Multi-source data fusion-based wind resource assessment method, medium and system

Abstract

The invention discloses a wind resource assessment method based on multi-source data fusion, a medium and a system. The method comprises the following steps: S1, acquiring project basic information and multi-source data of a wind power project; the project basic information comprises a project name, a geographic position and topographic information; the multi-source data comprises anemometer tower data, mesoscale data and CFD data; s2, preprocessing the anemometer tower data to generate corrected anemometer data; s3, performing multi-source data fusion on the corrected wind measurement data, mesoscale data and CFD data, and performing wind resource parameter analysis to obtain a machine site wind resource evaluation result; s4, performing reduction and uncertainty analysis based on the wind resource evaluation result of the machine site, and calculating the generating capacity; and S5, generating a wind resource assessment report according to the power generation amount calculation result and the project basic information. According to the method, the wind resource state of each machine site in the wind field can be evaluated more accurately.

Description

Technical Field

[0001] This invention mainly relates to the field of wind power technology, specifically to a wind resource assessment method, medium, and system based on multi-source data fusion. Background Technology

[0002] Currently, wind resource assessment methods are relatively simplistic, generally relying solely on meteorological tower data and mesoscale data to evaluate wind farm resource development. This approach is limited, and when refined to the specific wind farm turbine locations, the accuracy of the estimates is significantly flawed. It fails to accurately estimate the wind resource status of individual turbine locations, further leading to estimation errors in normal power generation. This negatively impacts the assessment of wind farm power generation and has a substantial influence on project development. Summary of the Invention

[0003] To address the technical problems existing in the prior art, the present invention provides a wind resource assessment method, medium, and system based on multi-source data fusion for accurately assessing the wind resource status of each turbine location within a wind farm.

[0004] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A wind resource assessment method based on multi-source data fusion includes the following steps: S1. Obtain basic project information and multi-source data for the wind power project; the basic project information includes project name, geographical location, and topographic information; the multi-source data includes wind measurement tower data, mesoscale data, and CFD data. S2. Preprocess the wind measurement tower data to generate corrected wind measurement data; S3. The corrected wind measurement data generated in step S2 is fused with the mesoscale data and CFD data to perform wind resource parameter analysis and obtain the wind resource assessment results of the turbine location. S4. Based on the wind resource assessment results of the turbine location obtained in step S3, perform reduction and uncertainty analysis, and calculate the power generation; S5. Based on the power generation calculation results and basic project information from step S4, generate a wind resource assessment report.

[0005] Preferably, the preprocessing in step S2 includes: Representative year selection: Based on the wind measurement duration of the wind measuring tower, the candidate representative year is selected using the sliding method; Data filtering: Filter the wind measurement data according to the filtering rules; Data interpolation: The filtered data is interpolated in the order of adjacent high-rise buildings with the same wind measurement data, then the nearest wind measurement data, and finally the mesoscale data. Interpolation compares multiple interpolation methods and selects the optimal interpolation method by combining multi-directional fitting evaluation indicators. The integrity rate of the wind tower data before and after each step of interpolation and the correlation with the interpolation data source are recorded. Representative Year Correction: Correction is performed on the representative year wind measurement data after interpolation. During correction, the difference and ratio between the mesoscale data's representative year average wind speed and the long-term average wind speed are first calculated. The wind measurement data are divided into multiple sectors, and the wind measurement data and mesoscale data of each sector are fitted separately to obtain the linear fitting relationship and correlation coefficient between the mesoscale data and the wind measurement data. Correction is performed on sectors with correlation coefficients greater than a specified threshold. Correction is performed based on the difference between the mesoscale data's representative year average wind speed and the long-term average wind speed and the linear fitting relationship between the mesoscale data and the wind measurement data, or based on the ratio between the mesoscale data's representative year average wind speed and the long-term average wind speed.

[0006] Preferably, the filtering rules in the data screening are as follows: filtering out data that exceeds a reasonable threshold range; filtering out data whose long-term trends do not conform to the rules; and filtering out data with poor correlation between different high-level data.

