Wind field feature detection method and device based on laser radar

Abstract

The invention relates to the technical field of laser radar meteorological detection, in particular to a wind field feature detection method and device based on a laser radar, and the method comprises the following steps: S1, controlling the laser radar to execute dynamic hierarchical scanning, and generating three-dimensional laser scanning data; s2, carrying out aerosol backscattering signal preprocessing on the three-dimensional laser scanning data; s3, extracting an aerosol motion vector of each range gate, and decomposing the aerosol motion vector into a horizontal wind field component and a vertical wind field component; s4, performing spatial gradient calculation to generate a spatial gradient distribution map; s5, respectively calculating a horizontal energy spectrum and a vertical energy spectrum; and S6, outputting wind field characteristic parameters. According to the invention, through fusion of the dynamic features of the laser radar point cloud, the wind shear space gradient and the energy spectrum analysis method, precise extraction of the turbulence intensity, the main energy scale and the three-dimensional vortex features is realized, and the accuracy and the spatial resolution of wind field structure identification are improved.

Description

Technical Field

[0001] The present invention relates to the field of laser radar meteorological detection technology, and in particular to a laser radar-based wind field feature detection method and device. Background Art

[0002] Accurately acquiring the spatial structural characteristics of near-ground wind fields is of great significance in fields such as atmospheric boundary layer research, wind energy assessment, and wind field monitoring in complex environments. Traditional wind speed measurement methods mainly rely on ground wind towers or single-point ultrasonic anemometers. These methods are limited by the limited point layout and cannot achieve high-resolution three-dimensional wind field detection. In recent years, LiDAR, as a non-contact remote sensing technology, has been widely used in wind field measurement. It has the advantages of high spatial coverage and high temporal resolution, and can realize multi-level and multi-angle monitoring of atmospheric aerosol movement, becoming an important means of obtaining wind field data.

[0003] However, existing lidar-based wind field detection technology still faces significant technical bottlenecks in key areas such as wind vector decomposition, wind shear quantification, and turbulence spectrum identification. On the one hand, the signal-to-noise ratio of point cloud data is low, resulting in large errors in motion vector extraction. On the other hand, the lack of a unified framework for integrating energy spectrum analysis with spatial gradient field modeling makes it difficult to accurately output wind field parameters including turbulence intensity, principal energy scale, and three-dimensional vortex characteristics. Therefore, a lidar-based wind field feature detection method and device are urgently needed to address these issues. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a method and device for detecting wind field characteristics based on laser radar.

[0005] The wind field characteristic detection method based on laser radar includes the following steps: S1: Controls the laser radar to perform dynamic layered scanning and generate three-dimensional laser scanning data including azimuth, elevation, and range gates; S2: Perform aerosol backscatter signal preprocessing on 3D laser scanning data to obtain point cloud data with improved signal-to-noise ratio; S3: Extract the aerosol motion vector of each range gate based on the preprocessed point cloud data, and decompose the aerosol motion vector into horizontal wind field components and vertical wind field components in the ground reference frame using the vector space projection algorithm; S4: performing spatial gradient calculations on the horizontal wind field component and the vertical wind field component according to a preset spatial grid to generate spatial gradient distribution maps of the horizontal wind shear and the vertical wind shear; S5: Perform turbulence energy spectrum analysis on horizontal wind shear and vertical wind shear, and calculate horizontal energy spectrum and vertical energy spectrum respectively; S6: Fusion of horizontal energy spectrum, vertical energy spectrum and spatial gradient distribution map to output wind field characteristic parameters including turbulence intensity, main energy scale and three-dimensional vortex characteristics.

[0006] Optionally, the S1 specifically includes: S11: Determine the initial scanning azimuth and elevation angle of the laser radar according to the set scanning target area range, set the initial azimuth angle to 0°, and the initial elevation angle to -5°; S12: Set the azimuth scanning increment to 0.5° and the azimuth scanning range to 0° to 360°, and drive the lidar to gradually adjust the azimuth to complete a 360° horizontal scan; S13: At each fixed azimuth position, the pitch angle is gradually adjusted according to the set pitch angle increment of 1°, and vertical scanning is performed within the pitch angle range of -5° to 20°; S14: The laser radar emits a pulsed laser signal at each azimuth and elevation angle, and sets the range gate width to 30 meters. The backscatter intensity of the aerosol in each range gate is recorded based on the received echo signal, forming the initial three-dimensional laser scanning data including azimuth, elevation and range gates.

