GIS data analysis method and system for wind resource assessment of offshore wind plant

Abstract

The invention relates to a GIS data analysis method and system for wind resource assessment of an offshore wind plant. The method comprises the following steps: obtaining wind measurement data of a target sea area through a global meteorological analysis data set; converting the acquired wind measurement data of the target sea area into a general format identified and processed by a GIS (Geographic Information System); converting the wind measurement data of the target sea area after format conversion into continuous grid data of the target sea area through an inverse distance weighted interpolation method; importing the raster data of the target sea area into a GIS platform, analyzing wind resource data in the target sea area, and dividing wind resource areas of different grades; creating a thematic map on a GIS platform according to the wind resource areas of different grades, and graphically displaying various results of wind resource evaluation by utilizing layer management and symbolization functions; and automatically generating a target sea area wind resource report according to various results of wind resource evaluation of the target sea area, and outputting the target sea area wind resource report according to a preset format.

Description

Technical Field

[0001] The present application relates to the field of offshore wind power technology, and more specifically, to a GIS data analysis method and system for wind resource assessment at an offshore wind farm. Background Art

[0002] With the development of renewable energy, the construction of offshore wind farms has received increasing attention. Offshore wind energy resources are abundant and stable. The development of offshore wind farms can provide society with a large amount of clean and renewable electricity, effectively reduce dependence on traditional fossil energy, reduce carbon emissions, and help cope with climate change. The rational development of offshore wind resources will help optimize the energy structure, improve the security and stability of energy supply, and make important contributions to achieving sustainable development goals. The assessment of wind resources in offshore wind farms is of great significance in many aspects. Accurate assessment of wind resources can determine the best site for offshore wind farms, make full use of wind-rich areas, and improve power generation efficiency and economic benefits. At the same time, the assessment can help determine the scale and layout of wind farms, make the installation of wind turbines more scientific and reasonable, and maximize wind energy capture. Traditional wind resource assessment methods often rely on limited observation site data to assess offshore wind resources.

[0003] With current technology, data from a single observation site cannot fully and accurately reflect the distribution of wind resources in an offshore wind farm, and cannot analyze the long-term changes and seasonal characteristics of wind resources. As a result, the assessment results lack an understanding of the overall trend of the wind farm, reducing the accuracy of wind resource assessments.

[0004] Existing technologies such as the Chinese patent with patent number "CN113743027A" disclose an ocean forecast information service method based on user interest points, which includes: generating a topographic map of the target area for CFD calculation; obtaining long-term data of several wind measurement points in the target area; performing CFD simulation calculations on the target area based on the long-term data and the topographic map to obtain wind resource data for multiple result points; wherein the number of result points is greater than the number of wind measurement points; generating a wind resource layer of the target area based on the wind resource data; merging and splicing wind resource layers of multiple different target areas to establish a wind resource map. This solution applies CFD technology, combined with actual topographic maps, to simulate wind farms and obtain relevant wind resource data; through less measured point data, more wind resource data for result points can be simulated and calculated, thereby realizing the establishment of a high-resolution onshore wind resource map, providing strong support for the subsequent macro-site selection of wind farms.

[0005] The problem with the above-mentioned prior art is that this method merges and splices the wind resource layers of multiple different target areas to establish a wind resource map. When merging and splicing the wind resource layers of multiple different target areas, the connection between the wind resource data of adjacent areas may be unnatural or inaccurate. The size and terrain complexity of different target areas may be different, which means that the resolution and calculation parameters used in the respective CFD simulation calculations are also different. These differences will cause some areas to appear too rough or fine in the map, affecting the practicality of the wind resource map. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a GIS data analysis method and system for wind resource assessment in offshore wind farms.

[0007] The technical solution of the present invention is as follows:

[0008] The present invention proposes a GIS data analysis method for offshore wind farm wind resource assessment, comprising the following steps:

[0009] Step S1, obtaining wind measurement data of the target sea area through a global meteorological analysis data set;

[0010] Step S2, converting the acquired wind measurement data of the target sea area into a common format for GIS recognition and processing;

[0011] Step S3, converting the wind measurement data of the target sea area after the conversion format into continuous target sea area raster data by using the inverse distance weighted interpolation method;

[0012] Step S4, importing the target sea area raster data into the GIS platform, analyzing the wind resource data in the target sea area, and dividing the wind resource areas into different levels;

[0013] Step S5, creating a thematic map on the GIS platform according to wind resource areas of different levels, and displaying the results of wind resource assessment graphically using layer management and symbolization functions;

[0014] Step S6, automatically generating a target sea area wind resource report based on various results of the target sea area wind resource assessment, and outputting the target sea area wind resource report in a preset format.