[0007] Preferably, the wind resource parameters in step S3 include at least one of wind speed, wind frequency, wind direction distribution, wind shear, air density, turbulence, and extreme winds.

[0008] Preferably, in step S3, the wind resource parameter analysis includes the following process: For wind speed, calculate the average wind speed at each high floor; For wind frequency, wind speed is divided into wind speed segments according to a specified width, and the wind speed frequency and wind energy frequency of each wind speed segment are statistically analyzed. Based on the wind speed frequency obtained from statistics, Weibull fitting is performed, and the optimal fitting method is selected by combining multiple fitting evaluation indicators to obtain the scale parameter k and the shape parameter A. For wind direction distribution, divide the wind direction into sectors according to the specified width, count the wind speed frequency and wind energy frequency in each sector, and draw wind rose diagrams and wind energy rose diagrams. For wind shear, calculate the wind shear index between two high-altitude wind speeds and fit the wind shear index for multiple high-altitude wind speeds. For air density, the air density and average air density at each moment are calculated based on the air temperature and air pressure data from the meteorological tower. For turbulence, the wind speed is divided into wind speed segments according to a specified width, and the average value of turbulence intensity and the standard deviation of turbulence intensity for each wind speed segment are calculated.

[0009] Preferably, in step S3, the wind resource parameter analysis further includes wind resource analysis at the turbine location: based on the CFD calculation results, wind parameter data for each turbine location are obtained, and the wind speed, wind frequency, wind direction, turbulence, and air density data for each turbine location are statistically analyzed based on the results.

[0010] Preferably, the specific process of step S4 is as follows: Based on the basic information of the project and the wind parameter data of each turbine location calculated by CFD, various reductions are calculated. Based on the completeness rate and correlation coefficient data of the wind measurement data processing process, the uncertainty of the wind measurement data is calculated. Based on the project topography, the uncertainty of CFD simulation is calculated. The total reduction and uncertainty are obtained by combining the results of various calculations, and the power generation and corresponding reduction coefficients under different probabilities are obtained. The power generation under the target reduction factor is calculated based on the reduction and uncertainty analysis results. The power loss due to noise is calculated based on the unit noise analysis results. The power generation change of each unit is calculated based on the control strategy scheme, and finally the on-grid power is obtained.

[0011] Preferably, after generating the wind resource assessment report, the system supports conditional retrieval of wind measurement tower data and CFD data. Users can set the corresponding search range according to specific search conditions to retrieve which wind measurement tower and corresponding turbine location the data is located on, providing a quick reference for users' subsequent data analysis.

[0012] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, the computer program performing the steps of the method described above when run by a processor.

[0013] The present invention further discloses a wind resource assessment system based on multi-source data fusion, including an interconnected memory and a processor, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0014] Compared with the prior art, the advantages of the present invention are as follows: This invention provides a multi-source data fusion method that integrates and analyzes anemometer data, mesoscale data, and wind farm CFD simulation data. Compared to traditional analysis methods, it can more accurately assess the wind resource status at each turbine location within a wind farm, providing a reference for wind farm construction and serving efficient and precise modern wind power development. This invention offers report generation and data retrieval services. Users can retrieve specific anemometer data and turbine location CFD data according to their research and development needs, improving the efficiency of subsequent data evaluation. It also provides a report generation function, allowing for customized report generation for users. The system platform in this invention provides diverse data displays, including visual analysis of anemometer wind speed, wind frequency, wind direction distribution, wind shear, air density, turbulence, and extreme winds, facilitating intuitive evaluation of various technical parameters. The system in this invention provides massive data storage capacity, integrating all scattered data related to wind resource assessment, supporting historical data storage and data traceability functions, and facilitating big data assessment of wind resources. Attached Figure Description

[0015] Figure 1 This is an example diagram of a specific application of the wind resource assessment method based on multi-source data fusion of the present invention.

[0016] Figure 2 The diagram shows the selection of representative years in this invention; (a) is for cases where the duration of the wind measurement tower is less than one year; (b) is for cases where the duration of the wind measurement tower is more than one year.