[0007] Optionally, the S2 specifically includes: S21: Smoothing the raw backscattered signals of each range gate in the initial 3D laser scanning data using a sliding window averaging method, with the width of the sliding window set to five adjacent range gates. S22: De-noising the smoothed backscattered signal using a threshold denoising algorithm based on wavelet transform, wherein the selected denoising threshold is the median of the absolute values of the signal wavelet coefficients; S23: Calculate the signal-to-noise ratio of the denoised backscattered signal, remove data points with a signal-to-noise ratio lower than a set threshold of 3 dB, and retain valid data points with a signal-to-noise ratio greater than or equal to 3 dB; S24: reorganizing the data points after smoothing, denoising, and signal-to-noise ratio threshold screening to form point cloud data with improved signal-to-noise ratio.

[0008] Optionally, the S3 specifically includes: S31: Match the aerosol scattering intensity distribution of the corresponding range gates at adjacent moments in the preprocessed point cloud data, calculate the displacement vector of the aerosol mass center of the range gate per unit time using the maximum cross-correlation method, and obtain the initial motion vector of the aerosol in three-dimensional space. ; S32: The azimuth of each point of the laser radar , pitch angle and distance Convert to a direction vector in a three-dimensional coordinate system with the ground as the reference system ; S33: According to the direction vector with aerosol motion vector , calculate the horizontal unit vector in the ground reference system. , the vertical unit vector ; S34: Calculate direction vectors separately Unit vector in the horizontal direction Perpendicular to the unit vector The projection amount in the direction is used to obtain the horizontal wind speed component and vertical wind speed component , the calculation formulas are: and ; S35: Set all the corresponding and The data are aggregated to form wind field component datasets.

[0009] Optionally, the S31 specifically includes: S311: Extract the aerosol echo signal intensity distribution corresponding to the same azimuth and elevation angles and the same range gate at two adjacent time points, and construct the data of these two time points into a two-dimensional signal matrix. S312: For the two signal matrices constructed in S311, one of the signal matrices is shifted within a limited sliding window range, and the correlation between the two sets of data is calculated point by point to obtain a correlation matrix for measuring the degree of similarity under different shift amounts; S313: In the correlation matrix, find the position corresponding to the maximum correlation, and determine the translation amount of this position as the displacement of the aerosol center of mass in the two-dimensional pixel space; S314: Based on the spatial resolution parameters of the lidar system, the displacement in the two-dimensional pixel space is converted into a displacement vector in the actual physical space to determine the actual displacement of the aerosol mass center in space; S315: Combine the actual sampling time intervals of two adjacent scans and divide the physical space displacement by the time interval to obtain the movement speed of the aerosol mass center per unit time, which is used as the initial movement vector of the aerosol in the three-dimensional space for the range gate. .

[0010] Optionally, the S4 specifically includes: S41: Map the horizontal wind speed component and the vertical wind speed component of each range gate obtained in S3 to a preset three-dimensional space grid according to the physical coordinates of the laser radar scanning space, where the volume of each grid cell of the space grid is 100m×100m×30m; S42: For each spatial grid node, the central difference method is used to calculate the spatial gradient of the wind speed component on the X-axis, Y-axis, and Z-axis respectively. The specific calculation formula is: For the horizontal wind speed component : ; ; For the vertical wind speed component : ; in, For nodes The horizontal wind speed component of For nodes The vertical wind speed component; are the step sizes of the spatial grid in the X, Y, and Z directions, with values of 100, 100, and 30 meters respectively; are the spatial gradients of wind speed in the X, Y, and Z directions respectively; S43: Visualize the wind speed spatial gradient results of all grid nodes to generate a horizontal wind shear spatial gradient distribution map and a vertical wind shear spatial gradient distribution map.

[0011] Optionally, the S5 specifically includes: S51: extract the horizontal wind shear data and vertical wind shear data of all nodes in the spatial grid obtained in S4, and organize them into a one-dimensional wind shear sequence along the main wind direction or the main vertical direction, respectively, to ensure that the sequence sampling interval is consistent with the spatial grid step size; S52: For each wind shear sequence, the fast Fourier transform method is used to convert the spatial domain data into the wavenumber frequency domain to obtain the corresponding amplitude spectrum and phase spectrum; S53: Based on the Fourier transform results, the energy density of each frequency component within the spatial wavenumber range is calculated. The horizontal energy spectrum and the vertical energy spectrum are equal to the normalized result of the square of the amplitude of the respective wind shear sequence and the sequence length. S54: Plotting the calculated horizontal energy spectrum and vertical energy spectrum as a spectrum curve showing energy varying with wave number.

[0012] Optionally, the S54 specifically includes: S541: Arrange the horizontal energy spectrum and vertical energy spectrum obtained in S53, and establish a numerical correspondence table between wave number sequence and corresponding energy spectrum density, respectively. The wave number sequence range covers all effective wave number components, and the energy spectrum density is arranged according to the normalized energy calculation result; S542: Use the drawing tool to draw the horizontal energy spectrum curve and the vertical energy spectrum curve with the wave number as the horizontal axis and the energy spectrum density as the vertical axis, respectively. During the process, ensure that the wave number and energy spectrum density of each data point correspond one to one; S543: Smoothing the drawn spectrum curve to remove abnormal fluctuation points, and marking the main peak position and characteristic points of the spectrum slope on the curve graph.

