or wind speed of a nearby wind tower according to the effective wind resource data of the adjacent wind towers, uses the mesoscale weather data to correct it into representative wind resource data, and finally obtains the final interpolated and corrected wind resource data of the target wind tower through tower shadow correction. This simplifies the data processing process, improves data consistency and accuracy, adapts to the diversity of heterogeneous data sources, and is more flexible and universal. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1It is a schematic diagram of the structure of the wind resource intelligent analysis and processing system according to an embodiment of the present invention; Figure 2 is a schematic diagram of a wind resource data import module according to an embodiment of the present invention; Figure 3 is a schematic diagram of a wind resource data processing module according to an embodiment of the present invention; Figure 4 This is an original image of a wind resource report generation module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0027] Figure 1 FIG. 1 is a schematic diagram of a wind resource intelligent analysis and processing system according to an embodiment of the present invention. Figure 1 The wind resource intelligent analysis and processing system in the embodiment shown may include: Wind resource data import module 101, wind resource data processing module 102 and wind resource report generation module 103; The wind resource data import module 101 is used to import wind resource data into a pre-created project set, and store the wind resource data in the project set using observation site, wind tower and observation time as key values; The wind resource data processing module 102 is used to retrieve the wind resource data in the project set to be processed, perform a primary detection on the retrieved wind resource data in the project set using the set prior rules, and mark abnormal data; perform a secondary quality assessment on the marked abnormal data using the trained data assessment model to determine the type of abnormal data; match, interpolate and correct the wind resource data according to the type of abnormal data until the interpolated and corrected wind resource data meets the preset quality requirements; The wind resource report generation module 103 is used to use any key value of the observation site, the wind tower and the observation time as an index condition, retrieve the corresponding wind resource data and fill it into the set report template, and generate a matching wind resource report with one click.

[0028] The wind resource intelligent analysis and processing system of this embodiment is composed of a wind resource data import module, a wind resource data processing module and a wind resource report generation module. It can realize the intelligent import and classified key-value storage of wind resource data, and can also use the wind resource data processing module to automatically process abnormal data, and use the wind resource report generation module to generate a matching wind resource report with one click, thereby improving the intelligence and analysis efficiency of the entire wind resource analysis.

[0029] In the wind resource data import module 101, the wind resource data includes wind tower data, wind radar data, mesoscale weather data and meteorological station data.

[0030] like Figure 2 As shown, the wind resource data import module 101 is a module for integrating external data into the system, and its main functions include: extracting data from external data sources, mainly including wind measurement site data (wind measurement towers, wind measurement radars, meteorological stations, mesoscale data, etc.); converting data to a standard format in the system; loading data into a target storage area in the system; supporting batch import of data; supporting common wind measurement data formats, and supporting the import of other common wind measurement data formats such as txt and csv.

[0031] When importing a file, the wind resource data import module 101 parses, verifies and validates the file content to obtain the data information and wind tower information in the file. At this time, the user can modify and confirm the channel information in the data and the location and time information of the wind tower.

[0032] When importing multiple files at the same time, if the channel information in the data changes, the wind resource data import module 101 will prompt to confirm the channel information matching. In addition, for the imported data, the system also supports appending data. If the channel information in the appended data is inconsistent with the original data, the wind resource data import module 101 will also prompt to confirm the channel information matching, and the final imported data is stored in the json file format.

[0033] In such Figure 3 In the wind resource data processing module 102 shown, the data evaluation model is constructed based on historical wind measurement data. The historical wind measurement data is wind resource related data collected by wind towers, wind radars, meteorological stations, etc. in history, including conventional observation variables such as wind speed, wind direction, temperature, and air pressure, as well as statistical data such as turbulence intensity, wind energy, and wind power density.

[0034] It should be noted here that the data evaluation model can be implemented using existing neural network models such as the random forest model.

[0035] In a specific implementation process, the types of abnormal data include missing wind resource data of the same tower at different heights and missing wind direction and / or wind speed in the wind resource data of the target wind measurement tower.

[0036] Among them, according to the wind resource data of the available height at the setting time of the target wind measurement tower, the wind resource data of different heights at the current time are interpolated on the same tower; the wind resource data of different heights interpolated on the same tower include wind speed data, wind direction data and temperature data.

