Wind resource measurement and evaluation method and system

Abstract

The invention discloses a wind resource measurement and evaluation method and system, and relates to the technical field of wind energy resource evaluation, and the method comprises the steps: collecting data through a sensor, and carrying out the preprocessing; performing data fusion based on the preprocessed data; performing wind resource evaluation through the fused data; and carrying out visual display on the evaluation result. Through cooperative work of multiple types of sensors and a dynamic data fusion technology based on weight distribution and Kalman filtering, the problems that in the prior art, wind resource measurement precision is insufficient, complex terrain area measurement adaptability is poor, and the space coverage range is limited are solved; through comprehensive application of wind power density calculation and a wind energy evaluation model, high-precision simulation and quantification results of wind speed distribution, wind power density distribution and wind direction frequency in a target area are provided; through the design of the visualization and interaction module, a user can visually check and dynamically adjust the evaluation data, thereby optimizing the planning and design of the wind power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of wind energy resource assessment, and in particular to a wind resource measurement and assessment method and system. Background Art

[0002] With the increasing global demand for clean energy, wind energy, as an important renewable energy source, has become a research hotspot and key development area in the power industry. However, the existing wind resource measurement and assessment technologies have significant limitations: traditional measurement methods mainly rely on observation data from meteorological stations, which have limited spatial coverage and cannot provide three-dimensional wind resource information under complex terrain; although the wind resource assessment method based on numerical simulation has a good theoretical basis, its accuracy is limited by the quality of model parameters and input data; although new measurement equipment such as lidar can provide high-resolution data, it is difficult for a single device to fully cover the target area. In addition, the existing multi-source data fusion technology lacks dynamic prediction and spatiotemporal consistency correction, resulting in the evaluation results cannot fully reflect the spatial distribution characteristics and temporal variation laws of regional wind resources. Therefore, the existing technology is difficult to meet actual needs in application scenarios such as complex terrain and offshore wind farms, which restricts the planning, design and operation efficiency of wind farms. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to solve the problems of insufficient wind resource measurement accuracy, limited spatial coverage and poor adaptability in complex terrain in the existing technology by using a wind resource measurement and evaluation method with multi-source data fusion, utilizing technical means combining multiple types of sensors such as wind measurement towers, lidar, satellite remote sensing and drones.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a method for measuring and evaluating wind resources, comprising the following steps:

[0006] Use sensors to collect data for pre-processing;

[0007] Perform data fusion based on preprocessed data;

[0008] Conduct wind resource assessment through fused data;

[0009] Visualize the evaluation results.

[0010] As a preferred solution of the wind resource measurement and evaluation method described in the present invention, the method of collecting data using sensors for preprocessing includes:

[0011] By laying out wind towers, make sure that the installation location of the wind towers covers the representative terrain in the target wind farm area, set the height of the wind towers to be more than 1.5 times the height of the wind turbine hub, and install wind speed sensors, wind direction sensors, temperature sensors and air pressure sensors on the wind towers to collect wind speed, wind direction, temperature and air pressure data;

[0012] Install the lidar equipment at a commanding point in the area to scan the target area in the horizontal and vertical directions, and collect wind speed and wind direction distribution data at different altitudes; obtain the digital elevation model of the target area through satellite remote sensing equipment to characterize the terrain features, obtain cloud cover images to reflect the regional meteorological conditions, and obtain thermal infrared data to provide surface temperature information; use drones equipped with wind speed sensors and temperature sensors to conduct low-altitude flight measurements in complex terrain areas according to the set flight route, and collect local wind speed, wind direction and temperature data that are not covered by fixed equipment.