[0013] Optionally, the S6 specifically includes: S61: Based on the horizontal energy spectrum and vertical energy spectrum obtained in S5, the total energy integral of each spectrum is calculated respectively, and the total energy integral value is defined as the turbulence intensity in the corresponding spatial direction; S62: Analyze the energy spectrum curve to find the main wave number component corresponding to the maximum value of the energy spectrum, and then convert it to obtain the main energy scale, which is equal to the spatial scale corresponding to the main peak of the energy spectrum; S63: Based on the spatial gradient distribution map, the vorticity calculation method is used to calculate the spatial partial derivatives of the three-dimensional wind speed components in each direction to obtain the three-dimensional vorticity vector field, and the three-dimensional vortex characteristics are statistically analyzed based on the modulus distribution of each vorticity component; S64: Output turbulence intensity, main energy scale and three-dimensional vortex characteristics as wind field characteristic parameters.

[0014] The wind field characteristic detection device based on laser radar is used to implement the above-mentioned wind field characteristic detection method based on laser radar, and includes the following modules: LiDAR scanning module: used to perform dynamic layered laser scanning to obtain three-dimensional laser scanning data containing azimuth, elevation and range gate information; Signal preprocessing module: connected to the laser radar scanning module, used to perform sliding window smoothing, wavelet denoising and signal-to-noise ratio threshold filtering on the three-dimensional laser scanning data to generate point cloud data with improved signal-to-noise ratio; Vector extraction module: connected to the signal preprocessing module, extracts the aerosol motion vector of each range gate based on the point cloud data, and decomposes it into horizontal wind speed components and vertical wind speed components in the ground reference system; Wind shear calculation module: connected to the vector extraction module, used to map wind speed components to a three-dimensional spatial grid and calculate the spatial gradients of horizontal and vertical wind shear based on the central difference method; Energy spectrum analysis module: connected to the wind shear calculation module, used to perform fast Fourier transform on horizontal wind shear and vertical wind shear, and calculate horizontal energy spectrum and vertical energy spectrum respectively; Wind field feature fusion module: bidirectionally connected to the energy spectrum analysis module and the wind shear calculation module, used to fuse the energy spectrum and spatial gradient, and output a wind field feature parameter set including turbulence intensity, main energy scale and three-dimensional vortex characteristics.

[0015] Beneficial effects of the present invention: The present invention significantly improves the quality of point cloud data through a precisely defined dynamic layered lidar scanning strategy, combined with data preprocessing methods such as sliding window averaging, wavelet denoising, and signal-to-noise ratio screening, laying a solid foundation for the high-precision extraction of wind field motion vectors and subsequent wind shear and energy spectrum analysis. By adopting the maximum cross-correlation method and spatial gridding modeling, the systematic decomposition of aerosol motion vectors in the ground reference system is achieved, effectively ensuring the consistency and comparability of physical quantities such as wind field components, spatial gradients, and energy distribution.

[0016] This invention quantitatively outputs key wind field parameters such as turbulence intensity, main energy scale and three-dimensional vortex characteristics through the fusion calculation of energy spectrum and spatial gradient, significantly improving the spatial resolution and physical characterization ability of wind field structure; the overall solution can achieve accurate characterization of wind field spatial changes and turbulence structure under complex wind environments, providing high-quality data support and scientific basis for applications such as wind energy development, atmospheric environment monitoring and meteorological safety warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 Schematic diagram of a wind field characteristic detection method according to an embodiment of the present invention; Figure 2 Schematic diagram of a wind field characteristic detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1 As shown, the wind field feature detection method based on laser radar includes the following steps: S1: Controls the laser radar to perform dynamic layered scanning and generate three-dimensional laser scanning data including azimuth, elevation, and range gates; S2: Perform aerosol backscatter signal preprocessing on 3D laser scanning data to obtain point cloud data with improved signal-to-noise ratio; S3: Extract the aerosol motion vector of each range gate based on the preprocessed point cloud data, and decompose the aerosol motion vector into horizontal wind field components and vertical wind field components in the ground reference frame using the vector space projection algorithm; S4: performing spatial gradient calculations on the horizontal wind field component and the vertical wind field component according to a preset spatial grid to generate spatial gradient distribution maps of the horizontal wind shear and the vertical wind shear; S5: Perform turbulence energy spectrum analysis on horizontal wind shear and vertical wind shear, and calculate horizontal energy spectrum and vertical energy spectrum respectively; S6: Fusion of horizontal energy spectrum, vertical energy spectrum and spatial gradient distribution map to output wind field characteristic parameters including turbulence intensity, main energy scale and three-dimensional vortex characteristics.

