Wind field inversion method and system based on laser radar and meteorological station data

Abstract

The invention provides a wind field inversion method and system based on laser radar and meteorological station data, and the method comprises the steps: carrying out the deviation correction of original radial wind speed data collected by a three-dimensional laser radar through the vertical profile data, collected by the three-dimensional laser radar, of a wind field, and obtaining radial wind speed data; performing horizontal wind vector inversion based on the radial wind speed data to obtain a background three-dimensional wind field; iteratively optimizing a background three-dimensional wind field and performing regular grid conversion by combining a three-dimensional variational method with an inverse distance weighted interpolation method and taking the spatial smoothness of the wind field and the original radial wind speed data as constraints to obtain a three-dimensional grid wind field; according to the method, the high-timeliness and high-resolution three-dimensional laser radar wind measurement data and the meteorological station wind measurement data are fused, so that the limitation of a single observation means on space continuity and timeliness is broken through, the real-time performance of the three-dimensional laser radar wind measurement data and the real-time performance of the meteorological station wind measurement data are improved, and the real-time performance of the three-dimensional laser radar wind measurement data and the real-time performance of the meteorological station wind measurement data are improved. And high-precision three-dimensional reconstruction of a complex convection wind field is realized.

Description

Technical Field

[0001] This invention relates to the field of wind field inversion technology, specifically to a wind field inversion method and system based on lidar and meteorological station data. Background Technology

[0002] Severe convective winds are among the most sudden, spatially smallest, and most destructive meteorological disasters. Their wind field structures are complex, often accompanied by intense vertical wind shear and micro-scale vortices, such as thunderstorms, tornadoes, and downbursts. This makes it extremely difficult for traditional fixed-station observation networks to effectively capture and warn of these rapidly changing extreme wind fields due to their inherent insufficient spatial representativeness and temporal resolution, thus making them one of the weakest links in the current monitoring and early warning system.

[0003] Currently, monitoring of severe convective winds mainly relies on wind field information provided by surface meteorological stations, wind profiler radars, and numerical weather prediction models. Surface meteorological stations provide precise wind vector data for a single point and serve as the baseline information source. However, due to the sparse distribution of surface meteorological stations and their limited spatial representativeness, and the drastic and highly localized changes in wind fields within severe convective systems, relying solely on sparse stations makes it difficult to capture small-scale wind speed gradients and wind shear characteristics. Active remote sensing devices (such as wind profiler radars and lidars), while capable of detecting wind fields within a certain vertical range, and three-dimensional lidars, which emit laser beams and receive backscattered signals from aerosols in the atmosphere to retrieve wind speed components along the line connecting the measuring point and the radar with high spatiotemporal resolution, only provide radial wind speed data and lack information on wind speed components perpendicular to the beam direction, thus failing to directly construct wind vectors. Furthermore, lidar's measuring points become sparse with increasing distance, failing to meet practical application requirements. Numerical weather prediction models provide large-scale, forecast-like wind field analysis based on physical equations, but their resolution for sudden, small-scale convective systems is limited.

[0004] Therefore, there is an urgent need for a high-precision wind field inversion method to solve the problems of insufficient wind field spatial coverage, discontinuous wind field data, and low wind field inversion accuracy in existing technologies, so as to realize the three-dimensional reconstruction of complex convective wind directions. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention proposes a wind field inversion method based on lidar and meteorological station data, comprising: Using the vertical profile data of the wind field collected by the three-dimensional lidar, the original radial wind speed data collected by itself is corrected for deviation to obtain radial wind speed data. Based on the radial wind speed data, the horizontal wind vector is inverted to obtain the background three-dimensional wind field. Using a three-dimensional variational method combined with inverse distance weighted interpolation, and taking the spatial smoothness of the wind field and the original radial wind speed data as constraints, the background three-dimensional wind field is iteratively optimized and transformed into a regular grid to obtain a three-dimensional grid wind field. By introducing layered constraints, the three-dimensional grid wind field and the measured wind speed data from the meteorological station are interpolated and fused to obtain the target three-dimensional wind field.

[0006] Optionally, the step of using a three-dimensional variational method combined with inverse distance weighted interpolation, with the spatial smoothness of the wind field and the original radial wind speed data as constraints, iteratively optimizing the background three-dimensional wind field and performing regular grid transformation to obtain a three-dimensional grid wind field includes: Using the three-dimensional variational method with the goal of minimizing the multi-constraint objective function, the wind speeds in the background three-dimensional wind field are iteratively optimized to obtain the optimized three-dimensional wind field. The optimized 3D wind field is transformed into a regular grid using the inverse distance weighted interpolation method to obtain a 3D grid wind field; The multi-constraint objective function is obtained by superimposing the background three-dimensional wind field as the background constraint term, the original radial wind speed data as the observation constraint term, and the spatial smoothness of the wind field as the smoothing constraint term. The expression of the multi-constraint objective function is as follows:

[0007] In the formula, Let represent the overall objective function. Represents the background field constraint terms. Represents the observation constraint term. Represents the smoothing constraint term. , and These represent the background error weight, observation error weight, and smoothing weight, respectively. , and The wind speed vector represents the background three-dimensional wind field. , and This represents the three-dimensional wind speed components at each measuring point during the iteration process. This represents the raw radial wind speed data. This represents the radial wind speed of the wind field during the iterative optimization process. This represents the divergence used to constrain the continuity of wind field quality.

[0008] Optionally, the step of transforming the optimized three-dimensional wind field into a regular grid using the inverse distance weighted interpolation method to obtain a three-dimensional grid wind field includes: Within the area of ​​the wind field, a regular three-dimensional coordinate grid consisting of equally spaced grid points is constructed; Based on each grid point in the rule-based three-dimensional coordinate grid, the wind speed estimate of the grid point is obtained by using the inverse distance weighted interpolation method, which calculates the weighted average based on the three-dimensional wind speed vector and distance of each measuring point in the neighborhood of the grid point. Traverse each grid point in the rule-based three-dimensional coordinate grid and output a three-dimensional grid wind field containing wind speed estimates for all grid points.