[0013] As a preferred solution of a wind resource measurement and evaluation method described in the present invention, the data preprocessing includes:

[0014] Convert the raw data collected from wind towers, lidar, satellite remote sensing and drones into a standardized time series format;

[0015] For the data collected by the wind tower, the 3σ criterion is used to eliminate outliers beyond the normal range based on the mean and standard deviation of each group of wind speed and wind direction data. For the wind speed distribution data collected by the lidar, random high-frequency noise is removed by a low-pass filter, and low-frequency offset is eliminated by a high-pass filter. For the terrain elevation data collected by satellite remote sensing and the wind speed data collected by the drone, interpolation algorithms are used to supplement the missing data points that occur during the collection process. Based on the collection timestamp of each sensor, the data are aligned in chronological order to make the wind speed, wind direction, temperature and terrain data have time synchronization.

[0016] As a preferred solution of the wind resource measurement and evaluation method described in the present invention, wherein: the data fusion based on the preprocessed data includes:

[0017] Map the cleaned and aligned wind tower data, lidar data, satellite remote sensing data and drone data in spatial dimensions, standardize each data source according to the global coordinate system and map it to the three-dimensional model of the target wind farm; set weight values ​​based on the measurement accuracy, timeliness and regional coverage of each data source, with wind tower data having a higher weight than drone data, and lidar data having a higher weight in terms of height distribution;

[0018] The wind speed and wind direction data are fused by weighted average method, and the fusion value calculation formula is:

[0019]

[0020] Among them, F i is the measurement value of a single data source, w i is the weight of the current data source;

[0021] The Kalman filter algorithm is used to dynamically adjust the preliminary fused data, and the fusion results are corrected through prediction equations and observation equations to generate a comprehensive data set reflecting the wind speed, wind direction and temperature at different heights and regional ranges of the target wind farm.

[0022] As a preferred embodiment of the wind resource measurement and evaluation method described in the present invention, the wind resource evaluation through the fused data includes: calculating the air density according to the wind speed, temperature and air pressure data contained in the fused data, and the calculation formula is:

[0023]

[0024] Among them, ρ is air density, p is air pressure, R is gas constant, and T is temperature;

[0025] The wind power density is calculated based on the air density, wind speed and impeller diameter of the fan. The calculation formula is:

[0026] P=0.5.ρ·A·V 3

[0027] Among them, A is the swept area of ​​the fan, and V is the wind speed;

[0028] Combined with the terrain roughness and wind direction frequency data in the fused data, the wind power density is input into the wind energy assessment model WASP or WindPRO. The wind power density distribution and wind speed frequency distribution in the target wind farm area are obtained through model operation. The wind speed range, wind power change law and wind energy utilization characteristics affected by terrain are extracted based on the model assessment data.

[0029] As a preferred solution of the wind resource measurement and evaluation method described in the present invention, wherein: the visual display of the evaluation results includes:

[0030] The wind speed, wind power density and wind direction frequency results are imported into GIS, and the wind speed contour map, wind power density distribution map and wind direction frequency rose diagram are generated using the GIS platform, and the maps are superimposed on the digital elevation model to form a three-dimensional spatial display; the time series changes of wind speed and wind power density are dynamically displayed through bar charts and line charts through the data visualization platform; users can select any area in the map, view the wind speed and wind power density distribution of the current area through the interactive function, and highlight the detailed data of the selected area.

[0031] As a preferred solution of a wind resource measurement and evaluation method described in the present invention, wherein: the user interaction function includes:

[0032] After the user selects the target area through the visual map, the wind energy resource data of the selected area is generated in real time based on the evaluation results, including wind speed curve, wind power density distribution and wind direction frequency chart;

[0033] After the user adjusts the wind turbine parameters, including impeller diameter, hub height and layout density, the system updates the wind resource distribution map, recalculates the power generation forecast in the area, and integrates the optimized evaluation data into a report that includes wind turbine layout recommendations, optimization data and forecast results.

[0034] Another object of the present invention is to provide a wind resource measurement and evaluation system, which can collect wind speed, wind direction, temperature and air pressure data through multiple types of sensors, and combine multi-source data preprocessing and dynamic fusion technology to obtain wind resource distribution information in the target area at different heights and time ranges, thereby solving the technical problems in the prior art of single measurement method, low data fusion accuracy and evaluation results that cannot accurately reflect the wind resource characteristics of complex areas.