[0017] Figure 3 This is a flowchart of an embodiment of the wind resource assessment method based on multi-source data fusion of the present invention.

[0018] Figure 4 This is a flowchart of the multi-source data fusion method in this invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 and Figure 3 As shown in the figure, the wind resource assessment method based on multi-source data fusion provided in this embodiment of the invention includes the following steps: S1. Obtain basic project information and multi-source data for wind power projects; basic project information includes project name, project location (geographical location) and project topography information; multi-source data includes meteorological tower data, mesoscale data and CFD data (fluid dynamics simulation data, such as high-precision, three-dimensional spatial distribution data of wind parameters such as wind speed, wind direction, and turbulence intensity obtained by simulating wind flow in the wind farm area using CFD software). In addition, it supports the inclusion of various project materials such as topographic maps and feasibility study reports.

[0021] S2. Preprocess the wind measurement tower data to generate corrected wind measurement data, and generate the turbulence matrix at the same time; Preprocessing includes the following methods: (1) Selection of representative year: Based on the wind measurement duration of the wind tower, the candidate representative year is selected by the sliding method (the candidate representative year cannot exceed the range of mesoscale data), and the following cases are used for selection: The wind measurement tower lasted less than a year, such as Figure 2 As shown in (a); The wind measurement tower lasts for exactly one year, such as Figure 2 (as shown in b) The duration of the wind measurement tower is used as the representative year.

[0022] Calculate the average wind speed for the long-term period of mesoscale data and for each candidate representative year. Calculate the difference and proportion between the average wind speed of each candidate representative year and the average wind speed for the long-term period. Select the candidate representative year with the smallest proportion as the final representative year.

[0023] (2) Data screening: The wind speed, wind direction, temperature and air pressure data of each high-rise building are screened. The screening rules are: ① Screening out data that exceeds the reasonable threshold range; ② Screening out data whose long-term trend does not conform to the rules; ③ Screening out data with poor correlation between different high-rise buildings.

[0024] (3) Data interpolation: Interpolate the filtered data. Interpolate in the order of adjacent high-rise buildings with the same wind measurement data, then the nearest wind measurement data, and finally the mesoscale data. Interpolation compares multiple interpolation methods and selects the optimal interpolation method by combining multi-directional fitting evaluation indicators. Record the completeness rate of the wind measurement tower data before and after each step of interpolation and the correlation of the interpolation data source.

[0025] (4) Representative year correction: Correct the wind measurement data of the representative year after interpolation. If the representative year is a normal wind year, no correction is required. When correcting, first calculate the difference and ratio between the average wind speed of the representative year and the average wind speed of the long-term period in the mesoscale data. Divide the wind measurement data into multiple sectors and fit the wind measurement data and mesoscale data of each sector respectively to obtain the linear fitting relationship and correlation coefficient between the mesoscale data and the wind measurement data. Correct the sector with the correlation coefficient greater than the specified threshold. Correct the wind measurement data based on the difference between the average wind speed of the representative year and the average wind speed of the long-term period in the mesoscale data and the linear fitting relationship between the mesoscale data and the wind measurement data, or correct the wind measurement data based on the ratio between the average wind speed of the representative year and the average wind speed of the long-term period in the mesoscale data.

[0026] It can export corrected wind measurement data that meets the input format requirements of the verification simulation software.

[0027] In addition, a turbulence matrix is ​​generated: wind speed is divided into wind speed segments with a specified width, and wind direction is divided into sectors with a specified width. The average turbulence intensity and relative standard deviation of turbulence intensity for each sector of each wind speed segment are calculated to obtain a turbulence intensity matrix and a turbulence intensity relative standard deviation matrix. The missing data for a certain wind speed segment in a certain mountainous area are filled according to the selected filling method, and the differences before and after filling are displayed.

[0028] It can export the turbulence intensity matrix and the relative standard deviation matrix of turbulence intensity as required by the input format of the verification simulation software.

