Low-altitude three-dimensional wind field inversion method and system based on single wind lidar

Abstract

The application discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data of a key region is lost due to a blocking effect, and detection nodes are uneven. The method specifically comprises the following steps: S1, using a wind measurement laser radar to perform a multi-elevation-angle volume scanning detection mode and a wind profile detection mode, and obtaining radial wind speed data of a target region; S2, performing quality control on the radial wind speed, eliminating isolated abnormal points, and filling isolated data missing points; S3, using a smoothing spline interpolation method to perform blocking correction on a single scanning surface; and S4, obtaining an initial background three-dimensional wind field based on the radial wind speed after the wind profile is corrected. The method can realize high-precision wind field inversion of a three-dimensional wind field by using multi-layer volume scanning radial wind speed of a single wind measurement laser radar.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of radar detection, and particularly relates to a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar. BACKGROUND

[0002] High-temporal and high-spatial resolution three-dimensional wind field inversion plays an important supporting role in the safe operation and efficient development of low-altitude economy, and its importance runs through the whole process of planning and construction, production and operation, and safety supervision of low-altitude economy. As a remote sensing device, the wind measurement laser radar has the advantages of long detection distance and high temporal and spatial resolution, and is widely used in atmospheric wind field detection.

[0003] In the process of low-altitude atmospheric wind field detection, the traditional wind measurement laser radar has the following problems:

[0004] 1) Limited by the principle of the wind measurement laser radar instrument itself, it can only directly measure the radial wind speed within the line of sight, and cannot obtain the velocity vector directly;

[0005] 2) During radar detection, the light beam is easily blocked by low-altitude obstacles such as buildings, terrain, and debris. This blocking effect will cause data missing in key areas;

[0006] 3) The detection nodes of the laser radar are not uniform, and as the distance increases, the measurement points become sparse, which cannot meet the actual application requirements. SUMMARY

[0007] The present application aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present application proposes a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, which is used to solve the technical problems proposed in the background art. The present application solves the above problems by performing high-precision inversion of three-dimensional spatial wind field based on the volume scanning detection results of a single wind measurement laser radar.

[0008] To achieve the above-mentioned purpose, the first aspect of the present application provides a low-altitude three-dimensional wind field inversion method based on a single wind measurement laser radar, comprising:

[0009] Collecting historical data of low-altitude three-dimensional wind field inversion of the wind measurement laser radar, and constructing an elevation step and azimuth step prediction model of the single wind measurement laser radar in the multi-elevation volume scanning detection mode based on the historical data; the elevation step and azimuth step prediction model outputs a predicted adjustment value; the predicted adjustment value is less than a preset value;

[0010] Based on a single wind measurement laser radar in a multi-elevation angle body scanning detection mode, the current scanning surface is adjusted to the next target scanning surface according to a predicted adjustment value, and detection is performed until the entire detection of the target region is completed, so as to obtain the radial wind speed data of the target region, wherein the radial wind speed data of the target region includes the radial wind speed data of a plurality of scanning surfaces.

[0011] The obtained radial wind speed data of the target region is preprocessed based on the spatial continuity of the wind field, and preprocessed radial wind speed data is obtained.

[0012] Based on a wind profile detection mode, the wind profile of the target region is obtained, and based on the wind profile, the vertical wind speed data of all radar measurement points is initialized, and the preprocessed radial wind speed data is corrected to obtain corrected radial wind speed data, and the horizontal wind vector is inverted using the corrected radial wind speed data to obtain the background three-dimensional wind field.

[0013] Another aspect of the present application provides a low-altitude three-dimensional wind field inversion system based on a single wind measurement laser radar, comprising:

[0014] A prediction model construction and training unit is used for construction and training of the elevation step and azimuth step prediction models, and the prediction model construction and training unit includes a data preprocessing module, a model architecture construction module, a training optimization module, a confidence evaluation module, and a feedback iteration module.

[0015] An elevation step and azimuth step prediction unit is used for deploying the elevation step and azimuth step prediction models to calculate the predicted adjustment value.

[0016] A detection control module is used for controlling the single wind measurement laser radar to detect based on the wind profile detection mode and to adjust the detection based on the multi-elevation angle body scanning detection mode according to the predicted adjustment value.

[0017] A radial wind speed quality control module is used for quality control of the radial wind speed, and isolated abnormal points are removed and isolated data missing points are filled.

