A wind power resource survey and site preliminary screening method fusing multi-source heterogeneous data

Abstract

The application discloses a kind of wind power resource census and site preliminary screening method of fusion multi-source heterogeneous data, first acquisition resource condition, geographic space, environmental constraint, four categories of multi-source heterogeneous data of infrastructure, complete standardized pretreatment and construct standardized spatial database with full life cycle traceability mechanism;Subsequently, through the multi-layer Boolean space superposition analysis of GPU acceleration, the automatic batch rejection of rigid constraint area is realized, and the candidate development unit is obtained;Again, a multi-dimensional wind farm site development suitability evaluation index system is constructed, the AHP-entropy weight combination weight method is used to calculate the index comprehensive weight, and the comprehensive suitability score of each candidate unit is obtained;Finally, the contiguous candidate site is identified through DBSCAN spatial clustering, and the priority is sorted according to the score and the output results are standardized. The whole process automation of wind power resource census and site preliminary screening is realized, which greatly improves the efficiency and evaluation accuracy of regional scale wind power census at municipal and county level.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of new energy power generation technology and geographic information system, specifically a method for wind power resource survey and site screening that integrates multi-source heterogeneous data. It can be widely applied to wind power resource surveys at the city and county levels, as well as preliminary planning and site selection for wind power projects. Background Technology

[0002] Driven by the "dual carbon" strategic goals, my country's wind power installed capacity has continued to grow rapidly, and wind power development has gradually expanded to low wind speed areas in the central and southern regions and distributed wind power, which has put forward higher requirements for the refined survey of wind power resources and rapid preliminary screening of sites in cities and counties.

[0003] Traditional wind power resource survey and site selection methods suffer from the following core pain points: First, the processing efficiency of multi-source heterogeneous data is low and the standardization is poor. Wind power site selection involves heterogeneous data from multiple sources, in multiple formats, and at multiple resolutions. Traditional manual processing is time-consuming, lacks unified spatial benchmarks, and has weak data traceability, making it difficult to support large-scale, detailed surveys. Second, the screening process is separated and lacks automation. Rigid constraint elimination and suitability evaluation are disconnected. Provincial-level surveys can take several months to complete, resulting in extremely low efficiency. Third, the evaluation weights are highly subjective and the results are inaccurate. Existing methods rely heavily on subjective weighting and fail to fully explore the objective laws of the data, leading to significant discrepancies between evaluation results and actual operational effects. Fourth, there is a lack of engineering output mechanisms, making it impossible to automatically identify contiguous developable sites and directly support project site surveys and detailed investigations.

[0004] Existing technologies mostly focus on a single aspect of wind power resource assessment, and have not formed an automated method covering the entire process, which cannot meet the current needs of the wind power industry for large-scale, refined, and efficient surveys and site selection. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned shortcomings of existing technologies by providing a method for wind power resource survey and site screening that integrates multi-source heterogeneous data. This method solves the problems of low data processing efficiency, poor standardization, insufficient automation in screening, strong subjectivity in evaluation, and weak engineering practicality of traditional methods. It achieves full-process standardization, automation, and intelligence in regional wind power resource survey and site screening, significantly improving work efficiency and evaluation accuracy, and providing reliable technical support for the early planning of wind power projects.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for wind power resource survey and preliminary site screening that integrates multi-source heterogeneous data, comprising the following steps: S1. Multi-source heterogeneous data acquisition and standardized preprocessing: Collect multi-source heterogeneous data related to wind power resources, and perform unified processing on the data in terms of coordinate system, spatiotemporal resolution, and data format. Complete outlier removal and missing value completion to obtain standardized multi-source data. S2. Construction of Standardized Spatial Database: Design a spatial database template for wind power resource survey, automatically import standardized multi-source data into the database, and build a spatial database with a full life cycle metadata traceability mechanism; S3. Automated screening of rigid constraints: The study area is divided into several grid evaluation units. Each rigid constraint layer is binarized. Boolean product superposition analysis is performed on the multi-layer binarized rigid constraint layers through GPU parallel computing architecture to automatically remove prohibited development areas and obtain candidate evaluation units that pass the rigidity screening. S4. Multi-factor suitability quantitative evaluation: Construct a wind farm site development suitability evaluation index system, normalize each index, use the AHP-entropy weight combination weight method to calculate the comprehensive weight of each index, and calculate the comprehensive suitability score of each candidate evaluation unit based on the comprehensive weight. S5. Site Priority Ranking and Results Output: The DBSCAN spatial clustering algorithm is used to perform spatial clustering on candidate evaluation units whose comprehensive scores reach a preset threshold. It automatically identifies contiguous candidate wind power sites, prioritizes them according to their comprehensive suitability scores, and generates and outputs the survey results.