[0037] Specifically, the process of interpolating wind speed data at different heights on the same tower is as follows: Determine whether there is at least one valid wind speed at the target height at a certain moment. If so, calculate the average of these valid wind speeds as the wind speed value of the target height at that moment; If there is no effective wind speed at the target height at a certain moment, determine whether there is an effective wind speed other than the target height at that moment; When there are effective wind speeds at at least two heights other than the target height at that moment, the Pearson correlation coefficients between the target height and the channels with effective wind speeds at that moment are calculated respectively, and the effective wind speeds of a set number of channels with the highest correlation are selected for same-tower interpolation of the wind speed data of the target height at that moment; when interpolating at the same tower, the wind shear coefficient at that moment is calculated using the selected effective wind speed, and the wind shear profile is calculated based on the wind shear coefficient at that moment, thereby inferring the wind speed data of the target height at that moment; When there is an effective wind speed at a height other than the target height at that moment, the wind shear profile is calculated using the average wind shear coefficient observed in the known wind resource data set at similar times and dates in the day, and then the wind speed data at the target height at that moment is extrapolated.

[0038] In this way, the wind speed data at different heights at the current moment can be accurately interpolated, thus improving the integrity of the data.

[0039] In some specific implementations, the process of interpolating wind direction data / temperature data at different heights at the current moment on the same tower is as follows: Determine the amount of wind direction data / temperature data at the target height at a certain moment; When the number of wind direction data / temperature data at the target height at a certain moment is at least two, the average of these wind direction data / temperature data is calculated as the wind direction data / temperature data at the target height at that moment; When there is only one wind direction data / temperature data at the target height at a certain moment, the wind direction data / temperature data at the target height at that moment are inferred by using the average wind direction deflection / temperature gradient observed at similar times and days in a known wind resource data set.

[0040] In this way, the wind direction data / temperature data at different heights at the current moment can be accurately interpolated, thereby improving the integrity of the data.

[0041] In other embodiments, wind direction and / or wind speed are interpolated from different towers based on wind resource data from adjacent wind towers; Among them, the process of interpolating wind direction from different towers based on the wind resource data of adjacent wind towers is as follows: The neighboring wind tower is used as the reference wind tower, and the wind direction data of the target wind tower is divided into different sectors according to the wind direction data of the reference wind tower. The change from the reference wind direction to the target wind direction at each moment in each sector is calculated respectively, and the average offset of each sector is calculated. The interpolation value at the corresponding moment can be obtained by adding the average sector offset to the reference wind direction.

[0042] For example, when dividing sectors, you first need to specify the number of sectors to be divided, which is generally 16 sectors. When set to 16 sectors, 360° is evenly divided into 16 parts, and the span of each sector is 22.5°. The first sector is 348.75-11.25 degrees, that is, it extends 11.25 degrees to the left and right from 0 degrees. The second sector is 22.5 degrees, which extends 11.25 degrees to the left and right, with a range of 11.25-33.75 degrees, and so on. For the target wind direction data, the corresponding sector can be determined based on the wind direction value of the reference wind tower at the corresponding time.

[0043] In other embodiments, the process of interpolating wind speed from different towers based on wind resource data of adjacent wind towers is as follows: The wind speed data of the target wind tower are divided into several sectors according to the interpolated wind direction data of the target wind tower. The neighboring wind towers are used as reference wind towers, and the relationship between the wind speed data of the target wind tower and the reference wind speed of the reference wind tower is fitted sector by sector. Based on the fitted wind speed relationship of each sector, the wind speed data in each sector of the target wind tower are interpolated.

[0044] In the application of wind speed interpolation, different fitting methods can be flexibly selected according to the specific data characteristics and interpolation requirements to improve the prediction accuracy. For example, the linear least squares method is simple and easy to use, and is suitable for wind speed data with obvious linear relationships. However, when the wind speed fluctuates greatly, the overall least squares method will be more effective because it can simultaneously consider the measurement errors of the target wind speed and the reference wind speed. For situations where the wind speed distribution is close to the Weibull distribution, the use of Weibull fitting can better reflect the probability characteristics of the wind speed, and is especially suitable for the fitting and prediction of long-term wind speed data.

[0045] As an implementation method, wind resource data is corrected to representative wind resource data; it is determined whether the representative wind resource data has a tower shadow effect, and if not, the final interpolated and corrected target wind tower wind resource data is obtained; otherwise, the representative wind resource data is subjected to tower shadow correction, and the result after tower shadow correction is used as the final interpolated and corrected target wind tower wind resource data.

[0046] Among them, if there are two wind measurement channels at the same height of the target wind measurement tower, this phenomenon is called the tower shadow effect. At this time, the tower shadow correction method needs to be used to correct the wind speed data at this height.