[0029] S3. The corrected wind measurement data, turbulence matrix, mesoscale data and CFD data generated in step S2 are fused from multiple sources to perform wind resource parameter analysis and obtain the wind resource assessment results of the turbine site. The system supports multi-source data fusion for comprehensive assessment of wind farm wind resources, that is, combining wind measurement tower data, mesoscale data, and CFD data for multi-source data fusion assessment to test wind farm wind resources. Specifically, such as... Figure 4As shown, the measured data from the anemometer tower (raw wind speed and direction information) is used as input to perform quality control on the anemometer tower data, filtering out invalid data to ensure the reliability of the subsequent analysis data; Simultaneously, mesoscale data from numerical weather prediction models are incorporated; The filtered meteorological tower data is fused with mesoscale data; the temporal and spatial continuity of the mesoscale data is used to fill in the missing periods that may occur in the meteorological tower data due to equipment failure or other reasons, so as to generate a complete and continuous set of mesoscale interpolated meteorological tower data. Since the measured height of the meteorological tower is usually not completely consistent with the hub height of the wind turbine, the interpolated meteorological tower data is vertically extrapolated to convert it to the actual hub height of the wind turbine. Based on the wind speed data at the hub height, and in accordance with the relevant standards (such as IEC61400-1), the key wind characteristic parameters, namely the turbulence matrix and the relative standard deviation matrix of turbulence intensity, are further calculated. The wind condition data and wind characteristic parameters obtained from the above processing, representing a specific point (the location of the wind measurement tower), are used as input conditions and imported into the computational fluid dynamics model. The CFD model will simulate the wind flow field distribution under the influence of complex terrain (such as hills and valleys) throughout the wind farm area. Through CFD simulation, a set of high-resolution, three-dimensional flow field data covering the entire wind farm was finally obtained.

[0030] The wind resource parameters include wind speed, wind frequency, wind direction distribution, wind shear, air density, turbulence, and extreme winds. The corresponding analysis process is as follows: (1) Wind speed: Calculate the average wind speed of each high-rise building. The average wind speed can be calculated separately according to monthly average, daily average, hourly average, etc., and the annual, monthly and daily trends of wind speed variation can be displayed. The above data can also be calculated according to a specified sector and the data in that sector can be displayed.

[0031] (2) Wind frequency: Divide the wind speed into wind speed segments according to the specified width, and count the wind speed frequency and wind energy frequency of each wind speed segment. Calculate them according to the following formulas:

[0032] - Number of wind speed samples in a certain wind speed range; - Total number of wind speed samples.

[0033]

[0034] - The wind speed measured within a certain wind speed range; - Wind speeds measured across all wind speed ranges.

[0035] Based on the wind speed frequency obtained from statistics, Weibull fitting was performed using multiple methods, and the optimal fitting method was selected by combining multiple fitting evaluation indicators to obtain the scale parameter k and shape parameter A.

[0036] Specifically, wind speed distribution typically follows a Weibull distribution, with its probability density function... for:

[0037] Cumulative distribution function for:

[0038] Then a specific interval frequency for:

[0039] The actual wind speed frequency data were fitted using the maximum likelihood method, the energy method, and the lowest squares method, respectively, to obtain the scale parameter k and the shape parameter A. Calculate the correlation coefficient R between the fitting results obtained by each method and the actual data, and select the method with the largest R for fitting.

[0040] The system provides statistics on the above data in different sectors according to specified sectors, and displays the data in that sector.

[0041] The system provides statistics on the above data for different time spans (months, quarters, etc.).

[0042] (3) Wind direction distribution: Divide the wind direction into sectors according to the specified width, count the wind speed frequency and wind energy frequency in each sector, and draw the wind rose diagram and wind energy rose diagram.

[0043] (4) Wind shear: Calculate the wind shear index between the wind speeds at two different heights using the following formula. :

[0044] :high Wind speed at the location; :high Wind speed at that location.

[0045] according to The wind shear index can be fitted to wind speeds at multiple heights, and any number of high-rise data points can be selected for fitting.

