A High-Resolution Nonlinear Three-Dimensional Wind Field Inversion Method Based on Wind Measurement Lidar

Abstract

This invention relates to the field of lidar wind field inversion technology, and provides a method for high-resolution nonlinear three-dimensional wind field inversion based on a wind-measuring lidar, comprising: acquiring and processing raw measured radial wind data to obtain effective measured radial wind data; data interpolation processing to obtain medium-to-high resolution measured radial wind data; dividing into linear grid cells and calculating the center nodes of the linear grid cells to obtain initial three-dimensional wind speed values; calculating the global optimal gradient field of the grid nodes within the three-dimensional fan-shaped cube of the glide path, and then using the initial three-dimensional wind speed values ​​and the global optimal gradient field to solve for the nonlinear optimized three-dimensional wind field; performing cyclic interpolation and optimization on the nonlinear optimized three-dimensional wind field to obtain the high-resolution nonlinear three-dimensional wind field of the glide path. This invention significantly improves the spatial resolution and physical fit of wind field inversion, and can balance coverage, data refresh rate, and reliability in practical applications, achieving accurate and high-resolution reconstruction of complex wind fields in dynamic environments.

Description

Technical Field

[0001] This invention relates to the field of lidar wind field inversion technology, and in particular to a high-resolution nonlinear three-dimensional wind field inversion method based on wind-measuring lidar on the glide slope. Background Technology

[0002] During the landing of civil airliners and carrier-based aircraft, encountering abnormal airflow fields, sudden changes in horizontal or vertical winds within the glide path can easily cause the aircraft to deviate from the ideal glide path. Because the aircraft's speed is still relatively high during landing, the time available for pilot reaction and maneuver is extremely short. Coupled with insufficient power and altitude, this greatly increases the risk of serious flight safety accidents. Therefore, there is an urgent need for nonlinear, three-dimensional, fine-grained wind field information covering the aircraft's approach airspace, especially within the glide path during the landing phase (generally a cylindrical space with a radius of 30m centered on the ideal glide path), with high spatial resolution (lateral, longitudinal, and radial, on the order of meters) and high temporal resolution (on the order of seconds). Wind-measuring lidar is currently the most effective remote sensing method for acquiring wind field information under clear-sky conditions, featuring high measurement accuracy, high spatiotemporal resolution, wide detection range, and fast response speed. The laser beam of wind-measuring lidar can acquire a series of radial winds within a certain range and at a certain radial distance resolution in the radial direction. Combined with different scanning strategies and inversion algorithms, it can achieve the function of acquiring a three-dimensional wind field based on one-dimensional radial wind.

[0003] Existing wind field inversion algorithms are mainly divided into two categories: 1) "Information-enhancing" methods based on conditional assumptions: Inversion methods based on assumptions of uniform, locally uniform, linear, and locally linear wind fields are mostly only applicable to inverting two-dimensional wind fields. A few methods that can invert three-dimensional wind fields can only obtain three-dimensional wind fields that satisfy a few simple wind field state assumptions. They cannot obtain three-dimensional, nonlinear wind fields that are more consistent with the complex actual environment, thus limiting their role in actual operational wind field forecasting. 2) Data assimilation algorithms based on atmospheric physical dynamics constraints: These need to consider the dynamic equations that atmospheric motion must follow, such as the three-dimensional variational assimilation method 3DVAR. By establishing a loss function constrained by multiple constraints (observation terms, mass conservation, smoothness, background wind field, vorticity equations, etc.), and by seeking the minimum value of the loss function, the problem of nonlinear, three-dimensional wind field inversion has been solved. However, the problem of obtaining wind fields with high spatial resolution required by the glide slope remains unsolved.

[0004] Under current technological constraints, wind-measuring lidar still faces the dilemma of balancing high spatial resolution and high measurement accuracy, limited by pulse width and window function width. For the detailed wind fields requiring meter-level and second-level spatiotemporal resolution during aircraft landing, simply relying on improvements in the performance of the wind-measuring lidar itself remains challenging. For the inversion of high spatial resolution detailed wind fields, numerical interpolation methods hold promise for improving the spatial resolution of measured radial wind or inverted wind fields by wind-measuring lidar. However, conventional interpolation processes do not consider the constraints of objective physical dynamic equations governing atmospheric motion; direct interpolation strategies that only satisfy numerical calculation rules are prone to significant uncertainty in the reliability of direct interpolation results, affecting their effectiveness in operational wind field forecasting. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a high-resolution nonlinear three-dimensional wind field inversion method based on wind-measuring lidar, which significantly improves the spatial resolution and physical fitting degree of wind field inversion, and can take into account coverage, data refresh rate and reliability in practical applications, and realizes accurate and high-resolution reconstruction of complex wind fields in dynamic environments.