[0009] Optionally, before correcting the deviation of the original radial wind speed data acquired by the system using the vertical profile data of the wind field collected by the three-dimensional lidar, the system further includes: Using the preset geographical information in the shading database, the missing measurement area of ​​the wind field is determined. Based on each missing measurement point in the missing measurement area, the effective wind speed sequence at the adjacent azimuth angle of the missing measurement point is extracted. The missing measurement point is reconstructed based on the effective wind speed sequence using the smooth spline interpolation method. Wind speed data at the same azimuth and height layer are extracted from the temporally adjacent scan surface of the original radial wind speed data to form a time series. Estimated wind speed data for missing moments in the time series are generated by temporal interpolation. The estimated wind speed data and the reconstructed missing measurement points are then used to fill in the original radial wind speed data; By introducing the spatial continuity of the wind field gradient as a constraint, spatial interpolation and smoothing analysis are performed on the filled original radial wind speed data to generate complete original radial wind speed data.

[0010] Optionally, the step of performing horizontal wind vector inversion based on the radial wind speed data to obtain the background three-dimensional wind field includes: The radial wind speed of each measuring point at different azimuth and elevation angles is obtained from the radial wind speed data of the wind field, and the vertical wind speed of each measuring point is extracted from the vertical profile data of the wind field. Based on each of the measuring points in the wind field: based on the radial wind speed of the measuring point at different azimuth and elevation angles, and combining the geometric projection relationship between the radial wind speed and the three-dimensional wind vector, the horizontal wind vector is inverted by least squares fitting to obtain the east-west wind speed and the north-south wind speed of the measuring point; and by combining the east-west wind speed, the north-south wind speed and the vertical wind speed of the measuring point, the initial three-dimensional wind field of the measuring point is constructed. The initial three-dimensional wind field of each measuring point in the wind field is collected to form the background three-dimensional wind field of the wind field.

[0011] Optionally, the expression for the geometric projection relationship between the radial wind speed and the three-dimensional wind vector is:

[0012] In the formula, Indicates the radial wind speed at the measuring point. This indicates the east-west wind speed at the measuring point. This indicates the wind speed in the north-south direction at the measuring point. This indicates the azimuth angle corresponding to the measuring point. This indicates the elevation angle corresponding to the measuring point.

[0013] Optionally, the introduction of layered constraints, and the interpolation and fusion of the three-dimensional grid wind field and the measured wind speed data from the meteorological station to obtain the target three-dimensional wind field, includes: Obtain the grid point corresponding to the location of each weather station in the three-dimensional grid wind field, as well as all grid points in the vertical column corresponding to the location, to complete the spatial registration between the weather station and the three-dimensional grid wind field; The measured wind speed data of each meteorological station is time-registered with the three-dimensional grid wind field using time interpolation. Based on each of the meteorological stations, the measured wind speed data of the meteorological stations and the three-dimensional wind speed vector of the vertical column of the location of the meteorological station in the three-dimensional grid wind field are used to perform wind speed data fusion by combining the optimal interpolation formula with stratification constraints to obtain the fused data of the vertical column. By aggregating the fused data from all the aforementioned weather stations in their respective vertical columns, the target three-dimensional wind field is obtained.

[0014] Optionally, after introducing layered constraints and interpolating and fusing the three-dimensional grid wind field with the measured wind speed data from the meteorological station to obtain the target three-dimensional wind field, the method further includes: Based on the target three-dimensional wind field, the objective function constrained by minimizing the wind field gradient difference and minimizing the measured residual is solved to obtain the final target three-dimensional wind field. The expression for the objective function is:

[0015] In the formula, Represents the objective function value. The three-dimensional wind speed vector representing the target three-dimensional wind field is continuously optimized during the iteration process. The three-dimensional wind speed vector representing the background three-dimensional wind field. and Let represent the wind field gradient tensors of the target 3D wind field and the background 3D wind field, respectively. Indicates weather station The measured three-dimensional wind speed This represents the estimated wind speed at the weather station for the target three-dimensional wind field. This indicates the weather station number. The first term constrains the consistency of the wind field gradient, and the second term constrains the residuals between the fused wind field and the measured station.

[0016] Based on the same inventive concept, this invention proposes a wind field inversion system based on lidar and meteorological station data, comprising: The preliminary inversion module is used to correct the deviation of the original radial wind speed data it collects by using the vertical profile data of the wind field collected by the three-dimensional lidar, to obtain the radial wind speed data, and to perform horizontal wind vector inversion based on the radial wind speed data to obtain the background three-dimensional wind field. The wind field data optimization module is used to iteratively optimize the background three-dimensional wind field and perform regular grid transformation using a three-dimensional variational method combined with inverse distance weighted interpolation, with the spatial smoothness of the wind field and the original radial wind speed data as constraints, to obtain a three-dimensional grid wind field. The wind field data fusion module is used to introduce layer constraints and interpolate and fuse the three-dimensional grid wind field with the measured wind speed data of the meteorological station to obtain the target three-dimensional wind field.

[0017] Optionally, the wind field data optimization module is specifically used for: Using the three-dimensional variational method with the goal of minimizing the multi-constraint objective function, the wind speeds in the background three-dimensional wind field are iteratively optimized to obtain the optimized three-dimensional wind field. The optimized 3D wind field is transformed into a regular grid using the inverse distance weighted interpolation method to obtain a 3D grid wind field; The multi-constraint objective function is obtained by superimposing the background three-dimensional wind field as the background constraint term, the original radial wind speed data as the observation constraint term, and the spatial smoothness of the wind field as the smoothing constraint term. The expression of the multi-constraint objective function is as follows:

[0018] In the formula, Let represent the overall objective function. Represents the background field constraint terms. Represents the observation constraint term. Represents the smoothing constraint term. , and These represent the background error weight, observation error weight, and smoothing weight, respectively. , and The wind speed vector represents the background three-dimensional wind field. , and This represents the three-dimensional wind speed components at each measuring point during the iteration process. This represents the raw radial wind speed data. This represents the radial wind speed of the wind field during the iterative optimization process. This represents the divergence used to constrain the continuity of wind field quality.

[0019] Optionally, the wind field data optimization module is specifically used for: Within the area of ​​the wind field, a regular three-dimensional coordinate grid consisting of equally spaced grid points is constructed; Based on each grid point in the rule-based three-dimensional coordinate grid, the wind speed estimate of the grid point is obtained by using the inverse distance weighted interpolation method, which calculates the weighted average based on the three-dimensional wind speed vector and distance of each measuring point in the neighborhood of the grid point. Traverse each grid point in the rule-based three-dimensional coordinate grid and output a three-dimensional grid wind field containing wind speed estimates for all grid points.