[0015] As a preferred implementation, the wind measurement data of the target sea area includes: wind speed, wind direction and climate data of the target sea area collected according to analysis requirements.

[0016] As a preferred implementation, in the process of converting the wind measurement data of the target sea area into continuous target sea area grid data, a process of quality inspection of the generated grid part is also included, which is specifically as follows:

[0017] According to the required grid generation period, the wind measurement data within the latitude and longitude grid points of the target sea area are obtained;

[0018] The single-period wind measurement data of the target sea area is used to place them in the grid points according to the longitude and latitude grids, and a single-period grid of wind measurement data is initially generated;

[0019] Data verification is performed on individual grid data in a single period grid to check whether there are null values ​​and wrong values. For grids with missing or erroneous data, data interpolation and revision algorithms are used to interpolate and revise grids with missing or erroneous data.

[0020] As a preferred implementation, the target sea area raster data is imported into the GIS platform to analyze the wind resource data in the target sea area and divide the wind resource areas of different levels, which specifically includes the following steps:

[0021] The GIS platform uses the geospatial data abstraction gdal library to read the target sea area raster data, determine the weight of the target sea area raster data, and perform weighted merging of the target sea area raster data;

[0022] The target sea area is divided into several sub-areas according to its characteristics. The Zonal Statistics as Table tool of the GIS platform is used to calculate the average wind resources of each sub-area with the divided sub-areas as statistical areas.

[0023] By dividing the wind resources into different levels, regional data including sub-region identification and corresponding level of average wind resources are output.

[0024] As a preferred implementation manner, after the step of dividing the wind resource areas into different levels, the method further includes:

[0025] Use the Graduated Colors classification method to select a color gradient of corresponding level for each wind resource area in the target sea area.

[0026] On the other hand, the present invention also provides a GIS data analysis system for offshore wind farm wind resource assessment, comprising:

[0027] The data acquisition module obtains wind measurement data of the target sea area through the global meteorological analysis data set;

[0028] Output processing module, which converts the acquired wind measurement data into a common format for GIS recognition and processing;

[0029] The data gridding module converts the wind measurement data of the target sea area into continuous grid data of the target sea area through the inverse distance weighted interpolation method;

[0030] GIS analysis module, which imports the target sea area raster data into the GIS platform to analyze the wind resource data in the target sea area and divide the wind resource areas into different levels;

[0031] The visualization module creates thematic maps on the GIS platform according to wind resource areas of different levels, and displays the results of wind resource assessment in an intuitive graphical manner using layer management and symbolization functions;

[0032] The report generation module automatically generates a target sea area wind resource report based on the wind resource assessment results of the target sea area, and outputs the target sea area wind resource report in a preset format.

[0033] As a preferred implementation, the data acquisition module acquires wind measurement data of the target sea area through a global meteorological analysis data set, and the wind measurement data of the target sea area includes: wind speed, wind direction and climate data of the target sea area collected according to analysis requirements.

[0034] As a preferred implementation, the data gridding module, in the process of converting the wind measurement data of the target sea area into continuous target sea area grid data, also includes a process of quality inspection of the generated grid part, which is as follows:

[0035] According to the required grid generation period, the wind measurement data within the latitude and longitude grid points of the target sea area are obtained;

[0036] The single-period wind measurement data of the target sea area is used to place them in the grid points according to the longitude and latitude grids, and a single-period grid of wind measurement data is initially generated;

[0037] Data verification is performed on individual grid data in a single period grid to check whether there are null values ​​and wrong values. For grids with missing or erroneous data, data interpolation and revision algorithms are used to interpolate and revise grids with missing or erroneous data.

[0038] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for GIS data analysis for offshore wind farm wind resource assessment as described in any embodiment of the present invention is implemented.