[0018] A radar measurement point three-dimensional wind field inversion module is used for blocking correction of a single scanning surface using a smoothing spline interpolation method, and the vertical wind speed of all radar measurement points is initialized based on the vertical wind speed obtained based on the wind profile detection mode, and the initial background three-dimensional wind field is obtained after the radial wind speed is corrected, and the three-dimensional wind speed of each radar measurement point is inverted based on the initial background wind field using a three-dimensional variation method.

[0019] A target region three-dimensional wind field interpolation module is used for interpolating the radar measurement point data into the three-dimensional space grid of the target region using the inverse distance weighting method to obtain the background three-dimensional wind field.

[0020] Compared with the prior art, the application has the beneficial effects that:

[0021] The application effectively improves the efficiency and accuracy of data acquisition of a single wind measurement laser radar by constructing the single wind measurement laser radar based on historical data, the elevation stepping and azimuth stepping prediction model in the multi-elevation body scanning detection mode, and then obtains the preprocessed radial wind speed data by using the continuity of the wind field, initializes the vertical wind speed data of all radar measurement points based on the wind profile collected by the single wind measurement laser radar, corrects the preprocessed radial wind speed data to obtain the corrected radial wind speed data, inverses the horizontal wind vector by using the corrected radial wind speed data, and obtains the background three-dimensional wind field, so as to realize high-precision wind field inversion of the three-dimensional wind field. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 The flowchart of the present application;

[0024] Figure 2 The schematic diagram of the wind measurement laser radar detection mode in the embodiment of the present application;

[0025] Figure 3 The effect diagram of quality control using the low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar provided by the embodiment of the present application;

[0026] Wherein (a) is the original radial wind speed effect diagram of a single PPI scan, (b) is the effect diagram of isolated point and missing point filling achieved by quality control, and (c) is the radial wind speed effect diagram after blocking correction;

[0027] Figure 4 The three-dimensional wind field slice schematic diagram in the embodiment of the present application;

[0028] Figure 5 The system construction schematic diagram of the present application.

[0029] Figure 6 The construction schematic diagram of the prediction model construction and training unit in the present application. DETAILED DESCRIPTION

[0030] The technical solutions of the present application will be described clearly and completely below in connection with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0031] Please refer to Figure 1 Figure 6 The present application provides a low-altitude three-dimensional wind field inversion system based on a single wind measurement laser radar, comprising:

[0032] A prediction model construction and training unit 10 is configured to construct and train the elevation step and azimuth step prediction models. The prediction model construction and training unit comprises a data preprocessing module 11, a model architecture construction module 12, a training optimization module 13, a confidence evaluation module 14, and a feedback iteration module 15.

[0033] An elevation step and azimuth step prediction unit 20 is configured to deploy the elevation step and azimuth step prediction models and calculate the prediction adjustment value.

[0034] A detection control module 30 is configured to control the single wind measurement laser radar, detect based on the wind profile detection mode, and perform adjustment detection based on the multi-elevation body scanning detection mode according to the prediction adjustment value.

[0035] A radial wind speed quality control module 40 is configured to perform quality control on the radial wind speed, eliminate isolated abnormal points, and fill in isolated data missing points.

[0036] A radar measurement point three-dimensional wind field inversion module 50 is configured to use a smoothing spline interpolation method to perform blocking correction on a single scanning surface, initialize the vertical wind speed of all radar measurement points based on the vertical wind speed obtained in the wind profile detection mode, obtain an initial background three-dimensional wind field after correcting the radial wind speed, and use a three-dimensional variation method to invert the three-dimensional wind speed of each radar measurement point based on the initial background wind field.

[0037] A target area three-dimensional wind field interpolation module 60 is configured to use an inverse distance weighting method to interpolate the radar measurement point data into a three-dimensional space grid of a target area, and obtain a background three-dimensional wind field.

[0038] ​In an exemplary embodiment, the data preprocessing module 11 receives data input from the probe control module 30, including radial wind speed data in the multi-elevation body scanning probe mode, the elevation and azimuth of the scanning plane, and the time interval of the scanning plane; and receives input data from the radar measurement point three-dimensional wind field inversion module 50, including the low-altitude wind field gradient (horizontal wind speed shear, vertical wind speed vertical change) obtained by inversion of the first N scanning planes, and radar equipment state parameters. The above data is cleaned by the data preprocessing module 11; also including data collected by the radial wind speed quality control module 40, data fed back by the target area three-dimensional wind field interpolation module 60, and the like.