[0006] To achieve the above objectives, the present invention provides the following solution: a high-resolution nonlinear three-dimensional wind field inversion method for the glide slope based on wind-measuring lidar, comprising:

[0007] Based on the preset wind-measuring lidar scanning strategy and parameters, the original measured radial wind data of the three-dimensional fan-shaped cube of the glide slope is obtained, and the original measured radial wind data is preprocessed and anomaly detected to obtain effective measured radial wind data.

[0008] Based on the effective measured radial wind data, an interpolation algorithm is used to interpolate the wind speeds in the radial, lateral, and longitudinal directions to obtain medium-to-high resolution measured radial wind data.

[0009] Based on the medium-to-high resolution measured radial wind data, linear grid cells are divided, and the center nodes of the linear grid cells are calculated to obtain the initial three-dimensional wind speed value of each center node.

[0010] By introducing atmospheric dynamic physical constraints, a nonlinear loss function is constructed, and the nonlinear loss function is optimized for extreme values ​​to obtain the global optimal gradient field of the grid nodes within the three-dimensional fan-shaped cube of the glide slope. Then, using the initial value of the three-dimensional wind speed and the global optimal gradient field, the medium-to-high resolution nonlinear optimized three-dimensional wind field is obtained.

[0011] The medium-to-high resolution nonlinear optimized three-dimensional wind field is restored to the original data format of the wind measurement lidar, and then cyclic interpolation and optimization are performed to obtain a glide slope high-resolution nonlinear three-dimensional wind field that meets the application requirements.

[0012] Optionally, based on a preset wind-measuring lidar scanning strategy and parameters, the raw measured radial wind data of the three-dimensional fan-shaped cube of the glide slope is obtained, including:

[0013] The wind-measuring lidar is set as the starting point, and the distance gate profile at a radial distance of 400 meters from the starting point is set as the scanning area. Within the scanning area, the elevation angle scanning range and azimuth angle scanning range of the wind-measuring lidar are set according to the relative positional relationship between the wind-measuring lidar and the ideal landing point.

[0014] Based on the requirements of spatial and temporal resolution, the elevation angle scanning interval and azimuth angle scanning interval of the wind-measuring lidar are set. Then, by using a scanning strategy that sequentially traverses the four elevation angle plane positions, the original measured radial wind data of the three-dimensional cube within the scanning area are obtained.

[0015] Optionally, the raw measured radial wind data is preprocessed and anomaly detected to obtain valid measured radial wind data, including:

[0016] The original measured radial wind data is preprocessed to obtain initial valid data. Then, according to the predefined anomaly threshold, four elevation angle profiles are traversed sequentially, and each elevation angle profile is scanned line by line to determine whether there are any anomalies and obtain abnormal data. Based on the abnormal data, the number of initial valid data in each line of the elevation angle profile is determined.

[0017] When there are two or more initial valid data points in each row of the elevation angle profile, calculate the average value of the two initial valid data points adjacent to the abnormal data point, and replace the abnormal data point with the average value.

[0018] When the number of initial valid data in each row of the elevation angle profile is one, all the abnormal data are replaced with the initial valid data;

[0019] When the number of initial valid data in each row of the elevation angle profile is zero, the anomaly detection ends and valid measured radial wind data is obtained.

[0020] Optionally, based on the medium-to-high resolution measured radial wind data, linear grid cells are divided, and the center nodes of the linear grid cells are calculated to obtain the initial three-dimensional wind speed value for each center node, including:

[0021] Based on the aforementioned medium-to-high resolution measured radial wind data, five beams and twelve grid nodes were selected for adjacent elevation angles, adjacent azimuth angles, and adjacent distance gates to construct multiple linear grid cells.

[0022] A linear loss function is constructed within the linear grid cell, and the linear loss function is optimized for extreme values ​​to calculate the linear three-dimensional wind field of the center node of the linear grid cell, thereby obtaining the initial three-dimensional wind speed value of each center node; the initial three-dimensional wind speed value includes radial wind data, horizontal tangential wind data, and vertical wind data.

[0023] Optionally, atmospheric dynamic physical constraints are introduced to construct a nonlinear loss function, and the nonlinear loss function is optimized for extreme values ​​to obtain the global optimal gradient field of the grid nodes within the three-dimensional fan-shaped cube of the glide slope. Then, using the initial three-dimensional wind speed and the global optimal gradient field, the medium-to-high resolution nonlinear optimized three-dimensional wind field is solved, including:

[0024] Within the three-dimensional fan-shaped cube of the glide slope scanned by the wind-measuring lidar, wind field constraint terms are selected to construct a nonlinear loss function controlled by the atmospheric dynamics equations.

