Wind power plant climate and ecological effect spatialization method, device, medium and equipment

Abstract

The invention provides a spatialization method and device for climate and ecological effects of a wind power plant, a medium and equipment, and relates to the technical field of planning and design of the wind power plant. The method comprises the following steps: acquiring multi-source climate and ecological data of a wind power plant region and a control group region, and extracting influence data caused by wind power plant operation based on the multi-source climate and ecological data; combining the wind speed data to screen out target influence data of the wind speed in the operation interval of the wind turbine generator; then wake flow simulation is carried out based on wind power plant space layout information and performance parameters, and wake flow intensity distribution is obtained; and finally, by taking the wake flow intensity as a weight, carrying out weighted calculation on the target influence data on the pixel scale of the wind power plant region to generate spatialized climate and ecological effect data. According to the invention, the spatial fine expression of the disturbance influence of the wind power plant is realized, and the optimization of wind power plant design and ecological restoration layout is facilitated.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind farm planning and design. Specifically, it relates to a method, device, computer-readable storage medium, and electronic device for spatializing the climate and ecological effects of a wind farm. Background Technique

[0002] With the continuous development of wind power generation technology, the scale of wind farms has been expanding, the layout density and installed capacity of wind turbines have been continuously increasing, and the impact of wind farms on the surrounding environment has gradually attracted attention. During the operation of wind turbines, the local wind field in the downwind direction will be significantly changed, resulting in wake effects such as wind speed deficit, wind direction deflection, and turbulence enhancement, which may further affect the atmospheric boundary layer structure and surface climate and ecosystem. In order to evaluate the environmental changes brought about by the operation of wind farms, some studies have proposed using meteorological observation or ecological investigation means, combined with the operation data of wind farms for analysis, to preliminarily reveal the correlation mechanism between the operation of wind farms and the local climate-ecological response.

[0003] Currently, some studies use on-site observation methods to evaluate the climate and ecological effects of wind farms. However, due to the limited number of observation stations and mostly fixed positions, the obtained data are difficult to comprehensively cover the entire area of the wind farm, resulting in an inability to form a spatial continuous understanding of the operation disturbance of the wind farm. In addition, although remote sensing observations of the impact of wind farms have the ability to cover regions, the spatial resolution of its commonly used products is generally not higher than 1 km, making it difficult to resolve the micro-scale disturbances brought about by the internal structure of the wind farm and the distribution of wind turbines, which limits its application value in the micro-site selection and layout optimization of wind farms. And related simulation methods mostly use mesoscale climate models to simulate the impact of wind farms. Although they have a certain prediction ability, their typical spatial resolution is several kilometers, making it difficult to capture the subtle differences such as local wind speed disturbances, thermal changes, and ecological responses caused by the operation of wind turbines.

[0004] Therefore, there is an urgent need for a new spatialization scheme for the climate and ecological effects of wind farms to more comprehensively and accurately present the disturbance effects caused by the operation of wind farms on the climate and ecosystem, and thus provide effective support for the site selection evaluation, micro-design, and operation regulation of wind farms.

[0005] It should be noted that the information disclosed in the above background technique section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the embodiments of the present disclosure is to provide a method, device, medium, and equipment for spatializing the climate and ecological effects of a wind farm, which can more comprehensively and accurately present the disturbance effects caused by the operation of wind farms on the climate and ecosystem, and thus provide effective support for the site selection evaluation, micro-design, and operation regulation of wind farms.

[0007] According to a first aspect of the embodiments of the present disclosure, a method for spatializing the climate and ecological effects of a wind farm is provided, including: Obtain multi-source climate and ecological data in the wind farm area and the control group area, and calculate initial wind farm impact data based on the multi-source climate and ecological data of each area; Obtain wind speed data corresponding to the initial wind farm impact data, and screen out target wind farm impact data from the initial wind farm impact data according to the wind speed data, where the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operation range; Based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines, conduct wake simulation of the wind farm area to obtain the wake intensity distribution of the wind farm area; Based on the target wind farm impact data, perform weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution to obtain the spatialized data of the climate and ecological effects of the wind farm.

[0008] In an exemplary embodiment of the present disclosure, the wake intensity distribution includes wake wind speed deficit; The step of performing weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution based on the target wind farm impact data to obtain the spatialized data of the climate and ecological effects of the wind farm includes: Calculate the number of spatial pixels in the wind farm area according to the area of the wind farm area and the grid size of the wake simulation model; Use the wake wind speed deficit as the weighting factor for each spatial pixel, and calculate the spatialized data of the climate and ecological effects of the wind farm according to the target wind farm impact data, the number of spatial pixels, and the weighting factor.

[0009] In an exemplary embodiment of the present disclosure, the step of calculating initial wind farm impact data based on the multi-source climate and ecological data of each area includes: Based on the multi-source climate and ecological data of each area, calculate the statistical values of the target climate and ecological elements in the wind farm area and the control group area respectively; Calculate the difference between the statistical values of the target climate and ecological elements in the wind farm area and the control group area to obtain the initial wind farm impact data.

[0010] In an exemplary embodiment of the present disclosure, the step of calculating the statistical values of the target climate and ecological elements in the wind farm area and the control group area respectively based on the multi-source climate and ecological data of each area includes: Based on the spatial distribution characteristics of various climate and ecological data in the multi-source climate and ecological data, spatially statistical processing is performed on the target climate and ecological elements within the wind farm area and the control group area respectively to obtain the statistical values of the target climate and ecological elements in the corresponding areas; Among them, the multi-source climate and ecological data include field observation data, satellite remote sensing data, and wind farm simulation data.

[0011] In an exemplary embodiment of the present disclosure, the spatial layout information includes the geographical location and geometric parameters of the wind turbines; Performing wind farm wake simulation based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines to obtain the wake intensity distribution of the wind farm, including: Performing wind farm wake simulation calculation based on the geographical location, geometric parameters, and performance parameters of the wind turbines to obtain the wake intensity distribution.

[0012] In an exemplary embodiment of the present disclosure, performing wind farm wake simulation calculation based on the geographical location, geometric parameters, and performance parameters of the wind turbines to obtain the wake intensity distribution includes: Based on the geographical location of the wind turbines, obtaining wind speed and wind direction data corresponding to the initial wind farm impact data; wherein, the initial wind farm impact data is determined based on the multi-source climate and ecological data in the wind farm area and the control group area; Determining a corresponding wake simulation model according to the response characteristics of the target climate and ecological elements; Based on the geometric parameters, performance parameters of the wind turbines, and the wind speed and wind direction data, using the wake simulation model to perform wind farm wake simulation calculation to obtain the wake intensity distribution.