[0035] In order to solve the above technical problems, the present invention provides the following technical solutions: a wind resource measurement and evaluation system, comprising:

[0036] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned wind resource measurement and evaluation method when executing the computer program.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the wind resource measurement and evaluation method as described above.

[0038] Beneficial effects of the present invention: The present invention solves the problems of insufficient wind resource measurement accuracy, poor adaptability in complex terrain areas, and limited spatial coverage in the prior art through the collaborative work of multiple types of sensors, combined with preprocessing methods for data cleaning, format unification, and time and space alignment, as well as dynamic data fusion technology based on weight distribution and Kalman filtering; through the comprehensive application of wind power density calculation and wind energy assessment model, high-precision simulation and quantitative results of wind speed distribution, wind power density distribution, and wind direction frequency in the target area are provided; through the design of visualization and interactive modules, users can intuitively view and dynamically adjust assessment data, thereby optimizing the planning and design of wind farms. The present invention improves the accuracy and applicability of wind resource measurement and enhances the comprehensiveness and dynamism of assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0040] Figure 1 An overall flow chart of a wind resource measurement and evaluation method provided for the first embodiment of the present invention;

[0041] Figure 2 A structural diagram of a wind resource measurement and evaluation system provided for the second embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] Example 1, reference Figure 1 According to an embodiment of the present invention, a method for measuring and evaluating wind resources is provided, comprising:

[0044] Use sensors to collect data for preprocessing; perform data fusion based on the preprocessed data; conduct wind resource assessment based on the fused data; and visualize the assessment results.

[0045] By laying out wind towers, make sure that the installation location of the wind towers covers the representative terrain in the target wind farm area, set the height of the wind towers to be more than 1.5 times the height of the wind turbine hub, and install wind speed sensors, wind direction sensors, temperature sensors and air pressure sensors on the wind towers to collect wind speed, wind direction, temperature and air pressure data;

[0046] Install the lidar equipment at a commanding point in the area to scan the target area in the horizontal and vertical directions, and collect wind speed and wind direction distribution data at different altitudes; obtain the digital elevation model of the target area through satellite remote sensing equipment to characterize the terrain features, obtain cloud cover images to reflect the regional meteorological conditions, and obtain thermal infrared data to provide surface temperature information; use drones equipped with wind speed sensors and temperature sensors to conduct low-altitude flight measurements in complex terrain areas according to the set flight route, and collect local wind speed, wind direction and temperature data that are not covered by fixed equipment.

[0047] Convert the raw data collected from wind towers, lidar, satellite remote sensing and drones into a standardized time series format;

[0048] For the data collected by the wind tower, the 3σ criterion is used to eliminate outliers beyond the normal range based on the mean and standard deviation of each group of wind speed and wind direction data. For the wind speed distribution data collected by the lidar, random high-frequency noise is removed by a low-pass filter, and low-frequency offset is eliminated by a high-pass filter. For the terrain elevation data collected by satellite remote sensing and the wind speed data collected by the drone, interpolation algorithms are used to supplement the missing data points that occur during the collection process. Based on the collection timestamp of each sensor, the data are aligned in chronological order to make the wind speed, wind direction, temperature and terrain data have time synchronization.

[0049] Map the cleaned and aligned wind tower data, lidar data, satellite remote sensing data and drone data in spatial dimensions, standardize each data source according to the global coordinate system and map it to the three-dimensional model of the target wind farm; set weight values ​​based on the measurement accuracy, timeliness and regional coverage of each data source, with wind tower data having a higher weight than drone data, and lidar data having a higher weight in terms of height distribution;

[0050] The wind speed and wind direction data are fused by weighted average method, and the fusion value calculation formula is:

[0051]

[0052] Among them, F i is the measurement value of a single data source, w i is the weight of the current data source;

[0053] The Kalman filter algorithm is used to dynamically adjust the preliminary fused data, and the fusion results are corrected through prediction equations and observation equations to generate a comprehensive data set reflecting the wind speed, wind direction and temperature at different heights and regional ranges of the target wind farm.

