Wind field data graph determination method and device, electronic equipment and medium

Abstract

The invention discloses a wind field data graph determination method and device, electronic equipment and a medium, and the method comprises the steps: obtaining an associated data set which is pre-constructed for a target region, and storing a plurality of different weather types and a plurality of wind field features corresponding to the weather types in the associated data set, a wind field data graph request sent by a user for the target area is received, the wind field data graph request comprises the target weather type, target wind field features corresponding to the target weather type are determined from the associated data set, a target wind field data graph of the target area is constructed according to the target wind field features, and the target wind field data graph is displayed to the user. According to the method and the device, accurate requirements on wind field characteristics under specific weather types can be met. According to the method, the target wind field features can be directly searched from the pre-constructed associated data set according to the target weather type, so that repeated consumption of computing resources is avoided, the response time is shortened, and the wind field data graph can be quickly acquired.

Description

Technical Field

[0001] This application belongs to the field of meteorological wind fields, specifically relating to a method, apparatus, electronic equipment, and medium for determining wind field data maps. Background Technology

[0002] With the rapid development of renewable energy, the planning and operation of wind farms are increasingly reliant on wind farm data maps. These maps can be used not only to assess wind energy resource potential but also to optimize wind farm layout, improve power generation efficiency, and predict power output, thereby ensuring the stable operation of the power grid.

[0003] However, existing wind field data prediction techniques have significant problems in practical applications. First, existing solutions lack the classification and integration of weather types, thus failing to effectively distinguish the differences in wind field characteristics under different atmospheric circulation backgrounds. This results in insufficient generalization ability of wind field data under different weather scenarios, failing to meet the accurate requirements for wind field data maps under specific weather types. Second, existing solutions generally adopt a real-time calculation mode, that is, data is reacquired and downscaled every time a wind field data map is needed, leading to redundant consumption of computing resources and prolonged response time, making it impossible to achieve rapid acquisition of wind field data maps. Summary of the Invention

[0004] In view of the above problems, this application proposes a method for determining wind field data maps, the method comprising: Obtain a pre-constructed associated dataset for the target area; the associated dataset stores several different weather types and several wind field features corresponding to each weather type; Receive a wind field data map request from a user for the target area; the wind field data map request includes the target weather type; Determine the target wind field characteristics corresponding to the target weather type from the associated dataset; Based on the target wind field characteristics, a target wind field data map of the target area is constructed, and the target wind field data map is displayed to the user.

[0005] Optionally, obtaining the pre-built associated dataset for the target region includes: Acquire historical meteorological and geographical data for the target area; The historical meteorological data is classified into weather types to obtain several weather types for the target area; The background wind field data of the target area is determined based on the historical meteorological data and the geographical data; For any of the aforementioned weather types, the wind field characteristics corresponding to the weather type are determined based on the background wind field data and the historical meteorological data, and the weather type and the wind field characteristics are stored as a set of associated data; Multiple sets of related data are combined into a related dataset.

[0006] Optionally, the historical meteorological data includes reanalysis meteorological data, and the geographic data includes first topographic surface data and second topographic surface data; determining the background wind field data of the target area based on the historical meteorological data and the geographic data includes: The first meteorological field data is determined based on the reanalysis meteorological data and the first topographic surface data; First background wind field data is obtained from the first meteorological field data; wherein, the resolution of the first background wind field data and the resolution of the first topographic surface data are the first resolution; The second background wind field data is determined based on the first background wind field data and the second topographic surface data; wherein, the resolution of the second background wind field data and the resolution of the second topographic surface data are the second resolution, which is higher than the first resolution; The first background wind field data or the second background wind field data is used as the background wind field data of the target area.

[0007] Optionally, determining the first meteorological field data based on the reanalysis meteorological data and the first topographic surface data includes: The initial meteorological field data corresponding to the target area and the boundary meteorological field data corresponding to the boundary of the target area are determined from the reanalysis meteorological data. Based on the first topographic surface data, a preset weather forecast model is configured to obtain the target meteorological simulation model; The boundary meteorological field data is used as the boundary condition of the target meteorological simulation model. The target meteorological simulation model is then used to perform dynamic downscaling simulation on the initial meteorological field data to obtain the first meteorological field data output by the target meteorological simulation model.

[0008] Optionally, determining the second background wind field data based on the first background wind field data and the second topographic surface data includes: Based on the second topographic surface data, a preset fluid dynamics model is configured to obtain the target fluid simulation model; The target fluid simulation model is used to perform fluid dynamics simulation on the first background wind field to obtain the second background wind field data output by the target fluid simulation model.

[0009] Optionally, for any weather type, the wind field characteristics corresponding to the weather type are determined based on the background wind field data and the historical meteorological data, and the weather type and the wind field characteristics are stored as a set of associated data, including: For any of the aforementioned weather types, the analytical wind field data corresponding to the weather type is determined based on the background wind field data and the historical meteorological data; The wind field features corresponding to the weather type are extracted from the analyzed wind field data, and the weather type and the wind field features are stored as a set of associated data.

[0010] Optionally, the historical meteorological data includes station observation data, and the step of determining the analytical wind field data corresponding to any weather type based on the background wind field data and the historical meteorological data includes: For any of the aforementioned weather types, acquire the station observation data for the target area; Observational wind field data is constructed based on the observation data from the aforementioned stations; The background wind field data and the observed wind field data are assimilated to obtain the analyzed wind field data.

[0011] Optionally, the target wind field characteristics include average wind speed, prevailing wind direction, wind speed distribution shape parameters, and wind speed distribution scale parameters. The step of constructing a target wind field data map of the target area based on the target wind field characteristics and displaying the target wind field data map to the user includes: For the target weather pattern, a wind speed spatial distribution map corresponding to the target area is generated based on the average wind speed and the prevailing wind direction; For the target weather pattern, a wind speed probability distribution map of the target location is generated based on the wind speed distribution shape parameter and the wind speed distribution scale parameter; wherein, the target location is a characteristic location in the target region; For the target weather type, obtain the analytical wind field data corresponding to the target weather type, and construct an analytical wind field map based on the analytical wind field data; At least one of the wind speed spatial distribution map, the wind speed probability distribution map, and the analyzed wind field map is displayed to the user as the target wind field data map.

[0012] Secondly, embodiments of this application provide a wind field data map determination device, the device comprising: The dataset acquisition module is used to acquire a pre-constructed associated dataset for the target area; the associated dataset stores several different weather types and several wind field features corresponding to each weather type. The weather acquisition module is used to receive wind field data map requests from users for the target area; the wind field data map requests include the target weather type. The wind field feature determination module is used to determine the target wind field features corresponding to the target weather type from the associated dataset; The data map determination module is used to construct a target wind field data map of the target area based on the target wind field characteristics, and to display the target wind field data map to the user.

[0013] Thirdly, embodiments of this application provide an electronic device, including a processor, a memory, and a program or instructions stored in the memory and capable of running on the processor, wherein the program or instructions, when executed by the processor, implement the method described above.

[0014] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, they implement the method described above.