[0046] You can also statistically analyze the above data in different sectors according to specified sectors and display the data in that sector.

[0047] The system supports statistical analysis of the above data within different time spans (months, quarters, etc.).

[0048] (5) Air density: Calculate the air density and average air density at each time based on the air temperature and air pressure data of the wind tower.

[0049] The system supports statistical analysis of the above data in different sectors according to specified sectors, and displays the data in that sector.

[0050] The system supports statistical analysis of the selected data within different time spans (monthly, quarterly, etc.).

[0051] (6) Turbulence: Divide the wind speed into wind speed segments according to a specified width, and statistically analyze the average turbulence intensity and standard deviation of turbulence intensity for each wind speed segment. Calculate the representative turbulence intensity using the following formula:

[0052] The system supports statistical analysis of the above data in different sectors according to specified sectors, and displays the data in that sector.

[0053] The system supports statistical analysis of the above data within different time spans (months, quarters, etc.).

[0054] (7) Extreme wind: The extreme wind of each wind tower is calculated using multiple methods.

[0055] In addition, it includes wind resource analysis for each aircraft location: based on CFD calculations, wind parameter data for each location is obtained, and statistical analysis is performed on the results for data such as wind speed, wind frequency, wind direction, turbulence, and air density. Data within different sectors can be statistically analyzed and displayed according to specified sectors.

[0056] S4. Based on the wind resource assessment results of the turbine location obtained in step S3, perform reduction and uncertainty analysis, and calculate the power generation; The reduction and uncertainty analysis includes: calculating various reductions based on the recorded basic project information and wind parameter data of each turbine location calculated by CFD; calculating the uncertainty of wind measurement data based on the completeness rate and correlation coefficient data of the wind measurement data processing process; calculating the uncertainty of CFD simulation based on the project terrain; and combining the results of various calculations to obtain the total reduction and uncertainty, and obtaining the power generation and corresponding reduction coefficients under different probabilities.

[0057] Specifically, the project's geographical location, climate conditions, turbulence level, number of turbine locations, and turbine configuration are categorized, and various reduction values ​​are obtained based on an experience database. Based on the completeness of the wind measurement data and the correlation between the wind measurement data and the reference data used in the interpolation process, the uncertainty of the wind measurement data is obtained by searching the empirical database. Calculate the horizontal and vertical distances from each location to the wind measurement tower, and, considering the complex terrain, consult an empirical database to obtain the CFD simulation uncertainty.

[0058] The total reduction is calculated based on each reduction item. :

[0059] Calculate the overall uncertainty based on the individual uncertainties. :

[0060] Calculate the reduction under different probabilities based on the basic reduction and uncertainty:

[0061] —probability The reduction below; —Probability of normal distribution The characteristic variables are obtained by looking up a table.

[0062] Power generation calculation: The power generation under the target reduction factor is calculated based on the reduction and uncertainty analysis results. The power loss due to noise is calculated based on the unit noise analysis results. The power generation change of each unit is calculated based on the control strategy scheme. Finally, the power to be fed into the grid is obtained.

[0063] Specifically, the distance between each turbine location and residents is obtained through on-site surveys or satellite image analysis, the noise impact of each turbine location on residents is obtained, and the noise loss of the turbine location is obtained after taking corresponding noise reduction measures; based on the wind parameters of each turbine location, corresponding control strategies are formulated to obtain the changes in power generation of each turbine location.

[0064] S5. Based on the power generation calculation results and basic project information from step S4, generate a wind resource assessment report.

[0065] Based on the above project information, wind measurement data processing and analysis (power generation, etc.), and the results obtained from CFD data analysis, select the system's built-in report template to generate the required wind resource assessment report.

[0066] It supports conditional retrieval of wind measurement tower data and CFD data. Users can set the corresponding search range according to specific search conditions to find out which wind measurement tower and corresponding machine location the data is located on, providing a quick reference for users' subsequent data analysis.