[0047] It should be noted here that the tower shadow correction can be based on the actual situation by using the method of correcting according to the area affected by the tower shadow, taking the maximum value method, and selecting the channel with the larger average value in the two wind speed channels. If the tower shadow influence is more obvious in wind speed channels A and B and the tower shadow area can be accurately divided, the correction method according to the area affected by the tower shadow is preferred. This method can correct the specific area affected by the tower shadow with higher accuracy. If it is difficult to clearly define the area affected by the tower shadow and the wind speed fluctuates greatly, it is recommended to use the maximum value method. This method is simple and can avoid the influence of the tower shadow on the reduction of wind speed, and is suitable for real-time correction.

[0048] Specifically, the process of correction according to the area affected by the tower shadow is as follows: Divide the two wind speed channels at the same height into several sectors according to the wind direction; calculate the ratio of the wind speeds of channels A and B at the same time for each sector; calculate the median of the ratio within each sector; For the values ​​obtained in the previous step, take the central angle of the sector where the maximum value is located, and take 15 degrees to the left and right, and regard it as the area where channel B is affected by the tower shadow. The wind speed channel B value in this area is corrected by the corresponding channel A value; similarly, take the sector where the minimum value is located, select the corresponding range, and regard it as the area where channel A is affected by the tower shadow.

[0049] Specifically, the maximum value method is used, that is, at each moment, the larger value of channels A and B is selected as the value at that moment.

[0050] In this embodiment, in the wind resource report generation module 103, a report template is set to be composed of a plurality of sections, each section has two states: hidden and displayed, and the position of each section in the report template is adjustable.

[0051] This enables the generation of personalized wind resource reports suitable for different wind farm planning needs.

[0052] The wind resource report supports the following data formats: (1) digital format, including integers and decimals, for data processing and analysis; (2) chart format, such as line charts, bar charts, scatter plots, etc., for intuitive display of data and analysis; (3) table format, such as Excel tables, CSV files, etc., for storage and management of data. For example, Figure 4 As shown, for the site data report, the site information can be retrieved, including the location, height, direction and other information of the wind tower, as well as the layout and installation of the wind tower; the corresponding charts are obtained based on the processed data and intelligent analysis.

[0053] In one or more embodiments, based on the wind resource intelligent analysis and processing system as described above, a wind resource intelligent analysis and processing method is provided, which includes: Step 1: Import wind resource data into a pre-created project set, and store the wind resource data in the project set using observation sites, wind towers, and observation times as key values; Step 2: retrieve the wind resource data in the project set to be processed, use the set prior rules to perform a primary detection on the retrieved wind resource data in the project set, and mark the abnormal data; use the trained data evaluation model to perform a secondary quality evaluation on the marked abnormal data to determine the type of abnormal data; match the wind resource data according to the type of abnormal data, interpolate and correct it until the interpolated and corrected wind resource data meets the preset quality requirements; Step 3: Use any key value from the observation site, wind tower, and observation time as the index condition, retrieve the corresponding wind resource data and fill it into the set report template, and generate the matching wind resource report with one click.

[0054] This embodiment uses a priori rule settings to perform a primary detection on wind resource data, and then uses a data evaluation model to perform a secondary quality evaluation on the initially marked abnormal data. The two-detection mechanism is used to timely discover and resolve data quality issues, thereby improving the integrity, accuracy and credibility of the data, thereby improving the quality and reliability of the data.

[0055] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wind resource intelligent analysis and processing system, characterized in that: include: Wind resource data import module, wind resource data processing module and wind resource report generation module; The wind resource data import module is used to import the wind resource data into a pre-created project set, and store the wind resource data in the project set using the observation site, wind tower and observation time as key values; The wind resource data processing module is used to retrieve the wind resource data in the project set to be processed, perform a primary detection on the retrieved wind resource data in the project set using the set prior rules, and mark abnormal data; perform a secondary quality assessment on the marked abnormal data using the trained data assessment model to determine the type of abnormal data; match, interpolate and correct the wind resource data according to the type of abnormal data until the interpolated and corrected wind resource data meets the preset quality requirements; The wind resource report generation module is used to use any key value of the observation site, wind tower and observation time as an index condition, retrieve the corresponding wind resource data and fill it into the set report template, and generate a matching wind resource report with one click.

2. The wind resource intelligent analysis and processing system according to claim 1, characterized in that: In the wind resource report generation module, the report template is set to consist of a number of sections, each section has two states: hidden and displayed, and the position of each section in the report template is adjustable.

3. The wind resource intelligent analysis and processing system according to claim 1, characterized in that: The wind resource data includes wind tower data, wind radar data, mesoscale weather data and meteorological station data.