[0015] The embodiments of this application have the following advantages: In this embodiment, a pre-constructed associated dataset for the target area is acquired. This dataset stores several different weather types and corresponding wind field features. A wind field data map request from a user for the target area, including the target weather type, is received. The target wind field features corresponding to the target weather type are determined from the associated dataset. A target wind field data map for the target area is constructed based on these features and displayed to the user. This application pre-constructs an associated dataset that reflects the relationship between weather types and wind field features, effectively distinguishing differences in wind field characteristics under different atmospheric circulation backgrounds. This enhances the generalization ability of wind field data under different weather scenarios and meets the precise requirements for wind field features under specific weather types. Furthermore, this application eliminates the need to re-acquire data and re-calculate downscaling each time a wind field data map is needed. Instead, it directly retrieves the target wind field features from the pre-constructed associated dataset based on the target weather type in the user's wind field data map request for the target area. This avoids redundant consumption of computational resources, shortens response time, and enables rapid acquisition of wind field data maps. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0017] Figure 1 This is a flowchart illustrating the steps of a wind field data determination method according to an embodiment of this application; Figure 2 This is a schematic diagram of a wind field data determination device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been presented in the various embodiments of this application to enable readers to better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and updates based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0019] In recent years, mountain wind field forecasting techniques have primarily relied on mesoscale numerical models for downscaling calculations. Nested grid techniques are used to enhance the resolution to the kilometer level, and global reanalysis data is used as input to perform downscaling simulations to obtain regional meteorological fields. Subsequently, the wind field data is processed using statistical interpolation (such as Kriging) or simple shape parameterization methods.

[0020] However, these existing solutions have significant drawbacks: First, the terrain processing is too simplistic, using only fixed roughness or elevation correction, which cannot reflect the interaction between different weather systems (such as cold fronts and cyclones) and micro-topography (slope and aspect), resulting in the inability to accurately capture the sudden changes in wind speed and complex flow field structures caused by mountain micro-topography.

[0021] Secondly, the lack of classification and integration of typical weather patterns (such as clear skies and light winds, and strong cold fronts) makes it impossible for wind field data to distinguish the characteristics of different atmospheric circulation backgrounds, making it difficult to adapt to the precise needs of specific weather scenarios.

[0022] Finally, traditional methods generally adopt a real-time computing model, which requires rerunning computationally intensive processes such as data cleaning and CFD downscaling for each forecast. This leads to repeated consumption of computing resources, extended response time, and an inability to meet the demand for timely meteorological services at mountain stations, resulting in high operation and maintenance costs.

[0023] To address the aforementioned issues, this invention proposes a method, apparatus, electronic device, and medium for determining wind field data maps. It pre-constructs a correlated dataset containing typical weather patterns and their corresponding wind speed characteristics. This approach achieves a breakthrough from general forecasting to weather-type-specific simulation, dynamically reflecting the interaction between different weather systems and terrain, and effectively distinguishing wind field characteristics under different atmospheric circulation backgrounds. This solution not only improves forecast accuracy but also significantly reduces computational resource consumption and response time, providing high-precision data support for wind farm site selection, power prediction, and disaster early warning. It also enables rapid acquisition of wind field data maps in scenarios such as mountainous areas.

[0024] Reference Figure 1 The diagram shows a flowchart of the steps of a method for determining wind field data maps according to an embodiment of this application.

[0025] The method may specifically include the following steps: Step 101: Obtain a pre-constructed associated dataset for the target area; the associated dataset stores several different weather types and several wind field features corresponding to each weather type.

[0026] In this embodiment of the application, a pre-constructed associated dataset for the target area can be obtained; the associated dataset stores several different weather types and several wind field features corresponding to each weather type.

[0027] The target area can refer to the specific geographical area where wind field forecasting is required. In practice, the target area can be a key area of ​​focus for wind farm site selection, meteorological services, or disaster early warning.

[0028] A correlated dataset can refer to a data storage structure used to associate and organize different types of data (such as weather patterns and wind field characteristics) to facilitate subsequent querying and analysis.

[0029] In a practical implementation, the associated dataset can be a multidimensional structured dataset, whose dimensions can include weather-related dimensions and latitude and longitude dimensions.

[0030] In the weather type dimension, each weather type can be treated as an independent data layer or variable. The weather type dimension is used to distinguish wind field characteristics under different weather conditions.

[0031] In the latitude and longitude dimension, the latitude and longitude dimension is represented in the form of grid points, and each grid point stores the wind field characteristics corresponding to each weather type.

[0032] This design can effectively organize and store wind field characteristics under different weather conditions, facilitating rapid querying and analysis, and meeting the refined requirements of wind field forecasting.

[0033] Furthermore, the dimensions of the associated dataset can also include a height dimension and a time dimension. The height dimension is used to represent wind field characteristics at different heights, such as 10 meters, 50 meters, and 100 meters, and is suitable for storing multi-layer wind field data. If the associated dataset contains time-series information, the time dimension can be used to record wind field characteristics at different points in time.

[0034] In this embodiment of the application, weather type can refer to weather type with specific atmospheric circulation background and meteorological characteristics, such as clear sky and light wind type, strong cold front passing type, warm and humid airflow control type, typhoon peripheral influence type, etc. In this embodiment of the application, the wind field characteristics of the corresponding weather type can be found in the associated dataset according to the weather type.

[0035] Wind field characteristics can refer to specific parameters or attributes related to a wind field, such as wind speed, wind direction, and turbulence intensity. Wind field characteristics can be used to describe the physical properties of a wind field. In the embodiments of this application, wind field characteristics may include average wind speed (m / s), prevailing wind direction (degrees), wind speed standard deviation (m / s), wind speed distribution shape parameters, and wind speed distribution scale parameters.

[0036] The average wind speed can be represented as the average wind speed of the current grid point under the corresponding weather type.

[0037] The prevailing wind direction can be represented by the main wind direction at a grid point under the corresponding weather pattern, and can be expressed as an angle.

[0038] The standard deviation of wind speed can represent the degree of fluctuation of wind speed at a given grid point under a corresponding weather pattern.

[0039] The shape parameter of wind speed distribution can represent the shape characteristics of wind speed distribution. The shape parameter of wind speed distribution can be the shape parameter of Weibull distribution. The larger the value of the shape parameter of Weibull distribution, the more concentrated the wind speed distribution.

[0040] The scale parameter of wind speed distribution can represent the scale characteristics of wind speed distribution. The scale parameter of wind speed distribution can be the Weibull distribution scale parameter. The larger the value of the Weibull distribution scale parameter, the higher the average wind speed.

[0041] Step 102: Receive a wind field data map request from the user for the target area; the wind field data map request includes the target weather type.

[0042] In this embodiment of the application, a wind field data map request sent by a user for a target area can be received, and the wind field data map request includes the target weather type.

[0043] The target area can refer to the specific geographical area that the user is interested in. The target area can be the area where the user needs to obtain wind farm data maps, such as a mountainous area, wind farm, city, or specific region.

[0044] A wind field data map request can refer to a request issued by a user. The purpose of a wind field data map request is to obtain wind field data maps of a specific area. A wind field data map request can include information such as the target area and the target weather pattern.

[0045] The target weather type can refer to the weather type specified by the user in the wind field data map request, such as clear sky and light wind, strong cold front passing through, etc. The target weather type can be used to filter wind field data related to that weather type.

[0046] In practical implementation, user requests can be received through a user interface (such as a webpage, application, or API interface). For example, a user can input a target area (such as a mountainous area in a certain city of a certain province, or a target area selected by latitude and longitude coordinates) and a target weather type (such as clear sky and light breeze) in the user interface. Step 103: Determine the target wind field characteristics corresponding to the target weather type from the associated dataset.