[0025] The gradient field in the partial differential form of the nonlinear loss function is numerically discretized using the central difference scheme to obtain the difference scheme nonlinear loss function. Then, the extreme value optimization of the difference scheme nonlinear loss function is performed using the quasi-Newton iteration method to obtain the global optimal gradient field. The global optimal gradient field includes six three-dimensional data cubes.

[0026] Using the initial three-dimensional wind speed and the global optimal gradient field, the nonlinear algebraic equations for horizontal tangential wind and vertical wind are solved respectively to obtain a medium-to-high resolution nonlinear optimized three-dimensional wind field.

[0027] Optionally, the medium-to-high resolution nonlinear optimized three-dimensional wind field is restored to the original data format of the wind-measuring lidar, and then cyclic interpolation and optimization are performed to obtain a glide slope high-resolution nonlinear three-dimensional wind field that meets application requirements, including:

[0028] The medium-to-high resolution, nonlinear optimized three-dimensional wind field at each grid node within the three-dimensional sector cube of the glide path is restored to a medium-to-high resolution restored radial wind speed value consistent with the original measured radial wind data format.

[0029] Based on the medium-high resolution restored radial wind speed value, an interpolation algorithm is used to iteratively interpolate and optimize the measured and quasi-measured radial winds at adjacent distance gates, adjacent azimuth angles, and adjacent elevation angles until the spatial resolution of the medium-high resolution nonlinear optimized three-dimensional wind field meets the application requirements, thus obtaining a high-resolution nonlinear three-dimensional wind field for the glide slope.

[0030] This invention discloses the following technical advantages by providing a high-resolution nonlinear three-dimensional wind field inversion method for the glide slope based on wind-measuring lidar:

[0031] 1. By acquiring and preprocessing the original measured radial wind data from the three-dimensional fan-shaped cube of the glide path, the integrity and accuracy of the original wind speed data are ensured, and the effectiveness of the basic data for subsequent processing is improved.

[0032] 2. Through interpolation algorithms, spatial resolution is significantly improved without adding hardware burden. The innovative introduction of physical constraints ensures the physical reality of the interpolated data, significantly improves interpolation reliability, and greatly enhances the spatial resolution of wind field data.

[0033] 3. By using linear wind field initial value inversion and gradient field optimization, we can utilize measured and quasi-measured wind speeds to reduce the initial value error of nonlinear wind field solutions. This eliminates reliance on a single data source and simplification assumptions, thereby improving the physical consistency and accuracy of the final inversion results.

[0034] 4. By combining "linear initial value + optimized gradient field", global physical equation constraint optimization can be introduced, making the nonlinear three-dimensional wind field reconstruction more consistent with the complex conditions of the actual environment and improving the actual effectiveness of prediction and early warning.

[0035] 5. By restoring the medium-to-high resolution nonlinear optimized three-dimensional wind field to the original data format of the wind measurement lidar, and continuously iterating to improve the resolution until the resolution reaches the meter level or as required, the synergistic output of "ultimate resolution + physical reliability" is guaranteed.

[0036] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the method flow provided in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of a wind-measuring lidar scanning glide slope provided in an embodiment of the present invention;

[0040] Figure 3 A flowchart of wind-measuring lidar beam scanning provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the cube architecture for measured radial wind data provided in an embodiment of the present invention;

[0042] Figure 5A schematic diagram of the architecture of a linear unit provided in an embodiment of the present invention;

[0043] Figure 6 A schematic diagram illustrating the solution of the linear three-dimensional wind field at the center node for each linear element in an embodiment of the present invention;

[0044] Figure 7 This is a schematic diagram of the process for obtaining high-resolution, nonlinear, three-dimensional wind fields through cyclic interpolation and optimization provided in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] like Figure 1 As shown, this invention provides a high-resolution nonlinear three-dimensional wind field inversion method for the glide slope based on wind-measuring lidar, comprising:

[0048] 1. Based on the preset wind-measuring lidar scanning strategy and parameters, acquire the original measured radial wind data of the three-dimensional fan-shaped cube of the glide slope, and preprocess and detect anomalies in the original measured radial wind data to obtain effective measured radial wind data.

[0049] Obtain raw measured radial wind data, including:

[0050] 1.1 Set the wind-measuring lidar as the starting point, and set the range gate profile at a radial distance of 400 meters from the starting point as the scanning area. Within the scanning area, set the elevation angle scanning range and azimuth angle scanning range of the wind-measuring lidar according to the relative positional relationship between the wind-measuring lidar and the ideal landing point.