[0020] Optionally, the system further includes a data preprocessing module for: Using the preset geographical information in the shading database, the missing measurement area of ​​the wind field is determined. Based on each missing measurement point in the missing measurement area, the effective wind speed sequence at the adjacent azimuth angle of the missing measurement point is extracted. The missing measurement point is reconstructed based on the effective wind speed sequence using the smooth spline interpolation method. Wind speed data at the same azimuth and height layer are extracted from the temporally adjacent scan surface of the original radial wind speed data to form a time series. Estimated wind speed data for missing moments in the time series are generated by temporal interpolation. The estimated wind speed data and the reconstructed missing measurement points are then used to fill in the original radial wind speed data; By introducing the spatial continuity of the wind field gradient as a constraint, spatial interpolation and smoothing analysis are performed on the filled original radial wind speed data to generate complete original radial wind speed data.

[0021] Optionally, the preliminary inversion module is specifically used for: The radial wind speed of each measuring point at different azimuth and elevation angles is obtained from the radial wind speed data of the wind field, and the vertical wind speed of each measuring point is extracted from the vertical profile data of the wind field. Based on each of the measuring points in the wind field: based on the radial wind speed of the measuring point at different azimuth and elevation angles, and combining the geometric projection relationship between the radial wind speed and the three-dimensional wind vector, the horizontal wind vector is inverted by least squares fitting to obtain the east-west wind speed and the north-south wind speed of the measuring point; and by combining the east-west wind speed, the north-south wind speed and the vertical wind speed of the measuring point, the initial three-dimensional wind field of the measuring point is constructed. The initial three-dimensional wind field of each measuring point in the wind field is collected to form the background three-dimensional wind field of the wind field.

[0022] Optionally, the expression for the geometric projection relationship between the radial wind speed and the three-dimensional wind vector in the preliminary inversion module is as follows:

[0023] In the formula, Indicates the radial wind speed at the measuring point. This indicates the east-west wind speed at the measuring point. This indicates the wind speed in the north-south direction at the measuring point. This indicates the azimuth angle corresponding to the measuring point. This indicates the elevation angle corresponding to the measuring point.

[0024] Optionally, the wind field data fusion module is specifically used for: Obtain the grid point corresponding to the location of each weather station in the three-dimensional grid wind field, as well as all grid points in the vertical column corresponding to the location, to complete the spatial registration between the weather station and the three-dimensional grid wind field; The measured wind speed data of each meteorological station is time-registered with the three-dimensional grid wind field using time interpolation. Based on each of the meteorological stations, the measured wind speed data of the meteorological stations and the three-dimensional wind speed vector of the vertical column of the location of the meteorological station in the three-dimensional grid wind field are used to perform wind speed data fusion by combining the optimal interpolation formula with stratification constraints to obtain the fused data of the vertical column. By aggregating the fused data from all the aforementioned weather stations in their respective vertical columns, the target three-dimensional wind field is obtained.

[0025] Optionally, the system further includes a wind field data optimization module, used for: Based on the target three-dimensional wind field, the objective function constrained by minimizing the wind field gradient difference and minimizing the measured residual is solved to obtain the final target three-dimensional wind field. The expression for the objective function is:

[0026] In the formula, Represents the objective function value. The three-dimensional wind speed vector representing the target three-dimensional wind field is continuously optimized during the iteration process. The three-dimensional wind speed vector representing the background three-dimensional wind field. and Let represent the wind field gradient tensors of the target 3D wind field and the background 3D wind field, respectively. Indicates weather station The measured three-dimensional wind speed This represents the estimated wind speed at the weather station for the target three-dimensional wind field. This indicates the weather station number. The first term constrains the consistency of the wind field gradient, and the second term constrains the residuals between the fused wind field and the measured station.

[0027] In another aspect, this application also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a wind field inversion method based on lidar and meteorological station data as described above is implemented.

[0028] Furthermore, this application also provides a computer-readable storage medium having an executable program stored thereon, which, when executed, implements the wind field inversion method based on lidar and meteorological station data as described above.

[0029] Compared with the closest existing technology, the present invention has the following beneficial effects: This invention provides a wind field inversion method and system based on lidar and meteorological station data, comprising: using vertical profile data of the wind field acquired by a three-dimensional lidar to correct the deviation of the original radial wind speed data acquired by itself to obtain radial wind speed data; performing horizontal wind vector inversion based on the radial wind speed data to obtain a background three-dimensional wind field; using a three-dimensional variational method combined with an inverse distance weighted interpolation method, with the spatial smoothness of the wind field and the original radial wind speed data as constraints, iteratively optimizing the background three-dimensional wind field and performing regular grid transformation to obtain a three-dimensional grid wind field; introducing stratification constraints, interpolating and fusing the three-dimensional grid wind field with the measured wind speed data from the meteorological station to obtain a target three-dimensional wind field. Vertical profile data obtained from lidar was used to correct the deviation of the original radial wind speed, eliminating the contribution of vertical wind speed mixed in with the radial wind speed and improving the accuracy of the observation data. A three-dimensional variational method was introduced, and iterative optimization was carried out with spatial smoothness and radial wind speed observation as constraints, which effectively enhanced the continuity and physical consistency of the wind field. Furthermore, wind field data obtained from high-time-efficiency and high-resolution lidar was fused with meteorological station measured data, which increased the coverage and reliability of wind field data, broke through the limitations of single observation methods in terms of spatial continuity and timeliness, realized the fine three-dimensional reconstruction of complex convective wind fields, and ensured the vertical continuity and physical rationality of the wind field between the ground and the lower atmosphere by introducing stratification constraints. Attached Figure Description

[0030] Figure 1 A flowchart illustrating a wind field inversion method based on lidar and meteorological station data provided by the present invention; Figure 2 A schematic diagram of the structure of a wind field inversion system based on lidar and meteorological station data provided by the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0031] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] Example 1 This invention provides a wind field inversion method based on lidar and meteorological station data, such as... Figure 1 As shown, it includes: S1. Using the vertical profile data of the wind field collected by the three-dimensional lidar, the original radial wind speed data collected by itself is corrected for deviation to obtain radial wind speed data. Based on the radial wind speed data, the horizontal wind vector is inverted to obtain the background three-dimensional wind field. S2. Using the three-dimensional variational method combined with the inverse distance weighted interpolation method, with the spatial smoothness of the wind field and the original radial wind speed data as constraints, the background three-dimensional wind field is iteratively optimized and transformed into a regular grid to obtain a three-dimensional grid wind field. S3. Introduce layered constraints and interpolate and fuse the three-dimensional grid wind field with the measured wind speed data from the meteorological station to obtain the target three-dimensional wind field.