[0013] In an exemplary embodiment of the present disclosure, the method further includes: Based on a preset spatial grid structure, selecting a wind farm area in the research area and constructing a candidate area outside the wind farm area; Screening a target area from the candidate areas whose similarity to the wind farm area meets a preset similarity condition, and obtaining a control group area according to the target area.

[0014] According to the second aspect of the embodiments of the present disclosure, there is provided a spatialization device for wind farm climate and ecological effects, including: A perturbation extraction module, configured to obtain multi-source climate and ecological data in the wind farm area and the control group area, and calculate initial wind farm impact data based on the multi-source climate and ecological data of each area; A wind speed matching module, configured to obtain wind speed data corresponding to the moment of the initial wind farm impact data, and screen out target wind farm impact data from the initial wind farm impact data according to the wind speed data, wherein the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operation range; A wake simulation module, configured to perform wake simulation of a wind farm based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines, so as to obtain the wake intensity distribution of the wind farm area; A spatial mapping module, configured to perform weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution based on the target wind farm impact data, so as to obtain spatialized data of the wind farm climate and ecological effects.

[0015] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any step of the method described in the first aspect is implemented.

[0016] According to a fourth aspect of the present disclosure, there is provided an electronic device, including: A processor and a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, any step of the method described in the first aspect is implemented.

[0017] The technical solutions provided in the embodiments of the present disclosure may include the following beneficial effects: In the spatialization method of the wind farm climate and ecological effects provided in the exemplary embodiments of the present disclosure, by constructing a multi-source climate and ecological data comparison framework between the wind farm area and the control group area, on the basis of ensuring the consistency of the regional environmental background, the disturbance information caused by the operation of the wind farm can be effectively extracted, thereby overcoming the limitation of regional effect identification caused by fixed and insufficient field observation points in the related art. Further, a wind speed operation range is set and the target wind farm impact data is screened accordingly, which helps to eliminate background interference under non-operating conditions, improve the accuracy of disturbance attribution, and avoid evaluation deviations caused by different wind conditions. At the same time, by introducing the spatial layout information and performance parameters of the wind turbines to carry out wake simulation, the disturbance characteristics of the wind farm layout on the downwind area wind field structure can be fully characterized, so that the spatial distribution of the disturbance information is more in line with the actual physical process. Furthermore, using the wake intensity as a spatial weighting factor, projecting the target impact data onto the pixel scale of the wind farm area, and carrying out weighted calculation, the spatialized expression of the climate and ecological effects can be realized at the microscale level, effectively making up for the deficiencies of remote sensing observation and mesoscale simulation in terms of spatial resolution. This method can be used not only to evaluate the climate and ecological disturbances caused by the operation of built or under-construction wind farms, provide references for ecological restoration and operation regulation, but also to predict the potential ecological and climate response mechanisms of proposed wind farms, and provide decision-making support for wind turbine arrangement and site selection.

[0018] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0020] Figure 1 The system architecture diagram showing a method for spatializing the climate and ecological effects of a wind farm to which the embodiments of the present disclosure can be applied is shown.

[0021] Figure 2 The flowchart showing a method for spatializing the climate and ecological effects of a wind farm in the embodiments of the present disclosure is shown.

[0022] Figure 3 The schematic diagram showing an initial wind farm area in the embodiments of the present disclosure is shown.

[0023] Figure 4 The schematic diagram showing an initial control group area in the embodiments of the present disclosure is shown.

[0024] Figure 5 The schematic diagram showing an intermediate wind farm area and an intermediate control group area in the embodiments of the present disclosure is shown.

[0025] Figure 6 The schematic diagram showing a final wind farm area and a final control group area in the embodiments of the present disclosure is shown.

[0026] Figure 7 The schematic diagram showing the spatialization result of the influence of a wind farm on the surface temperature under different unit selection and layout scenarios in the embodiments of the present disclosure is shown.

[0027] Figure 8 The schematic diagram showing the average spatialization result of the influence of a wind farm on the surface temperature under different unit selection and layout scenarios in the embodiments of the present disclosure is shown.

[0028] Figure 9 The block diagram showing a device for spatializing the climate and ecological effects of a wind farm in the embodiments of the present disclosure is shown.

[0029] Figure 10 The structural diagram showing an electronic device suitable for implementing the embodiments of the present disclosure is shown.

[0030] In the accompanying drawings, the same or corresponding reference numerals denote the same or corresponding parts. Detailed implementation manners

[0031] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "said" used in this specification and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0032] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0033] Figure 1 The system architecture diagram of a method for spatializing the climate and ecological effects of a wind farm to which the embodiments of the present disclosure can be applied is shown.

[0034] As Figure 1 shown, the system architecture 100 may include one or more of terminal devices such as a smart phone 101, a portable computer 102, a desktop computer 103, etc., a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or fiber optic cables, etc.

[0035] The terminal device may be various electronic devices having data processing functions, and a display screen is provided on the electronic device, and the display screen is used to display to the user the wind farm area, the control group area, multi-source climate and ecological data in each area, and spatialized data of the climate and ecological effects of the wind farm, etc., to facilitate the user to evaluate and make decisions. The electronic device includes but is not limited to the above-mentioned desktop computer, portable computer, smart phone, and tablet computer, etc.

[0036] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0037] The method for spatializing the climate and ecological effects of a wind farm provided by the embodiments of the present disclosure can be executed by a terminal device. Correspondingly, the device for spatializing the climate and ecological effects of a wind farm can be disposed in the terminal device. However, those skilled in the art can easily understand that the method for spatializing the climate and ecological effects of a wind farm provided by the embodiments of the present disclosure can also be executed by the server 105. Correspondingly, the device for spatializing the climate and ecological effects of a wind farm can also be disposed in the server 105. No special limitation is made in this exemplary embodiment.

