Inversion method of three-dimensional wind field based on coherent Doppler wind lidar

Abstract

The invention relates to the technical field of laser radar wind field inversion, in particular to a coherent Doppler anemometry laser radar-based three-dimensional wind field inversion method, which comprises the following steps of: acquiring original power spectrum data of an anemometry laser radar to be inversed in each direction, and sequentially performing interpolation processing on the original power spectrum data in each direction to obtain an inversion result; obtaining power spectrum data after interpolation in each direction; calculating to obtain a cumulative power spectrum function; and optimizing the obtained vector three-dimensional wind field by using the cumulative power spectrum function so as to calculate the maximum function value of the cumulative power spectrum function, and the three-dimensional wind field corresponding to the maximum function value is the optimal three-dimensional wind field. According to the method, all the power spectrum data after azimuth interpolation are directly processed as a whole, random noise is averaged by using an accumulation process, a real signal is highlighted, and stable and accurate three-dimensional wind field inversion can be realized even when the signal-to-noise ratio is extremely low.

Description

Technical Field

[0001] This invention relates to the field of lidar wind field inversion technology, and in particular to an inversion method for three-dimensional wind fields based on coherent Doppler wind measurement lidar. Background Technology

[0002] Currently, driven by the increasing demand for atmospheric environmental monitoring, coherent Doppler wind lidar has become a key technology for acquiring high spatiotemporal resolution three-dimensional wind fields. This technology emits a laser and receives the backscattered echoes from aerosol particles in the atmosphere, using Doppler efficiency to accurately estimate the radial wind speed along the laser beam direction. To achieve three-dimensional wind field detection, the radar typically employs a conical scanning mode. The core objective of three-dimensional wind field inversion technology is to reconstruct the complete motion state of the atmosphere in three-dimensional space from the radial velocity information directly measured by the Doppler radar through a series of algorithms and models, that is, to obtain the horizontal wind (i.e., meridional wind) at any point in space. and latitude wind ) and vertical wind Three components.

[0003] Currently, the method for retrieving three-dimensional wind fields from lidar radial velocity measurements is based on a velocity-azimuth display using a single lidar unit's conical scanning. VAD (Various Aspects of Radial Velocity) is a method that fits the sinusoidal curves of radial velocity at different azimuth angles. Sine wave fitting is the most basic and widely used method. It solves wind field information by fitting the sinusoidal relationship between radial velocity and azimuth angle. The starting point of its technical path is that the radial velocity must first be estimated from a single Doppler spectrum. However, this prerequisite directly leads to its inherent defects: when the atmospheric echo signal is weak (i.e., the signal-to-noise ratio (SNR) is reduced), the noise peak intensity in the spectrum may exceed or be comparable to the peak value of the real signal. Therefore, the method of determining the radial velocity based on finding the maximum value of the spectrum will likely produce absurd "bad spots" (i.e., abnormal radial wind speeds). This method has no ability to distinguish and directly brings these "bad spots" into the fitting. Under low SNR, it will produce completely wrong wind field results (i.e., it will cause the inversion results to diverge). Especially in complex wind fields such as typhoons and wind shear, the error amplification effect is significant. At the same time, when the SNR is low, this method is difficult to accurately extract the Doppler frequency shift, resulting in the failure of detection under clean upper atmosphere or severe weather conditions. Summary of the Invention

[0004] The technical problem to be solved by this invention is: in order to solve the problem of low accuracy of existing three-dimensional wind field inversion results under low signal-to-noise ratio, this invention provides a three-dimensional wind field inversion method based on coherent Doppler wind measurement lidar. Through the improvement of lidar three-dimensional wind field inversion method, reliable three-dimensional wind field inversion results can still be obtained under the extreme condition of low signal-to-noise ratio.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for inverting three-dimensional wind fields based on coherent Doppler wind lidar, comprising the following steps: S1. Obtain the original power spectrum data of the wind measurement lidar to be inverted in each direction, and perform interpolation processing on the original power spectrum data of each direction in sequence to obtain the interpolated power spectrum data of each direction. S2, Set the vector three-dimensional wind field And utilize this vector three-dimensional wind field The cumulative power spectrum function is calculated by combining the interpolated power spectrum data from each direction in S1. ; S3. Utilize the cumulative power spectrum function in S2 The desired three-dimensional vector wind field is optimized to calculate the cumulative power spectral function. The maximum function value, and the three-dimensional wind field corresponding to the maximum function value, is the desired optimal three-dimensional wind field.

[0006] Therefore, the raw power spectrum data of all azimuth angles are directly interpolated and used as the overall input, and a vector three-dimensional wind field is set. All spectra are coherently summed to obtain the cumulative power spectral function. Then, for the cumulative power spectrum function This method, which seeks to obtain the optimal 3D wind field, is superior to existing inversion methods based on velocity and azimuth display using a single radar cone scan. It effectively averages out noise and highlights the signal, bypassing the unavoidable intermediate product of "bad pixels." It directly performs noise reduction and optimization at the level of the original power spectrum data, thus obtaining reliable 3D wind field inversion results even under extreme conditions of low signal-to-noise ratio. This overcomes the physical limitations of convergence without initial values ​​and detection of extremely weak signals, achieving stable inversion under low signal-to-noise ratio conditions. This provides a more reliable wind measurement method for fields such as weather forecasting, aviation safety, and emergency operations. Furthermore, it deeply couples Fourier interpolation with the cumulative power spectrum function construction, enabling the subsequent optimizer to more easily and accurately lock the global maximum value, thereby improving the accuracy of the final 3D wind field inversion results.