[0033] In step S1, S1-1, the original radial wind speed data of the wind field is collected using a three-dimensional lidar in three-dimensional volume scanning mode, and the vertical profile data of the wind field is collected in wind profile detection mode.

[0034] To eliminate the effects of noise and obstruction in the observation data, the raw radial wind speed data were subjected to quality control and preprocessing based on the principle of wind field spatial continuity.

[0035] Specifically, outlier identification and removal: isolated abnormal wind speeds are removed using statistical thresholding and spatiotemporal consistency tests to obtain pre-processed raw radial wind speed data, which is then used to remove the identified abnormal wind speeds.

[0036] Obstruction correction: For the missing measurement areas caused by building and terrain obstruction, the missing measurement areas of the wind field are determined by using the preset geographical information in the obstruction database. Based on each missing measurement point in the missing measurement area, the effective wind speed sequence at the adjacent azimuth angle of the missing measurement point is extracted. The missing measurement point is reconstructed based on the effective wind speed sequence using the smooth spline interpolation method. The wind speed fitting function for missing measurement points in the smooth spline interpolation method is obtained by minimizing the following formula:

[0037] In the formula, This represents the cost function to be minimized to balance fitting accuracy and curve smoothness. This represents the effective wind speed sequence that is not obstructed. This represents the azimuth angle corresponding to the effective wind speed sequence that is not obstructed. Represents the wind speed fitting function In azimuth The predicted value at that location, This represents the second derivative of the wind speed fitting function with respect to the azimuth angle. Indicates azimuth. This represents the smoothing parameter, which is adjusted... The smoothness of the control curve is used to achieve continuous interpolation of missing measurement points. The first term in the formula is the fitting error, and the second term is the smoothing penalty term.

[0038] Missing data filling: Wind speed data of the same azimuth and height layer are extracted from the time-adjacent scan surface of the original radial wind speed data to form a time series. Estimated wind speed data for missing moments in the time series are generated by temporal interpolation. The estimated wind speed data and the reconstructed missing measurement points are then used to fill in the original radial wind speed data; By introducing the spatial continuity of the wind field gradient as a constraint, spatial interpolation and smoothing analysis are performed on the filled original radial wind speed data to generate complete original radial wind speed data.

[0039] After preprocessing, the radial wind speed data is spatially continuous and temporally smooth, laying the foundation for subsequent three-dimensional wind vector inversion.

[0040] S1-2. Using the vertical profile data of the wind field collected by the three-dimensional lidar, the original radial wind speed data collected by itself is corrected for deviation to obtain the radial wind speed data.

[0041] Because the radial wind speed measured by 3D scanning contains contributions from vertical motion, these vertical contributions must be removed to accurately invert the horizontal wind. Vertical profile data includes horizontal wind direction, horizontal wind speed, and vertical wind speed. A vertical profile refers to a sequence of wind speed / direction observations at different altitudes within the same horizontal position; a single vertical profile contains a set of measurement points in multiple vertical directions.

[0042] Obtain vertical wind speed in wind profiler mode. To eliminate the influence of the vertical component on the radial wind speed, the original radial wind speed was... The correction is made, and its correction expression is:

[0043] In the formula, This represents the radial wind speed obtained after correction for measurement point deviation. This represents the original radial wind speed at the measuring point. Indicates the elevation angle corresponding to the measuring point, after correction. Used to invert horizontal wind vectors.

[0044] The above method uses the wind profile to initialize the vertical wind speed components of all measuring points and corrects them during the radial wind speed inversion process, eliminating the interference components of the vertical velocity in the radial projection.

[0045] S1-3. Based on the radial wind speed data, perform horizontal wind vector inversion to obtain the background three-dimensional wind field.

[0046] The corrected radial wind speed is used for horizontal wind vector inversion on each scanning surface of the lidar, calculating the east-west and north-south wind components to form a preliminary background three-dimensional wind field. This background wind field can reflect the local rotation characteristics and wind shear structure of the strong convective system.

[0047] Specifically, the radial wind speed of each measuring point at different azimuth and elevation angles is obtained from the radial wind speed data of the wind field, and the vertical wind speed of each measuring point is extracted from the vertical profile data of the wind field. Based on each of the measuring points in the wind field: based on the radial wind speed of the measuring point at different azimuth and elevation angles, and combining the geometric projection relationship between the radial wind speed and the three-dimensional wind vector, the horizontal wind vector is inverted by least squares fitting to obtain the east-west wind speed and the north-south wind speed of the measuring point; and by combining the east-west wind speed, the north-south wind speed and the vertical wind speed of the measuring point, the initial three-dimensional wind field of the measuring point is constructed. The initial three-dimensional wind field of each measuring point in the wind field is collected to form the background three-dimensional wind field of the wind field.

[0048] The expression for the geometric projection relationship between the radial wind speed and the three-dimensional wind vector is:

[0049] In the formula, Indicates the radial wind speed at the measuring point. This indicates the east-west wind speed at the measuring point. This indicates the wind speed in the north-south direction at the measuring point. This indicates the azimuth angle corresponding to the measuring point. This indicates the elevation angle corresponding to the measuring point.

[0050] In step S2, to improve the physical consistency of the wind field, a three-dimensional variational method is used to optimize the background three-dimensional wind field.

[0051] Using a three-dimensional variational method combined with inverse distance weighted interpolation, and taking the spatial smoothness of the wind field and the original radial wind speed data as constraints, the background three-dimensional wind field is iteratively optimized and transformed into a regular grid to obtain a three-dimensional grid wind field.

[0052] Using the three-dimensional variational method with the goal of minimizing the multi-constraint objective function, the wind speeds in the background three-dimensional wind field are iteratively optimized to obtain the optimized three-dimensional wind field. The optimized 3D wind field is transformed into a regular grid using the inverse distance weighted interpolation method to obtain a 3D grid wind field; The multi-constraint objective function is obtained by superimposing the background three-dimensional wind field as the background constraint term, the original radial wind speed data as the observation constraint term, and the spatial smoothness of the wind field as the smoothing constraint term. The expression of the multi-constraint objective function is as follows:

[0053] In the formula, Let represent the overall objective function. Represents the background field constraint terms. Represents the observation constraint term. This represents a smoothing constraint term used to control wind field divergence. , and These represent the background error weight, observation error weight, and smoothing weight, respectively. , and The wind speed vector represents the background three-dimensional wind field. Indicates wind speed in the east-west direction. Indicates wind speed in the north-south direction. Indicates vertical wind speed. , and This represents the three-dimensional wind speed components at each measuring point during the iteration process. This represents the raw radial wind speed data. This represents the radial wind speed of the wind field during the iterative optimization process. This represents the divergence used to constrain the continuity of wind field quality.