[0038] Embodiments of the present disclosure provide a method for spatializing the climate and ecological effects of a wind farm. Referring to Figure 2 as shown, the method may include steps S210 to S240: Step S210, obtaining multi-source climate and ecological data in the wind farm area and the control group area, and calculating initial wind farm impact data based on the multi-source climate and ecological data of each area; Step S220, obtaining wind speed data corresponding to the moment of the initial wind farm impact data, and screening out target wind farm impact data from the initial wind farm impact data according to the wind speed data, where the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operation range; Step S230, performing wake simulation of the wind farm based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines to obtain the wake intensity distribution of the wind farm area; Step S240, based on the target wind farm impact data, performing weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution to obtain the spatialized data of the climate and ecological effects of the wind farm.

[0039] By executing the method for spatializing the climate and ecological effects of a wind farm provided by the present disclosure, by constructing a comparison framework of multi-source climate and ecological data between the wind farm area and the control group area, on the premise of ensuring consistent regional environmental conditions, the disturbance information caused by the operation of the wind farm can be extracted, effectively overcoming the problems of limited measurement points and insufficient coverage of existing measurement methods. Setting a wind speed operation range to screen target data helps to exclude interference factors in non-operating states and improve the accuracy of disturbance attribution. Conducting wake simulation in combination with the layout information and performance parameters of the wind turbines can reflect the actual impact of the wind farm layout on the wind field structure, making the spatial distribution of disturbances more physically based. Using the wake intensity as the pixel weighting factor to perform weighted processing on the impact data realizes the high-resolution spatial expression of climate and ecological effects, making up for the limitations in resolution of remote sensing and mesoscale simulation. This method is applicable to both evaluating the environmental effects of existing wind farms and predicting the potential impacts of proposed projects, providing support for wind farm planning and operation and maintenance.

[0040] Next, a spatialization method for the climate and ecological effects of a wind farm in this exemplary embodiment will be described in detail.

[0041] In step S210, multi-source climate and ecological data in the wind farm area and the control group area are obtained, and initial wind farm impact data is calculated based on the multi-source climate and ecological data of each area.

[0042] In the exemplary embodiment of the present disclosure, the wind farm area refers to the research area where the wind turbine layout range is located, and the control group area refers to the external area selected as a reference for the wind farm area. The selection locations and area sizes of the wind farm area and the control group area can be flexibly changed according to actual needs, and the present disclosure does not limit this.

[0043] Exemplarily, based on a preset spatial grid structure, a wind farm area can be selected in the research area, and a candidate area can be constructed outside the wind farm area. Then, a target area whose similarity to the wind farm area meets a preset similarity condition is screened from the candidate area, and the control group area is obtained according to the target area.

[0044] Among them, the spatial grid structure refers to a regular grid constructed in the research area according to a unified spatial resolution (such as 1 km × 1 km), which is used to carry and normalize various spatial data to ensure the comparability of spatial data between different areas. On this basis, multiple consecutive grid cells containing the wind turbine layout area can be selected from the research area to form the wind farm area. For example, the wind turbine layout information extracted from high-definition remote sensing images or the wind turbine planning layout map formed in the design stage can be input into a geographic information system, and a grid structure of a preset scale, such as a 1 km × 1 km regular grid, can be constructed based on its spatial distribution range. The wind turbine positions and the regular grid are spatially overlapped, and a set of grid cells containing at least one wind turbine is screened out, and these sets of grid cells are defined as the initial wind farm area for subsequent construction of the control group area and difference analysis processing.

[0045] Then, a candidate area is constructed within a certain range outside the initial wind farm area (such as 3 km to 8 km), and this range should avoid the area directly affected by the wake of wind turbines to maintain its original background state as much as possible. It can be understood that the candidate area is a buffer zone established within a preset distance outside the initial wind farm area and is used as a potential control area. Within the candidate area, based on the basic geographical attributes of each grid cell, such as land cover type, terrain undulation, vegetation type, altitude, etc., a grid-by-grid comparison is made with the initial wind farm area. By calculating the similarity between each candidate area and the initial wind farm area and screening according to the preset similarity conditions, the target areas with satisfactory similarity are retained. Finally, the set of these target areas is defined as the control group area, which is used to represent the comparison benchmark not affected by the operation of wind turbines. It can be understood that at the same time, areas in the initial wind farm area that are significantly different from the main elements of the wind farm area also need to be excluded to obtain the final wind farm area.

[0046] For example, first, for each grid cell in the candidate area, the numerical features of each grid cell in multiple basic geographical attribute dimensions are extracted, including but not limited to the classification code of land cover type, the standard deviation of terrain undulation, the classification index of vegetation type, and the average value of altitude, etc. Then, the multi-dimensional attribute features of each grid cell are compared with the statistical features of all grid cells in the initial wind farm area in the corresponding attribute dimensions. Next, based on a distance function or a similarity metric function, the similarity score of each grid cell in the candidate area and the wind farm area in the attribute space is calculated. Screening conditions are constructed according to the tolerance range or preset similarity threshold of each geographical attribute feature in the initial wind farm area, and grid cells with large deviation in similarity are excluded, and the set of grid cells with similarity scores meeting the screening conditions is retained. Finally, a spatial connectivity analysis is performed on the grid cells passing the screening, and scattered or discontinuous areas are excluded to form a control group area with a complete spatial structure and matching attribute features.

[0047] This setting method can more effectively identify the non-background perturbation effects caused by the operation of the wind farm on the basis of keeping the regional environmental conditions as consistent as possible.

[0048] Reference Figure 3 As shown, a schematic diagram of an initial wind farm area is shown, in which the initial wind farm area 301 is formed by selecting a set of grid cells containing at least one wind turbine. Reference Figure 4 As shown, a schematic diagram of an initial control group area is shown. By setting a fixed buffer distance outside the initial wind farm area 301, the initial control group area 401 is constructed within this buffer range with the same grid specification as the wind farm area. Reference Figure 5As shown, a schematic diagram of an intermediate wind farm area and an intermediate control group area is presented. Based on the initial wind farm area 301 and the initial control group area 401, grid cells 501 with significant differences in the main attributes of the wind farm are deleted from each area. For example, grid cells with inconsistent main characteristics of the wind farm area in terms of attributes such as land cover type, terrain relief, and vegetation type are deleted, thereby obtaining a more homogeneous intermediate wind farm area 502 and an intermediate control group area 503. Refer to Figure 6 As shown, a schematic diagram of a final wind farm area and a final control group area is presented. By further merging the remaining grid cells in the intermediate wind farm area 502 and the intermediate control group area 503 and performing spatial connectivity constraints to ensure continuous regional structure and regular morphology, a final wind farm area 601 and a final control group area 602 for subsequent comparative analysis are respectively formed.