4. The wind resource intelligent analysis and processing system according to claim 1, characterized in that: The types of abnormal data include missing wind resource data of the same tower at different heights and missing wind direction and / or wind speed in the wind resource data of the target wind measurement tower.

5. The wind resource intelligent analysis and processing system according to claim 4, characterized in that: According to the wind resource data of the available height at the target wind tower setting time, the wind resource data of different heights at the current time are interpolated on the same tower; the wind resource data of different heights interpolated on the same tower include wind speed data, wind direction data and temperature data.

6. The wind resource intelligent analysis and processing system according to claim 5, characterized in that: The process of interpolating wind speed data at different heights on the same tower is as follows: Determine whether there is at least one valid wind speed at the target height at a certain moment. If so, calculate the average of these valid wind speeds as the wind speed value of the target height at that moment; If there is no effective wind speed at the target height at a certain moment, determine whether there is an effective wind speed other than the target height at that moment; When there are effective wind speeds at at least two heights other than the target height at that moment, the Pearson correlation coefficient between the target height and the channels with effective wind speeds at that moment is calculated respectively, and the effective wind speeds of a set number of channels with the highest correlation are selected for same-tower interpolation of the wind speed data of the target height at that moment; when interpolating at the same tower, the wind shear coefficient at that moment is calculated using the selected effective wind speed, and the wind shear profile is calculated based on the wind shear coefficient at that moment, thereby inferring the wind speed data of the target height at that moment; When there is an effective wind speed at a height other than the target height at that moment, the wind shear profile is calculated using the average wind shear coefficient observed in the known wind resource data set at similar times and dates in the day, and then the wind speed data at the target height at that moment is extrapolated.

7. The wind resource intelligent analysis and processing system according to claim 5, characterized in that: The process of interpolating wind direction data / temperature data at different heights on the same tower is as follows: Determine the amount of wind direction data / temperature data at the target height at a certain moment; When the number of wind direction data / temperature data at the target height at a certain moment is at least two, the average of these wind direction data / temperature data is calculated as the wind direction data / temperature data at the target height at that moment; When there is only one wind direction data / temperature data at the target height at a certain moment, the wind direction data / temperature data at the target height at that moment are inferred by using the average wind direction deflection / temperature gradient observed at similar times and days in a known wind resource data set.

8. The wind resource intelligent analysis and processing system according to claim 4, characterized in that: Perform interpolation of wind direction and / or wind speed based on wind resource data from adjacent wind towers; Among them, the process of interpolating wind direction from different towers based on the wind resource data of adjacent wind towers is as follows: The neighboring wind tower is used as the reference wind tower, and the wind direction data of the target wind tower is divided into different sectors according to the wind direction data of the reference wind tower; the change from the reference wind direction to the target wind direction at each moment in each sector is calculated respectively, and the average offset of each sector is calculated; the interpolation value at the corresponding moment can be obtained by adding the average offset of the sector to the reference wind direction; Or the process of interpolating wind speed from different towers based on the wind resource data of adjacent wind towers is: The wind speed data of the target wind tower are divided into several sectors according to the interpolated wind direction data of the target wind tower. The neighboring wind towers are used as reference wind towers, and the relationship between the wind speed data of the target wind tower and the reference wind speed of the reference wind tower is fitted sector by sector. Based on the fitted wind speed relationship of each sector, the wind speed data in each sector of the target wind tower are interpolated.

9. The wind resource intelligent analysis and processing system according to claim 1, characterized in that: The interpolated wind resource data are corrected into representative wind resource data using the mesoscale weather data in the wind resource data; it is determined whether the representative wind resource data has a tower shadow effect, and if not, the final interpolated and corrected target wind resource data of the wind tower is obtained; otherwise, the representative wind resource data is corrected for the tower shadow, and the result of the tower shadow correction is used as the final interpolated and corrected target wind resource data of the wind tower.

10. A method for intelligent analysis and processing of wind resources, characterized in that: A wind resource intelligent analysis and processing system based on any one of claims 1 to 9; comprising: Import wind resource data into a pre-created project set, and store the wind resource data in the project set using observation sites, wind towers, and observation times as key values; Retrieve the wind resource data in the project set to be processed, perform a primary test on the wind resource data in the retrieved project set using the set of prior rules, and mark the abnormal data; perform a secondary quality assessment on the marked abnormal data using the trained data assessment model to determine the type of abnormal data; match, interpolate and correct the wind resource data according to the type of abnormal data until the interpolated and corrected wind resource data meets the preset quality requirements; Using any key value of the observation site, wind tower and observation time as the index condition, the corresponding wind resource data is retrieved and filled into the set report template, and the matching wind resource report is generated with one click.

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