[0047] In this embodiment, the target wind field characteristics corresponding to the target weather type can be determined from the associated dataset. The target wind field characteristics refer to the wind field characteristics corresponding to the target weather type selected from the associated dataset. These characteristics may include average wind speed, prevailing wind direction, wind speed standard deviation, Weibull distribution shape parameters, and Weibull distribution scale parameters, etc.

[0048] Step 104: Construct a target wind field data map of the target area based on the target wind field characteristics, and display the target wind field data map to the user.

[0049] In this embodiment of the application, a target wind field data map of the target area can be constructed based on the target wind field characteristics, and the target wind field data map can be displayed to the user.

[0050] Target wind field data maps refer to visual charts or images generated based on the characteristics of a target wind field, used to intuitively display the wind field characteristics of a target area under specific weather patterns. Target wind field data maps can include wind speed spatial distribution maps, wind speed probability distribution maps, analytical wind field maps, and wind speed standard deviation maps, helping users quickly understand the spatial distribution and statistical characteristics of the wind field.

[0051] In practice, the target wind field data map can be displayed to the user through a user interface (such as a webpage, application, or graphical interface), allowing the user to intuitively view and analyze the target wind field data map.

[0052] This application pre-constructs a correlation dataset that reflects the relationship between weather patterns and wind field characteristics. This dataset effectively distinguishes the differences in wind field characteristics under different atmospheric circulation backgrounds, thereby enhancing the generalization ability of wind field data under different weather scenarios and meeting the precise requirements for wind field characteristics under specific weather patterns. Secondly, this application eliminates the need to reacquire data and recalculate downscaling each time a wind field data map is needed. Instead, it directly retrieves the target wind field characteristics from the pre-constructed correlation dataset based on the target weather pattern in the user's wind field data map request for the target area. This avoids redundant consumption of computational resources, shortens response time, and enables rapid acquisition of wind field data maps.

[0053] In one optional embodiment of this application, step 101 further includes the following steps: S11, acquire historical meteorological and geographical data of the target area; S12, perform weather classification on the historical meteorological data to obtain several weather types for the target area; S13, determine the background wind field data of the target area based on the historical meteorological data and the geographical data; S14, for any of the weather types, determine the wind field characteristics corresponding to the weather type based on the background wind field data and the historical meteorological data, and store the weather type and the wind field characteristics as a set of associated data; S15, combine multiple sets of related data into a related dataset.

[0054] In this embodiment of the application, historical meteorological data and geographical data of the target area can be obtained. After obtaining the historical meteorological data and geographical data, completeness, rationality, correlation checks, and abnormal data processing can be performed on the historical meteorological data and geographical data.

[0055] Historical meteorological data refers to meteorological observation data recorded in the target area over a period of time. This data can be used to analyze the meteorological characteristics and patterns of change in the target area. Historical meteorological data can include observation data under different weather conditions and cover a period of at least one full year, thus allowing the capture of seasonal changes and various weather processes. Preferably, the time period for historical meteorological data can be 10 years or more, or 30 years or more.

[0056] Historical meteorological data can include meteorological (surface) observation data, remote sensing data, and reanalysis meteorological data.

[0057] Meteorological (ground) observation data can include ground station measurement data (wind observation data from local meteorological observation equipment under typical weather processes of wind fields), data from three-dimensional ultrasonic anemometers, etc.

[0058] Remote sensing data can provide information such as cloud cover and precipitation (satellite cloud images, radar reflectivity). It is also used to help identify and confirm key weather systems (such as cold fronts, cyclones, and high-pressure ridges) and weather types affecting the study area.

[0059] Reanalysis meteorological data can be global reanalysis datasets such as ERA5 (ECMWF Reanalysis). Reanalysis meteorological data can be used to obtain gridded data such as sea level pressure field, 10-meter wind field, and 500hPa / 700hPa geopotential height field, which can provide the daily atmospheric circulation background field.

[0060] Geographic data can be used to analyze the impact of geographical features of a target area on wind fields. Geographic data can include topographic and surface data of the target area. Topographic and surface data includes both topographic data and surface data.

[0061] Topographic data should cover the target area and include sufficiently detailed topographic features (such as elevation, slope, and topographic relief) to reflect the impact of geographical features on the wind field. The spatial resolution of the topographic data should be high enough to capture the topographic details of the target area. For example, the topographic data can be a high-resolution digital elevation model (DEM), typically with a resolution between tens and hundreds of meters. The temporal range of the topographic data should match the temporal range of the meteorological data to ensure dataset consistency; that is, the topographic data should cover at least one full year of meteorological data, preferably more than 10 or 30 years.

[0062] Surface data can be surface roughness data, which refers to parameters describing the influence of the surface on wind fields or other atmospheric flows. Surface roughness data can be expressed as roughness length, with the unit being meters. Surface roughness data is an important input parameter in computational fluid dynamics models and meteorological models, used to simulate the exchange of momentum, heat, and moisture between the surface and the atmosphere.

[0063] Surface features (such as vegetation height, building height, and terrain undulation) can be obtained through remote sensing technologies (such as satellite imagery and lidar), and roughness length can be retrieved by inversion. Remote sensing inversion methods can provide large-scale, high-resolution surface roughness data.

[0064] Surface roughness data can also be obtained by referring to existing empirical values ​​of roughness length based on land use type (such as forest, grassland, farmland, city, etc.).

[0065] In this embodiment of the application, historical meteorological data can be classified into weather types to obtain several weather types for the target area.

[0066] Weather typology refers to the process of classifying the weather types of a target area based on historical meteorological data. Weather typology can divide complex meteorological data into several typical weather types, such as clear skies and light winds, and strong cold fronts.

[0067] In practice, based on key meteorological variables (such as wind speed, wind direction, air pressure, temperature, precipitation, etc.) in historical meteorological data, statistical analysis, clustering algorithms or machine learning methods can be used to classify similar meteorological conditions into the same weather type.

[0068] Statistical analysis can identify typical meteorological patterns by analyzing the distribution characteristics of meteorological variables.

[0069] Clustering algorithms can be used to classify meteorological variables (such as K-means, hierarchical clustering, etc.) to obtain several weather patterns.

[0070] Machine learning methods can be used to classify meteorological variables and generate weather patterns by using machine learning models (such as decision trees, random forests, etc.).

[0071] In this embodiment of the application, background wind field data of the target area can be determined based on historical meteorological data and geographical data.

[0072] Background wind field data refers to the wind field characteristics of the target area without the influence of specific weather patterns. It can be calculated based on historical meteorological and geographical data. Background wind field data is used to describe the normal wind field characteristics of the target area. In practice, background wind field data can be obtained by dynamically downscaling reanalysis meteorological data.

[0073] In this embodiment of the application, for any weather type, the wind field characteristics corresponding to the weather type can be determined based on background wind field data and historical meteorological data, and the weather type and wind field characteristics can be stored as a set of associated data. Then, multiple sets of associated data are combined to form an associated dataset.