[0051] 1.2 Based on the requirements of spatial and temporal resolution, the elevation angle scanning interval and azimuth angle scanning interval of the wind-measuring lidar are set. Then, by using a scanning strategy that sequentially traverses the four elevation angle plane positions, the original measured radial wind data of the three-dimensional cube within the scanning area are obtained.

[0052] Obtain valid measured radial wind data, including:

[0053] 1.3 Due to the influence of precipitation, ground clutter, bird and insect echoes, etc., the original measured radial wind data needs to be preprocessed to obtain initial valid data. Then, based on a predefined anomaly threshold, the four elevation angle profiles are traversed sequentially, and each line in the elevation angle profile is scanned to determine if there are any anomalies, thus obtaining anomaly data. Based on the anomaly data, the number of initial valid data in each line of the elevation angle profile is determined.

[0054] When there are two or more initial valid data points in each row of the elevation angle profile, calculate the average value of the two initial valid data points adjacent to the abnormal data point, and replace the abnormal data point with the average value.

[0055] When the number of initial valid data in each row of the elevation angle profile is one, all the abnormal data are replaced with the initial valid data;

[0056] When the number of initial valid data in each row of the elevation angle profile is zero, the anomaly detection ends and valid measured radial wind data is obtained.

[0057] 2. Based on the effective measured radial wind data, an interpolation algorithm is used to interpolate the wind speeds in the radial, lateral, and longitudinal directions to obtain medium-to-high resolution measured radial wind data.

[0058] 3. Based on the aforementioned medium-to-high resolution measured radial wind data, linear grid cells are generated, and the center nodes of the linear grid cells are calculated to obtain the initial three-dimensional wind speed value for each center node; including:

[0059] Based on the aforementioned medium-to-high resolution measured radial wind data, five beams and twelve grid nodes were selected for adjacent elevation angles, adjacent azimuth angles, and adjacent distance gates to construct multiple linear grid cells.

[0060] A linear loss function is constructed within the linear grid cell, and extreme value optimization is performed on the linear loss function to calculate the linear three-dimensional wind field at the center node of the linear grid cell (at a certain elevation angle, azimuth angle, and distance gate). This yields the initial values ​​of the grid nodes, i.e., the initial values ​​of the three-dimensional wind speed, which are used for gradient field optimization and solving the nonlinear algebraic equations. The initial values ​​of the three-dimensional wind speed include radial wind data, horizontal tangential wind data, and vertical wind data. Within the three-dimensional fan-shaped cube of the entire glide slope region scanned by the wind-measuring lidar, the linear three-dimensional wind field at each center node is solved for each linear grid cell.

[0061] 4. Introducing atmospheric dynamics physical constraints, a nonlinear loss function is constructed, and extreme value optimization is performed on the nonlinear loss function to obtain the globally optimal gradient field of the grid nodes within the three-dimensional fan-shaped cube of the glide slope. Then, using the initial three-dimensional wind speed and the globally optimal gradient field, the medium-to-high resolution nonlinear optimized three-dimensional wind field is solved; including:

[0062] Within the three-dimensional fan-shaped cube of the glide slope scanned by the wind-measuring lidar, wind field constraints such as wind field smoothness and mass continuity are selected to construct a nonlinear loss function controlled by the atmospheric dynamics equation (partial differential form).

[0063] The gradient field in the partial differential form of the nonlinear loss function is numerically discretized using the central difference scheme to obtain the difference scheme nonlinear loss function. Then, the extreme value optimization of the difference scheme nonlinear loss function is performed using the quasi-Newton iteration method to obtain the global optimal gradient field. The global optimal gradient field includes six three-dimensional data cubes.

[0064] Using the initial three-dimensional wind speed and the global optimal gradient field, the nonlinear algebraic equations for horizontal tangential wind and vertical wind are solved respectively to obtain a medium-to-high resolution nonlinear optimized three-dimensional wind field.

[0065] 5. The medium-to-high resolution nonlinear optimized three-dimensional wind field is restored to the original data format of the wind-measuring lidar, and then cyclic interpolation and optimization are performed to obtain a glide slope high-resolution nonlinear three-dimensional wind field that meets application requirements. This includes:

[0066] The medium-to-high resolution, nonlinear optimized three-dimensional wind field at each grid node within the three-dimensional sector cube of the glide path is restored to a medium-to-high resolution restored radial wind speed value consistent with the original measured radial wind data format.

[0067] Based on the medium-high resolution restored radial wind speed value, an interpolation algorithm is used to iteratively interpolate and optimize the measured and quasi-measured radial winds at adjacent distance gates, adjacent azimuth angles, and adjacent elevation angles until the spatial resolution of the medium-high resolution nonlinear optimized three-dimensional wind field meets the application requirements, thus obtaining a high-resolution nonlinear three-dimensional wind field for the glide slope.