[0007] Furthermore, the multi-source heterogeneous data in step S1 includes four main categories: Wind resource data, including annual average wind speed, annual average wind power density, effective wind speed hours, and turbulence intensity, are sourced from reanalysis meteorological data, wind tower measured data, and satellite remote sensing inversion data. Geospatial data, including digital elevation model (DEM), slope, aspect, and topographic relief, sourced from sources including spaceborne remote sensing DEM data; Environmental constraint data, including vector data for ecological protection red lines, permanent basic farmland, nature reserves, drinking water source protection areas, cultural relics protection units, military zones, and unusable land types; Infrastructure data, including vector data of substations, transmission lines, highways, and railway freight stations.

[0008] Furthermore, the standardization preprocessing in step S1 specifically includes: All vector and raster data coordinate systems were uniformly converted to the CGCS2000 National Geodetic Coordinate System, and the projection was uniformly converted to the 3-degree zone Gaussian projection. All raster data were resampled to a preset spatial resolution using bilinear interpolation, and the time dimension was unified to continuous observation data of the past 10 years. The 3σ criterion was used to remove outliers from numerical data, and missing values ​​were filled using ordinary kriging interpolation. All vector data will be standardized to FileGDB format, and raster data will be standardized to LZW compressed TIFF format.

[0009] Furthermore, the spatial database template in step S2 is divided into vector datasets and raster datasets. The vector datasets include constraint layers and infrastructure layers, while the raster datasets include resource layers and geographic data layers. Each layer has a pre-defined standardized attribute field structure and encoding rules. The data entry process is automated through Python + ArcPy scripts, and metadata is automatically generated. The metadata includes data source, collection time, preprocessing process, resolution, and coordinate system information, enabling full lifecycle traceability of the data.

[0010] Furthermore, step S3 specifically includes: The study area was divided into 30m×30m grid evaluation units, with each unit serving as an independent evaluation object. For each rigid constraint layer, set control rules, assign a value of 0 to the prohibited construction area and a value of 1 to the construction area, and generate a binary raster layer; A GPU-accelerated spatial overlay analysis tool performs a Boolean product operation on all binary rigid constraint layers. The calculation formula is as follows:

[0011] in, This represents the rigid screening result for the i-th evaluation unit. This indicates that a rigid screening process has been implemented. This indicates that it has been removed; This represents the binarized result of the i-th evaluation unit in the j-th rigid constraint layer, where n is the total number of rigid constraint layers. extract The evaluation unit generates a candidate region vector layer.

[0012] Furthermore, the suitability evaluation index system in step S4 includes four criterion layers: resource endowment conditions, geographical and topographical conditions, power grid access conditions, and transportation accessibility conditions. Each criterion layer has corresponding quantitative indicators. The normalization process for the aforementioned indicators is as follows: For positive indicators, the normalization formula is:

[0013] For negative indicators, the normalization formula is:

[0014] in, Let k be the normalized value of the k-th indicator for the i-th evaluation unit. The original value of the indicator. The maximum value of the k-th indicator. It is the minimum value of the k-th indicator.