[0049] Figures 3 to 6 It successively shows the whole process of the wind farm area and the control group area from initial construction, candidate screening to final formation, gradually realizing the delineation of the optimized area from two dimensions of spatial constraints and attribute matching, laying a foundation for subsequent spatial analysis.

[0050] In the exemplary embodiment of the present disclosure, multi-source climate and ecological data refer to climate and ecological data with different sources but relevant reflected content. Among them, climate and ecological data include climate elements and ecological elements. Climate elements include but are not limited to temperature, humidity, surface temperature, soil humidity, etc. Climate elements usually show instantaneous or quasi-instantaneous response characteristics to the operation of the wind farm and the resulting local wind field changes. Through continuous observation or simulation, spatialization processing can be further completed to support real-time or short-term scale applications in wind farm site selection design and operation and maintenance assessment. Ecological elements include but are not limited to carbon flux, vegetation index, etc. Taking carbon flux as a characterization index of vegetation status as an example, carbon flux refers to the carbon dioxide exchange flux per unit area per unit time, which can reflect the instantaneous intensity of photosynthesis and respiration in the vegetation system. Its value can produce an immediate response when the climate conditions change caused by the wind farm, and it has high time resolution and sensitivity. Through spatialization processing of the observation or simulation results of carbon flux, the instantaneous disturbance caused by the operation of the wind farm to the surface ecosystem can be effectively characterized in the ecological dimension, providing valuable auxiliary information for micro-site selection of wind farms, ecological impact assessment, and operation and maintenance regulation.

[0051] Among them, multi-source climate and ecological data can include field observation data, satellite remote sensing data, and wind farm simulation data.

[0052] For field observation data, to effectively identify the operation disturbances of a wind farm, it is usually necessary to set up observation points in the wind farm area and the control group area respectively and carry out long-term synchronous observations to obtain climate and ecological data with comparative significance. For satellite remote sensing data, observation products with corresponding spatial and temporal resolutions can be selected as data sources according to actual needs. Taking the Moderate Resolution Imaging Spectroradiometer (MODIS) as an example, its daily products have a spatial resolution of 500 meters to 1000 meters and can provide observation information at the scale of a wind farm. For wind farm simulation data, a mesoscale numerical simulation method driven by physical mechanisms, such as the Weather Research and Forecasting (WRF) model, can be used. In the application of wind farm impact assessment, this model usually outputs relevant climate elements such as wind speed, turbulence, and temperature at a spatial resolution of 1 km to 3 km, and has advantages such as strong controllability and adjustable parameters. In addition, a simulation model can also be constructed using machine learning or statistical regression methods to establish a disturbance prediction relationship based on a training set, so as to output climate and ecological element data related to the operation of the wind farm. This disclosure does not limit this.

[0053] In addition, to ensure the accuracy and consistency of subsequent analysis, various types of data need to be preprocessed separately. For field observation data, the historical record data collected by the observation stations arranged in the wind farm area and the control group area can be selected. This type of data is usually output in text format and can be read and parsed with the help of data processing tools. During the processing, missing values, duplicate values, and abnormal records outside the reasonable range need to be identified and excluded to obtain representative high-quality observation data.

[0054] For satellite remote sensing observation data, remote sensing products of different orbital types can be selected according to the spatio-temporal resolution requirements of the target climate and ecological elements. Taking sun-synchronous orbit remote sensing data as an example, common products have a daily observation frequency and a spatial resolution of 500 meters to 1 kilometer. High-quality images meeting the cloud-free conditions can be screened and obtained through a data processing platform with quality control functions, and the output format can be set as an information raster data with geographic coordinate references. For geostationary meteorological satellites, their remote sensing products are usually in a file format with a standard grid structure. By reading the georeferencing information contained in the remote sensing products, the matching between the image and the geographic coordinates can be completed, and a spatial data product in a unified format can be generated through spatial reprojection. During this process, low-quality pixels affected by clouds or occlusion can be excluded according to the data quality flags attached to the remote sensing products to ensure the observation accuracy and stability of the included data.

[0055] For wind farm simulation data, a mesoscale meteorological simulation model constructed based on physical mechanisms can be selected for calculation. Taking a typical mesoscale meteorological model as an example, when simulating the climate and ecological responses in the wind farm area, usually at the same nesting level, dual simulation scenarios with and without a wind farm parameterization scheme are respectively constructed. The difference between the simulation results of the two cases can be used to quantify the environmental perturbation effects caused by the operation of the wind farm. The file format of such simulation results is often the standard raster format, which contains spatial coordinate information. The coordinate parsing and unified spatial reference system conversion can be completed through geographic information processing tools to convert it into raster data with spatial positioning capabilities, which is convenient for alignment and fusion processing with data from other observation sources.

[0056] The initial wind farm impact data is the preliminary perturbation information obtained by analyzing the differences in climate and ecology between the wind farm area and the control group area, and is used to characterize the regional differential responses caused by the operation of the wind farm.

[0057] In some exemplary embodiments, when calculating the initial wind farm impact data based on multi-source climate and ecological data of each region, specifically, based on the multi-source climate and ecological data of each region, the statistical values of the target climate and ecological elements in the wind farm area and the control group area can be calculated respectively. Among them, the target climate and ecological elements can be ecological elements or climate elements, such as at least one of temperature, humidity, surface temperature, soil humidity, carbon flux, and vegetation index. The present disclosure does not limit this. The statistical values can be average values, maximum values, minimum values, standard deviations, etc. The specific statistical form can be set according to the physical properties of the target climate and ecological elements and the evaluation requirements.

[0058] Exemplarily, based on the spatial distribution characteristics of various climate and ecological data in the multi-source climate and ecological data, the target climate and ecological elements can be spatially statistically processed in the wind farm area and the control group area respectively to obtain the statistical values of the target climate and ecological elements in the corresponding areas.

[0059] It should be noted that the multi-source climate and ecological data includes satellite remote sensing data and wind farm simulation data with a rasterized spatial structure, as well as field observation data in the form of point observations. For satellite remote sensing data and wind farm simulation data, based on the boundary ranges of the wind farm area and the control group area, the grid cells contained in each area can be extracted, and the statistical values of the target climate or ecological elements in each area can be calculated. For field observation data, spatial statistical processing refers to directly statistically calculating the observed values by screening the observation points falling within the regional boundaries in the wind farm area and the control group area to obtain the statistical values of the field observation data in the area, such as statistically obtaining the average temperature of all meteorological stations in each area.