[0054] Specifically, the optimized 3D wind field is transformed into a regular grid using inverse distance weighted interpolation to obtain a 3D grid wind field, including: Within the area of ​​the wind field, a regular three-dimensional coordinate grid consisting of equally spaced grid points is constructed; Based on each grid point in the rule-based three-dimensional coordinate grid, the wind speed estimate of the grid point is obtained by using the inverse distance weighted interpolation method, which calculates the weighted average based on the three-dimensional wind speed vector and distance of each measuring point in the neighborhood of the grid point. Traverse each grid point in the rule-based three-dimensional coordinate grid and output a three-dimensional grid wind field containing wind speed estimates for all grid points.

[0055] For any point to be inserted (i.e., each grid point), the estimated wind speed is:

[0056] in, Indicates the insertion point The estimated wind speed, Point to be inserted No. Three-dimensional wind speed vectors of neighboring measuring points For the first Neighboring measurement points and distance, This is the distance index (usually taken as 2). As a smoothing factor, Indicates the first Nearby measuring point pairs The weights are determined by the distance; the smaller the distance, the greater the weight. Interpolation then forms a spatially continuous three-dimensional radar wind field background.

[0057] In the aforementioned multi-constraint objective function, the observation constraint term ensures that the inversion result closely matches the original observed radial wind speed data, preserving true observational characteristics. The smoothing constraint term suppresses wind field noise, enhancing the spatial continuity and structural rationality of the wind field. The background constraint term serves as the initial reference state, ensuring that the optimized wind field closely approximates the prior estimate, thus achieving wind speed data optimization. Furthermore, through inverse distance weighted interpolation, the wind speed of the grid nodes is estimated by weighted averaging of the wind speeds of the irregularly distributed radar measurement points and the known scattered points (radar measurement points) around the blank points in the regular grid. This forms a complete, continuous, and regular three-dimensional wind field image, effectively enhancing wind field continuity and providing a high-precision background field for subsequent multi-source data fusion.

[0058] In step S3, layered constraints are introduced, and the three-dimensional grid wind field and the measured wind speed data from the meteorological station are interpolated and fused to obtain the target three-dimensional wind field.

[0059] This step further incorporates measured wind speed and direction data from ground meteorological stations into the fusion framework to improve the surface layer accuracy and overall reliability of the wind field reconstruction. First, it is necessary to complete the data matching and spatiotemporal registration between radar wind measurement data and measured wind speed from meteorological stations. Specifically, real-time wind speed and direction observation data from automatic weather stations within the target area are collected, a unified coordinate system is established, and the data is synchronized with the radar data in time. For asynchronous observation periods, temporal alignment is achieved through time interpolation or matching of the most recent observations to ensure consistent spatiotemporal resolution during fusion.

[0060] Specifically, the location of each weather station in the three-dimensional grid wind field is obtained, as well as all grid points in the vertical column corresponding to its location, to complete the spatial registration between the weather station and the three-dimensional grid wind field. The measured wind speed data of each meteorological station is time-registered with the three-dimensional grid wind field using time interpolation.

[0061] On the background 3D wind field retrieved by radar, grid areas spatially overlapping with meteorological stations are selected. A weighted interpolation fusion method is used to incorporate ground station observations into the background wind field. A stratification constraint is introduced during the fusion process: the weight of ground data is increased below the boundary layer height, while radar-dominant information is retained in the mid-to-high-level regions. This ensures the vertical continuity and physical plausibility of the wind fields at the ground and lower atmospheres.

[0062] Specifically, based on each weather station, the measured wind speed data of the weather station and the three-dimensional wind speed vector of the vertical column of the weather station's location in the three-dimensional grid wind field are used to perform wind speed data fusion by combining the optimal interpolation formula with stratification constraints, so as to obtain the fused data of the vertical column. By aggregating the fused data from all the aforementioned weather stations in their respective vertical columns, the target three-dimensional wind field is obtained.

[0063] The optimal interpolation formula combining the introduced layer constraint is as follows:

[0064] In the formula, This represents the three-dimensional wind speed vector of the fused grid points, i.e., the wind speed vector of the target three-dimensional wind field. The stratification weighting function varies with altitude, typically taking a larger value at the surface and gradually decreasing at higher altitudes to achieve physical continuity in the vertical direction. This represents the wind speed data measured by the weather station. This represents the three-dimensional wind speed vector at a grid point in a three-dimensional grid wind field.

[0065] After obtaining the target three-dimensional wind field, the following is also included: Based on the target three-dimensional wind field, the objective function constrained by minimizing the wind field gradient difference and minimizing the measured residual is solved to obtain the final target three-dimensional wind field. The expression for the objective function is:

[0066] In the formula, Represents the objective function value. The three-dimensional wind speed vector representing the target three-dimensional wind field is continuously optimized during the iteration process. The three-dimensional wind speed vector representing the background three-dimensional wind field. and Let represent the wind field gradient tensors of the target 3D wind field and the background 3D wind field, respectively. Indicates weather station The measured three-dimensional wind speed This represents the estimated wind speed at the weather station for the target three-dimensional wind field. This indicates the weather station number. The first term constrains the consistency of the wind field gradient, and the second term constrains the residuals between the fused wind field and the measured station.

[0067] By solving for the optimal fusion coefficient matrix, adaptive fusion of multi-source wind fields is achieved, resulting in a final high-precision fused wind field.

[0068] Example 2 Based on the same inventive concept, this invention also provides a wind field inversion system based on lidar and meteorological station data, such as... Figure 2 As shown, it includes: The preliminary inversion module is used to correct the deviation of the original radial wind speed data it collects by using the vertical profile data of the wind field collected by the three-dimensional lidar, to obtain the radial wind speed data, and to perform horizontal wind vector inversion based on the radial wind speed data to obtain the background three-dimensional wind field. The wind field data optimization module is used to iteratively optimize the background three-dimensional wind field and perform regular grid transformation using a three-dimensional variational method combined with inverse distance weighted interpolation, with the spatial smoothness of the wind field and the original radial wind speed data as constraints, to obtain a three-dimensional grid wind field. The wind field data fusion module is used to introduce layer constraints and interpolate and fuse the three-dimensional grid wind field with the measured wind speed data of the meteorological station to obtain the target three-dimensional wind field.