[0054] It is further explained that:

[0055] The Kalman filter algorithm is used to dynamically adjust the initially fused data. Specifically, based on the cleaned and aligned wind tower data, lidar data, satellite remote sensing data and drone data, these data are sorted by time dimension, and a serialized data input stream is established with timestamp as index, and weights are assigned according to the characteristics of each data source. The multi-source data is standardized to the global coordinate system through spatial mapping, and combined with the three-dimensional grid model of the target wind farm, the data corresponds to each grid point in the target area in the spatial dimension. On this basis, the wind speed and wind direction data are fused by the weighted average method to calculate the fusion result. Among them, the wind tower data has a higher weight in the weighting due to its long-term observation stability, and the lidar data has a higher weight in the spatial distribution fusion due to its high distribution fineness. The drone data has a certain weight in the dynamic adjustment of the local area due to its strong flexibility.

[0056] After the fusion data is generated, the Kalman filter algorithm is used to dynamically adjust the data at each moment in the time series. The Kalman filter uses the wind speed, wind direction and temperature of the grid point as state variables, combines the predicted value of the current grid point (estimated based on the state of the previous moment and the possible change trend in the time interval) and the actual observation value of the sensor (the current value in the fusion data), and realizes the dynamic adjustment of the state estimation through weighted correction. The correction process combines the covariance of the sensor measurement noise and the credibility of the predicted value to calculate and finally generate the corrected state value. After the state estimation is completed at each moment, the adjusted wind speed, wind direction and temperature values ​​are written into the comprehensive data set of the grid point, and the results are fed back to the subsequent time series processing steps for dynamic iteration.

[0057] This adjustment process ensures that the changes in wind speed, wind direction and temperature at each grid point in the time series are consistent with the dynamic characteristics of the actual wind resources in the wind farm, while ensuring that the spatial distribution is consistent with the high-resolution match of the lidar and drone data. The resulting comprehensive dataset can accurately reflect the wind resource status at different heights and regional ranges within the target wind farm area.

[0058] The air density is calculated based on the wind speed, temperature and air pressure data contained in the fused data. The calculation formula is:

[0059]

[0060] Among them, ρ is air density, p is air pressure, R is gas constant, and T is temperature;

[0061] The wind power density is calculated based on the air density, wind speed and impeller diameter of the fan. The calculation formula is:

[0062] P=0.5.ρ·A·V 3

[0063] Among them, A is the swept area of ​​the fan, and V is the wind speed;

[0064] Combined with the terrain roughness and wind direction frequency data in the fused data, the wind power density is input into the wind energy assessment model WASP or WindPRO. The wind power density distribution and wind speed frequency distribution in the target wind farm area are obtained through model operation. The wind speed range, wind power change law and wind energy utilization characteristics affected by terrain are extracted based on the model assessment data.

[0065] The wind speed, wind power density and wind direction frequency results are imported into GIS, and the wind speed contour map, wind power density distribution map and wind direction frequency rose diagram are generated using the GIS platform, and the maps are superimposed on the digital elevation model to form a three-dimensional spatial display; the time series changes of wind speed and wind power density are dynamically displayed through bar charts and line charts through the data visualization platform; users can select any area in the map, view the wind speed and wind power density distribution of the current area through the interactive function, and highlight the detailed data of the selected area.

[0066] After the user selects the target area through the visual map, the wind energy resource data of the selected area is generated in real time based on the evaluation results, including wind speed curve, wind power density distribution and wind direction frequency chart;

[0067] After the user adjusts the wind turbine parameters, including impeller diameter, hub height and layout density, the system updates the wind resource distribution map, recalculates the power generation forecast in the area, and integrates the optimized evaluation data into a report that includes wind turbine layout recommendations, optimization data and forecast results.