[0068] Example 2

[0069] S1: Obtain the raw measured radial wind data cube of the glide slope, such as the 3D data cube uu_raw (94×8×4)The system includes 94 range gates (radial), 8 azimuth angles (lateral), and 4 elevation angles (longitudinal). The scanning strategy and parameters for the wind-measuring lidar are set according to application requirements. For example, using the wind-measuring lidar as the starting point, the range gate profile at a radial distance of 400m is the key area of ​​focus within the glide slope. Based on the relative position of the wind-measuring lidar and the ideal landing point, the elevation angle scanning range (e.g., 0°-9°, corresponding to a height range of 64m at 400m) and azimuth angle scanning range (e.g., 10°-24°, corresponding to an azimuth range of 84m at 400m) are set. Based on the requirements for spatial resolution (meter-level) and temporal resolution (second-level), the elevation angle scanning interval (e.g., 3°, with an average longitudinal spatial resolution of 16m within the range gate profile) and azimuth angle scanning interval (e.g., 2°, with an average lateral spatial resolution of 10.5m within the range gate profile) of the wind-measuring lidar are initially set. The system then scans sequentially through the four elevation angle PPIs, such as... Figure 2 , Figure 3 , Figure 4 As shown, measured radial wind data of the three-dimensional sector cube of the aircraft glide slope were obtained. According to the above scanning strategy, a complete scan requires 32 measurement beams. If the beam scanning rate is 0.25s / beam, the data update rate of the glide slope region is 8s / time.

[0070] S2: Raw measured radial wind data cube uu_raw obtained from S1 (94×8×4) By preprocessing the radial wind measured by wind-measuring lidar, an effective measured radial wind data cube uu is obtained. (94×8×4) When measuring radial wind speed, wind-measuring lidar often encounters problems such as precipitation, ground clutter, and bird / insect echoes, leading to missing or abnormal radial wind data. For abnormal radial wind readings at the same radial distance, the mean of the effective radial wind readings at adjacent azimuth angles is used to fill in the gaps or replace the missing values.

[0071] S21: Abnormal Measured Radial Wind Detection. The abnormal threshold for measured radial wind speed is defined as 30 m / s, and processing is performed on each elevation angle profile. Within each elevation angle profile, anomaly detection is performed on each distance gate, i.e., scanning line by line to obtain the measured radial wind speed of the current line. If there is no abnormal measured radial wind speed in the line (i.e., wind speed greater than 30 m / s or data missing), the loop exits. If there is abnormal measured radial wind speed in the line, abnormal measured radial wind speed processing is performed according to the number of valid measured radial wind speeds (i.e., wind speed less than or equal to 30 m / s) in each line.

[0072] S22: Three cases are handled: (1) When there are two or more valid measured radial winds in each row, find the two valid measured radial winds in that row that are closest to each abnormal measured radial wind, and replace the abnormal measured radial wind with the average value of these two valid measured radial winds; (2) When there is only one valid measured radial wind in each row, replace each abnormal measured radial wind directly with that valid measured radial wind; (3) When there is no valid measured radial wind in each row, skip directly. All abnormal measured radial winds are processed in sequence using the above method.

[0073] S3: Cube of effective measured radial wind data obtained from S2 (94×8×4) Through the first interpolation process (such as cubic spline interpolation), an effective measured radial wind data cube uu_int1 with medium to high spatial resolution is obtained. (187×15×7) For the 400m gate section within the effective measured radial wind data cube, interpolation is performed on the effective measured radial wind in the radial, lateral, and longitudinal directions of this section. For example, cubic spline interpolation is used to interpolate the measured radial wind (as the radial range resolution of a typical wind-measuring lidar is 30m). The first interpolation doubles the radial spatial resolution (15m). Simultaneously, at the 400m gate section, the azimuth and elevation angle scanning intervals of the wind-measuring lidar, corresponding to a lateral spatial resolution (10.5m) and a longitudinal spatial resolution (16m), still do not meet application requirements. Therefore, cubic spline interpolation is used to interpolate the effective measured radial wind in both the lateral and longitudinal directions. The first interpolation doubles both the lateral and longitudinal spatial resolutions, resulting in a lateral spatial resolution of 5.25m and a longitudinal spatial resolution of 8m.

[0074] S4: Using the effective measured radial wind data cube with medium to high spatial resolution obtained in S3, the 3DVAR method is used to process it to obtain a medium to high resolution, nonlinear, three-dimensional wind field.