[0069] In one possible implementation, the aforementioned wind field data optimization module is specifically used for: Using the three-dimensional variational method with the goal of minimizing the multi-constraint objective function, the wind speeds in the background three-dimensional wind field are iteratively optimized to obtain the optimized three-dimensional wind field. The optimized 3D wind field is transformed into a regular grid using the inverse distance weighted interpolation method to obtain a 3D grid wind field; The multi-constraint objective function is obtained by superimposing the background three-dimensional wind field as the background constraint term, the original radial wind speed data as the observation constraint term, and the spatial smoothness of the wind field as the smoothing constraint term. The expression of the multi-constraint objective function is as follows:

[0070] In the formula, Let represent the overall objective function. Represents the background field constraint terms. Represents the observation constraint term. Represents the smoothing constraint term. , and These represent the background error weight, observation error weight, and smoothing weight, respectively. , and The wind speed vector represents the background three-dimensional wind field. , and This represents the three-dimensional wind speed components at each measuring point during the iteration process. This represents the raw radial wind speed data. This represents the radial wind speed of the wind field during the iterative optimization process. This represents the divergence used to constrain the continuity of wind field quality.

[0071] In one possible implementation, the aforementioned wind field data optimization module is specifically used for: Within the area of ​​the wind field, a regular three-dimensional coordinate grid consisting of equally spaced grid points is constructed; Based on each grid point in the rule-based three-dimensional coordinate grid, the wind speed estimate of the grid point is obtained by using the inverse distance weighted interpolation method, which calculates the weighted average based on the three-dimensional wind speed vector and distance of each measuring point in the neighborhood of the grid point. Traverse each grid point in the rule-based three-dimensional coordinate grid and output a three-dimensional grid wind field containing wind speed estimates for all grid points.

[0072] In one possible implementation, the system further includes a data preprocessing module for: Using the preset geographical information in the shading database, the missing measurement area of ​​the wind field is determined. Based on each missing measurement point in the missing measurement area, the effective wind speed sequence at the adjacent azimuth angle of the missing measurement point is extracted. The missing measurement point is reconstructed based on the effective wind speed sequence using the smooth spline interpolation method. Wind speed data at the same azimuth and height layer are extracted from the temporally adjacent scan surface of the original radial wind speed data to form a time series. Estimated wind speed data for missing moments in the time series are generated by temporal interpolation. The estimated wind speed data and the reconstructed missing measurement points are then used to fill in the original radial wind speed data; By introducing the spatial continuity of the wind field gradient as a constraint, spatial interpolation and smoothing analysis are performed on the filled original radial wind speed data to generate complete original radial wind speed data.

[0073] In one possible implementation, the aforementioned preliminary inversion module is specifically used for: The radial wind speed of each measuring point at different azimuth and elevation angles is obtained from the radial wind speed data of the wind field, and the vertical wind speed of each measuring point is extracted from the vertical profile data of the wind field. Based on each of the measuring points in the wind field: based on the radial wind speed of the measuring point at different azimuth and elevation angles, and combining the geometric projection relationship between the radial wind speed and the three-dimensional wind vector, the horizontal wind vector is inverted by least squares fitting to obtain the east-west wind speed and the north-south wind speed of the measuring point; and by combining the east-west wind speed, the north-south wind speed and the vertical wind speed of the measuring point, the initial three-dimensional wind field of the measuring point is constructed. The initial three-dimensional wind field of each measuring point in the wind field is collected to form the background three-dimensional wind field of the wind field.

[0074] In one possible implementation, the expression for the geometric projection relationship between the radial wind speed and the three-dimensional wind vector in the aforementioned preliminary inversion module is as follows:

[0075] In the formula, Indicates the radial wind speed at the measuring point. This indicates the east-west wind speed at the measuring point. This indicates the wind speed in the north-south direction at the measuring point. This indicates the azimuth angle corresponding to the measuring point. This indicates the elevation angle corresponding to the measuring point.

[0076] In one possible implementation, the aforementioned wind field data fusion module is specifically used for: Obtain the grid point corresponding to the location of each weather station in the three-dimensional grid wind field, as well as all grid points in the vertical column corresponding to the location, to complete the spatial registration between the weather station and the three-dimensional grid wind field; The measured wind speed data of each meteorological station is time-registered with the three-dimensional grid wind field using time interpolation. Based on each of the meteorological stations, the measured wind speed data of the meteorological stations and the three-dimensional wind speed vector of the vertical column of the location of the meteorological station in the three-dimensional grid wind field are used to perform wind speed data fusion by combining the optimal interpolation formula with stratification constraints to obtain the fused data of the vertical column. By aggregating the fused data from all the aforementioned weather stations in their respective vertical columns, the target three-dimensional wind field is obtained.

[0077] In one possible implementation, the system further includes a wind field data optimization module, used for: Based on the target three-dimensional wind field, the objective function constrained by minimizing the wind field gradient difference and minimizing the measured residual is solved to obtain the final target three-dimensional wind field. The expression for the objective function is:

[0078] In the formula, Represents the objective function value. The three-dimensional wind speed vector representing the target three-dimensional wind field is continuously optimized during the iteration process. The three-dimensional wind speed vector representing the background three-dimensional wind field. and Let represent the wind field gradient tensors of the target 3D wind field and the background 3D wind field, respectively. Indicates weather station The measured three-dimensional wind speed This represents the estimated wind speed at the weather station for the target three-dimensional wind field. This indicates the weather station number. The first term constrains the consistency of the wind field gradient, and the second term constrains the residuals between the fused wind field and the measured station.

[0079] Example 3 like Figure 3As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0080] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the wind field inversion method based on lidar and meteorological station data in the above embodiments.

[0081] Example 4 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the wind field inversion method based on lidar and meteorological station data in the above embodiments.

[0082] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0083] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function 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.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable 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.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A wind field inversion method based on lidar and meteorological station data, characterized in that, include: Using the vertical profile data of the wind field collected by the three-dimensional lidar, the original radial wind speed data collected by itself is corrected for deviation to obtain radial wind speed data. Based on the radial wind speed data, the horizontal wind vector is inverted to obtain the background three-dimensional wind field. Using a three-dimensional variational method combined with inverse distance weighted interpolation, and taking the spatial smoothness of the wind field and the original radial wind speed data as constraints, the background three-dimensional wind field is iteratively optimized and transformed into a regular grid to obtain a three-dimensional grid wind field. By introducing layered constraints, the three-dimensional grid wind field and the measured wind speed data from the meteorological station are interpolated and fused to obtain the target three-dimensional wind field.