[0015] Furthermore, the combined weighting method in step S4 is a combined weighting calculation method that combines the Analytic Hierarchy Process (AHP) with the entropy weighting method, specifically including: The subjective weights of each indicator were calculated using the analytic hierarchy process (AHP). Consistency checks were performed to ensure CR < 0.1; The objective weights of each indicator are calculated using the entropy weight method. The calculation formula is:

[0016]

[0017]

[0018]

[0019] in, Let represent the weight of the i-th evaluation unit under the k-th indicator, m be the total number of evaluation units, and n be the total number of indicators. Let the entropy value be the k-th index. The difference coefficient for the k-th indicator; Calculate the portfolio weights:

[0020] Where α is the subjective weight coefficient, and its value ranges from 0.4 to 0.6; The formula for calculating the overall suitability score is as follows:

[0021] in, The comprehensive suitability score is the i-th evaluation unit, and its value ranges from 0 to 1.

[0022] Furthermore, step S5 specifically includes: The DBSCAN spatial clustering algorithm is adopted, with a neighborhood radius of 2 grid cells and a minimum sample size of MinPts of 100. Candidate evaluation cells with a comprehensive score greater than or equal to a preset threshold are spatially clustered. Spatial adjacent cells with similar scores are merged into contiguous candidate sites, while fragmented sites with an area smaller than the preset threshold are removed. For each contiguous candidate site, its total area, average comprehensive score, average annual wind speed, average wind power density, average slope, and distance from the nearest substation are calculated as core parameters. Candidate sites are sorted in descending order based on their average comprehensive score to generate a priority list of recommended wind power sites.

[0023] Furthermore, in step S5, the preset threshold is a comprehensive score greater than or equal to 0.6, and the preset area threshold is 1 square kilometer; after arranging the contiguous candidate sites in descending order of average comprehensive score, the first-level recommended sites are those with a score greater than or equal to 0.8, the second-level recommended sites are those with a score between 0.7 and 0.8, and the third-level recommended sites are those with a score between 0.6 and 0.7.

[0024] Furthermore, the method is applied to the survey and preliminary screening of onshore wind power resources at the city and county level. The survey results output in step S5 include a list of recommended priority wind power sites, a wind power resource distribution map, and a priority distribution map of candidate sites. The GPU-accelerated spatial overlay analysis adopts the CUDA parallel computing architecture, which divides the binarized rigid constraint layer into several data blocks, allocates them to the GPU for multi-threaded parallel execution of Boolean multiplication operations, and then merges and outputs the calculation results of each data block.

[0025] Compared with the prior art, the beneficial effects of the present invention are: 1. Significantly improved data processing efficiency and standardization: Through fully automated and standardized processing, the problem of multi-source heterogeneous data fusion is solved, and data processing efficiency is improved by more than 85%. At the same time, the entire life cycle of data is traceable, meeting the planning and compliance requirements of wind power projects.

[0026] 2. Significantly enhanced large-area survey capabilities: It can support detailed wind power surveys at the city and county levels with a high resolution of 30m, and the entire process is automated. The survey cycle is shortened from several months to 3-5 days, greatly reducing labor costs.

[0027] 3. Improved the accuracy and reliability of initial site screening: The evaluation model was optimized by combining weights, taking into account both expert experience and data objectivity. The evaluation results are highly consistent with the actual operation of the wind farm, effectively reducing the decision-making risk of project site selection and reducing the amount of ineffective work in subsequent detailed exploration.

[0028] 4. High engineering applicability: The priority list of contiguous sites, core parameter statistics, and thematic maps output by this invention can be directly applied to the early planning, site selection, and project approval of wind power projects, which greatly shortens the development cycle of wind power projects, reduces project development costs, and has promotion and application value. Attached Figure Description

[0029] Figure 1 This is an overall flowchart of the wind power resource survey and site screening method that integrates multi-source heterogeneous data according to the present invention.

[0030] Figure 2 This is a flowchart illustrating the multi-source heterogeneous data fusion logic and standardized preprocessing process of this invention.

[0031] Figure 3 This is a schematic diagram of the rigid constraint superposition analysis and candidate unit selection of the present invention. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments described are only for explaining the present invention and are not intended to limit the scope of protection of the present invention.