[0075] S41: Utilizing the effective measured radial wind data cube uu_int1 obtained from S3 with medium to high spatial resolution (187×15×7) By constructing a series of linear elements and using effective measured radial wind to solve linearly within the linear elements (as shown in Equations 1.1, 1.2, and 1.3), the center point (elevation angle φ) of the linear element is obtained. i Azimuth θ j Distance gate r k Place, such as Figure 5 Linear horizontal tangential wind (vv at the Zhongxing node) lmn and vertical wind ww lmn In the effective measured radial wind data cube uu_int1 (187×15×7) Within, solve for each linear unit individually (e.g.) Figure 6As shown), the final result is a medium-to-high spatial resolution, linear, three-dimensional wind field, including an effective measured radial wind data cube uu_int1_line. (187×15×7) Horizontal tangential wind data cube vv_int1_line (187×15×7) Vertical wind data cube ww_int1_line (187×15×7) .

[0076] (1.1);

[0077] (1.2);

[0078] (1.3);

[0079] In the formula, This represents a node in a 3D data cube, with coordinates as follows: Or, simply (i,j,k), It is the collection of all detection units within the radar scanning area.

[0080] For example, by selecting 5 beams and 12 nodes (each node corresponding to a valid measured radial wind speed value) based on adjacent elevation angles, adjacent azimuth angles, and adjacent distance gates, a series of linear units can be constructed, such as... Figure 5 As shown. The solution process within the linear element is as follows: Using the measured radial wind speed values ​​uu of the six nodes at position coordinates (i,j-1,k-1), (i,j-1,k), (i,j-1,k+1), (i,j+1,k-1), (i,j+1,k), (i,j+1,k+1), calculate the horizontal tangential wind speed values ​​vv at the node at position k with elevation angle i, azimuth angle j, and distance k; then using the measured radial wind speed values ​​uu of the six nodes at position (i-1,j,k-1), (i-1,j,k), (i-1,j,k+1), (i+1,j,k-1), (i+1,j,k), (i+1,j,k+1), calculate the vertical wind speed value ww at the node with position coordinates (i,j,k). For example, for the center point of a certain linear element (elevation angle... Azimuth Distance Gate The formulas for calculating the three-dimensional linear wind speed are as follows (equations 2, 3, and 4):

[0081] (2);

[0082] in, For the elevation angle Azimuth Distance Gate The measured radial wind speed value at that time.

[0083] (3);

[0084] in, For the elevation angle Azimuth Distance Gate The horizontal tangential wind speed at that time.

[0085] (4);

[0086] in, For the elevation angle Azimuth Distance Gate The vertical wind speed value at that time.

[0087] S42: Utilizes S41 to obtain a medium-to-high spatial resolution, linear, three-dimensional wind field, including an effective measured radial wind data cube uu_int1_line (187×15×7) Horizontal tangential wind data cube vv_int1_line (187×15×7) Vertical wind data cube ww_int1_line (187×15×7) By constructing a nonlinear loss function and performing gradient field optimization, a medium-to-high spatial resolution, nonlinear, three-dimensional wind field is obtained, including an effective measured radial wind data cube uu_int1_nonl. (187×15×7) Horizontal tangential wind data cube vv_int1_ nonl (187×15×7) Vertical wind data cube ww_int1_ nonl (187×15×7) .

[0088] S421: Within the glide path of the wind-measuring lidar scan (corresponding to a three-dimensional fan-shaped cubic coverage area), a nonlinear loss function (as shown in Equation 5) controlled by the atmospheric dynamics equation (partial differential form) is constructed by selecting constraints such as wind field smoothness and mass continuity.

[0089] (5);

[0090] In the formula, The wind field is represented by three-dimensional Cartesian coordinates in the radar-scanned space.

[0091] S422: The gradient field in the partial differential form of the nonlinear loss function is numerically discretized using the central difference scheme (as shown in Equation 6), transforming the nonlinear loss function of the partial differential scheme into a nonlinear loss function of the difference scheme. The nonlinear loss function of the difference scheme is then optimized for extrema using a quasi-Newton iteration method, yielding the optimized gradient field (including six three-dimensional data cubes). , , , , , ).

[0092] (6);

[0093] in, V Different three-dimensional wind speeds uu, vv, ww can be taken respectively; p Different spatial dimensions x, y, and z can be taken respectively.

[0094] S423: Utilizing the medium-to-high spatial resolution, linear, three-dimensional wind field obtained in S41, including the effective measured radial wind data cube uu_int1_line (187×15×7) Horizontal tangential wind data cube vv_int1_line (187×15×7) Vertical wind data cube ww_int1_line (187×15×7) As initial values, and the optimized gradient field obtained in S422 (comprising six 3D data cubes), , , , , , Solving the nonlinear algebraic equations for horizontal tangential wind (as shown in Equation 7) and vertical wind (as shown in Equation 8) yields a medium-to-high spatial resolution, nonlinear, three-dimensional wind field, including an effective measured radial wind data cube uu_int1_nonl (187×15×7) Horizontal tangential wind data cube vv_int1_ nonl (187×15×7) Vertical wind data cube ww_int1_ nonl (187×15×7) .