[0060] It is understandable that any one of the climate and ecological data, such as satellite remote sensing data, wind farm simulation data, and field observation data, can be used to perform spatial statistical processing in the wind farm area and the control group area to obtain the statistical values of the target climate and ecological elements in the corresponding areas. Of course, the three types of climate and ecological data, namely satellite remote sensing data, wind farm simulation data, and field observation data, can also be combined to obtain the final statistical values of the target climate and ecological elements. For example, based on satellite remote sensing data, wind farm simulation data, and field observation data respectively, the initial statistical values of the target climate and ecological elements in the wind farm area and the control group area are obtained, and taking the field observation data as the reference benchmark, the differences between the initial statistical values are compared to identify possible biases in the satellite remote sensing data and the wind farm simulation data. Based on the bias identification results, the statistical values of the target climate and ecological elements in the satellite remote sensing data and / or the simulation data are corrected for bias, and the corrected statistical values are used as the final statistical values of the target climate and ecological elements for subsequent regional difference analysis and impact attribution processing.

[0061] Under the influence of the climate background, the climate and ecological elements in the wind farm area often show obvious seasonal changes and a certain degree of irregular fluctuations. Since the wind farm area and the control group area are under the same climate background conditions, the corresponding elements in the control group area will also be affected similarly. Therefore, by calculating the difference between the statistical values of the target climate and ecological elements in the wind farm area and the control group area, the common disturbances brought about by background climate change can be effectively offset, and then the difference characteristics related to the existence of the wind farm can be extracted, so as to ensure that the difference information more accurately reflects the local impacts caused by the operation of the wind farm.

[0062] For example, there is: (1) Among them, is the initial wind farm impact data, that is, the impact of the wind farm on the target climate and ecological elements at a certain moment, is the average value of the target climate and ecological elements in the wind farm area at a certain moment, is the average value of the target climate and ecological elements in the control group area at a certain moment.

[0063] In step S220, the wind speed data corresponding to the moment of the initial wind farm impact data is obtained, and the target wind farm impact data is screened from the initial wind farm impact data according to the wind speed data, where the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operation range.

[0064] Since a wind farm mainly disturbs the local climate and ecosystem by generating wake effects during the operation of the turbines, during the actual operating wind speed conditions of the wind turbines, their operation is the main driving factor causing changes in the disturbances in the observations or simulations. If the wind speed is in the non-operating range of the turbines, such as below the cut-in wind speed or above the cut-out wind speed, the operation of the wind farm is not the main environmental impact factor, and such data is not suitable as a basis for spatialization.

[0065] Therefore, after the disturbance calculation between the wind farm area and the control group area is completed, that is, after the generation stage of the initial wind farm impact data, the wind speed data at the corresponding time should be introduced as a screening basis to match all the impact data by time, and retain the data whose corresponding wind speed values are within the operating range of the wind farm as the target wind farm impact data. For the impact data with wind speeds not within the operating range, it can be directly set to invalid or assigned a value of 0 to avoid misjudging the differences under non-operating conditions as environmental impacts caused by the operation of the wind farm. Among them, the wind speed operating range can be set according to the technical parameters of specific wind turbines, and the present disclosure does not limit this.

[0066] This step can ensure that the input impact data in the subsequent spatialization process has the physical relevance of wind power operation disturbances, thereby improving the interpretability and accuracy of the spatialization results.

[0067] In step S230, based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines, wake simulations of the wind farm are carried out to obtain the wake intensity distribution of the wind farm area.

[0068] In the exemplary embodiment of the present disclosure, the spatial layout information includes the geographical location and geometric parameters of the wind turbines. Exemplarily, based on the geographical location, geometric parameters and performance parameters of the wind turbines, wake simulation calculations of the wind farm can be carried out to obtain the wake intensity distribution. Among them, the geographical location of the wind turbine refers to the longitude and latitude coordinates of each wind turbine in the geographical space, which is used to determine the actual installation position of each wind turbine in the wind farm. It can be understood that the geographical location of the wind turbine not only determines the distribution of the wake initial points, but also affects the superposition relationship and spatial propagation path between the wakes of different turbines, and is one of the basic inputs for wake modeling. The geometric parameters of the wind turbine are used to describe the main dimensional characteristics of the wind turbine structure, including hub height, blade length, swept diameter of the wind turbine, turbine spacing, layout arrangement method, etc. The performance parameters of the wind turbine include rated power, power curve and thrust curve, etc. Among them, the rated power refers to the maximum electric power that the wind turbine can continuously output at the rated wind speed, the power curve is a function curve describing the output power response relationship of the wind turbine at different wind speeds, and the thrust curve is a curve representing the thrust coefficient generated by the wind turbine at different wind speeds.

[0069] Exemplarily, based on the geographical location of the wind turbine, the wind speed and wind direction data corresponding to the initial wind farm impact data at a specific moment can be obtained, and the corresponding wake simulation model can be determined according to the response characteristics of the target climate and ecological elements. Specifically, before conducting wake simulation, first, based on the geographical location of the wind turbine, the specific spatial location of the wind turbine in the geographic information system can be determined, and using this location as an index, combined with the time point corresponding to the initial wind farm impact data, the wind speed and wind direction data at the corresponding time and spatial points can be extracted from the measured meteorological data and meteorological reanalysis data, which are used to determine the inflow conditions for wake propagation. Among them, the wind speed determines the degree of wake center wind speed deficit, wake length, etc., and the wind direction affects the wake propagation path and superposition characteristics. The wind speed and wind direction data can be sourced from the wind measurement towers installed within the wind farm or obtained through means such as meteorological reanalysis data and numerical weather prediction models.

[0070] Then, according to the response characteristics of the target climate and ecological elements to be evaluated, the model type for simulating wake disturbance characteristics is determined. If the target climate and ecological elements have a time-delay response characteristic to wind speed disturbance, and the evaluation focuses on the steady-state impact of the wake, for example, for surface temperature or soil moisture, etc., a steady-state wake model with simple structure and low computational cost can be selected for simulation, such as the Niels Otto Jensen model, Bastankhah Gaussian model, Niayifar Gaussian model, etc., to capture the average characteristics of the wake. For target climate and ecological elements with obvious time-varying characteristics, such as air temperature, humidity, etc., a transient wake model that can reflect the temporal evolution characteristics of the wake can be selected.