[0033] The wind power resource survey and site screening method of the present invention, which integrates multi-source heterogeneous data, has the following overall process: Figure 1 As shown, the specific implementation steps are as follows: Step 1: Multi-source heterogeneous data acquisition and standardized preprocessing The multi-source heterogeneous data fusion logic and standardized preprocessing flowchart proposed in this invention are as follows: Figure 2 As shown, the study area was first defined, and four main categories of core multi-source heterogeneous data were collected, specifically including: Wind resource data: ERA5 reanalysis meteorological data of the study area for the past 10 years, hourly wind speed data measured by the wind tower, and wind power density data retrieved by satellite remote sensing. The core indicators include annual average wind speed, annual average wind power density, effective wind speed hours, and turbulence intensity. Geospatial data: Collect 30m resolution digital elevation model (DEM) data, and calculate and generate slope, aspect, and topographic relief raster data based on the DEM; Environmental constraint data: collect vector data of prohibited development areas such as ecological protection red lines, permanent basic farmland, nature reserves, drinking water source protection areas, cultural relics protection units, and military restricted areas; Infrastructure data: Collect vector data of 110kV and above substations, 35kV and above transmission lines, grade highways, and railway freight stations.

[0034] After data collection is completed, standardized preprocessing is performed. The specific process is as follows: 1. Coordinate and projection unification: The coordinate system of all vector and raster data is uniformly converted to the CGCS2000 national geodetic coordinate system, and the projection is uniformly converted to the 3-degree zone Gauss-Kruger projection to ensure that the spatial reference of all data is consistent. 2. Spatiotemporal resolution uniformity: All raster data are resampled to a spatial resolution of 30m using bilinear interpolation, and the time dimension is uniformly set to the annual average of the past 10 years to ensure the temporal consistency of the data; 3. Data Cleaning and Completion: Outlier removal is performed on numerical raster data using the 3σ criterion. The calculation formula is as follows:

[0035] Remove excess For outliers within the range, missing values ​​are filled in using ordinary kriging interpolation to ensure data integrity. 4. Unified Format: All vector data is converted to FileGDB format, and raster data is uniformly converted to LZW compressed TIFF format to ensure data format compatibility and reading efficiency.

[0036] Step 2: Construction of a standardized spatial database The design is for a standardized spatial database template for wind power resource surveys. It consists of two main modules: vector datasets and raster datasets. The vector datasets include constraint layers, infrastructure layers, and administrative division layers. The raster datasets include resource layers and topographic layers. Each layer has a pre-defined standardized attribute field structure and encoding rules (including data name, resolution, coordinate system, time range, and data source).

[0037] An automated data import script was developed using Python and ArcPy to automatically write preprocessed multi-source data into the corresponding dataset. Metadata covering information such as data source, collection time, preprocessing process, and spatial parameters was generated simultaneously, enabling full lifecycle traceability of the data. At the same time, a spatial index was built for vector data and a pyramid index was built for raster data to improve the efficiency of subsequent spatial analysis. Step 3: Automated screening of rigid constraints The rigid constraint superposition analysis and candidate element selection method proposed in this invention is as follows: Figure 3 As shown, the study area is first divided into 30m×30m grid evaluation units, with each unit serving as an independent evaluation object. Control rules are set for each rigid constraint layer, assigning 0 to undevelopable areas such as legally prohibited construction areas, slopes >30°, and altitudes >3500m, and assigning 1 to developable areas, thereby generating a binary grid layer. A GPU-accelerated spatial overlay analysis tool performs a Boolean product operation on all binary rigid constraint layers, using the following formula:

[0038] in, This represents the rigid screening result for the i-th evaluation unit. This indicates that a rigid screening process has been implemented. This indicates that it has been removed; This represents the binarized result of the i-th evaluation unit in the j-th rigid constraint layer, where n is the total number of rigid constraint layers.

[0039] Extract all The evaluation unit generates a candidate region vector layer for subsequent suitability evaluation.

[0040] Step 4: Multi-factor suitability quantitative evaluation The following table shows the evaluation index system for the suitability of wind farm development:

[0041] For the raw data of each indicator, the maximum-minimum normalization process is performed according to the positive / negative attributes to map all indicator values ​​to the 0-1 range and eliminate the influence of dimensions.

[0042] Positive index normalization formula:

[0043] Normalization formula for negative indices:

[0044] in, Let k be the normalized value of the k-th indicator for the i-th evaluation unit. The original value of the indicator. The maximum value of the k-th indicator. It is the minimum value of the k-th indicator.