[0039] The model architecture construction module 12 is used to design a model and input the model into the training optimization module 13;

[0040] The training optimization module 13 is used to receive the cleaned data output by the data preprocessing module 11, to train the model output by the model architecture construction module 12; and output the trained model to the confidence evaluation module 14 for performance evaluation;

[0041] The confidence evaluation module 14 is used to receive the model data input by the training optimization module 13, and compare and verify the prediction data generated based on the model data input by the training optimization module 13 with the data output by the data preprocessing module 11; if the model performance meets the standard, the qualified model data is fed back to the elevation step and azimuth step prediction unit 20 for deployment; if the model performance does not meet the standard, the evaluation structure is fed back to the feedback iteration module 15 for triggering the optimization process;

[0042] The feedback iteration module 15 is used to receive the evaluation structure of the model performance not meeting the standard output by the confidence evaluation module 14, and perform mechanism operation based on the result of the confidence evaluation module 14; if the model performance does not meet the standard (such as error exceeding threshold value, insufficient stability), the iteration mechanism is triggered as follows: feedback to the data preprocessing module to optimize data cleaning or division method, feedback to the model architecture construction module to adjust sub-network structure or neuron number, feedback to the training optimization module to correct loss function or hyperparameter.

[0043] Please refer to Figure 1 Figure 6 Another aspect of the embodiment of the present application provides a low-altitude three-dimensional wind field inversion method based on a single wind measurement laser radar, comprising:

[0044] ​S1, the wind lidar performs a multi-elevation body scan detection mode and a wind profile detection mode, based on a single wind lidar in a multi-elevation body scan detection mode, the target region is detected to obtain the radial wind speed data of the target region, the complete observation data corresponding to the first N scanning surfaces including the current scanning surface is collected after the opening detection, the complete observation data includes the radial wind speed data, the elevation angle and the azimuth angle of the corresponding scanning surface, the complete observation data further includes the enhanced input data including the low-altitude wind field gradient, the time interval between the current scanning surface and the previous scanning surface and the radar equipment state parameter, the low-altitude wind field gradient includes the horizontal wind speed shear and the vertical wind speed vertical change, the prediction adjustment value is obtained by calculating the elevation step and the azimuth step prediction model; wherein the multi-elevation body scan detection mode is composed of a scanning probe with a preset elevation sequence plane position, the elevation sequence is from 0° to 90°, the elevation step is , the azimuth step is ; the wind profile detection mode is a multi-beam detection mode with an elevation angle of 70°; the schematic diagram of the wind lidar detection mode is shown in Figure 2 .

[0045] In an exemplary embodiment, the elevation step and azimuth step prediction model construction method includes,

[0046] Collecting the historical data of low-altitude three-dimensional wind field inversion of the wind lidar, based on the historical data, the elevation step and azimuth step prediction model of a single wind lidar in a multi-elevation body scan detection mode is constructed; the elevation step and azimuth step prediction model outputs the prediction adjustment value; the prediction adjustment value is less than the preset value, the prediction adjustment value includes the elevation step value and the azimuth step value from the current scanning surface to the next target scanning surface, specifically, the construction method of the elevation step and azimuth step prediction model includes data cleaning, data division, model architecture, model training and optimization, and model verification and iteration;

[0047] The elevation step and azimuth step model architecture includes a core network, a feature input layer, a regional sub-module, and a fusion and output layer, the elevation step and azimuth step model architecture selects a long short-term memory network LSTM; in the regional sub-module, the near-surface and the near-high-altitude are set in the hidden layer, two parallel sub-networks respectively use different numbers of neurons;

[0048] The elevation step and azimuth step prediction model minimizes the radial wind speed data inversion error of the next target scanning surface as the loss function.

[0049] S2. Quality control of radial wind speed, including removing isolated outliers and filling missing values, specifically includes: Based on the continuity of the wind field, this quality control method uses a sliding window, combined with sub-interval statistics and threshold judgment, to identify, remove, and fill missing values ​​of abnormal wind speeds. Its core idea is to use spatial continuity to preprocess the acquired radial wind speed data of the target area to obtain preprocessed radial wind speed data. The preprocessing methods include: identifying, removing, and filling missing values ​​of abnormal wind speeds in the target area; and correcting the obstruction of each scanning surface in the target area to fill missing measurement points in the obstruction area of ​​the corresponding scanning surface.