[0068] Embodiment 2 is an embodiment of the present invention, which provides a system for a wind resource measurement and evaluation method, including: a data acquisition module, a data processing module, a data fusion and evaluation module, and a visualization and interaction module;

[0069] The data acquisition module collects raw data related to wind resources in different spatial and height ranges through various types of sensors and observation equipment;

[0070] The data processing module cleans, converts the format, and performs time and space alignment on the collected multi-source data. By eliminating outliers, removing noise, and filling in missing data, the raw data generated by different sensors are converted into a unified standardized time series format. By correcting the timestamps of each data source, each observation data is synchronized on the same time basis; spatial alignment maps the collected geographic information to a unified global coordinate system;

[0071] The data fusion and evaluation module is based on the processed multi-source data, assigns weights to each data source according to measurement accuracy, timeliness and regional coverage, adopts weighted average method to fuse wind speed, wind direction and temperature data, dynamically adjusts the preliminary fusion results through Kalman filtering, generates a time-continuous wind resource comprehensive data set covering the target area and height range, calculates wind power density using wind speed and air density, simulates and analyzes the distribution of wind energy resources in the target area in combination with the evaluation model, and obtains wind speed frequency distribution, wind power density distribution and wind direction influence characteristics;

[0072] The visualization and interaction module is based on the geographic information system and data visualization platform to visualize the evaluation results and intuitively display the wind resource distribution through a three-dimensional terrain map.

[0073] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0074] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0075] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0076] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A wind resource measurement and evaluation method, characterized in that: include: Use sensors to collect data for pre-processing; Perform data fusion based on preprocessed data; Conduct wind resource assessment through fused data; Visualize the evaluation results.

2. A wind resource measurement and evaluation method according to claim 1, characterized in that: The use of sensors to collect data for preprocessing includes: By laying out wind towers, make sure that the installation location of the wind towers covers the representative terrain in the target wind farm area, set the height of the wind towers to be more than 1.5 times the height of the wind turbine hub, and install wind speed sensors, wind direction sensors, temperature sensors and air pressure sensors on the wind towers to collect wind speed, wind direction, temperature and air pressure data; Install the lidar equipment at a commanding point in the area to scan the target area in the horizontal and vertical directions, and collect wind speed and wind direction distribution data at different altitudes; obtain the digital elevation model of the target area through satellite remote sensing equipment to characterize the terrain features, obtain cloud cover images to reflect the regional meteorological conditions, and obtain thermal infrared data to provide surface temperature information; use drones equipped with wind speed sensors and temperature sensors to conduct low-altitude flight measurements in complex terrain areas according to the set flight route, and collect local wind speed, wind direction and temperature data that are not covered by fixed equipment.

3. A wind resource measurement and evaluation method according to claim 2, characterized in that: The data preprocessing includes: Convert the raw data collected from wind towers, lidar, satellite remote sensing and drones into a standardized time series format; For the data collected by the wind tower, the 3σ criterion is used to eliminate outliers beyond the normal range based on the mean and standard deviation of each group of wind speed and wind direction data. For the wind speed distribution data collected by the lidar, random high-frequency noise is removed by a low-pass filter, and low-frequency offset is eliminated by a high-pass filter. For the terrain elevation data collected by satellite remote sensing and the wind speed data collected by the drone, interpolation algorithms are used to supplement the missing data points that occur during the collection process. Based on the collection timestamp of each sensor, the data are aligned in chronological order to make the wind speed, wind direction, temperature and terrain data have time synchronization.