2. The method as described in claim 1, characterized in that, The method utilizes a three-dimensional variational method combined with inverse distance weighted interpolation, taking the spatial smoothness of the wind field and the original radial wind speed data as constraints, to iteratively optimize the background three-dimensional wind field and perform regular grid transformation to obtain a three-dimensional grid wind field, including: Using the three-dimensional variational method with the goal of minimizing the multi-constraint objective function, the wind speeds in the background three-dimensional wind field are iteratively optimized to obtain the optimized three-dimensional wind field. The optimized 3D wind field is transformed into a regular grid using the inverse distance weighted interpolation method to obtain a 3D grid wind field; The multi-constraint objective function is obtained by superimposing the background three-dimensional wind field as the background constraint term, the original radial wind speed data as the observation constraint term, and the spatial smoothness of the wind field as the smoothing constraint term. The expression of the multi-constraint objective function is as follows: In the formula, Let represent the overall objective function. Indicates the background field constraint terms. Represents the observation constraint term. Represents the smoothing constraint term. , and These represent the background error weight, observation error weight, and smoothing weight, respectively. , and The wind speed vector represents the background three-dimensional wind field. , and This represents the three-dimensional wind speed components at each measuring point during the iteration process. This represents the raw radial wind speed data. This represents the radial wind speed of the wind field during the iterative optimization process. This represents the divergence used to constrain the continuity of wind field quality.

3. The method as described in claim 2, characterized in that, The process of transforming the optimized 3D wind field into a regular grid using inverse distance weighted interpolation to obtain a 3D grid wind field includes: Within the area of ​​the wind field, a regular three-dimensional coordinate grid consisting of equally spaced grid points is constructed; Based on each grid point in the rule-based three-dimensional coordinate grid, the wind speed estimate of the grid point is obtained by using the inverse distance weighted interpolation method, which calculates the weighted average based on the three-dimensional wind speed vector and distance of each measuring point in the neighborhood of the grid point. Traverse each grid point in the rule-based three-dimensional coordinate grid and output a three-dimensional grid wind field containing wind speed estimates for all grid points.

4. The method as described in claim 1, characterized in that, Before correcting the deviation of the raw radial wind speed data acquired by the system using the vertical profile data of the wind field collected by the three-dimensional lidar, the process also includes: Using the preset geographical information in the shading database, the missing measurement area of ​​the wind field is determined. Based on each missing measurement point in the missing measurement area, the effective wind speed sequence at the adjacent azimuth angle of the missing measurement point is extracted. The missing measurement point is reconstructed based on the effective wind speed sequence using the smooth spline interpolation method. Wind speed data at the same azimuth and height layer are extracted from the temporally adjacent scan surface of the original radial wind speed data to form a time series. Estimated wind speed data for missing moments in the time series are generated by temporal interpolation. The estimated wind speed data and the reconstructed missing measurement points are then used to fill in the original radial wind speed data; By introducing the spatial continuity of the wind field gradient as a constraint, spatial interpolation and smoothing analysis are performed on the filled original radial wind speed data to generate complete original radial wind speed data.

5. The method as described in claim 1, characterized in that, The process of performing horizontal wind vector inversion based on the radial wind speed data to obtain the background three-dimensional wind field includes: The radial wind speed of each measuring point at different azimuth and elevation angles is obtained from the radial wind speed data of the wind field, and the vertical wind speed of each measuring point is extracted from the vertical profile data of the wind field. Based on each of the measuring points in the wind field: based on the radial wind speed of the measuring point at different azimuth and elevation angles, and combining the geometric projection relationship between the radial wind speed and the three-dimensional wind vector, the horizontal wind vector is inverted by least squares fitting to obtain the east-west wind speed and the north-south wind speed of the measuring point; and by combining the east-west wind speed, the north-south wind speed and the vertical wind speed of the measuring point, the initial three-dimensional wind field of the measuring point is constructed. The initial three-dimensional wind field of each measuring point in the wind field is collected to form the background three-dimensional wind field of the wind field.

6. The method as described in claim 5, characterized in that, The expression for the geometric projection relationship between the radial wind speed and the three-dimensional wind vector is: In the formula, Indicates the radial wind speed at the measuring point. This indicates the east-west wind speed at the measuring point. This indicates the wind speed in the north-south direction at the measuring point. This indicates the azimuth angle corresponding to the measuring point. This indicates the elevation angle corresponding to the measuring point.

7. The method as described in claim 1, characterized in that, The introduction of layered constraints involves interpolating and fusing the three-dimensional grid wind field with the measured wind speed data from the meteorological station to obtain the target three-dimensional wind field, including: Obtain the grid point corresponding to the location of each weather station in the three-dimensional grid wind field, as well as all grid points in the vertical column corresponding to the location, to complete the spatial registration between the weather station and the three-dimensional grid wind field; The measured wind speed data of each meteorological station is time-registered with the three-dimensional grid wind field using time interpolation. Based on each of the meteorological stations, the measured wind speed data of the meteorological stations and the three-dimensional wind speed vector of the vertical column of the location of the meteorological station in the three-dimensional grid wind field are used to perform wind speed data fusion by combining the optimal interpolation formula with stratification constraints to obtain the fused data of the vertical column. By aggregating the fused data from all the aforementioned weather stations in their respective vertical columns, the target three-dimensional wind field is obtained.

8. The method as described in claim 1, characterized in that, After introducing layered constraints and interpolating and fusing the three-dimensional grid wind field with the measured wind speed data from the meteorological station to obtain the target three-dimensional wind field, the method further includes: Based on the target three-dimensional wind field, the objective function constrained by minimizing the wind field gradient difference and minimizing the measured residual is solved to obtain the final target three-dimensional wind field. The expression for the objective function is: In the formula, Represents the objective function value. The three-dimensional wind speed vector representing the target three-dimensional wind field is continuously optimized during the iteration process. The three-dimensional wind speed vector representing the background three-dimensional wind field. and Let represent the wind field gradient tensors of the target 3D wind field and the background 3D wind field, respectively. Indicates weather station The measured three-dimensional wind speed This represents the estimated wind speed at the weather station for the target three-dimensional wind field. This indicates the weather station number. The first term constrains the consistency of the wind field gradient, and the second term constrains the residuals between the fused wind field and the measured station.