[0023] S1 specifically includes: S11: Determine the initial scanning azimuth and elevation angle of the laser radar according to the set scanning target area range, set the initial azimuth angle to 0°, and the initial elevation angle to -5°; S12: Set the azimuth scanning increment to 0.5° and the azimuth scanning range to 0° to 360°, and drive the lidar to gradually adjust the azimuth to complete a 360° horizontal scan; S13: At each fixed azimuth position, the pitch angle is gradually adjusted according to the set pitch angle increment of 1°, and vertical scanning is performed within the pitch angle range of -5° to 20°; S14: The laser radar emits a pulsed laser signal at each azimuth and elevation angle, and sets the range gate width to 30 meters. The backscatter intensity of the aerosol in each range gate is recorded according to the received echo signal, forming initial three-dimensional laser scanning data including azimuth, elevation and range gates. Through the specific operations of the above steps S11 to S14, accurate and complete spatial three-dimensional laser scanning data can be acquired, ensuring that the input data for subsequent wind field feature detection has high resolution and high quality.

[0024] S2 specifically includes: S21: The original backscattered signal of each range gate in the initial 3D laser scanning data is smoothed using the sliding window averaging method. The width of the sliding window is set to 5 adjacent range gates. The calculation formula is: , where Indicates the smoothed backscatter signal strength in dB; Indicates the The raw backscatter signal strength of each range gate; Indicates the sliding window width, the value is 5; Indicates the The location of the range gate; S22: De-noising the smoothed backscattered signal using a threshold denoising algorithm based on wavelet transform, where the selected denoising threshold is the median of the absolute values of the signal wavelet coefficients, and the calculation formula is: ; Where, Represents the signal after wavelet transformation scale coefficients; represents the threshold coefficient; represents the wavelet coefficients after threshold denoising; represents the median; S23: Calculate the signal-to-noise ratio of the denoised backscattered signal, remove data points with a signal-to-noise ratio lower than the set threshold of 3 dB, and retain valid data points with a signal-to-noise ratio greater than or equal to 3 dB. The calculation formula is: , where Indicates the signal-to-noise ratio in dB; Indicates signal power; represents the noise power; S24: The data points after smoothing, denoising and signal-to-noise ratio threshold screening are reorganized to form point cloud data with improved signal-to-noise ratio, which is used for the subsequent accurate calculation of wind field characteristics. Through the specific operations of the above steps S21 to S24, the signal-to-noise ratio of the three-dimensional laser scanning data is effectively improved, and the data noise interference is reduced, thereby ensuring the improvement of the subsequent wind field detection accuracy and stability.

[0025] S3 specifically includes: S31: Match the aerosol scattering intensity distribution of the corresponding range gates at adjacent moments in the preprocessed point cloud data, calculate the displacement vector of the aerosol mass center of the range gate per unit time using the maximum cross-correlation method, and obtain the initial motion vector of the aerosol in three-dimensional space. ; S32: The azimuth of each point of the laser radar , pitch angle and distance Convert to a direction vector in a three-dimensional coordinate system with the ground as the reference system , the direction vector calculation formula is: ; S33: According to the direction vector with aerosol motion vector , calculate the horizontal unit vector in the ground reference system. , the expression is: , the vertical unit vector , the expression is: ; S34: Calculate direction vectors separately Unit vector in the horizontal direction Perpendicular to the unit vector The projection amount in the direction is used to obtain the horizontal wind speed component and vertical wind speed component , the calculation formulas are: and ; S35: Set all the corresponding and The data are aggregated to form a wind field component dataset, which provides an input basis for the subsequent wind shear gradient calculation. Through the processing flow of steps S31 to S35 above, the aerosol motion vector can be accurately mapped to the horizontal and vertical wind field components in the ground reference system while maintaining the consistency of the directional information and the measurement reference, ensuring that the physical meaning of the wind field components is clear and the calculation process is stable and reliable.

[0026] S31 specifically includes: S311: Extract the aerosol echo signal intensity distribution corresponding to the same azimuth and elevation angles and the same range gate at two adjacent time points, and construct the data of these two time points into a two-dimensional signal matrix. S312: For the two signal matrices constructed in S311, one of the signal matrices is shifted within a limited sliding window range, and the correlation between the two sets of data is calculated point by point to obtain a correlation matrix for measuring the degree of similarity under different shift amounts; S313: In the correlation matrix, find the position corresponding to the maximum correlation, and determine the translation amount of this position as the displacement of the aerosol center of mass in the two-dimensional pixel space; S314: Based on the spatial resolution parameters of the lidar system, the displacement in the two-dimensional pixel space is converted into a displacement vector in the actual physical space to determine the actual displacement of the aerosol mass center in space; S315: Combine the actual sampling time intervals of two adjacent scans and divide the physical space displacement by the time interval to obtain the movement speed of the aerosol mass center per unit time, which is used as the initial movement vector of the aerosol in the three-dimensional space for the range gate. .