[0045] The comprehensive weight of the indicators is calculated using the AHP-entropy weight combination method: 1. Subjective Weight Calculation: Five senior experts in wind power development, resource assessment, GIS, and power systems were invited to construct an indicator judgment matrix using the 1-9 scaling method, and the subjective weight of each indicator was calculated using the square root method. And perform a consistency check to ensure that the consistency ratio CR < 0.1, and pass the check.

[0046] 2. Objective Weight Calculation: Based on the indicator data of candidate units that have passed the rigid screening, the objective weight is calculated using the entropy weight method. The calculation formula is:

[0047]

[0048]

[0049]

[0050] in, Let represent the weight of the i-th evaluation unit under the k-th indicator, m be the total number of evaluation units, and n be the total number of indicators. Let the entropy value be the k-th index. Let be the difference coefficient of the k-th indicator; when hour, .

[0051] Combined weight calculation: Setting the subjective weight coefficient α=0.5, and balancing expert experience with data objectivity, the final combined weight is calculated as follows:

[0052] Based on the comprehensive weight and normalized index value, the comprehensive suitability score of each candidate evaluation unit is calculated:

[0053] in, The comprehensive suitability score is the i-th evaluation unit, with a value range of 0-1. The higher the score, the better the development suitability.

[0054] Step 5: Site Prioritization and Output The DBSCAN spatial clustering algorithm was adopted, with a neighborhood radius of ε=2 grid cells and a minimum sample size of MinPts=100. Candidate evaluation cells with a comprehensive score ≥0.6 were spatially clustered, and spatially adjacent cells with similar scores were merged into contiguous candidate sites, while fragmented sites with an area of ​​less than 1km² were removed.

[0055] For each contiguous candidate site, core engineering parameters such as total area, average comprehensive score, average annual wind speed, average wind power density, average slope, and distance to the nearest substation are statistically calculated. Sites are then sorted in descending order of average comprehensive score and classified into Level 1 (≥0.8), Level 2 (0.7-0.8), and Level 3 (0.6-0.7) recommended sites. A priority list of recommended wind power sites is generated, and thematic maps such as wind power resource distribution map and candidate site priority distribution map are automatically generated simultaneously, completing the entire process of general survey and initial screening.

[0056] Example 1: This example uses a city as the study area, with a total area of ​​approximately 19,700 km². The method described in this invention is used to conduct a wind power resource survey and preliminary site screening. The specific implementation process is as follows: 1. Data Acquisition and Preprocessing: ERA5 reanalysis data, SRTM 30m DEM data, vector data of ecological protection red lines and permanent basic farmland released by the Department of Natural Resources of the Autonomous Region, data of 110kV and above substations and transmission lines of the power grid company, and data of grade highways of the Department of Transportation were collected from the study area over the past 10 years. The coordinates, resolution and format were standardized, outliers were removed by the 3σ criterion, and missing values ​​were filled by Kriging interpolation to obtain standardized multi-source data.

[0057] 2. Spatial Database Construction: Based on a pre-set database template, all data was automatically imported into the database using Python + ArcPy scripts, and metadata was generated synchronously. The spatial database for the wind power resource survey of the study area was completed, with a total data volume of approximately 15GB. After the spatial index and pyramid index were constructed, the data reading efficiency was improved by more than 60%.

[0058] 3. Rigid Constraint Screening: The study area was divided into 30m×30m grid evaluation units, totaling approximately 21.9 million units. Twelve rigid constraint binarization layers were constructed, and prohibited development areas were automatically eliminated through GPU-accelerated Boolean overlay analysis. Finally, approximately 3.9 million candidate units passed the rigid constraint screening, corresponding to a developable area of ​​approximately 3,500 km², with a rejection rate of 91%. The entire screening process took approximately 25 minutes, far less than the tens of hours required for traditional manual processing.