4. A wind resource measurement and evaluation method as claimed in claim 3, characterized in that: The data fusion based on the preprocessed data includes: Map the cleaned and aligned wind tower data, lidar data, satellite remote sensing data and drone data in spatial dimensions, standardize each data source according to the global coordinate system and map it to the three-dimensional model of the target wind farm; set weight values ​​based on the measurement accuracy, timeliness and regional coverage of each data source, with wind tower data having a higher weight than drone data, and lidar data having a higher weight in terms of height distribution; The wind speed and wind direction data are fused by weighted average method, and the fusion value calculation formula is: Among them, F i is the measurement value of a single data source, w i is the weight of the current data source; The Kalman filter algorithm is used to dynamically adjust the preliminary fused data, and the fusion results are corrected through prediction equations and observation equations to generate a comprehensive data set reflecting the wind speed, wind direction and temperature at different heights and regional ranges of the target wind farm.

5. A wind resource measurement and evaluation method as claimed in claim 4, characterized in that: The wind resource assessment by the fused data includes: calculating the air density according to the wind speed, temperature and air pressure data contained in the fused data, and the calculation formula is: Among them, ρ is air density, p is air pressure, R is gas constant, and T is temperature; The wind power density is calculated based on the air density, wind speed and impeller diameter of the fan. The calculation formula is: P60.5.ρ·A·V 3 Among them, A is the swept area of ​​the fan, and V is the wind speed; Combined with the terrain roughness and wind direction frequency data in the fused data, the wind power density is input into the wind energy assessment model WASP or WindPRO. The wind power density distribution and wind speed frequency distribution in the target wind farm area are obtained through model operation. The wind speed range, wind power change law and wind energy utilization characteristics affected by terrain are extracted based on the model assessment data.

6. A wind resource measurement and evaluation method according to claim 5, characterized in that: The visual display of the evaluation results includes: The wind speed, wind power density and wind direction frequency results are imported into GIS, and the wind speed contour map, wind power density distribution map and wind direction frequency rose map are generated using the GIS platform, and the maps are superimposed on the digital elevation model to form a three-dimensional spatial display; the time series changes of wind speed and wind power density are dynamically displayed through bar charts and line charts through the data visualization platform; Users can select any area on the map and use the interactive function to view the wind speed and wind power density distribution in the current area, and highlight the detailed data of the selected area.

7. A wind resource measurement and evaluation method according to claim 6, characterized in that: The user interaction function includes: After the user selects the target area through the visual map, the wind energy resource data of the selected area is generated in real time based on the evaluation results, including wind speed curve, wind power density distribution and wind direction frequency chart; After the user adjusts the wind turbine parameters, including impeller diameter, hub height and layout density, the system updates the wind resource distribution map, recalculates the power generation forecast in the area, and integrates the optimized evaluation data into a report that includes wind turbine layout recommendations, optimization data and forecast results.

8. A system using a wind resource measurement and evaluation method as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition module, data processing module, data fusion and evaluation module, and visualization and interaction module; The data acquisition module collects raw data related to wind resources in different spatial and height ranges through various types of sensors and observation equipment; The data processing module cleans, converts the format, and performs time and space alignment on the collected multi-source data. By eliminating outliers, removing noise, and filling in missing data, the raw data generated by different sensors are converted into a unified standardized time series format. By correcting the timestamps of each data source, each observation data is synchronized on the same time basis; spatial alignment maps the collected geographic information to a unified global coordinate system; The data fusion and evaluation module is based on the processed multi-source data, assigns weights to each data source according to measurement accuracy, timeliness and regional coverage, adopts weighted average method to fuse wind speed, wind direction and temperature data, dynamically adjusts the preliminary fusion results through Kalman filtering, generates a time-continuous wind resource comprehensive data set covering the target area and height range, calculates wind power density using wind speed and air density, simulates and analyzes the distribution of wind energy resources in the target area in combination with the evaluation model, and obtains wind speed frequency distribution, wind power density distribution and wind direction influence characteristics; The visualization and interaction module is based on the geographic information system and data visualization platform to visualize the evaluation results and intuitively display the wind resource distribution through a three-dimensional terrain map.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a wind resource measurement and evaluation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the wind resource measurement and evaluation method according to any one of claims 1 to 7 are implemented.

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