[0027] The specific calculation steps are as follows: S311: Extract two adjacent time points and When the aerosol echo intensity distribution image of the corresponding range gate at the same azimuth and elevation angle is , it is recorded as a two-dimensional grayscale distribution matrix. and ; S312: Calculation within the sliding window and The normalized mutual correlation coefficient matrix between is: , in, is the mutual correlation coefficient, reflecting and In translation The following correlations; : 2D pixel coordinates; : The horizontal and vertical translation of the sliding window; For the region The average value of For the region The average value of S313: In Find the maximum value in , and record its corresponding offset as , i.e., the maximum cross-correlation position, is taken as the pixel displacement of the aerosol centroid; S314: Pixel displacement Converted into actual displacement in space , the conversion relationship is: ,in, is the actual spatial displacement vector of the aerosol mass center; is the spatial resolution coefficient corresponding to a single pixel; is the pixel displacement corresponding to the maximum cross-correlation; are the unit vectors in the horizontal and vertical directions of the image respectively; S315: According to the time interval , calculate the three-dimensional initial motion vector, the formula is: ,in, is the initial motion vector of the aerosol in three-dimensional space; is the actual space displacement vector; is the sampling time interval between adjacent frames; through the continuous processing of the above steps S311 to S315, accurate measurement of the spatial motion state of aerosols based on time series radar echo data is achieved, providing high-precision basic data for wind field component decomposition and subsequent spatial wind field characteristic analysis, effectively improving the physical accuracy and spatial resolution of wind field measurement results.

[0028] S4 specifically includes: S41: Map the horizontal wind speed component and vertical wind speed component of each range gate obtained in S3 to a preset three-dimensional spatial grid according to the physical coordinates of the lidar scanning space. The volume of each grid cell in the spatial grid is 100m×100m×30m. The grid coverage area is consistent with the lidar observation range to ensure that all wind speed data are assigned to the corresponding grid nodes. S42: For each spatial grid node, the central difference method is used to calculate the spatial gradient of the wind speed component on the X axis (east-west direction), Y axis (north-south direction), and Z axis (vertical direction). The specific calculation formula is: For the horizontal wind speed component : ; ; For the vertical wind speed component : ; in, For nodes The horizontal wind speed component of For nodes The vertical wind speed component; are the step sizes of the spatial grid in the X, Y, and Z directions, with values of 100, 100, and 30 meters respectively; are the spatial gradients of wind speed in the X, Y, and Z directions respectively; S43: Visualize the spatial gradient results of wind speeds of all grid nodes to generate horizontal wind shear spatial gradient distribution maps and vertical wind shear spatial gradient distribution maps to reflect the distribution characteristics of wind speed changes in space; through the specific process of the above steps S41 to S43, it is possible to quantitatively characterize the spatial gradient characteristics of each component of the wind field based on a three-dimensional spatial grid with uniform resolution, and intuitively reflect the structural laws of horizontal and vertical wind shear with spatial distribution maps, thereby providing high-precision basic data for wind field change trends and local turbulence research.

[0029] S5 specifically includes: S51: extract the horizontal wind shear data and vertical wind shear data of all nodes in the spatial grid obtained in S4, and organize them into a one-dimensional wind shear sequence along the main wind direction or the main vertical direction, respectively, to ensure that the sequence sampling interval is consistent with the spatial grid step size; S52: For each wind shear sequence, the fast Fourier transform (FFT) method is used to convert the spatial domain data into the wavenumber frequency domain to obtain the corresponding amplitude spectrum and phase spectrum. The calculation formula of the fast Fourier transform is: ,in, The wind shear sequence is The complex amplitude of the wave number component; The wind shear sequence Wind shear value at each sampling point; is the wind shear sequence length; is the index of the wavenumber component; is an imaginary unit; S53: Based on the Fourier transform results, calculate the energy density of each frequency component within the spatial wavenumber range. The horizontal energy spectrum and vertical energy spectrum are equal to the normalized result of the square of the amplitude of the respective wind shear sequence and the sequence length. The calculation formula of the energy spectrum is: ,in, For the The energy spectral density corresponding to the wave number component; For the The complex amplitude modulus of the wave number components; is the wind shear sequence length; S54: The calculated horizontal energy spectrum and vertical energy spectrum are plotted as spectral curves showing energy variation with wave number, and key characteristic parameters such as the main peak of the spectrum and the spectral slope are extracted for subsequent quantitative analysis of the turbulence intensity and scale of the wind field. Through the continuous processing of the above steps S51 to S54, the energy distribution of the wind shear spatial gradient at different spatial scales is measured, and the energy spectrum curves and their characteristic parameters in the horizontal and vertical directions are obtained respectively, which provides a direct physical basis for the discrimination of turbulent structure and the identification of the main energy scale.