[0071] Finally, based on the collected wind speed and wind direction data, as well as the geometric parameters and performance parameters of the wind turbine, they are input into the wake simulation model for simulation calculation to simulate the interaction and superposition process between wakes of different turbines, and finally the wake intensity distribution covering the entire wind farm area is output. Among them, the wake intensity distribution can be manifested as the degree of wind speed deficit and the enhancement amplitude of turbulence intensity within each spatial pixel, which is used to quantitatively reflect the disturbance ability of the wind turbine operation on the climate and ecological state of this area.

[0072] Taking the wake simulation using the Bastankhah Gaussian model as an example, there are: (2) (3) (4) Wherein, is the wake wind speed deficit, is the incoming flow wind speed at the wind turbine, is the wake velocity deficit ratio, is the maximum wind speed deficit at the wake center, is the wake width, is the thrust coefficient of the wind turbine, is the swept diameter of the wind turbine, is the distance from the pixel at the wake center in the downwind direction to the wind turbine, is the wake diffusion coefficient, is a constant, such as 0.2.

[0073] By introducing the geographical location and geometric parameters of the wind turbines, the distribution pattern and structural characteristics of each wind turbine in the actual space can be accurately described. Combining the performance parameters to construct the wind source input conditions that fit the actual operating state provides a boundary basis for the subsequent wake perturbation propagation. Furthermore, by performing the wake simulation process, the perturbation characteristics such as wind speed deficit and wind direction deviation caused by the operation of the wind turbines can be captured, and the difference in perturbation intensity presented in each area of the wind farm due to the influence of the wake can be finely reflected. Compared with the static processing method without introducing wake information, this process can effectively reveal the spatial heterogeneity of the internal perturbation of the wind farm, making the subsequent spatialized results of climate and ecological impacts closer to the actual interference ability distribution of the wind turbines, thereby improving the physical rationality of the perturbation effect estimation and the fineness of the spatial expression.

[0074] In step S240, based on the target wind farm impact data, weighted calculation is performed on the pixel scale of the wind farm area according to the wake intensity distribution to obtain the spatialized data of the wind farm climate and ecological effects.

[0075] In an exemplary implementation, the wake intensity distribution includes the wake wind speed deficit, which can reflect the degree of wind speed attenuation caused by the operation of the wind turbines and is the core index to measure the perturbation intensity of the wind farm operation. It can be used as a factor for the weighted distribution of perturbation effects in space.

[0076] Exemplarily, the number of spatial pixels in the wind farm area can be calculated according to the area of the wind farm area and the grid size of the wake simulation model. Specifically, the area of the wind farm area is determined according to the number of grid cells in the wind farm area, and the number of spatial pixels in the area is obtained according to the ratio between the area of the wind farm area and the spatial resolution of the selected wake simulation model.

[0077] Then, the wake wind speed deficit is used as the weighting factor for each spatial pixel, and based on the target wind farm impact data, the number of spatial pixels, and the weighting factor, the spatialized data of the wind farm's climate and ecological effects is calculated. Specifically, the wake intensity information at the corresponding position is extracted for each spatial pixel, specifically the value of the wake wind speed deficit. Based on this wake wind speed deficit value, a corresponding weighting factor is assigned to each pixel. Then, taking the target wind farm impact data as the basis for the overall perturbation intensity, combined with the weighting factor, a weighted allocation operation is performed on all spatial pixels to obtain the spatialized data of the wind farm's climate and ecological effects, thereby reconstructing the spatial distribution map of the wind farm's climate and ecological effects at the pixel scale.

[0078] For example, there is: (5) Where, is the spatialized data of the wind farm's climate and ecological effects, that is, the impact of the target climate and ecological elements caused by the wind farm after spatial allocation, [[ID=!2]]is the target wind farm impact data, is the number of spatial pixels in the wind farm area, is the wake wind speed deficit, which reflects the strength of the influence of each spatial pixel by the wind turbine wake. It can be seen from formula (5) that based on the target wind farm impact data and the number of spatial pixels in the wind farm area, a reference value of the overall perturbation intensity of the wind farm can be constructed, that is , and then, this overall perturbation intensity is weighted and allocated among the spatial pixels according to the wake wind speed deficit to finally obtain the impact of the target climate and ecological elements caused by the wind farm on each spatial pixel .

[0079] This process completes the spatial projection of the target perturbation information within the wind farm area, enabling the spatialized result to reflect the differences in the degree of influence of different areas by the operation of wind turbines. This spatialized result can be used as an important input basis for subsequent optimization of the wind farm's micro layout, division of ecological restoration areas, and environmental impact assessment.

[0080] After obtaining the spatialized data of the instantaneous wind farm's climate and ecological effects, to further analyze the continuous impact of the wind farm operation on the regional climate and ecological system, statistical processing can be performed on this type of data in the time dimension to form a time integration result reflecting the cumulative or average state of the effects within a certain time scale.

[0081] Specifically, the spatio-temporal effect data corresponding to consecutive multiple time points can be averaged or accumulated over time on the basis of consistent spatial pixels, so as to generate statistical layers at daily or monthly scales, which are used to characterize the temporal response characteristics of the operation of the wind farm to the target climate and ecological elements. For example, when conducting an impact assessment of surface air temperature, the hourly generated temperature impact data can be averaged over 24 hours to obtain the spatial distribution of the average perturbation of the wind farm on the surface air temperature on that day. On this basis, monthly average or seasonal average can be further calculated to support the trend analysis of the impact of the wind farm on climate processes.

[0082] For another example, in the evaluation of ecological indicators, elements such as carbon flux often have the characteristics of change per unit time. To quantify the regulatory effect of the operation of the wind farm on the carbon budget of the ecosystem over a long time series, the carbon flux impact data at each moment can be accumulated over time to obtain the cumulative spatial distribution reflecting the impact of the wind farm on the carbon sink function during the growing season, the whole year or other target periods, so as to provide a decision-making basis for ecosystem management and the optimization of wind farm operation strategies.