[0039] On the other hand, the present invention also provides a computer-readable medium for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement a GIS data analysis method for offshore wind farm wind resource assessment as described in any embodiment of the present invention.

[0040] The present invention has the following beneficial effects:

[0041] 1. The wind measurement data collected in the target sea area are processed into point data grids, and quality inspection and interpolation revision are carried out at the same time to ensure the accuracy and completeness of the data. The method of multi-source data fusion and unified processing overcomes the limitations of a single data source and improves the reliability of wind resource assessment.

[0042] 2. Comprehensively analyze the wind grid data of the target sea area through GIS, determine the data weights for weighted merging, and obtain comprehensive wind resource data. Divide the sub-areas according to the characteristics of the target sea area, calculate the average wind resources of each sub-area, and divide the wind resources into different levels, so as to achieve a more detailed understanding of the spatial distribution characteristics of wind resources and provide more specific guidance for the site selection, layout and operation of wind farms.

[0043] 3. According to the different levels of wind resource areas, the Graduated Colors classification method is used to select the corresponding level of color gradient for each sub-area, which enhances the visualization effect and intuitively displays the differences and changing trends of wind resource levels. At the same time, the wind resource level division results are output as a report in the form of a map area screenshot in a specified format, achieving visual display and report output, and improving the readability and dissemination efficiency of wind resource assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 The figure is a schematic diagram of the method flow of embodiment 1. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0048] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0049] The terms "include" and "comprises" indicate that the described features, integers, steps, operations, elements, and / or

[0050] The existence of a component does not exclude the existence or addition of one or more other features, integers, steps, operations, elements, components and / or their collections.

[0051] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0052] Embodiment 1:

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the specific embodiments of the present application and refer to the attached Figure 1 , clearly and completely describe the technical solution of the present invention.

[0054] To solve the problems in the prior art, the present invention provides a GIS data analysis method for wind resource assessment in an offshore wind farm, comprising the following steps:

[0055] Step S1, obtaining wind measurement data of the target sea area through a global meteorological analysis data set;

[0056] The wind measurement data of the target sea area includes: wind speed, wind direction and climate data of the target sea area collected according to analysis requirements.

[0057] In this embodiment, the real-time wind measurement data of the latitude and longitude points of the target sea area is obtained through the ERA5 data of OpenMeteo, which can provide the latest information for the analysis of the current wind resource status. This is necessary for timely understanding of the dynamic changes of the wind farm to make short-term decisions. At the same time, the historical wind measurement data related information of the target sea area is collected to grasp the long-term wind resource characteristics of the area, including seasonal changes, interannual changes, etc. This helps to make more reasonable decisions in the planning and design stage of the wind farm. By integrating real-time data and historical data, the wind resource status of the target sea area can be evaluated more comprehensively and accurately. Real-time data provides current wind condition information, and historical data reveals long-term wind resource trends. The combination of the two can better determine the optimal installation location of the wind turbine and predict long-term power generation. Storing data in the database can achieve efficient data management and retrieval, can store a large amount of wind resource data, and provide fast data query and analysis functions to improve the efficiency of subsequent evaluation.

[0058] For the planning and construction of wind farms, long-term sustainability needs to be considered. Historical wind measurement data of 5-20 years can provide decision makers with sufficient information to evaluate the stability and reliability of wind farms in long-term operation, and help predict the changing trend of wind resources in the future. By analyzing long-term historical data, periodic changes or long-term trends in wind resources can be found, providing forward-looking suggestions for the future development of wind farms. Wind speed information can help evaluate the changes in wind intensity in the sea area over a long period of time, while wind direction information helps determine the optimal layout direction of wind turbines. Finally, climate information comprehensively considers the impact of factors such as temperature, humidity, and precipitation on wind resources.

[0059] Step S2, converting the acquired wind measurement data of the target sea area into a common format for GIS recognition and processing;

[0060] In this embodiment, the ERA5 real-time wind measurement data and historical wind measurement data are converted into a common format for GIS recognition and processing, so that these data can be comprehensively analyzed on the same GIS platform, avoiding problems such as inability to read and processing difficulties caused by inconsistent data formats, and realizing the integration of multi-source data.