[0050] S21. Methods for removing isolated outliers include:

[0051] Measurement points based on effective wind speed values Centered on a point, a 5×3 sliding window (window1) is constructed, where 5 and 3 represent the radial and tangential window sliding numbers, respectively. The radar's wind speed measurement range is set to [-60, 60]. This range is divided into 30 wind speed sub-intervals with a wind speed interval of 4 m / s. -60 m / s and 60 m / s represent the minimum and maximum radial wind speeds detectable by the radar. The frequency of wind speed values ​​in window1 within each sub-interval is counted. When the measuring point... radial wind speed If the frequency of a given interval is less than 4, it is identified as an isolated point, removed, and set as a missing value. This process is repeated for all measurement points to complete the removal of all isolated points within the detection area.

[0052] S22. Methods for filling missing values ​​include:

[0053] Missing test points with missing values Centered on a 7×5 sliding window window2, 7 and 5 represent the radial and tangential sliding numbers of the window, respectively. When the proportion of effective wind speed values ​​within window2 is greater than 50%, the window has the ability to fill in missing values. The frequency of wind speed values ​​in window2 in each sub-interval is counted. When the maximum frequency of an interval is greater than 5, the wind speed of the corresponding sub-interval is used to fill in the missing value. All missing measurement points are traversed to complete the processing of fillable missing values ​​within the detection area.

[0054] After repeating operations S21 and S22 three times, all abnormal wind speeds are identified, eliminated, and missing values ​​are filled.

[0055] S3. Use smooth spline interpolation to achieve occlusion correction for a single scan surface. Methods include:

[0056] Extract all radial wind speeds at a distance of i from the gate. and corresponding azimuth angle Where j = 1, 2, ..., N, and N is the total number of azimuth angles, the sequence of missing wind speed values ​​caused by obstruction is: The corresponding azimuth sequence ,in =1, 2, ..., L; the sequence of effective wind speed values ​​that are not obstructed is... The corresponding azimuth sequence Where k = 1, 2, ..., NL;

[0057] When L / N is less than 20% of the threshold, the blocking correction condition is met. A smooth spline interpolation method is used to fill in the missing measurement points in the blocking area, based on the sequence of effective wind speed values. The corresponding azimuth sequence The goal of smooth spline fitting is to find a function. This minimizes the following objective function:

[0058]

[0059] in, This represents the fitting error of the data points, i.e., the sum of squared residuals. This represents the smoothness penalty term of the curve, where The second derivative of a function measures the curvature of a curve, and is obtained by adjusting the smoothing parameter. Controlling the trade-off between fitting error and smoothness, The larger the value, the smoother the curve. Take 0.2.

[0060] Furthermore, the sequence of missing wind speed values Fill in .

[0061] S4. Calculate the background 3D wind field based on radial wind speed correction using wind profilers. This is done by initializing the vertical wind speed at all radar measurement points based on the vertical wind speed obtained from the wind profiler detection mode. And correct the radial wind speed, and further use the radial wind speed to invert the horizontal wind vector. , and The background wind field was obtained by identifying east-west and north-south winds, respectively. Specifically, this includes:

[0062] Wind profiler detection mode obtains wind profiles, utilizing vertical wind speed. Initialize the vertical wind speed at all radar measurement points, and use the vertical wind speed Correct the radial wind speed at each measuring point To eliminate the projection of the vertical velocity onto the radial direction, the specific method is as follows:

[0063]

[0064] Based on the corrected radial wind speed inversion of the two-dimensional wind field of each PPI scanning surface the two-dimensional wind field of each measuring point as the initial background wind field.

[0065] S5, using three-dimensional variational method to invert the three-dimensional wind speed of each node: ), the specific way includes:

[0066] Based on the initial background wind field, the three-dimensional wind field component is calculated using variational method, and the three-dimensional wind field component is obtained by optimizing the objective function , the objective function The expression is as follows:

[0067]

[0068] Among them:

[0069]

[0070]

[0071]

[0072] Among them, is the background velocity term, is the radial velocity term, is the smoothing term, which is determined by divergence, vorticity and Laplace operator, , and are the corresponding weight coefficients, which are respectively 1, 2 and 10.