[0059] 4. Suitability Evaluation: An evaluation system with 4 criteria and 12 indicators was constructed. The weights of each indicator were calculated using the AHP-entropy weight combination method. The annual average wind speed had the highest weight (0.18), and the annual average wind power density had a weight of 0.16, which is consistent with the core logic of wind power development. The comprehensive suitability score of all candidate units was calculated. There are approximately 2.8 million units with a score ≥0.6, corresponding to an area of ​​approximately 2,520 km².

[0060] 5. Output: Through DBSCAN spatial clustering, 42 contiguous candidate sites were obtained, including 8 first-level recommended sites (average score ≥ 0.8), with a total area of ​​approximately 320 km², an average annual wind speed of 7.8 m / s, and an average wind power density of 380 W / m², possessing excellent development value; 15 second-level recommended sites, with a total area of ​​approximately 580 km²; and 19 third-level recommended sites, with a total area of ​​approximately 450 km². A priority list and four types of thematic maps were automatically generated. The entire process took approximately 4 days, significantly less than the 2-3 months of traditional methods.

[0061] The performance comparison between this embodiment and the traditional method is as follows:

[0062] This embodiment verifies the efficiency, accuracy, and engineering applicability of the method described in this invention, and it can be widely applied to various large-area wind power resource surveys and project site selection work.

[0063] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for wind power resource survey and preliminary site screening that integrates multi-source heterogeneous data, characterized in that, Includes the following steps: S1. Multi-source heterogeneous data acquisition and standardized preprocessing: Collect multi-source heterogeneous data related to wind power resources, and perform unified processing on the data in terms of coordinate system, spatiotemporal resolution, and data format. Complete outlier removal and missing value completion to obtain standardized multi-source data. S2. Construction of Standardized Spatial Database: Design a spatial database template for wind power resource survey, automatically import standardized multi-source data into the database, and build a spatial database with a full life cycle metadata traceability mechanism; S3. Automated screening of rigid constraints: The study area is divided into several grid evaluation units. Each rigid constraint layer is binarized. Boolean product superposition analysis is performed on the multi-layer binarized rigid constraint layers through GPU parallel computing architecture to automatically remove prohibited development areas and obtain candidate evaluation units that pass the rigidity screening. S4. Multi-factor suitability quantitative evaluation: Construct a wind farm site development suitability evaluation index system, normalize each index, use the AHP-entropy weight combination weight method to calculate the comprehensive weight of each index, and calculate the comprehensive suitability score of each candidate evaluation unit based on the comprehensive weight. S5. Site Priority Ranking and Results Output: The DBSCAN spatial clustering algorithm is used to perform spatial clustering on candidate evaluation units whose comprehensive scores reach a preset threshold. It automatically identifies contiguous candidate wind power sites, prioritizes them according to their comprehensive suitability scores, and generates and outputs the survey results.

2. The method according to claim 1, characterized in that, The multi-source heterogeneous data in step S1 includes four main categories: Wind resource data, including annual average wind speed, annual average wind power density, effective wind speed hours, and turbulence intensity, are sourced from reanalysis meteorological data, wind tower measured data, and satellite remote sensing inversion data. Geospatial data, including digital elevation model (DEM), slope, aspect, and topographic relief, sourced from sources including spaceborne remote sensing DEM data; Environmental constraint data, including vector data for ecological protection red lines, permanent basic farmland, nature reserves, drinking water source protection areas, cultural relics protection units, military zones, and unusable land types; Infrastructure data, including vector data of substations, transmission lines, highways, and railway freight stations.

3. The method according to claim 1, characterized in that, The standardization preprocessing in step S1 specifically includes: All vector and raster data coordinate systems were uniformly converted to the CGCS2000 National Geodetic Coordinate System, and the projection was uniformly converted to the 3-degree zone Gaussian projection. All raster data were resampled to a preset spatial resolution using bilinear interpolation, and the time dimension was unified to continuous observation data of the past 10 years. The 3σ criterion was used to remove outliers from numerical data, and missing values ​​were filled using ordinary kriging interpolation. All vector data will be standardized to FileGDB format, and raster data will be standardized to LZW compressed TIFF format.