[0067] The multi-source data fusion method provided by this invention integrates and analyzes wind measurement tower data, mesoscale data, and wind farm CFD simulation data. Compared with traditional analysis methods, it can more accurately assess the wind resource status of each turbine site in the wind farm, provide a reference for wind farm construction, and serve efficient and accurate modern wind power development.

[0068] This invention provides report generation and data retrieval services, allowing users to retrieve specific wind measurement tower data and machine site CFD data according to their own R&D needs, thereby improving the efficiency of subsequent data evaluation; it also provides a report generation function, which can generate customized reports for users.

[0069] The system platform in this invention provides diverse data displays, including visual analysis of wind speed, wind frequency, wind direction distribution, wind shear, air density, turbulence, and extreme winds from the wind measurement tower, which helps users to intuitively evaluate various technical parameters.

[0070] The system in this invention provides massive data storage capacity, can integrate all scattered data related to wind resource assessment, supports historical data storage and data traceability functions, and is conducive to big data assessment of wind resources.

[0071] This invention employs a joint evaluation method combining CFD, meteorological tower data, and mesoscale data to accurately assess wind resources at wind turbine sites. It utilizes integrated approaches to enhance the accuracy of wind resource assessment by fusing multi-source data. Furthermore, the integrated evaluation methods, combining multi-source data and modular calculations, improve computational efficiency and accuracy. This effectively enhances the efficiency of wind resource assessment for wind farms and provides assistance for wind farm design.

[0072] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, the computer program performing the steps of the method described above when run by a processor.

[0073] The present invention further discloses a wind resource assessment system based on multi-source data fusion, including an interconnected memory and a processor, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0074] The medium and system of the present invention, corresponding to the methods described above, also have the advantages described above.

[0075] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0076] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A wind resource assessment method based on multi-source data fusion, characterized in that, The method comprises the steps of: S1, obtaining project basic information and multi-source data of a wind power project; the project basic information comprises a project name, a geographic position and terrain information; the multi-source data comprises wind tower data, mesoscale data and CFD data; S2, preprocessing the wind tower data to generate corrected wind data; S3, performing multi-source data fusion on the corrected wind data generated in step S2 and the mesoscale data and CFD data, performing wind resource parameter analysis, and obtaining wind resource assessment results of a site; S4, based on the wind resource assessment results of the site obtained in step S3, performing reduction and uncertainty analysis, and calculating power generation; S5, generating a wind resource assessment report according to the power generation calculation results in step S4 and the project basic information.

2. The wind resource assessment method based on multi-source data fusion according to claim 1, characterized in that, The preprocessing in step S2 comprises: representative year selection: according to the wind measurement time length of the wind tower, a sliding method is used to select a representative year; data screening: the wind measurement data are screened according to a screening rule; data interpolation: the data after screening are interpolated, and the interpolation is performed in the order of adjacent high layers of the wind measurement data, adjacent wind measurement data, and finally mesoscale data. The interpolation is compared with multiple interpolation methods, and the optimal interpolation method is selected according to a multi-direction fitting evaluation index. The completeness rate before and after each step of interpolation of the wind tower data and the correlation of the interpolation data source are recorded; representative year correction: the wind measurement data of the representative year after interpolation are corrected. When correcting, the difference and proportion of the average wind speed of the representative year and the long-term period of the mesoscale data are calculated. The wind measurement data are divided into multiple sectors. The wind measurement data and the mesoscale data of each sector are fitted respectively to obtain the linear fitting relationship and the correlation coefficient of the mesoscale data and the wind measurement data. The sectors with a correlation coefficient greater than a specified threshold value are corrected. The correction is performed according to the difference of the average wind speed of the representative year and the long-term period of the mesoscale data and the linear fitting relationship of the mesoscale data and the wind measurement data, or according to the proportion of the average wind speed of the representative year and the long-term period of the mesoscale data.

3. The wind resource assessment method based on multi-source data fusion according to claim 2, characterized in that, The screening rule in the data screening is to screen out data outside a reasonable threshold range, data with a long-term trend not meeting a rule, and data with poor correlation between different high layers.