[0074] This application ensures data quality and consistency by acquiring historical meteorological and geographical data of the target area and performing integrity, rationality, and relevance checks, as well as anomaly processing. Based on meteorological elements (pressure field, temperature, advection), this application predefines weather pattern categories and integrates them into the data processing workflow. This simplifies complex meteorological conditions into several typical weather patterns, ensuring a high degree of matching between wind field data and specific weather scenarios, overcoming the generalization limitations of existing technologies. By combining background wind field data and historical meteorological data, this application accurately extracts the wind field characteristics corresponding to each weather pattern and associates and stores the weather patterns with the wind field characteristics to form a correlated dataset. This achieves systematic management of complex meteorological conditions, organizes wind field parameters according to weather pattern dimensions, supports efficient retrieval and real-time application, reduces computational redundancy, and improves engineering practicality.

[0075] In one optional embodiment of this application, the historical meteorological data includes reanalysis meteorological data, and the geographic data includes first topographic surface data and second topographic surface data. The first topographic surface data and the second topographic surface data are topographic surface data with different resolutions.

[0076] Step S13 also includes the following steps: S21, determine the first meteorological field data based on the reanalysis meteorological data and the first topographic surface data; S22, Obtain first background wind field data from the first meteorological field data; wherein, the resolution of the first background wind field data and the resolution of the first topographic surface data are the first resolution; S23, determine the second background wind field data based on the first background wind field data and the second topographic surface data; wherein, the resolution of the second background wind field data and the resolution of the second topographic surface data are the second resolution, and the second resolution is higher than the first resolution; S24, use the first background wind field data or the second background wind field data as the background wind field data of the target area.

[0077] In this embodiment of the application, the first meteorological field data can be determined based on the reanalysis meteorological data and the first topographic surface data.

[0078] In practical implementation, the first meteorological field data can be obtained by dynamically downscaling the reanalysis meteorological data and the first topographic surface data. The first meteorological field data can reflect the detailed meteorological conditions formed after the interaction between the weather system and the large-scale topography described by the first topographic surface data. The first meteorological field data can include physical quantities such as three-dimensional wind field (including U / V / W wind speed components), temperature field, pressure field, humidity field, and precipitation field.

[0079] The resolution of the first meteorological field data can be the highest, which can be 1-3 kilometers, significantly higher than that of the input reanalysis meteorological data (usually tens of kilometers in resolution).

[0080] In this embodiment of the application, first background wind field data can be obtained from first meteorological field data, wherein the resolution of the first background wind field data and the resolution of the first topographic surface data are the first resolution.

[0081] Specifically, the first background wind field data is a subset of wind field information extracted from the first meteorological field data, and its spatial resolution is consistent with that of the first meteorological field data, i.e., the first resolution. At the same time, the first topographic surface data used to generate the first background wind field data also has the same or matching first resolution, thereby ensuring the consistency between the underlying surface information and the meteorological field resolution during the dynamic simulation process.

[0082] In this embodiment, the second background wind field data can be determined based on the first background wind field data and the second topographic surface data. The resolution of the second background wind field data and the resolution of the second topographic surface data are both defined as a second resolution, which is higher than the first resolution.

[0083] In practical implementation, the second background wind field data can refer to ultra-high resolution wind field data simulated by a computational fluid dynamics model based on the first background wind field data and the second topographic surface data. The second background wind field data can more accurately reproduce the dynamic modulation effect of micro-topography on the near-surface wind field and is suitable for high-precision application scenarios such as micro-site selection of wind farms.

[0084] The second resolution can reach the 100-meter level. At this resolution, the second background wind field data can explicitly resolve fine flow field structures such as flow around the terrain, tube acceleration effect, and flow separation vortex caused by topography.

[0085] In this embodiment, either the first background wind field data or the second background wind field data can be used as the background wind field data for the target area. That is, this application can use not only the first background wind field data as the background wind field data for the target area, but also the second background wind field data.

[0086] This application generates background wind field data for a target area by combining reanalysis meteorological data, first topographic surface data, and second topographic surface data. First, this application uses a dynamic downscaling method to generate high-resolution first meteorological field data, which can reflect the detailed meteorological conditions resulting from the interaction between the weather system and the terrain. Second, this application uses a computational fluid dynamics model combined with second topographic surface data to generate ultra-high-resolution second background wind field data, which can accurately characterize the modulation effect of micro-topography on the wind field. Finally, this application provides flexible options, allowing the use of either the first background wind field data to meet regional needs or the second background wind field data to meet high-precision micro-application requirements, significantly improving the accuracy and applicability of the wind field data.

[0087] In practical implementation, for most scenarios affected by weather systems and large-scale circulation, simple spatial interpolation is ineffective. In such cases, numerical weather prediction (NWP) models can be used, employing mesoscale models such as WRF. Reanalysis data is used as the initial field and boundary conditions to run high-resolution (e.g., 1 km) simulations over the target area, obtaining gridded three-dimensional wind, temperature, and pressure fields. The specific implementation method is as follows: In one optional embodiment of this application, step S21 further includes the following steps: S31, determine the initial meteorological field data corresponding to the target area and the boundary meteorological field data corresponding to the boundary of the target area from the reanalysis meteorological data; S32, Configure a preset weather forecast model based on the first topographic surface data to obtain the target meteorological simulation model; S33, the boundary meteorological field data is used as the boundary condition of the target meteorological simulation model, and the target meteorological simulation model is used to perform dynamic downscaling simulation on the initial meteorological field data to obtain the first meteorological field data output by the target meteorological simulation model.

[0088] In this embodiment of the application, the initial meteorological field data corresponding to the target area and the boundary meteorological field data corresponding to the boundary of the target area can be determined from the reanalysis meteorological data.

[0089] The initial meteorological field data is an instantaneous snapshot of the atmospheric state covering the entire three-dimensional space of the target area at the beginning of dynamic downscaling. The initial meteorological field data includes gridded data of elements such as wind speed, temperature, air pressure and humidity, which can be used as the initial field of the WRF model.

[0090] Boundary meteorological field data are time-evolving sequences of meteorological elements applied to the boundary of the computational domain of the target region during dynamic downscaling. They serve as boundary conditions for the WRF model and are used to continuously characterize the impact of large-scale circulation systems on the interior of the region during the simulation process, ensuring that the simulated evolution within the region remains dynamically consistent with the external atmospheric environment.

[0091] In this embodiment of the application, a preset weather forecast model can be configured based on the first topographic surface data to obtain a target meteorological simulation model.

[0092] Among these, the weather forecasting model can be a WRF model. The WRF (Weather Research and Forecasting Model) is a widely used high-resolution numerical weather prediction and climate simulation system. WRF models are highly flexible and scalable, suitable for meteorological simulation and forecasting from regional to global scales.

[0093] Specifically, the first topographic surface data provides topographic and surface data for the target area, which is used to configure a pre-defined weather forecasting model. By combining the first topographic surface data with the parameters or grid system of the weather forecasting model, a target meteorological simulation model optimized specifically for the topographic features of the target area can be generated. The target meteorological simulation model can more accurately simulate the interaction between weather systems and topography in the target area, thereby generating simulation results that better reflect actual meteorological conditions.

[0094] In this embodiment of the application, the boundary meteorological field data can be used as the boundary condition of the target meteorological simulation model. The target meteorological simulation model is used to perform dynamic downscaling simulation on the initial meteorological field data to obtain the first meteorological field data output by the target meteorological simulation model.