4. The method according to claim 1, characterized in that, The spatial database template in step S2 is divided into vector datasets and raster datasets. The vector datasets include constraint layers and infrastructure layers, while the raster datasets include resource layers and geographic data layers. Each layer has a pre-defined standardized attribute field structure and encoding rules. The data entry process is automated through Python + ArcPy scripts, and metadata is automatically generated. The metadata includes data source, collection time, preprocessing process, resolution, and coordinate system information, enabling full lifecycle traceability of the data.

5. The method according to claim 1, characterized in that, Step S3 specifically includes: The study area was divided into 30m×30m grid evaluation units, with each unit serving as an independent evaluation object. For each rigid constraint layer, set control rules, assign a value of 0 to the prohibited construction area and a value of 1 to the construction area, and generate a binary raster layer; A GPU-accelerated spatial overlay analysis tool performs a Boolean product operation on all binary rigid constraint layers. The calculation formula is as follows: in, This represents the rigid screening result for the i-th evaluation unit. This indicates that a rigid screening process has been implemented. This indicates that it has been removed; This represents the binarized result of the i-th evaluation unit in the j-th rigid constraint layer, where n is the total number of rigid constraint layers. extract The evaluation unit generates a candidate region vector layer.

6. The method according to claim 1, characterized in that, The suitability evaluation index system in step S4 includes four criteria layers: resource endowment conditions, geographical and topographical conditions, power grid access conditions, and transportation accessibility conditions. Each criterion layer has corresponding quantitative indicators. The normalization process for the aforementioned indicators is as follows: For positive indicators, the normalization formula is: For negative indicators, the normalization formula is: in, Let be the normalized value of the k-th indicator for the i-th evaluation unit. The original value of the indicator. The maximum value of the k-th indicator. It is the minimum value of the k-th indicator.

7. The method according to claim 1, characterized in that, The combined weighting method in step S4 is a combined weighting calculation method that combines the Analytic Hierarchy Process (AHP) with the entropy weighting method, specifically including: The subjective weights of each indicator were calculated using the analytic hierarchy process (AHP). Consistency checks were performed to ensure CR < 0.1; The objective weights of each indicator are calculated using the entropy weight method. The calculation formula is: in, Let represent the weight of the i-th evaluation unit under the k-th indicator, m be the total number of evaluation units, and n be the total number of indicators. Let the entropy value be the k-th index. The difference coefficient for the k-th indicator; Calculate the portfolio weights: Where α is the subjective weighting coefficient, with a value ranging from 0.4 to 0.6; The formula for calculating the overall suitability score is as follows: in, The comprehensive suitability score is the i-th evaluation unit, and its value ranges from 0 to 1.

8. The method according to claim 1, characterized in that, Step S5 specifically includes: The DBSCAN spatial clustering algorithm is adopted, with a neighborhood radius of 2 grid cells and a minimum sample size of MinPts of 100. Candidate evaluation cells with a comprehensive score greater than or equal to a preset threshold are spatially clustered. Spatial adjacent cells with similar scores are merged into contiguous candidate sites, while fragmented sites with an area smaller than the preset threshold are removed. For each contiguous candidate site, its total area, average comprehensive score, average annual wind speed, average wind power density, average slope, and distance from the nearest substation are calculated as core parameters. Candidate sites are sorted in descending order based on their average comprehensive score to generate a priority list of recommended wind power sites.

9. The method according to claim 8, characterized in that, In step S5, the preset threshold is a comprehensive score greater than or equal to 0.6, and the preset area threshold is 1 square kilometer. After arranging the contiguous candidate sites in descending order of average comprehensive score, the first-level recommended sites are those with a score greater than or equal to 0.8, the second-level recommended sites are those with a score between 0.7 and 0.8, and the third-level recommended sites are those with a score between 0.6 and 0.

7.

10. The method according to claim 1, characterized in that, The method is applied to the survey and preliminary screening of onshore wind power resources at the city and county level. The survey results output in step S5 include a wind power site priority list, a wind power resource distribution map, and a candidate site priority distribution map. The GPU-accelerated spatial overlay analysis adopts the CUDA parallel computing architecture, which divides the binarized rigid constraint layer into several data blocks, allocates them to the GPU for multi-threaded parallel execution of Boolean multiplication operations, and then merges and outputs the calculation results of each data block.

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