[0095] (7)

[0096] (8)

[0097] Where A is the coefficient matrix, b1 is the initial value vector of the horizontal tangential wind, and b2 is the initial value vector of the vertical wind.

[0098] S5: Utilizing the medium-to-high spatial resolution, nonlinear, three-dimensional wind field obtained from S423, including the effective measured radial wind data cube uu_int1_nonl (187×15×7) Horizontal tangential wind data cube vv_int1_ nonl (187×15×7) Vertical wind data cube ww_int1_ nonl (187×15×7)Through repeated interpolation and optimization, the spatial resolution is further improved, thereby obtaining a high-resolution, nonlinear, three-dimensional wind field within the glide slope channel that meets application requirements, including an effective measured radial wind data cube uu_int5_nonl (2977×113×49) Horizontal tangential wind data cube uu_int5_nonl (2977×113×49) Vertical wind data cube ww_int5_nonl (2977×113×49) .

[0099] In step S3, the new interpolated radial wind obtained through the first interpolation, after being constrained by the nonlinear loss function in step S4, can be considered as the quasi-measured radial wind of the wind-measuring lidar. Therefore, for the new reconstructed radial wind data cube (composed of effective measured radial wind and quasi-measured radial wind), subsequent interpolation can be performed on the reconstructed radial wind in the radial, lateral, and longitudinal directions of the 400m distance gate profile.

[0100] like Figure 7 As shown, the spatial resolution is doubled again through a second interpolation (i.e., at a gate profile at a distance of 400m, the radial distance resolution is increased from 15m to 7.5m, the lateral distance resolution from 5.25m to 2.625m, and the longitudinal distance resolution from 8m to 4m). If the above spatial resolution still does not reach the meter level or meet the application requirements, then the updated quasi-measured radial wind is used, and steps S3-S5 are repeated. For example, the radial spatial resolution is increased from 30m to 0.9375m through five interpolations. The lateral spatial resolution is increased from 10.5m to 0.6563m through four interpolations, and the longitudinal spatial resolution is increased from 16m to 1m through four interpolations. If the new reconstructed three-dimensional wind field data cube includes the effective measured radial wind data cube uu_int5_nonl (2977×113×49) Horizontal tangential wind data cube vv_int5_ nonl (2977×113×49) Vertical wind data cube ww_int5_ nonl (2977×113×49) Once the spatial resolution reaches the meter level or meets the application requirements, interpolation stops.

[0101] Therefore, this invention provides a high-resolution nonlinear three-dimensional wind field inversion method based on wind-measuring lidar, which significantly improves the spatial resolution and physical fit of wind field inversion. It can balance coverage, data refresh rate and reliability in practical applications, and achieve accurate and high-resolution reconstruction of complex wind fields in dynamic environments.

[0102] 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.

[0103] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for high-resolution nonlinear three-dimensional wind field inversion on the glide slope based on wind-measuring lidar, characterized in that, include: Based on the preset wind-measuring lidar scanning strategy and parameters, the original measured radial wind data of the three-dimensional fan-shaped cube of the glide slope is obtained, and the original measured radial wind data is preprocessed and anomaly detected to obtain effective measured radial wind data. Based on the effective measured radial wind data, an interpolation algorithm is used to interpolate the wind speeds in the radial, lateral, and longitudinal directions to obtain medium-to-high resolution measured radial wind data. Based on the medium-to-high resolution measured radial wind data, linear grid cells are divided, and the center nodes of the linear grid cells are calculated to obtain the initial three-dimensional wind speed value of each center node. By introducing atmospheric dynamic physical constraints, a nonlinear loss function is constructed, and the nonlinear loss function is optimized for extreme values ​​to obtain the global optimal gradient field of the grid nodes within the three-dimensional fan-shaped cube of the glide slope. Then, using the initial value of the three-dimensional wind speed and the global optimal gradient field, the medium-to-high resolution nonlinear optimized three-dimensional wind field is obtained. The medium-to-high resolution nonlinear optimized three-dimensional wind field is restored to the original data format of the wind measurement lidar, and then cyclic interpolation and optimization are performed to obtain a glide slope high-resolution nonlinear three-dimensional wind field that meets the application requirements. By introducing atmospheric dynamic physical constraints, a nonlinear loss function is constructed, and extreme value optimization is performed on the nonlinear loss function to obtain the global optimal gradient field of the grid nodes within the three-dimensional fan-shaped cube of the glide slope. Then, using the initial three-dimensional wind speed and the global optimal gradient field, a medium-to-high resolution nonlinear optimized three-dimensional wind field is obtained, including: Within the three-dimensional fan-shaped cube of the glide slope scanned by the wind-measuring lidar, wind field constraint terms are selected to construct a nonlinear loss function controlled by the atmospheric dynamics equations. The gradient field in the partial differential form of the nonlinear loss function is numerically discretized using the central difference scheme to obtain the difference scheme nonlinear loss function. Then, the extreme value optimization of the difference scheme nonlinear loss function is performed using the quasi-Newton iteration method to obtain the global optimal gradient field. The global optimal gradient field includes six three-dimensional data cubes. Using the initial three-dimensional wind speed and the global optimal gradient field, the nonlinear algebraic equations for horizontal tangential wind and vertical wind are solved respectively to obtain a medium-to-high resolution nonlinear optimized three-dimensional wind field.