[0095] Specifically, boundary meteorological field data provides the meteorological conditions (such as wind speed, wind direction, temperature, and air pressure) at the boundary of the target area. This data is used as the boundary conditions for the target meteorological simulation model to ensure that meteorological changes within the target area remain consistent with the external environment during the dynamic downscaling simulation. Subsequently, the target meteorological simulation model uses the initial meteorological field data as the initial field and combines it with the boundary conditions to perform dynamic downscaling simulation. Dynamic downscaling is a technique to improve spatial resolution, which can convert low-resolution meteorological data (such as reanalysis data) into high-resolution meteorological field data, thereby more accurately simulating the interaction between local topography and weather systems. Ultimately, the first meteorological field data output by the target meteorological simulation model has higher resolution and can reflect the meteorological conditions of the target area in more detail, making it suitable for applications such as regional weather forecasting and wind energy resource assessment.

[0096] This application generates a target meteorological simulation model by extracting initial and boundary meteorological field data from reanalysis meteorological data and configuring a WRF model based on first topographic surface data. This model can more accurately simulate the interaction between topographic features and weather systems in the target area, and converts low-resolution data into high-resolution first meteorological field data through dynamic downscaling techniques. This improves simulation accuracy, captures the impact of local topography on meteorology, and ensures that internal meteorological changes and the external environment remain dynamically coordinated. It is suitable for regional weather forecasting and wind energy resource assessment, providing support for refined meteorological services.

[0097] In practical implementation, for smaller scales (hundreds of meters) and more complex terrains, computational fluid dynamics (CFD) models (such as those based on the RANS equations) can be used for simulation. The WRF output or site observations are used as input boundary conditions for the CFD to simulate the fine wind field structures caused by terrain, such as flow around the surface, acceleration (narrowing effect), deceleration, and vortices. Considering the significant influence of mountainous terrain elements, a CFD model is used to simulate the flow field in complex terrain, and it can be calibrated by incorporating measured data. The specific process is as follows: In one optional embodiment of this application, step S22 further includes the following steps: S41, Configure a preset fluid dynamics model based on the second terrain surface data to obtain the target fluid simulation model; S42, the target fluid simulation model is used to perform fluid dynamics simulation on the first background wind field to obtain the second background wind field data output by the target fluid simulation model.

[0098] In this embodiment of the application, a preset fluid dynamics model can be configured based on the second topographic surface data to obtain the target fluid simulation model.

[0099] The fluid dynamics model can be a CFD (Computational Fluid Dynamics) model. A CFD model is a simulation tool that uses numerical methods to solve for fluid motion and related physical phenomena. Based on the fundamental equations of fluid dynamics (such as the Navier-Stokes equations), CFD models simulate the flow, heat transfer, and mass transfer processes of fluids (such as air and water) under complex geometries and boundary conditions through discretization and numerical algorithms.

[0100] Specifically, the second topographic surface data provides the topographic features and surface attributes of the target area (such as terrain height, slope, and surface roughness). This data is used to optimize the parameters or mesh system of the target fluid simulation model. By combining the second topographic surface data with the physical equations of the fluid dynamics model, a target fluid simulation model specifically optimized for the topographic features of the target area can be generated. The target fluid simulation model can more accurately simulate the movement and distribution of fluids in the target area. The output of the target fluid simulation model can be used for wind energy resource assessment.

[0101] In this embodiment of the application, a target fluid simulation model can be used to perform fluid dynamics simulation on the first background wind field to obtain the second background wind field data output by the target fluid simulation model.

[0102] Specifically, the target fluid simulation model is based on CFD (Computational Fluid Dynamics) technology. It uses a primary background wind field as the input boundary condition and combines it with the topographic features of the target area. Through numerical solution of fluid dynamics equations (such as the Navier-Stokes equations and RANS equations), it simulates the flow and evolution of the wind field within the target area. During the simulation, the CFD model considers the disturbance effects of terrain on the wind field (such as valley winds and terrain acceleration effects), thereby generating more accurate secondary background wind field data. This secondary background wind field data has higher resolution and more detailed wind field characteristics, and can more accurately reflect the wind speed and direction distribution in the target area, making it suitable for applications such as wind energy resource assessment and weather forecasting.

[0103] In this embodiment of the application, the station observation data can also be spatially interpolated and quality controlled to obtain the observed wind field data. Then, the target fluid simulation model can be used to perform fluid dynamics simulation on the first background wind field data and the observed wind field data to obtain the second background wind field data output by the target fluid simulation model.

[0104] Spatial interpolation and quality control refer to the preprocessing steps of transforming discretely distributed station observation data into continuously distributed gridded data using algorithms such as Kriging interpolation, and removing outlier observations. Observed wind field data refers to the spatially continuous gridded wind field data obtained after the above processing.

[0105] The primary background wind field data and the observed wind field data are used together as input conditions for fluid dynamics simulation. The primary background wind field data provides a large-scale wind field background, while the observed wind field data provides local measured wind field constraints.

[0106] Specifically, this embodiment determines the second background wind field data through the following steps: First, spatial interpolation and quality control processing is performed on the station observation data to transform the discrete station observation data into spatially continuous observed wind field data; then, the first background wind field data is used as the basic background field, combined with the local wind field constraints provided by the observed wind field data, to jointly drive the target fluid simulation model to perform fluid dynamics simulation; in this process, the observed wind field data is integrated into the simulation process through data assimilation technology or directly as boundary conditions, so that the simulation results not only conform to the laws of fluid dynamics but also closely resemble the actual observed data; finally, through the solution calculation of the target fluid simulation model, the second background wind field data that can accurately reflect the influence of micro-topography and local wind field characteristics is output.

[0107] In this embodiment of the application, after obtaining the second background wind field data, the second background wind field data can be fine-tuned or corrected based on the station observation data.

[0108] This application combines second topographic surface data, first background wind field data, and observed wind field data, and uses a target fluid simulation model to perform fluid dynamics simulation, generating high-precision second background wind field data. This application fully utilizes topographic surface data to optimize the parameters and grid system of the fluid dynamics model, considers the disturbance effect of topography on the wind field, and can capture subtle changes in the local wind field. The output second background wind field data has high resolution and detailed wind field characteristics, suitable for refined application scenarios such as wind energy resource assessment and weather forecasting, providing a scientific basis for research and decision-making in related fields.

[0109] In one optional embodiment of this application, step S14 includes the following steps: S51, for any of the weather types, determine the analytical wind field data corresponding to the weather type based on the background wind field data and the historical meteorological data; S52, extract the wind field features corresponding to the weather type from the analyzed wind field data, and store the weather type and the wind field features as a set of associated data.

[0110] In this embodiment, for any weather type, the analytical wind field data corresponding to the weather type can be determined based on background wind field data and historical meteorological data. The analytical wind field data can refer to the optimal wind field estimate for a specific weather type obtained through data assimilation techniques. Specifically, by fusing background wind field data with historical meteorological data, analytical wind field data that conforms to both fluid dynamics and closely reflects actual observations can be constructed. Analytical wind field data can more completely demonstrate the three-dimensional wind field structure under the influence of specific weather patterns, including the vertical distribution characteristics of wind speed and the spatial variation patterns of wind direction.

[0111] In this embodiment of the application, wind field features corresponding to weather patterns can be extracted from analyzed wind field data, and the weather patterns and wind field features can be stored as a set of associated data.

[0112] Specifically, the wind field analysis data includes high-quality wind field information that has undergone data assimilation, while wind field characteristics are representative statistical parameters and distribution patterns extracted from this data. Through spatial analysis and statistical processing of the wind field data, key features such as mean wind speed, prevailing wind direction, wind speed standard deviation, and Weibull distribution parameters can be extracted. These features effectively characterize the statistical properties of wind fields under specific weather patterns, such as the central tendency, dispersion, and probability distribution characteristics of wind speed.