[0061] Step S3, converting the wind measurement data of the target sea area after the conversion format into continuous target sea area raster data by using the inverse distance weighted interpolation method;

[0062] In the process of converting the wind measurement data of the target sea area into continuous target sea area grid data, a process of quality inspection of the generated grid part is also included, which is as follows:

[0063] According to the required grid generation period, the wind measurement data within the latitude and longitude grid points of the target sea area are obtained;

[0064] The single-period wind measurement data of the target sea area is used to place them in the grid points according to the longitude and latitude grids, and a single-period grid of wind measurement data is initially generated;

[0065] Data verification is performed on individual grid data in a single period grid to check whether there are null values ​​and wrong values. For grids with missing or erroneous data, data interpolation and revision algorithms are used to interpolate and revise grids with missing or erroneous data.

[0066] In this embodiment, the wind field grid data generated by the interpolation method of the discrete ERA5 wind measurement data points can cover the entire assessed offshore area, ensuring that no missing parts are converted into continuous wind field grid data, making the wind field information more continuous and complete in space. This helps to more comprehensively understand the distribution of wind resources in the entire assessed offshore area, avoiding the problem of incomplete and inaccurate information that may be caused by relying solely on discrete point data. After that, the real-time grid data and historical grid data are stored in the database, realizing centralized management and long-term preservation of data. The database can provide efficient data retrieval, query and analysis functions, which is convenient for users to access and use these data at any time, and extract them to analyze and evaluate wind resources.

[0067] Quality testing of the generated raster data can timely identify problems in the data, such as missing data, outliers, insufficient precision, etc. Quality testing can ensure the reliability and accuracy of the data and improve the credibility of wind resource assessment results.

[0068] Step S4, importing the target sea area raster data into the GIS platform, analyzing the wind resource data in the target sea area, and dividing the wind resource areas into different levels;

[0069] The target sea area raster data is imported into the GIS platform to analyze the wind resource data in the target sea area and divide the wind resource areas of different levels, which specifically includes the following steps:

[0070] The GIS platform uses the geospatial data abstraction gdal library to read the target sea area raster data, determine the weight of the target sea area raster data, and perform weighted merging of the target sea area raster data;

[0071] The target sea area is divided into several sub-areas according to its characteristics. The Zonal Statistics as Table tool of the GIS platform is used to calculate the average wind resources of each sub-area with the divided sub-areas as statistical areas.

[0072] By dividing the wind resources into different levels, regional data including sub-region identification and corresponding level of average wind resources are output.

[0073] After dividing the wind resource areas into different levels, the following steps are also included:

[0074] Use the Graduated Colors classification method to select a color gradient of corresponding level for each wind resource area in the target sea area.

[0075] In this embodiment, the real-time grid data of the wind farm and the historical wind measurement grid data are imported into the GIS platform. The GIS platform can integrate wind resource information of different time scales and conduct a comprehensive wind resource assessment and analysis. It not only takes into account the real-time wind farm conditions, but also combines the long-term wind resource characteristics reflected by the historical data, so that the assessment results are more accurate and reliable. By adopting the weighted summation method, multiple wind resource indicators can be comprehensively considered, and the wind resources in different regions can be comprehensively evaluated. The comprehensive wind resource score of each region is calculated, and the wind resource areas of different levels are divided according to the comprehensive score. The distribution of wind resources can be intuitively displayed, and clear guidance can be provided for the planning and construction of wind farms. That is, in areas with high wind resource levels, more wind turbines can be arranged to improve the power generation capacity of wind farms; in areas with low wind resource levels, the number of wind turbines can be reduced or more suitable wind turbine models can be selected to reduce construction costs and improve the reliability and power generation efficiency of wind turbines.

[0076] Specifically, using the gdal library to read the real-time raster data of the wind farm and the historical wind measurement raster data can integrate data from different sources for analysis. Determining the weights and performing weighted merging can comprehensively consider the real-time situation and historical performance of the wind farm to obtain more comprehensive and accurate comprehensive wind resource data. According to the characteristics of the target sea area, it is divided into several sub-areas, which can be used for a more detailed analysis of wind resources. Different sub-areas may have different wind resource characteristics. By calculating the average wind resources of each sub-area separately, we can better understand the spatial distribution differences of wind resources. Using ArcGIS's Zonal Statistics as Table tool for statistical analysis, we can quickly calculate indicators such as the average wind resources of each sub-area. Through the division of wind resources into different levels, complex wind resource data can be simplified into easy-to-understand and easy-to-apply level information, and output results containing sub-area identifiers and corresponding levels of average wind resources, which is convenient for decision makers and relevant personnel to intuitively understand the wind resource status of each sub-area, provide a clear basis for the management and development of wind resources, and provide more specific guidance for the site selection, layout and operation of wind farms.