[0030] S54 specifically includes: S541: Arrange the horizontal energy spectrum and vertical energy spectrum obtained in S53, and establish a numerical correspondence table between wave number sequence and corresponding energy spectrum density, respectively. The wave number sequence range covers all effective wave number components, and the energy spectrum density is arranged according to the normalized energy calculation result; S542: Use the drawing tool to draw the horizontal energy spectrum curve and the vertical energy spectrum curve with the wave number as the horizontal axis and the energy spectrum density as the vertical axis, respectively. During the process, ensure that the wave number and energy spectrum density of each data point correspond one to one; S543: Smooth the drawn spectral curve and remove abnormal fluctuation points so that the curve reflects the true law of energy distribution. At the same time, mark the characteristic points of the main peak position and the spectral slope on the curve graph; and save the completed spectral curve as a structured image file or editable data format to provide a visual basis for subsequent wind field characteristic parameter extraction and result analysis. Through the above steps, the energy spectrum data of each component of the wind field can be displayed in the form of an intuitive curve, realizing a clear expression of the relationship between energy and wave number, thereby improving the intuitiveness and scientific nature of the wind field turbulence characteristic analysis.

[0031] S6 specifically includes: S61: Based on the horizontal energy spectrum and vertical energy spectrum obtained in S5, calculate the total energy integral of each spectrum respectively, and define the total energy integral value as the turbulence intensity in the corresponding spatial direction; the calculation formula is: ,in, is the turbulence intensity, For the The energy spectral density of the wave number components is is the energy spectrum length; S62: Analyze the energy spectrum curve and find the main wavenumber component corresponding to the maximum value of the energy spectrum, and then convert it to obtain the main energy scale. The main energy scale is equal to the spatial scale corresponding to the main peak of the energy spectrum. The calculation formula is: ,in, is the main energy scale, is the wave number corresponding to the main peak of the energy spectrum; S63: Based on the spatial gradient distribution diagram, the vorticity calculation method is used to calculate the spatial partial derivatives of the three-dimensional wind speed components in each direction to obtain the three-dimensional vorticity vector field. The three-dimensional vortex characteristics are statistically analyzed based on the modulus distribution of each vorticity component. The calculation formula for the three-dimensional vorticity is: ,in, is the three-dimensional vorticity vector; is the three-dimensional wind speed vector field; is the gradient operator; S64: Output the turbulence intensity, main energy scale, and three-dimensional vortex characteristics as wind field characteristic parameters and organize them into structured data result files for subsequent wind field diagnosis and application analysis. Through the processing flow of the above steps, it is possible to efficiently output wind field characteristic parameters with spatial resolution and physical representativeness based on the quantitative fusion of energy spectrum and spatial gradient characteristics, thereby significantly improving the accuracy and scientific nature of wind field structure analysis and complex wind environment identification.

[0032] like Figure 2 As shown, the wind field characteristic detection device based on laser radar is used to implement the above-mentioned wind field characteristic detection method based on laser radar, and includes the following modules: LiDAR scanning module: used to perform dynamic layered laser scanning to obtain three-dimensional laser scanning data containing azimuth, elevation and range gate information; Signal preprocessing module: Communicates with the LiDAR scanning module and performs sliding window smoothing, wavelet denoising, and signal-to-noise ratio threshold filtering on the 3D laser scanning data to generate point cloud data with improved signal-to-noise ratio; Vector extraction module: connected to the signal preprocessing module, extracts the aerosol motion vector of each range gate based on the point cloud data, and decomposes it into horizontal wind speed components and vertical wind speed components in the ground reference system; Wind shear calculation module: connected to the vector extraction module, used to map wind speed components to a three-dimensional spatial grid and calculate the spatial gradients of horizontal and vertical wind shear based on the central difference method; Energy spectrum analysis module: connected to the wind shear calculation module, used to perform fast Fourier transform on horizontal wind shear and vertical wind shear, and calculate horizontal energy spectrum and vertical energy spectrum respectively; Wind field feature fusion module: bidirectionally connected to the energy spectrum analysis module and the wind shear calculation module, used to fuse the energy spectrum and spatial gradient, and output a wind field feature parameter set including turbulence intensity, main energy scale and three-dimensional vortex characteristics.