[0113] In practice, for each weather type, wind field characteristics such as spatial characteristics, statistical characteristics, and typical wind field structures can be extracted from simulation results and observation data.

[0114] Spatial characteristics can include the average wind speed and prevailing wind direction under this weather pattern, and can be represented by a wind speed spatial distribution map. Strong wind areas (such as ridges and passes) and weak wind areas (such as leeward slopes and valley bottoms) can be identified on the wind speed spatial distribution map.

[0115] Statistical characteristics may include wind speed standard deviation, Weibull distribution shape parameters, and Weibull distribution scale parameters.

[0116] A typical wind field structure can be represented by an analytical wind field diagram, which can be combined with a flow field diagram to describe whether phenomena such as foehn winds, canyon winds, and bypassing are likely to occur under this weather pattern. After extraction, a mapping relationship can be established between weather patterns and corresponding wind field characteristic parameters, storing the associated data in the form of key-value pairs or structures. This storage method allows for quick retrieval of corresponding wind field characteristics through weather patterns during subsequent queries, providing directly usable characteristic parameters for applications such as wind farm planning and design, and wind turbine selection and layout.

[0117] This application generates high-quality analytical wind field data by fusing background wind field data and historical meteorological data. This analytical wind field data combines the regularity of numerical simulation with the accuracy of observational data, providing a more complete representation of the wind field structure under specific weather patterns. Extracting wind field features corresponding to weather patterns from the analytical wind field data effectively characterizes the statistical properties of the wind field. Storing weather patterns and wind field features as correlated data allows for rapid retrieval of the corresponding wind field features based on the weather pattern during subsequent queries.

[0118] In one optional embodiment of this application, the historical meteorological data includes station observation data. In this embodiment of the application, the station observation data can be meteorological (ground) observation data.

[0119] Step S51 includes the following steps: S61, for any of the aforementioned weather types, acquire the station observation data for the target area; S62, construct observation wind field data based on the station observation data; S63, the background wind field data and the observed wind field data are assimilated to obtain the analyzed wind field data.

[0120] In this embodiment of the application, station observation data of the target area can be obtained for any weather type, and observation wind field data can be constructed based on the station observation data.

[0121] Among them, station observation data can refer to the time series data of meteorological elements such as wind speed and wind direction obtained by actual measurement at various meteorological stations distributed in the target area. Station observation data can be meteorological (ground) observation data.

[0122] Observational wind field data refers to gridded wind field data covering the entire target area, obtained by spatial interpolation and gridding of discrete station observation data.

[0123] Specifically, this application first selects station observation data from a historical database for multiple time periods that match the characteristics of a chosen weather pattern. Then, it performs quality control and consistency checks on the selected multi-site observation data, eliminating outliers. Finally, it uses spatial interpolation algorithms such as Kriging interpolation and inverse distance weighted interpolation to reconstruct the quality-controlled discrete station observation data into continuous gridded wind field data covering the entire target area. The wind field data constructed in this way retains the authenticity of the station observation data while forming a complete spatial distribution field, providing a reliable observation field input for the subsequent data assimilation process.

[0124] In this embodiment of the application, background wind field data and observed wind field data can be assimilated to obtain analytical wind field data.

[0125] Data assimilation is a data fusion technique that combines numerical simulation results with observational data. The purpose of data assimilation is to generate meteorological data that most closely approximates the actual atmospheric conditions.

[0126] Analyzing wind field data can refer to optimized wind field data that is physically self-consistent, most closely approximates the real atmospheric state, and includes all meteorological elements, obtained after data assimilation processing.

[0127] In practice, a data assimilation system can be used to integrate observational data and numerical models from multiple sources, including but not limited to air pressure, temperature, humidity, satellite, and radar, to obtain analytical wind field data.

[0128] Data assimilation is not a simple reverse deduction, but rather a combination of observational data from various sources (air pressure, temperature, humidity, satellites, radar, etc.) with the complex physical equations that control atmospheric motion. Through the "data assimilation" technique, a physically self-consistent analytical field is generated that includes all meteorological elements (including wind fields) and is closest to the real atmospheric state.

[0129] Specifically, background wind field data can be used as the background field, and observed wind field data as the observation field. Using data assimilation algorithms such as three-dimensional variational analysis and ensemble Kalman filtering, and considering the respective error statistics, an optimization problem is solved to minimize the overall difference between the analytical field and the background and observation fields, thus obtaining the analytical wind field data. This process simultaneously considers the error characteristics of the observed data and the uncertainties of the numerical model. Iterative calculations ensure that the analytical wind field data maintains both the dynamic characteristics of the numerical simulation and closely approximates actual observation data. The final analytical wind field data combines the completeness of the numerical simulation with the accuracy of the observation data, more realistically reflecting the actual wind field conditions under specific weather patterns.

[0130] Furthermore, within a complete assimilation cycle, the data assimilation process can be iteratively optimized: the background wind field data corresponding to the initial moment of the assimilation cycle is used as the initial field, compared with the observed wind field data at that moment, and optimized to obtain preliminary analytical wind field data. This preliminary analytical wind field data is then fed into the WRF model as the initial field for the next time step calculation, propelling the model to obtain an updated and more accurate background field for the next moment, and then assimilated with the new observed wind field data at the next moment. This process is repeated cyclically, continuously correcting the simulation trajectory, and ultimately obtaining a set of spatiotemporally complete and consistent analytical wind field data. When using the variational assimilation algorithm, the stopping condition for data assimilation can be: the change in the cost function value during the optimization process is less than a preset first threshold, or the number of iterations reaches a preset maximum number. When using the ensemble Kalman filter assimilation algorithm, the stopping condition for data assimilation can be: the algorithm has processed all time-series data within the preset assimilation cycle.

[0131] This application significantly improves the accuracy and reliability of wind field data by incorporating site observation data and performing data assimilation processing. It combines the completeness of numerical simulation with the authenticity of measured data, preserving the physical consistency of the numerical model while integrating the accuracy of observational data. This effectively overcomes the limitations of relying solely on numerical simulation or site observation, providing a high-quality data foundation for subsequent wind field feature extraction and greatly improving the accuracy and reliability of wind energy resource assessment and wind farm terrain adaptability analysis.

[0132] In one optional embodiment of this application, the target wind field characteristics include average wind speed, prevailing wind direction, wind speed distribution shape parameter, and wind speed distribution scale parameter.

[0133] Step 104 includes the following steps: S71, For the target weather type, generate a wind speed spatial distribution map corresponding to the target area based on the average wind speed and the prevailing wind direction; S72, for the target weather pattern, generate a wind speed probability distribution map of the target location based on the wind speed distribution shape parameter and the wind speed distribution scale parameter; wherein, the target location is a feature location in the target area; S73, For the target weather type, obtain the analysis wind field data corresponding to the target weather type, and construct the analysis wind field map based on the analysis wind field data; S74, at least one of the wind speed spatial distribution map, the wind speed probability distribution map, and the analyzed wind field map is displayed to the user as the target wind field data map.

[0134] In this embodiment of the application, for a target weather type, a spatial distribution map of wind speed corresponding to the target area can be generated based on the average wind speed and the prevailing wind direction.