[0083] In some specific example embodiments, to achieve the spatialized assessment of the possible surface temperature perturbation caused by an unbuilt wind farm, first, candidate areas with high wind energy resource potential can be identified in the study area, and the limiting factors such as traffic accessibility, terrain conditions and ecological protection can be comprehensively considered to screen out the target sites with layout feasibility. After determining the site, the spatial layout of the wind turbines can be carried out according to the typical turbine layout specifications. Exemplarily, a layout rule with an in-row spacing of not less than 3D and an inter-row spacing of not less than 6D can be adopted, where D represents the swept diameter of the wind turbine.

[0084] For a fixed total installed capacity (such as 30 MW), different layout scenarios with different single-unit capacities can be set to carry out comparative analysis. For example, a 3 MW scenario composed of 10 3 MW turbines and a 5 MW scenario composed of 6 5 MW turbines can be set.

[0085] After the layout of the wind turbines is completed, a corresponding wind farm area can be constructed based on a 1 km × 1 km spatial grid, and candidate areas can be established within a range of 3 km to 8 km around it. Further, in combination with basic geographical elements such as terrain undulation and land cover type, grid cells that are significantly different from the wind farm area can be screened out, and only areas with similar basic attributes are retained as the control group area to construct a comparable spatial control system.

[0086] Subsequently, the disturbance information of the wind farm operation on the surface temperature within a set time period can be obtained through multi-source data fusion. The multi-source climate and ecological data may include field observation data, satellite remote sensing data, and wind farm climate simulation data. Of course, mesoscale numerical models such as WRF can also be used to simulate each of the two layout scenarios hour by hour, obtain the disturbance values of the wind farm on the surface temperature during the corresponding time period, and extract the wind speed and wind direction data at the corresponding times as the input conditions for the wake simulation model. After completing the climate disturbance calculation, based on a high-resolution wake simulation model (such as the Bastankhah Gaussian model), combined with the geometric dimensions, operating power, thrust coefficient curve of the unit, and the wind speed and wind direction information at that time, the wake intensity distribution can be calculated. The simulated wake results have a spatial resolution of the order of 10 m, which can be used as the weight basis for the spatial allocation of disturbance values to support high-precision spatial mapping.

[0087] With the support of the wake results, spatial weighting processing of the surface temperature impact data can be carried out to achieve a high-resolution expression of the disturbance effect. Refer to Figure 7 As shown, a schematic diagram of the spatialization results of the impact of the wind farm on the surface temperature under different unit selection and layout scenarios is shown. In Figure 7 it can be seen from the spatialization result 701 of the impact of the wind farm on the surface temperature in the 3MW scenario that the impact of the wind farm on the surface temperature in the 3MW scenario is more dispersed and more average. It can be seen from the spatialization result 702 of the impact of the wind farm on the surface temperature in the 5MW scenario that the impact of the wind farm on the surface temperature in the 5MW scenario is more concentrated and the wake spacing is larger.

[0088] In addition, the simulation results at specific times throughout the year (such as 3:00 every day) can be integrated in time to obtain the average disturbance distribution representing the long-term impact level. Refer to Figure 8 As shown, a schematic diagram of the average spatialization results of the impact of the wind farm on the surface temperature under different unit selection and layout scenarios is shown. It can be seen from Figure 8 that the dominant wind direction of the simulated wind farm is westerly, and the impact of the wind farm on the surface temperature is mainly concentrated in the downwind area on the east side of the wind turbines. It can be seen from the average spatialization result 801 of the impact of the wind farm on the surface temperature in the 3MW scenario that the maximum surface temperature impact in the 3MW scenario is about 0.8 - 1 °C, and the spatial impact range is about twice the swept diameter of the wind turbines. The impact of the wind farm on the surface temperature in the remaining areas is about 0.4 °C. It can be seen from the average spatialization result 802 of the impact of the wind farm on the surface temperature in the 5MW scenario that the maximum surface temperature impact in the 5MW scenario is about 1 - 1.2 °C, and the spatial distribution is more concentrated. In addition, Figure 7 and Figure 8 The 500 meters marked in it is the scale mark, indicating that this length in the figure corresponds to a distance of 500 meters in the actual geographical space, which is used to assist in understanding the layout scheme of the wind farm and the spatial scale distribution of its disturbance impact.

[0089] Finally, the spatialized results of the surface temperature impact of the wind farm can be integrated with elements such as regional topography, land use, and vegetation distribution, and comprehensive analysis can be carried out in combination with power generation performance indicators to provide support for the optimization of unit selection and micro-siting strategies. At the same time, during the construction or operation stage of the wind farm, key areas of concern can be identified based on this, assisting in the scientific formulation of environmental collaborative control measures and promoting the coordinated development of wind power resource development and ecological protection.

[0090] In this exemplary embodiment, a spatialization device for the climate and ecological effects of a wind farm is also provided. Refer to Figure 9 As shown, the spatialization device 900 for the climate and ecological effects of a wind farm may include a disturbance extraction module 910, a wind speed matching module 920, a wake simulation module 930, and a spatial mapping module 940, where: The disturbance extraction module 910 is configured to obtain multi-source climate and ecological data in the wind farm area and the control group area, and calculate initial wind farm impact data based on the multi-source climate and ecological data of each area; The wind speed matching module 920 is configured to obtain wind speed data corresponding to the moment of the initial wind farm impact data, and screen out target wind farm impact data from the initial wind farm impact data according to the wind speed data, where the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operating range; The wake simulation module 930 is configured to perform wind farm wake simulation based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines to obtain the wake intensity distribution of the wind farm area; The spatial mapping module 940 is configured to perform weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution based on the target wind farm impact data to obtain the spatialized data of the climate and ecological effects of the wind farm.

[0091] The specific details of each module of the above spatialization device for the climate and ecological effects of a wind farm have been described in detail in the corresponding spatialization method for the climate and ecological effects of a wind farm, so they will not be elaborated here.

[0092] The exemplary embodiment of the present disclosure also provides a computer-readable storage medium, on which a program product capable of implementing the above method of this specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on an electronic device, the program code is used to cause the electronic device to execute the steps described in the above "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure.

[0093] The program product can be in the form of a portable compact disc read-only memory (CD-ROM) and include program code, and can run on an electronic device such as a personal computer. However, the program product of the present disclosure is not limited thereto. In the present disclosure, a readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0094] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the readable storage medium (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0095] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, and the readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0096] The program code contained on the readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0097] The program code for performing the operations of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C#, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0098] In addition, an exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above spatialization method for the climate and ecological effects of a wind farm.