[0033] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0034] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A wind field feature detection method based on laser radar, characterized in that: The following steps are involved: S1: Controls the laser radar to perform dynamic layered scanning and generate three-dimensional laser scanning data including azimuth, elevation, and range gates; S2: Perform aerosol backscatter signal preprocessing on 3D laser scanning data to obtain point cloud data with improved signal-to-noise ratio; S3: Extract the aerosol motion vector of each range gate based on the preprocessed point cloud data, and decompose the aerosol motion vector into horizontal wind field components and vertical wind field components in the ground reference frame using the vector space projection algorithm; S4: performing spatial gradient calculations on the horizontal wind field component and the vertical wind field component according to a preset spatial grid to generate spatial gradient distribution maps of the horizontal wind shear and the vertical wind shear; S5: Perform turbulence energy spectrum analysis on horizontal wind shear and vertical wind shear, and calculate horizontal energy spectrum and vertical energy spectrum respectively; S6: Fusion of horizontal energy spectrum, vertical energy spectrum and spatial gradient distribution map to output wind field characteristic parameters including turbulence intensity, main energy scale and three-dimensional vortex characteristics.

2. The method for detecting wind field characteristics based on laser radar according to claim 1, characterized in that: Said S1 specifically includes: S11: Determine the initial scanning azimuth and elevation angle of the laser radar according to the set scanning target area range, set the initial azimuth angle to 0°, and the initial elevation angle to -5°; S12: Set the azimuth scanning increment to 0.5° and the azimuth scanning range to 0° to 360°, and drive the lidar to gradually adjust the azimuth to complete a 360° horizontal scan; S13: At each fixed azimuth position, the pitch angle is gradually adjusted according to the set pitch angle increment of 1°, and vertical scanning is performed within the pitch angle range of -5° to 20°; S14: The laser radar emits a pulsed laser signal at each azimuth and elevation angle, and sets the range gate width to 30 meters. The backscatter intensity of the aerosol in each range gate is recorded based on the received echo signal, forming the initial three-dimensional laser scanning data including azimuth, elevation and range gates.

3. The method for detecting wind field characteristics based on laser radar according to claim 1, characterized in that: The S2 specifically includes: S21: Smoothing the raw backscattered signals of each range gate in the initial 3D laser scanning data using a sliding window averaging method, with the width of the sliding window set to five adjacent range gates. S22: De-noising the smoothed backscattered signal using a threshold denoising algorithm based on wavelet transform, wherein the selected denoising threshold is the median of the absolute values of the signal wavelet coefficients; S23: Calculate the signal-to-noise ratio of the denoised backscattered signal, remove data points with a signal-to-noise ratio lower than a set threshold of 3 dB, and retain valid data points with a signal-to-noise ratio greater than or equal to 3 dB; S24: reorganizing the data points after smoothing, denoising, and signal-to-noise ratio threshold screening to form point cloud data with improved signal-to-noise ratio.

4. The method for detecting wind field characteristics based on laser radar according to claim 1, characterized in that: The S3 specifically includes: S31: Match the aerosol scattering intensity distribution of the corresponding range gates at adjacent moments in the preprocessed point cloud data, calculate the displacement vector of the aerosol mass center of the range gate per unit time using the maximum cross-correlation method, and obtain the initial motion vector of the aerosol in three-dimensional space. ; S32: The azimuth of each point of the laser radar , pitch angle and distance Convert to a direction vector in a three-dimensional coordinate system with the ground as the reference system ; S33: According to the direction vector with aerosol motion vector , calculate the horizontal unit vector in the ground reference system. , the vertical unit vector ; S34: Calculate direction vectors separately Unit vector in the horizontal direction Perpendicular to the unit vector The projection amount in the direction is used to obtain the horizontal wind speed component and vertical wind speed component , the calculation formulas are: and ; S35: Set all the corresponding and The data are aggregated to form wind field component datasets.

5. The method for detecting wind field characteristics based on laser radar according to claim 4, characterized in that: The S31 specifically includes: S311: Extract the aerosol echo signal intensity distribution corresponding to the same azimuth and elevation angles and the same range gate at two adjacent time points, and construct the data of these two time points into a two-dimensional signal matrix. S312: For the two signal matrices constructed in S311, one of the signal matrices is shifted within a limited sliding window range, and the correlation between the two sets of data is calculated point by point to obtain a correlation matrix for measuring the degree of similarity under different shift amounts; S313: In the correlation matrix, find the position corresponding to the maximum correlation, and determine the translation amount of this position as the displacement of the aerosol center of mass in the two-dimensional pixel space; S314: Based on the spatial resolution parameters of the lidar system, the displacement in the two-dimensional pixel space is converted into a displacement vector in the actual physical space to determine the actual displacement of the aerosol mass center in space; S315: Combine the actual sampling time intervals of two adjacent scans and divide the physical space displacement by the time interval to obtain the movement speed of the aerosol mass center per unit time, which is used as the initial movement vector of the aerosol in the three-dimensional space for the range gate. .