[0077] Specifically, by using the Graduated Colors classification method to select a color gradient of the corresponding level for each sub-region, the differences in wind resource levels in different sub-regions can be intuitively presented, and the color gradient can highlight key areas with higher or lower wind resource levels, helping people better understand the spatial distribution characteristics of wind resources. Compared with a single color or simple classification method, the color gradient can provide more information details, which helps to better convey the results of wind resource assessment in reports, presentations and exchanges, and improve communication efficiency and effectiveness.

[0078] Step S5, creating a thematic map on the GIS platform according to wind resource areas of different levels, and displaying the results of wind resource assessment graphically using layer management and symbolization functions;

[0079] In this embodiment, creating a thematic map can display complex wind resource assessment results in an intuitive graphical manner, making it easy to understand the distribution of wind resources and assessment results, and more clearly understand the distribution of wind resources and potential risks, thereby quickly formulating a more reasonable wind power layout plan.

[0080] Step S6, automatically generating a target sea area wind resource report based on various results of the target sea area wind resource assessment, and outputting the target sea area wind resource report in a preset format.

[0081] In this embodiment, the automatically generated wind resource report can systematically summarize and record the analysis results of the wind resource assessment. The report directly reflects the wind resources at different locations within the target sea area. At the same time, it supports the functions of saving and downloading files, so that the wind resource report can be easily archived and shared. The report can be used as part of the project archive and stored for a long time for subsequent reference.

[0082] Specifically, the wind resource report output in PDF format is stable and versatile. PDF files can be easily saved, printed and transmitted, making it easy to archive and preserve wind resource assessment results for a long time.

[0083] Embodiment 2:

[0084] This embodiment provides a GIS data analysis system for offshore wind farm wind resource assessment, including:

[0085] The data acquisition module obtains wind measurement data of the target sea area through the global meteorological analysis data set;

[0086] Output processing module, which converts the acquired wind measurement data into a common format for GIS recognition and processing;

[0087] The data gridding module converts the wind measurement data of the target sea area into continuous grid data of the target sea area through the inverse distance weighted interpolation method;

[0088] GIS analysis module, which imports the target sea area raster data into the GIS platform to analyze the wind resource data in the target sea area and divide the wind resource areas into different levels;

[0089] The visualization module creates thematic maps on the GIS platform according to wind resource areas of different levels, and displays the results of wind resource assessment in an intuitive graphical manner using layer management and symbolization functions;

[0090] The report generation module automatically generates a target sea area wind resource report based on the wind resource assessment results of the target sea area, and outputs the target sea area wind resource report in a preset format.

[0091] Embodiment three:

[0092] This embodiment provides an electronic device having a computer program stored thereon, and when the computer program is executed by a processor, a GIS data analysis method for offshore wind farm wind resource assessment as described in any embodiment of the present invention is implemented.

[0093] Embodiment 4:

[0094] This embodiment provides a computer-readable medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement a GIS data analysis method for offshore wind farm wind resource assessment as described in any embodiment of the present invention.

[0095] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0096] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0097] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0098] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, which is stored in a storage medium and includes 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 application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0099] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A GIS data analysis method for offshore wind farm wind resource assessment, characterized in that: The following steps are involved: Step S1, obtaining wind measurement data of the target sea area through a global meteorological analysis data set; Step S2, converting the acquired wind measurement data of the target sea area into a common format for GIS recognition and processing; Step S3, converting the wind measurement data of the target sea area after the conversion format into continuous target sea area raster data by using the inverse distance weighted interpolation method; Step S4, importing the target sea area raster data into the GIS platform, analyzing the wind resource data in the target sea area, and dividing the wind resource areas into different levels; Step S5, creating a thematic map on the GIS platform according to wind resource areas of different levels, and displaying the results of wind resource assessment graphically using layer management and symbolization functions; Step S6, automatically generating a target sea area wind resource report based on various results of the target sea area wind resource assessment, and outputting the target sea area wind resource report in a preset format.