2. The method for high-resolution nonlinear three-dimensional wind field inversion on the glide slope based on wind-measuring lidar according to claim 1, characterized in that, Based on the preset wind-measuring lidar scanning strategy and parameters, the raw measured radial wind data of the three-dimensional fan-shaped cube of the glide slope are obtained, including: The wind-measuring lidar is set as the starting point, and the distance gate profile at a radial distance of 400 meters from the starting point is set as the scanning area. Within the scanning area, the elevation angle scanning range and azimuth angle scanning range of the wind-measuring lidar are set according to the relative positional relationship between the wind-measuring lidar and the ideal landing point. Based on the requirements of spatial and temporal resolution, the elevation angle scanning interval and azimuth angle scanning interval of the wind-measuring lidar are set. Then, by using a scanning strategy that sequentially traverses the four elevation angle plane positions, the original measured radial wind data of the three-dimensional cube within the scanning area are obtained.

3. The method for high-resolution nonlinear three-dimensional wind field inversion on the glide slope based on wind-measuring lidar according to claim 2, characterized in that, The raw measured radial wind data is preprocessed and anomaly detected to obtain valid measured radial wind data, including: The original measured radial wind data is preprocessed to obtain initial valid data. Then, according to the predefined anomaly threshold, four elevation angle profiles are traversed sequentially, and each elevation angle profile is scanned line by line to determine whether there are any anomalies and obtain abnormal data. Based on the abnormal data, the number of initial valid data in each line of the elevation angle profile is determined. When there are two or more initial valid data points in each row of the elevation angle profile, calculate the average value of the two initial valid data points adjacent to the abnormal data point, and replace the abnormal data point with the average value. When the number of initial valid data in each row of the elevation angle profile is one, all the abnormal data are replaced with the initial valid data; When the number of initial valid data in each row of the elevation angle profile is zero, the anomaly detection ends and valid measured radial wind data is obtained.

4. The method for high-resolution nonlinear three-dimensional wind field inversion on the glide slope based on wind-measuring lidar according to claim 3, characterized in that, Based on the aforementioned medium-to-high resolution measured radial wind data, linear grid cells are generated, and the center nodes of the linear grid cells are calculated to obtain the initial three-dimensional wind speed values ​​for each center node, including: Based on the aforementioned medium-to-high resolution measured radial wind data, five beams and twelve grid nodes were selected for adjacent elevation angles, adjacent azimuth angles, and adjacent distance gates to construct multiple linear grid cells. A linear loss function is constructed within the linear grid cell, and the linear loss function is optimized for extreme values ​​to calculate the linear three-dimensional wind field of the center node of the linear grid cell, thereby obtaining the initial three-dimensional wind speed value of each center node; the initial three-dimensional wind speed value includes radial wind data, horizontal tangential wind data, and vertical wind data.

5. The method for high-resolution nonlinear three-dimensional wind field inversion on the glide slope based on wind-measuring lidar according to claim 4, characterized in that, The medium-to-high resolution nonlinear optimized three-dimensional wind field is restored to the original data format of the wind measurement lidar, and then cyclic interpolation and optimization are performed to obtain a glide slope high-resolution nonlinear three-dimensional wind field that meets application requirements, including: The medium-to-high resolution, nonlinear optimized three-dimensional wind field at each grid node within the three-dimensional sector cube of the glide path is restored to a medium-to-high resolution restored radial wind speed value consistent with the original measured radial wind data format. Based on the medium-high resolution restored radial wind speed value, an interpolation algorithm is used to iteratively interpolate and optimize the measured and quasi-measured radial winds at adjacent distance gates, adjacent azimuth angles, and adjacent elevation angles until the spatial resolution of the medium-high resolution nonlinear optimized three-dimensional wind field meets the application requirements, thus obtaining a high-resolution nonlinear three-dimensional wind field for the glide slope.

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