9. A wind field inversion system based on lidar and meteorological station data, characterized in that, include: The preliminary inversion module is used to correct the deviation of the original radial wind speed data it collects by using the vertical profile data of the wind field collected by the three-dimensional lidar, to obtain the radial wind speed data, and to perform horizontal wind vector inversion based on the radial wind speed data to obtain the background three-dimensional wind field. The wind field data optimization module is used to use a three-dimensional variational method combined with an inverse distance weighted interpolation method, with the spatial smoothness of the wind field and the original radial wind speed data as constraints, to iteratively optimize the background three-dimensional wind field and perform regular grid transformation to obtain a three-dimensional grid wind field. The wind field data fusion module is used to introduce layer constraints and interpolate and fuse the three-dimensional grid wind field with the measured wind speed data of the meteorological station to obtain the target three-dimensional wind field.

10. The system as described in claim 9, characterized in that, The wind field data optimization module is specifically used for: Using the three-dimensional variational method with the goal of minimizing the multi-constraint objective function, the wind speeds in the background three-dimensional wind field are iteratively optimized to obtain the optimized three-dimensional wind field. The optimized 3D wind field is transformed into a regular grid using the inverse distance weighted interpolation method to obtain a 3D grid wind field; The multi-constraint objective function is obtained by superimposing the background three-dimensional wind field as the background constraint term, the original radial wind speed data as the observation constraint term, and the spatial smoothness of the wind field as the smoothing constraint term. The expression of the multi-constraint objective function is as follows: In the formula, Let represent the overall objective function. Indicates the background field constraint terms. Represents the observation constraint term. Represents the smoothing constraint term. , and These represent the background error weight, observation error weight, and smoothing weight, respectively. , and The wind speed vector represents the background three-dimensional wind field. , and This represents the three-dimensional wind speed components at each measuring point during the iteration process. This represents the raw radial wind speed data. This represents the radial wind speed of the wind field during the iterative optimization process. This represents the divergence used to constrain the continuity of wind field quality.

11. The system as claimed in claim 10, characterized in that, The wind field data optimization module is specifically used for: Within the area of ​​the wind field, a regular three-dimensional coordinate grid consisting of equally spaced grid points is constructed; Based on each grid point in the rule-based three-dimensional coordinate grid, the wind speed estimate of the grid point is obtained by using the inverse distance weighted interpolation method, which calculates the weighted average based on the three-dimensional wind speed vector and distance of each measuring point in the neighborhood of the grid point. Traverse each grid point in the rule-based three-dimensional coordinate grid and output a three-dimensional grid wind field containing wind speed estimates for all grid points.

12. The system as described in claim 9, characterized in that, It also includes a data preprocessing module, used for: Using the preset geographical information in the shading database, the missing measurement area of ​​the wind field is determined. Based on each missing measurement point in the missing measurement area, the effective wind speed sequence at the adjacent azimuth angle of the missing measurement point is extracted. The missing measurement point is reconstructed based on the effective wind speed sequence using the smooth spline interpolation method. Wind speed data at the same azimuth and height layer are extracted from the temporally adjacent scan surface of the original radial wind speed data to form a time series. Estimated wind speed data for missing moments in the time series are generated by temporal interpolation. The estimated wind speed data and the reconstructed missing measurement points are then used to fill in the original radial wind speed data; By introducing the spatial continuity of the wind field gradient as a constraint, spatial interpolation and smoothing analysis are performed on the filled original radial wind speed data to generate complete original radial wind speed data.

13. The system as described in claim 9, characterized in that, The preliminary inversion module is specifically used for: The radial wind speed of each measuring point at different azimuth and elevation angles is obtained from the radial wind speed data of the wind field, and the vertical wind speed of each measuring point is extracted from the vertical profile data of the wind field. Based on each of the measuring points in the wind field: based on the radial wind speed of the measuring point at different azimuth and elevation angles, and combining the geometric projection relationship between the radial wind speed and the three-dimensional wind vector, the horizontal wind vector is inverted by least squares fitting to obtain the east-west wind speed and the north-south wind speed of the measuring point; and by combining the east-west wind speed, the north-south wind speed and the vertical wind speed of the measuring point, the initial three-dimensional wind field of the measuring point is constructed. The initial three-dimensional wind field of each measuring point in the wind field is collected to form the background three-dimensional wind field of the wind field.

14. The system as described in claim 13, characterized in that, The expression for the geometric projection relationship between radial wind speed and three-dimensional wind vector in the preliminary inversion module is as follows: In the formula, Indicates the radial wind speed at the measuring point. This indicates the east-west wind speed at the measuring point. This indicates the wind speed in the north-south direction at the measuring point. This indicates the azimuth angle corresponding to the measuring point. This indicates the elevation angle corresponding to the measuring point.

15. The system as described in claim 9, characterized in that, The wind field data fusion module is specifically used for: Obtain the grid point corresponding to the location of each weather station in the three-dimensional grid wind field, as well as all grid points in the vertical column corresponding to the location, to complete the spatial registration between the weather station and the three-dimensional grid wind field; The measured wind speed data of each meteorological station is time-registered with the three-dimensional grid wind field using time interpolation. Based on each of the meteorological stations, the measured wind speed data of the meteorological stations and the three-dimensional wind speed vector of the vertical column of the location of the meteorological station in the three-dimensional grid wind field are used to perform wind speed data fusion by combining the optimal interpolation formula with stratification constraints to obtain the fused data of the vertical column. By aggregating the fused data from all the aforementioned weather stations in their respective vertical columns, the target three-dimensional wind field is obtained.

16. The system as described in claim 9, characterized in that, It also includes a wind field data optimization module, used for: Based on the target three-dimensional wind field, the objective function constrained by minimizing the wind field gradient difference and minimizing the measured residual is solved to obtain the final target three-dimensional wind field. The expression for the objective function is: In the formula, Represents the objective function value. The three-dimensional wind speed vector representing the target three-dimensional wind field is continuously optimized during the iteration process. The three-dimensional wind speed vector representing the background three-dimensional wind field. and Let represent the wind field gradient tensors of the target 3D wind field and the background 3D wind field, respectively. Indicates weather station The measured three-dimensional wind speed This represents the estimated wind speed at the weather station for the target three-dimensional wind field. This indicates the weather station number. The first term constrains the consistency of the wind field gradient, and the second term constrains the residuals between the fused wind field and the measured station.

17. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a wind field inversion method based on lidar and meteorological station data as described in any one of claims 1 to 8 is implemented.

18. A computing device readable storage medium, characterized in that, It contains an execution program, which, when executed, implements a wind field inversion method based on lidar and meteorological station data as described in any one of claims 1 to 8.

Images