2. The GIS data analysis method for offshore wind farm wind resource assessment according to claim 1, characterized in that: The wind measurement data of the target sea area includes: wind speed, wind direction and meteorological data of the target sea area collected according to analysis requirements.

3. The GIS data analysis method for offshore wind farm wind resource assessment according to claim 1, characterized in that: In the process of converting the wind measurement data of the target sea area into continuous target sea area grid data, a process of quality inspection of the generated grid part is also included, which is as follows: According to the required grid generation period, the wind measurement data within the latitude and longitude grid points of the target sea area are obtained; The single-period wind measurement data of the target sea area is used to place them in the grid points according to the longitude and latitude grids, and a single-period grid of wind measurement data is initially generated; Data verification is performed on individual grid data in a single period grid to check whether there are null values ​​and wrong values. For grids with missing or erroneous data, data interpolation and revision algorithms are used to interpolate and revise grids with missing or erroneous data.

4. The GIS data analysis method for offshore wind farm wind resource assessment according to claim 1, characterized in that: The target sea area raster data is imported into the GIS platform to analyze the wind resource data in the target sea area and divide the wind resource areas of different levels, which specifically includes the following steps: The GIS platform uses the geospatial data abstraction gdal library to read the target sea area raster data, determine the weight of the target sea area raster data, and perform weighted merging of the target sea area raster data; The target sea area is divided into several sub-areas according to its characteristics. The Zonal Statistics as Table tool of the GIS platform is used to calculate the average wind resources of each sub-area with the divided sub-areas as statistical areas. By dividing the wind resources into different levels, regional data including sub-region identification and corresponding level of average wind resources are output.

5. The GIS data analysis method for offshore wind farm wind resource assessment according to claim 4 is characterized by: After the step of dividing wind resource areas into different levels, the method further includes: Use the Graduated Colors classification method to select a color gradient of corresponding level for each wind resource area in the target sea area.

6. A GIS data analysis system for offshore wind farm wind resource assessment, characterized in that: include: The data acquisition module obtains wind measurement data of the target sea area through the global meteorological analysis data set; Output processing module, which converts the acquired wind measurement data into a common format for GIS recognition and processing; The data gridding module converts the wind measurement data of the target sea area into continuous grid data of the target sea area through the inverse distance weighted interpolation method; GIS analysis module, which imports the target sea area raster data into the GIS platform to analyze the wind resource data in the target sea area and divide the wind resource areas into different levels; The visualization module creates thematic maps on the GIS platform according to wind resource areas of different levels, and displays the results of wind resource assessment in an intuitive graphical manner using layer management and symbolization functions; The report generation module automatically generates a target sea area wind resource report based on the wind resource assessment results of the target sea area, and outputs the target sea area wind resource report in a preset format.

7. A GIS data analysis system for offshore wind farm wind resource assessment according to claim 6, characterized in that: The data acquisition module acquires the wind measurement data of the target sea area through the global meteorological analysis data set. The wind measurement data of the target sea area includes: wind speed, wind direction and climate data of the target sea area collected according to analysis requirements.

8. The GIS data analysis system for offshore wind farm wind resource assessment according to claim 6, characterized in that: The data gridding module, in the process of converting the wind measurement data of the target sea area into continuous target sea area grid data, also includes a process of quality inspection of the generated grid part, which is as follows: According to the required grid generation period, the wind measurement data within the latitude and longitude grid points of the target sea area are obtained; The single-period wind measurement data of the target sea area is used to place them in the grid points according to the longitude and latitude grids, and a single-period grid of wind measurement data is initially generated; Data verification is performed on individual grid data in a single period to check whether there are null values ​​and wrong values. For grids with missing or erroneous data, data interpolation and revision algorithms are used to interpolate and revise grids with missing or erroneous data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the GIS data analysis method for offshore wind farm wind resource assessment as described in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a GIS data analysis method for offshore wind farm wind resource assessment as described in any one of claims 1 to 5 is implemented.

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