[0135] Among them, the wind speed spatial distribution map can refer to a thematic map that uses a map as the base map and intuitively displays the spatial changes of average wind speed and prevailing wind direction in a target area through contour lines, color patches, or wind arrows.

[0136] In practical implementation, for each weather type, a spatial distribution map of the average wind speed across the entire region can be drawn, represented by contour lines or color patches. Specifically, the average wind speed and prevailing wind direction data for each grid point under the target weather type can be obtained from the target wind field characteristics. Then, based on a geographic information system platform, the average wind speed data can be color-rendered to generate a color patch map, where different colors represent different wind speed levels. Simultaneously, wind arrows or streamline symbols are used to mark the prevailing wind direction at the corresponding grid points. Finally, the generated wind speed color patch map and wind direction labels are overlaid onto the topographic base map of the target area to form a complete spatial distribution map of wind speed. This spatial distribution map of wind speed can intuitively reveal the spatial distribution patterns of wind energy resources and the characteristics of the prevailing wind system under the influence of specific weather types, providing direct scientific basis for macro-level site selection and turbine layout optimization for wind farms.

[0137] In this embodiment, for a target weather type, a wind speed probability distribution map of the target location can be generated based on wind speed distribution shape parameters and wind speed distribution scale parameters. The target location refers to a characteristic location within the target area. Target locations can refer to specific, representative geographical locations selected from the target area; these locations can reflect the typical wind field characteristics of the area under the influence of the target weather type.

[0138] In practical implementation, wind rose diagrams, or wind speed probability distribution maps, can be drawn for key locations within a region under each weather type, visually displaying wind direction frequency and wind speed distribution. Specifically, the shape parameter of the wind speed distribution determines the shape of the distribution curve, reflecting the stability of wind speed at that location, while the scale parameter roughly determines the distribution range of the curve, reflecting the average wind speed level at that location. After constructing a probability distribution function using the shape and scale parameters of the wind speed distribution, the wind speed probability distribution map can be generated. The wind speed probability distribution map can visually display the probability of any wind speed range occurring, providing the most direct statistical basis for accurately calculating the average wind power density at that location and assessing wind energy resource potential.

[0139] In this embodiment of the application, for a target weather type, analytical wind field data corresponding to the target weather type is obtained, and an analytical wind field map is constructed based on the analytical wind field data. The analytical wind field map can be a chart that graphically displays the distribution of wind speed and direction within the target area, and can use arrows, colors, or other symbols to represent the direction and intensity of the wind.

[0140] In practical implementation, the most representative weather pattern can be selected to draw its wind field map, demonstrating how airflow interacts with the terrain. First, the most representative moment or hourly average data for the typical circulation characteristics of the target weather pattern can be extracted from the analytical wind field data obtained through data assimilation. Then, the wind speed and direction data at the grid points can be converted into visual elements, using arrows (wind arrows) or streamlines to represent wind direction and airflow paths, and the color or length of the arrows to represent wind speed. Finally, these visual elements are overlaid on the elevation topographic map of the target area to form the analytical wind field map.

[0141] In this embodiment of the application, at least one of the wind speed spatial distribution map, wind speed probability distribution map, and analyzed wind field map can be displayed to the user as the target wind field data map.

[0142] This application's embodiments utilize target wind field characteristics such as average wind speed, prevailing wind direction, wind speed distribution shape parameters, and wind speed distribution scale parameters to generate wind speed spatial distribution maps, wind speed probability distribution maps, and analytical wind field maps, which are then presented to users as target wind field data maps. This multi-dimensional and multi-level wind field data display method not only intuitively reveals the spatial distribution patterns and dominant wind system characteristics of wind energy resources under specific weather patterns, but also provides a scientific basis for wind farm site selection, turbine layout optimization, and wind energy resource potential assessment. Furthermore, by combining the wind speed probability distribution map and the analytical wind field map, users can gain a more comprehensive understanding of wind speed stability and wind direction trends, thereby improving the accuracy and efficiency of wind energy resource utilization.

[0143] Reference Figure 2 The diagram shows a structural schematic of a wind field data map determination device according to an embodiment of this application. The device includes: The dataset acquisition module 201 is used to acquire a pre-constructed associated dataset for the target area; the associated dataset stores several different weather types and several wind field features corresponding to the weather types respectively; The weather type acquisition module 202 is used to receive a wind field data map request from a user for the target area; the wind field data map request includes the target weather type; The wind field feature determination module 203 is used to determine the target wind field features corresponding to the target weather type from the associated dataset; The data map determination module 204 is used to construct a target wind field data map of the target area based on the target wind field characteristics, and to display the target wind field data map to the user.

[0144] In one optional embodiment of this application, the dataset acquisition module 201 includes: The data acquisition submodule is used to acquire historical meteorological and geographical data of the target area; The weather classification submodule is used to classify the historical meteorological data into weather types to obtain several weather types for the target area. The background wind field determination submodule is used to determine the background wind field data of the target area based on the historical meteorological data and the geographical data. The associated storage submodule is used to determine the wind field characteristics corresponding to any weather type based on the background wind field data and the historical meteorological data, and store the weather type and the wind field characteristics as a set of associated data. The dataset construction submodule is used to combine multiple sets of related data into a related dataset.

[0145] In one optional embodiment of this application, the historical meteorological data includes reanalysis meteorological data, and the geographic data includes first topographic surface data and second topographic surface data; the background wind field determination submodule includes: The first meteorological field acquisition unit is used to determine the first meteorological field data based on the reanalysis meteorological data and the first topographic surface data. The first background wind field acquisition unit is used to acquire first background wind field data from the first meteorological field data; wherein, the resolution of the first background wind field data and the resolution of the first topographic surface data are the first resolution; The second background wind field acquisition unit is used to determine the second background wind field data based on the first background wind field data and the second terrain surface data; wherein, the resolution of the second background wind field data and the resolution of the second terrain surface data are the second resolution, and the second resolution is higher than the first resolution; The background wind field determination unit is used to use the first background wind field data or the second background wind field data as the background wind field data of the target area.

[0146] In one optional embodiment of this application, the first meteorological field acquisition unit includes: The meteorological field determination subunit is used to determine, from the reanalysis meteorological data, the initial meteorological field data corresponding to the target area and the boundary meteorological field data corresponding to the boundary of the target area; The meteorological simulation model configuration subunit is used to configure a preset weather forecast model based on the first topographic surface data to obtain the target meteorological simulation model; The first meteorological field acquisition subunit is used to use the boundary meteorological field data as the boundary condition of the target meteorological simulation model, and to perform dynamic downscaling simulation on the initial meteorological field data using the target meteorological simulation model to obtain the first meteorological field data output by the target meteorological simulation model.

[0147] In one optional embodiment of this application, the second background wind field acquisition unit includes: The fluid simulation model configuration subunit is used to configure a preset fluid dynamics model based on the second terrain surface data to obtain the target fluid simulation model. The second background wind field acquisition subunit is used to perform fluid dynamics simulation on the first background wind field using the target fluid simulation model to obtain the second background wind field data output by the target fluid simulation model.

[0148] In one optional embodiment of this application, the associated storage submodule includes: The wind field analysis acquisition unit is used to determine the wind field analysis data corresponding to any weather type based on the background wind field data and the historical meteorological data. An associated storage unit is used to extract the wind field features corresponding to the weather type from the analyzed wind field data, and store the weather type and the wind field features as a set of associated data.