4. The wind resource assessment method based on multi-source data fusion according to claim 1 or 2 or 3, characterized in that, In step S3, the specific process of multi-source data fusion is as follows: taking the wind tower measured data as input, performing quality control on the wind tower data, and screening out invalid data; at the same time, introducing mesoscale data from a numerical weather prediction model; fusing the screened wind tower data and the mesoscale data; using the continuity of the mesoscale data in time and space to fill in the missing period of the wind tower data due to equipment failure, and generating a complete and continuous set of wind tower data after mesoscale interpolation; vertically extrapolating the interpolated wind tower data and converting to the actual hub height of the wind turbine; based on the wind speed data at the hub height, further calculating key wind characteristic parameters, including a turbulence matrix and a turbulence intensity relative standard deviation matrix; The wind condition data and wind characteristic parameters of the wind tower position are imported into a computational fluid dynamics model as input conditions to simulate the wind flow field distribution in the entire wind farm range under the influence of complex terrain. Through CFD simulation, a set of high-resolution, three-dimensional flow field data covering the entire wind farm is finally obtained.

5. The wind resource assessment method based on multi-source data fusion of claim 4, wherein, The wind resource parameters in step S3 include at least one of wind speed, wind frequency, wind direction distribution, wind shear, air density, turbulence and extreme wind; and the wind resource parameter analysis includes the following processes: For wind speed, the average wind speed of each high layer is calculated; For wind frequency, the wind speed is divided into wind speed sections according to a specified width, and the wind speed frequency and wind energy frequency of each wind speed section are counted; According to the counted wind speed frequency, Weibull fitting is performed, the optimal fitting mode is selected in combination with a plurality of fitting evaluation indexes, and scale parameter k and shape parameter A are obtained; For wind direction distribution, the wind direction is divided into sectors according to a specified width, and the wind speed frequency and wind energy frequency in each sector are counted to draw a wind direction rose diagram and a wind energy rose diagram; For wind shear, the wind shear index between the wind speeds of two high layers is calculated, and the wind shear index of the multi-high-layer wind speed is fitted; For air density, the air density at each time and the average air density are calculated according to the wind tower temperature and pressure data; For turbulence, the wind speed is divided into wind speed sections according to a specified width, and the average value and standard deviation of the turbulence intensity of each wind speed section are counted.

6. The wind resource assessment method based on multi-source data fusion of claim 5, wherein, In step S3, the wind resource parameter analysis further includes airport wind resource analysis: according to the CFD calculation results, wind parameter data of each airport are obtained, and wind speed, wind frequency, wind direction, turbulence and air density data of each airport are statistically analyzed according to the results.

7. The wind resource assessment method based on multi-source data fusion according to claim 1 or 2 or 3, characterized in that, The specific process of step S4 is as follows: According to the project basic information, the wind parameter data of each airport calculated by CFD, the wind data uncertainty is calculated according to the completeness rate and correlation coefficient data of the wind data processing process, the CFD simulation uncertainty is calculated according to the project terrain, and the total reduction and uncertainty are obtained by comprehensively calculating the results to obtain the power generation capacity under different probabilities and the corresponding reduction coefficient; According to the reduction and uncertainty analysis results, the power generation capacity under the target reduction coefficient is calculated, the noise loss power is calculated according to the airport noise analysis results, and the change of power generation capacity of each airport is calculated according to the control strategy scheme, and finally the on-grid power is obtained.

8. The wind resource assessment method based on multi-source data fusion according to claim 1 or 2 or 3, characterized in that, After generating the wind resource evaluation report, the wind tower data and CFD data are supported for conditional retrieval, and the user can set the corresponding retrieval range according to the specific retrieval conditions to retrieve which wind tower and corresponding airport point the data is located in, thereby providing a quick reference for the user's later data analysis.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, performs the steps of the method of any one of claims 1-8. 10.A wind resource assessment system based on multi-source data fusion, comprising a memory and a processor connected to each other, and a computer program is stored on the memory, characterized in that, The computer program, when executed by a processor, performs the steps of the method of any one of claims 1-8.

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