[0099] Next, reference is made to Figure 10 to describe the electronic device 1000 according to such an embodiment of the present disclosure. Figure 10 The illustrated electronic device 1000 is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present disclosure.

[0100] As Figure 10 shown, the electronic device 1000 is presented in the form of a general-purpose computing device. The components of the electronic device 1000 may include, but are not limited to: the above at least one processing unit 1010, the above at least one storage unit 1020, a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010), and a display unit 1040.

[0101] The storage unit 1020 stores program code, and the program code can be executed by the processing unit 1010, so that the processing unit 1010 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 1010 can execute the method steps in the exemplary embodiments of the present disclosure.

[0102] The storage unit 1020 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 1021 and / or a cache storage unit (Cache) 1022, and may further include a read-only storage unit (ROM) 1023.

[0103] The storage unit 1020 may further include a program / utility 1024 having a set (at least one) of program modules 1025. Such program modules 1025 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0104] The bus 1030 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0105] The electronic device 1000 can also communicate with one or more external devices 1070 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 1000, and / or communicate with any device that enables the electronic device 1000 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 1050. Moreover, the electronic device 1000 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 1060. As shown in the figure, the network adapter 1060 communicates with other modules of the electronic device 1000 through the bus 1030. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Array of Independent Disks) systems, tape drives, and data backup storage systems, etc.

[0106] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by the way of software combined with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0107] In addition, the above drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It can be easily understood that the processes shown in the above drawings do not indicate or limit the time sequence of these processes. Additionally, it can also be easily understood that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0108] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or can be implemented by the way of software combined with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0109] Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0110] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A spatialization method for the climate and ecological effects of a wind farm, characterized in that Including: Obtain multi-source climate and ecological data in the wind farm area and the control group area, and calculate the initial wind farm impact data based on the multi-source climate and ecological data of each area; Obtain the wind speed data corresponding to the time of the initial wind farm impact data, and screen out the target wind farm impact data from the initial wind farm impact data according to the wind speed data, wherein the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operation interval; Based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines, conduct wind farm wake simulation to obtain the wake intensity distribution of the wind farm area; Based on the target wind farm impact data, perform weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution to obtain the spatialized data of the wind farm climate and ecological effects.

2. The spatialization method of the wind farm climate and ecological effects according to claim 1, wherein The wake intensity distribution includes wake wind speed deficit; The performing weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution based on the target wind farm impact data to obtain the spatialized data of the wind farm climate and ecological effects includes: Calculate the number of spatial pixels in the wind farm area according to the area of the wind farm area and the grid size of the wake simulation model; Use the wake wind speed deficit as the weighting factor for each spatial pixel, and calculate the spatialized data of the wind farm climate and ecological effects according to the target wind farm impact data, the number of spatial pixels and the weighting factor.

3. The spatialization method of wind farm climate and ecological effects according to claim 1, wherein The calculating the initial wind farm impact data based on the multi-source climate and ecological data of each area includes: Based on the multi-source climate and ecological data of each area, calculate the statistical values of the target climate and ecological elements in the wind farm area and the control group area respectively; Calculate the difference between the statistical values of the target climate and ecological elements in the wind farm area and the control group area to obtain the initial wind farm impact data.

4. The method for spatializing the climate and ecological effects of a wind farm according to claim 3, wherein The calculating the statistical values of the target climate and ecological elements in the wind farm area and the control group area respectively based on the multi-source climate and ecological data of each area includes: Based on the spatial distribution characteristics of various climate and ecological data in the multi-source climate and ecological data, perform spatial statistical processing on the target climate and ecological elements in the wind farm area and the control group area respectively to obtain the statistical values of the target climate and ecological elements in the corresponding areas; Wherein, the multi-source climate and ecological data includes field observation data, satellite remote sensing data and wind farm simulation data.

5. The spatialization method of the wind farm climate and ecological effects according to claim 1, characterized in that The spatial layout information includes the geographical location and geometric parameters of the wind turbines; The conducting wind farm wake simulation based on the spatial layout information of the wind farm area and the performance parameters of the wind turbines to obtain the wake intensity distribution of the wind farm includes: Based on the geographical location, geometric parameters and performance parameters of the wind turbines, conduct wind farm wake simulation calculation to obtain the wake intensity distribution.

6. The spatialization method of the wind farm climate and ecological effects according to claim 5, wherein The conducting wind farm wake simulation calculation based on the geographical location, geometric parameters and performance parameters of the wind turbines to obtain the wake intensity distribution includes: Obtain the wind speed and wind direction data corresponding to the initial wind farm impact data at the moment based on the geographical location of the wind turbine; wherein, the initial wind farm impact data is determined based on multi-source climate and ecological data in the wind farm area and the control group area; Determine the corresponding wake simulation model according to the response characteristics of the target climate and ecological elements; Based on the geometric parameters, performance parameters of the wind turbine and the wind speed and wind direction data, use the wake simulation model to perform wind farm wake simulation calculation to obtain the wake intensity distribution.

7. The method for spatializing the climate and ecological effects of a wind farm according to claim 1, characterized in that, The method further includes: Based on a preset spatial grid structure, select a wind farm area in the research area and construct a candidate area outside the wind farm area; Screen a target area from the candidate areas whose similarity with the wind farm area meets the preset similarity conditions, and obtain the control group area according to the target area.

8. A spatialization device for the climate and ecological effects of a wind farm, characterized in that, Includes: A perturbation extraction module, configured to obtain multi-source climate and ecological data in the wind farm area and the control group area, and calculate the initial wind farm impact data based on the multi-source climate and ecological data of each area; A wind speed matching module, configured to obtain the wind speed data corresponding to the initial wind farm impact data, and screen out the target wind farm impact data from the initial wind farm impact data according to the wind speed data, wherein the wind speed data corresponding to the target wind farm impact data is within a preset wind speed operation interval; A wake simulation module, configured to perform wind farm wake simulation based on the spatial layout information of the wind farm area and the performance parameters of the wind turbine to obtain the wake intensity distribution of the wind farm area; A spatial mapping module, configured to perform weighted calculation on the pixel scale of the wind farm area according to the wake intensity distribution based on the target wind farm impact data to obtain the spatialized data of the wind farm climate and ecological effects.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-7.

10. An electronic device, characterized in that, Includes: A processor; and A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the method according to any one of claims 1-7 is implemented.

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