6. The method for detecting wind field characteristics based on laser radar according to claim 1, characterized in that: The S4 specifically includes: S41: Map the horizontal wind speed component and the vertical wind speed component of each range gate obtained in S3 to a preset three-dimensional space grid according to the physical coordinates of the laser radar scanning space, where the volume of each grid cell of the space grid is 100m×100m×30m; S42: For each spatial grid node, the central difference method is used to calculate the spatial gradient of the wind speed component on the X-axis, Y-axis, and Z-axis respectively. The specific calculation formula is: For the horizontal wind speed component : ； ； For the vertical wind speed component : ; in, For nodes The horizontal wind speed component of For nodes The vertical wind speed component; are the step sizes of the spatial grid in the X, Y, and Z directions, with values of 100, 100, and 30 meters respectively; are the spatial gradients of wind speed in the X, Y, and Z directions respectively; S43: Visualize the wind speed spatial gradient results of all grid nodes to generate a horizontal wind shear spatial gradient distribution map and a vertical wind shear spatial gradient distribution map.

7. The method for detecting wind field characteristics based on laser radar according to claim 1, characterized in that: The S5 specifically includes: S51: extract the horizontal wind shear data and vertical wind shear data of all nodes in the spatial grid obtained in S4, and organize them into a one-dimensional wind shear sequence along the main wind direction or the main vertical direction, respectively, to ensure that the sequence sampling interval is consistent with the spatial grid step size; S52: For each wind shear sequence, the fast Fourier transform method is used to convert the spatial domain data into the wavenumber frequency domain to obtain the corresponding amplitude spectrum and phase spectrum; S53: Based on the Fourier transform results, the energy density of each frequency component within the spatial wavenumber range is calculated. The horizontal energy spectrum and the vertical energy spectrum are equal to the normalized result of the square of the amplitude of the respective wind shear sequence and the sequence length. S54: Plotting the calculated horizontal energy spectrum and vertical energy spectrum as a spectrum curve showing energy varying with wave number.

8. The method for detecting wind field characteristics based on laser radar according to claim 7, characterized in that: The S54 specifically includes: S541: Arrange the horizontal energy spectrum and vertical energy spectrum obtained in S53, and establish a numerical correspondence table between wave number sequence and corresponding energy spectrum density, respectively. The wave number sequence range covers all effective wave number components, and the energy spectrum density is arranged according to the normalized energy calculation result; S542: Use the drawing tool to draw the horizontal energy spectrum curve and the vertical energy spectrum curve with the wave number as the horizontal axis and the energy spectrum density as the vertical axis, respectively. During the process, ensure that the wave number and energy spectrum density of each data point correspond one to one; S543: Smoothing the drawn spectrum curve to remove abnormal fluctuation points, and marking the main peak position and characteristic points of the spectrum slope on the curve graph.

9. The method for detecting wind field characteristics based on laser radar according to claim 1, characterized in that: The S6 specifically includes: S61: Based on the horizontal energy spectrum and vertical energy spectrum obtained in S5, the total energy integral of each spectrum is calculated respectively, and the total energy integral value is defined as the turbulence intensity in the corresponding spatial direction; S62: Analyze the energy spectrum curve to find the main wave number component corresponding to the maximum value of the energy spectrum, and then convert it to obtain the main energy scale, which is equal to the spatial scale corresponding to the main peak of the energy spectrum; S63: Based on the spatial gradient distribution map, the vorticity calculation method is used to calculate the spatial partial derivatives of the three-dimensional wind speed components in each direction to obtain the three-dimensional vorticity vector field, and the three-dimensional vortex characteristics are statistically analyzed based on the modulus distribution of each vorticity component; S64: Output turbulence intensity, main energy scale and three-dimensional vortex characteristics as wind field characteristic parameters.

10. A wind field characteristic detection device based on laser radar, used to implement the wind field characteristic detection method based on laser radar according to any one of claims 1 to 9, characterized in that: Includes the following modules: LiDAR scanning module: used to perform dynamic layered laser scanning to obtain three-dimensional laser scanning data containing azimuth, elevation and range gate information; Signal preprocessing module: connected to the laser radar scanning module, used to perform sliding window smoothing, wavelet denoising and signal-to-noise ratio threshold filtering on the three-dimensional laser scanning data to generate point cloud data with improved signal-to-noise ratio; Vector extraction module: connected to the signal preprocessing module, extracts the aerosol motion vector of each range gate based on the point cloud data, and decomposes it into horizontal wind speed components and vertical wind speed components in the ground reference system; Wind shear calculation module: connected to the vector extraction module, used to map wind speed components to a three-dimensional spatial grid and calculate the spatial gradients of horizontal and vertical wind shear based on the central difference method; Energy spectrum analysis module: connected to the wind shear calculation module, used to perform fast Fourier transform on horizontal wind shear and vertical wind shear, and calculate horizontal energy spectrum and vertical energy spectrum respectively; Wind field feature fusion module: bidirectionally connected to the energy spectrum analysis module and the wind shear calculation module, used to fuse the energy spectrum and spatial gradient, and output a wind field feature parameter set including turbulence intensity, main energy scale and three-dimensional vortex characteristics.

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