[0073] S6, the radar measuring point data is interpolated into the three-dimensional space grid by using the inverse distance weighting method, wherein the three-dimensional space grid is the three-dimensional space grid of the target area, and the specific way includes.

[0074] The coordinates of the radar measuring points are converted into Cartesian coordinates (X, Y, Z), and the coordinates of the points to be interpolated are also converted into Cartesian coordinates , find the nearest 9 radar adjacent measuring points of the point to be interpolated, and the wind speed value of the point to be interpolated , can be expressed as the weighted sum of the neighborhood point value to calculate, that is:

[0075] Among them, is the wind speed value of the adjacent point, is the weight function;

[0076]

[0077] Among them, ​​From the nearest point q to the point to be interpolated The distance is p, where p is the distance exponent. The larger p is, the more significant the effect of proximity. Here, p is set to 2, and ε is a smoothing factor, set to 0.2. Figure 4 The diagram shows a three-dimensional wind field slice, where colors represent wind speed values.

[0078] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for low-altitude three-dimensional wind field inversion based on a single wind-measuring lidar, characterized in that, include, Historical data of low-altitude three-dimensional wind field inversion from wind-measuring lidar are collected. Based on the historical data, an elevation angle stepping and azimuth stepping prediction model for a single wind-measuring lidar in multi-elevation angle volume scan detection mode is constructed. The elevation angle stepping and azimuth stepping prediction models output prediction adjustment values. The prediction adjustment values ​​are less than preset values. Based on a single wind-measuring lidar in multi-elevation angle volume scan detection mode, the detection is activated and the current scanning surface is adjusted to the next target scanning surface according to the predicted adjustment value, and detection is carried out until the entire target area is detected, and the radial wind speed data of the target area is obtained. The radial wind speed data of the target area includes the radial wind speed data of several scanning surfaces. Based on the spatial continuity of the wind field, the radial wind speed data of the acquired target area is preprocessed to obtain preprocessed radial wind speed data. Based on the wind profile detection mode, the wind profile of the target area is acquired. Based on the wind profile, the vertical wind speed data of all radar measuring points are initialized, and the preprocessed radial wind speed data is corrected to obtain the corrected radial wind speed data. The horizontal wind vector is inverted using the corrected radial wind speed data to obtain the background three-dimensional wind field.

2. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 1, characterized in that, Based on a single wind-measuring lidar in multi-elevation angle volume scan detection mode, the target area is activated for detection; After the detection is started, complete observation data corresponding to the first N scanning planes, including the current scanning plane, are collected. The predicted adjustment value is obtained by calculating the elevation step and azimuth step prediction models. The complete observation data includes radial wind speed data, elevation angle and azimuth angle of the corresponding scanning surface, and the prediction adjustment value includes elevation angle step value and azimuth step value from the current scanning surface to the next target scanning surface.

3. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 2, characterized in that, The complete observation data also includes enhanced input data, including the low-altitude wind field gradient obtained from the inversion of the first N scanning surfaces, the time interval between the current scanning surface and the previous scanning surface, and radar equipment status parameters. The low-altitude wind field gradient includes horizontal wind speed shear and vertical wind speed variation.

4. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 1, characterized in that, The construction method of the elevation step and azimuth step prediction model includes: data cleaning, data partitioning, model architecture, model training and optimization, and model validation and iteration; The elevation step and azimuth step prediction model architecture includes a core network, a feature input layer, regional sub-modules, and a fusion and output layer. The elevation step and azimuth step prediction model architecture adopts a Long Short-Term Memory (LSTM) network. In the regional sub-modules, the hidden layers are set to near the ground and near the high altitude, and the two parallel sub-networks use different numbers of neurons. The elevation step and azimuth step prediction models use minimizing the radial wind speed data inversion error of the next target scanning surface as the loss function.

5. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 1, characterized in that, The method for preprocessing radial wind speed data of the acquired target area based on the spatial continuity of the wind field includes: identifying, removing and filling in missing values ​​of abnormal wind speeds in the target area; correcting the obstruction of each scanning surface in the target area and filling in the missing measurement points in the obstruction area of ​​the corresponding scanning surface.

6. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 5, characterized in that, The method for correcting the obstruction of each scanning surface within the target area and filling in the missing measurement points in the obstructed area of ​​the corresponding scanning surface includes: Extract all radial wind speeds at a distance of i from the gate. and corresponding azimuth angle , where j = 1, 2, ..., N, and N is the total number of azimuth angles; The sequence of missing wind speed values ​​caused by obstruction is as follows The corresponding azimuth sequence ,in =1, 2, ..., L; the sequence of effective wind speed values ​​that are not obstructed is... The corresponding azimuth sequence Where k = 1, 2, ..., NL; When L / N is less than the threshold P3, the conditions for obstruction correction are met. A smooth spline interpolation method is used to fill in the missing measurement points in the obstruction area, based on the sequence of effective wind speed values. The corresponding azimuth sequence The objective function is obtained by fitting smooth splines. : in, The fitting error of the data points. This represents the smoothness penalty term of the curve, where The second derivative of a function measures the curvature of a curve. For smoothing parameters.

7. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 1, characterized in that, The methods for obtaining the corrected radial wind speed data include: Wind profiler detection mode obtains wind profiles, utilizing vertical wind speed. Initialize the vertical wind speed at all radar measurement points; Utilizing vertical wind speed Correct the radial wind speed at each measuring point Remove the projection of the vertical velocity onto the radial direction to obtain the corrected radial wind speed. : Based on the corrected radial wind speed Invert the horizontal wind vector of each PPI scan surface , and The winds are east-west and north-south respectively, and each measuring point... As the initial background wind field.

8. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 7, characterized in that, The methods for obtaining the background three-dimensional wind field include: The three-dimensional wind speed at each radar measuring point is inverted using a three-dimensional variational method. ( The variational method is used to calculate the three-dimensional wind field components, and the three-dimensional wind speed is obtained by optimizing the objective function. The expression is as follows: in: in, For background speed, For radial velocity, It is a smoothing term, determined by divergence, vorticity, and the Laplace operator. , and These are the corresponding weighting coefficients; The inverse range weighting method is used to interpolate radar measurement data into a three-dimensional spatial grid of the target area to obtain the background three-dimensional wind field.

9. The low-altitude three-dimensional wind field inversion method based on a single wind-measuring lidar according to claim 8, characterized in that, The method of interpolating radar measurement point data into the three-dimensional spatial grid of the target area using the inverse range weighting method includes: The coordinates of the radar measurement points are converted to Cartesian coordinates (X, Y, Z), and the coordinates of the points to be interpolated are converted to Cartesian coordinates. ; Find the distance from the point to be interpolated The wind speed values ​​at the nearest Q radar measurement points to be interpolated. It is calculated as a weighted sum of the values ​​of neighboring points, that is: in, Nearby wind speed value, It is a weighting function; in, From the nearest point q to the point to be interpolated The distance is p, where p is the distance exponent and ε is the smoothing factor.

10. A low-altitude three-dimensional wind field inversion system based on a single wind-measuring lidar, characterized in that, include: The prediction model building and training unit is used to build and train the elevation step and azimuth step prediction models. The prediction model building and training unit includes a data preprocessing module, a model architecture building module, a training optimization module, a confidence evaluation module, and a feedback iteration module. Elevation step and azimuth step prediction units are used to deploy elevation step and azimuth step prediction models and calculate prediction adjustment values; The detection and control module is used to control a single wind-measuring lidar, to perform detection based on the wind profile detection mode and to perform adjustment detection based on the predicted adjustment value in the multi-elevation angle body scan detection mode. A radial wind speed quality control module is used to perform quality control on radial wind speed, remove isolated outliers, and fill in isolated missing data points. The radar measuring point three-dimensional wind field inversion module is used to perform obstruction correction on a single scanning surface using a smooth spline interpolation method, initialize the vertical wind speed of all radar measuring points based on the vertical wind speed obtained from the wind profile detection mode, correct the radial wind speed, obtain the initial background three-dimensional wind field, and invert the three-dimensional wind speed of each radar measuring point based on the initial background wind field using a three-dimensional variational method. The target area three-dimensional wind field interpolation module is used to interpolate radar measurement point data into the three-dimensional spatial grid of the target area using the inverse range weighting method to obtain the background three-dimensional wind field.

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