[0149] In one optional embodiment of this application, the historical meteorological data includes station observation data, and the wind field analysis acquisition unit includes: The observation data acquisition node is used to acquire the station observation data of the target area for any of the aforementioned weather types. The wind field observation construction node is used to construct wind field observation data based on the observation data of the stations. The wind field analysis node is used to assimilate the background wind field data and the observed wind field data to obtain the analyzed wind field data.

[0150] In one optional embodiment of this application, the target wind field characteristics include average wind speed, prevailing wind direction, wind speed distribution shape parameters, and wind speed distribution scale parameters. The data map determination module 204 includes: The wind speed spatial distribution map acquisition submodule is used to generate a wind speed spatial distribution map corresponding to the target area based on the average wind speed and the prevailing wind direction for the target weather type. The wind speed probability distribution map acquisition submodule is used to generate a wind speed probability distribution map of the target point based on the wind speed distribution shape parameter and the wind speed distribution scale parameter for the target weather pattern; wherein, the target point is a feature point in the target region; The wind field map acquisition submodule is used to acquire the wind field data corresponding to the target weather type and construct the wind field map based on the wind field data. The target wind field data map display submodule is used to display at least one of the wind speed spatial distribution map, the wind speed probability distribution map, and the analyzed wind field map as the target wind field data map to the user.

[0151] As the apparatus embodiment is basically similar to the method embodiment, it is described in a relatively simple manner. For relevant details, please refer to the description of the method embodiment.

[0152] An embodiment of this application also provides an electronic device, which may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the method described above.

[0153] An embodiment of this application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the method described above.

[0154] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0155] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0156] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0158] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0160] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other modifications and updates to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all modifications and updates falling within the scope of the embodiments of the present application.

[0161] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the aforementioned element.

[0162] The above provides a detailed description of the wind field data map determination method, apparatus, electronic device, and medium. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of determining a wind farm data map, characterized by, The method comprises: acquiring a pre-constructed association data set for a target region; the association data set stores a plurality of different weather types and a plurality of wind field characteristics corresponding to the weather types respectively; receiving a wind field data graph request issued by a user for the target region; the wind field data graph request includes a target weather type; determining a target wind field characteristic corresponding to the target weather type from the association data set; constructing a target wind field data graph of the target region according to the target wind field characteristic, and displaying the target wind field data graph to the user.

2. The method of claim 1, wherein, The acquisition of the pre-constructed association data set for the target region comprises: acquiring historical meteorological data and geographical data of the target region; weather typing the historical meteorological data to obtain a plurality of weather types of the target region; determining background wind field data of the target region according to the historical meteorological data and the geographical data; for any weather type, determining a wind field characteristic corresponding to the weather type according to the background wind field data and the historical meteorological data, and storing the weather type and the wind field characteristic as a set of association data; forming a plurality of sets of association data into an association data set.

3. The method of claim 2, wherein, The historical meteorological data includes reanalysis meteorological data, and the geographical data includes first topographic surface data and second topographic surface data; the determination of the background wind field data of the target region according to the historical meteorological data and the geographical data comprises: determining first meteorological field data according to the reanalysis meteorological data and the first topographic surface data; acquiring first background wind field data from the first meteorological field data; wherein the resolution of the first background wind field data and the resolution of the first topographic surface data are a first resolution; determining second background wind field data according to the first background wind field data and the second topographic surface data; wherein the resolution of the second background wind field data and the resolution of the second topographic surface data are a second resolution, and the second resolution is higher than the first resolution; taking the first background wind field data or the second background wind field data as the background wind field data of the target region.

4. The method of claim 3, wherein, The determination of the first meteorological field data according to the reanalysis meteorological data and the first topographic surface data comprises: determining initial meteorological field data corresponding to the target region and boundary meteorological field data corresponding to the boundary of the target region from the reanalysis meteorological data; configuring a preset weather forecast model according to the first topographic surface data to obtain a target meteorological simulation model; taking the boundary meteorological field data as the boundary condition of the target meteorological simulation model, and performing dynamic downscaling simulation on the initial meteorological field data by using the target meteorological simulation model to obtain the first meteorological field data output by the target meteorological simulation model.

5. The method of claim 3, wherein, The determination of the second background wind field data according to the first background wind field data and the second topographic surface data comprises: configuring a preset fluid mechanics model according to the second topographic surface data to obtain a target fluid simulation model; The first background wind field is simulated by using the target fluid simulation model to obtain second background wind field data output by the target fluid simulation model.

6. The method of claim 3, wherein, For any of the weather types, the wind field characteristics corresponding to the weather type are determined according to the background wind field data and the historical meteorological data, and the weather type and the wind field characteristics are stored as a set of associated data, including: For any of the weather types, the wind field characteristics corresponding to the weather type are determined according to the background wind field data and the historical meteorological data, and the weather type and the wind field characteristics are stored as a set of associated data, including: For any of the weather types, the wind field characteristics corresponding to the weather type are determined according to the background wind field data and the historical meteorological data, and the weather type and the wind field characteristics are stored as a set of associated data, including:

7. The method of claim 6, wherein, The historical meteorological data includes station observation data, and the analysis wind field data corresponding to any of the weather types is determined according to the background wind field data and the historical meteorological data, including: For any of the weather types, the station observation data of the target region is obtained; The observation wind field data is constructed according to the station observation data; The background wind field data and the observation wind field data are data assimilated to obtain the analysis wind field data.

8. The method of claim 7, wherein, The target wind field characteristics include average wind speed, dominant wind direction, wind speed distribution shape parameter and wind speed distribution scale parameter, the target wind field data graph of the target region is constructed according to the target wind field characteristics, and the target wind field data graph is displayed to the user, including: For the target weather type, the wind speed spatial distribution graph corresponding to the target region is generated according to the average wind speed and the dominant wind direction; For the target weather type, the wind speed probability distribution graph of the target point is generated according to the wind speed distribution shape parameter and the wind speed distribution scale parameter; wherein the target point is a feature point in the target region; For the target weather type, the analysis wind field data corresponding to the target weather type is obtained, and an analysis wind field graph is constructed according to the analysis wind field data; At least one of the wind speed spatial distribution graph, the wind speed probability distribution graph and the analysis wind field graph is displayed to the user as the target wind field data graph.

9. A wind farm data map determination apparatus characterized by comprising: The device comprises: A data set acquisition module is configured to acquire a pre-constructed associated data set for a target region; the associated data set stores a plurality of different weather types and a plurality of wind field characteristics corresponding to the weather types respectively; A weather type acquisition module is configured to receive a wind field data graph request issued by a user for the target region; the wind field data graph request includes a target weather type; A wind field characteristic determination module is configured to determine target wind field characteristics corresponding to the target weather type from the associated data set; A data graph determination module is configured to construct a target wind field data graph of the target region according to the target wind field characteristics, and display the target wind field data graph to the user.

10. An electronic device, comprising: The device comprises a processor, a memory and a program or instruction stored on the memory and executable on the processor, and the program or instruction is executed by the processor to implement the method of any one of claims 1-8. The device comprises a processor, a memory and a program or instruction stored on the memory and executable on the processor, and the program or instruction is executed by the processor to implement the method of any one of claims 1-8.

11. A readable storage medium, characterized by, The program or instruction is stored on the readable storage medium, and when executed by the processor, the program or instruction implements the method according to any one of claims 1-8.

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