[0007] Further, S1 includes the following steps: S1-1. Obtain the raw power spectrum data of the wind measurement lidar in eight directions to be inverted; S1-2, Perform inverse Fourier transform on the original power spectrum data in eight directions obtained from S1-1 respectively; S1-3. Perform time-domain zero-padding on the power spectrum data in eight directions after inverse Fourier transform according to the target interpolation factor. S1-4. Perform a Fourier transform on the power spectrum data in eight directions after time-domain zero-padding to obtain the interpolated power spectrum data in eight directions. Therefore, the purpose of interpolating the original power spectrum data is to improve the spectral resolution, thereby enabling more accurate location of the Doppler frequency shift and, consequently, more accurate estimation of the radial velocity. .

[0008] Further, S2 includes the following steps: S2-1, Setting the Vector Three-Dimensional Wind Field , Scan azimuth To calculate the radial velocity ; S2-2, Radial velocity Converted to Doppler frequency shift And obtain the Doppler frequency shift Corresponding frequency index ; S2-3. Set M azimuth angles and find the frequency index of the original power spectrum data for each azimuth angle. Corresponding spectral intensity value M azimuth angles correspond to M spectral intensity values Accumulate the sums to obtain the cumulative power spectral function. ; in: Indicates the first Azimuth angle Indicates the number of azimuth angles.

[0009] Furthermore, in S2-1, the radial velocity The expression is: ; in: This represents a unit vector along the direction of the laser beam. This indicates the elevation angle measured by the lidar.

[0010] Furthermore, in S2-2, the Doppler frequency shift The expression is: ; Frequency Index The expression is: ; in: This indicates the laser emission wavelength of the wind-measuring lidar to be retrieved. This represents the index of the spectrum after rounding. This indicates the intermediate frequency of the wind-measuring lidar to be retrieved. This indicates the spectral resolution after interpolation.

[0011] Furthermore, in S2-3, the cumulative power spectral function The expression is: .

[0012] Furthermore, in S3, the optimization process refers to: In three-dimensional wind speed space The search is performed using the cumulative power spectrum function. The maximum function value obtained is the three-dimensional wind field corresponding to the maximum function value, which is the desired optimal three-dimensional wind field.

[0013] Furthermore, in S3, the optimal wind speed vector Corresponding cumulative power spectral function value The expression is:

[0014] Furthermore, in S3, in the three-dimensional wind speed space The process of conducting a search includes the following steps: S3-1. Initialization: In the search space of the three-dimensional wind field, a group of particles is randomly generated to obtain the initial position of each particle. Initial velocity Calculate the initial position of each particle Corresponding objective function value Each particle's current position is initialized to its own historical best position. Then the global optimal position of the entire group Initialized to the objective function value among all particles The position of the particle corresponding to the maximum value; S3-2, Iterative Update: In each iteration, each particle... The updated velocity is obtained by using the velocity update formula. The updated position is obtained by using the position update formula. ; S3-3, Update and Optimal Value Evaluation: Calculate the value for each particle. Updated location Corresponding objective function value If the updated objective function value Greater than the individual's historical best position Corresponding objective function value Then update the individual optimal value; if the updated objective function value... Greater than the global historical best position The corresponding objective function value Then the global optimal position of the entire group will be determined. Updated to the updated location ; S3-4. Repeat S3-2 and S3-3 until the global optimum position of the entire population is reached. objective function value Convergence occurs when the global optimum of the entire population is reached. That is, the globally optimal wind speed vector ; in: This represents the individual's historical best position. This represents the global optimal position of the entire population. Therefore, due to the cumulative power spectral function... For non-continuous problems with multiple local extrema, direct gradient optimization will fail. Therefore, an optimizer that does not require gradient information and has global search capabilities is needed. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm inspired by the collective behavior of biological groups such as flocks of birds or schools of fish. It is simple in concept, has few parameters, and converges quickly, making it very suitable for handling complex global optimization problems in continuous spaces. In addition, PSO can efficiently and reliably find the global optimal solution representing the real wind field under complex objective function "terrain", ensuring both extreme detection capabilities and the reliability and authenticity of the inversion results.

[0015] Furthermore, in S3-2, the expression for the velocity update formula is: ; The expression for the position update formula is: ; in: Represents particles The speed in the next moment, Indicates inertia weight, Represents particles The speed at the current moment, Represents individual learning factors. , Represents two random numbers uniformly distributed between [0, 1]. Represents the group learning factor. This represents the optimal position of an individual. This represents the globally optimal position of the entire group. Represents particles The position at the next moment, Represents particles Current position.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention directly interpolates the raw power spectrum data of all azimuth angles and uses it as the overall input, and sets a vector three-dimensional wind field. All spectra are coherently summed to obtain the cumulative power spectral function. Then, for the cumulative power spectrum function This method, which seeks to obtain the optimal 3D wind field, is superior to existing inversion methods based on velocity and azimuth display using a single radar cone scan. It effectively averages out noise and highlights the signal, bypassing the unavoidable intermediate product of "bad pixels." It directly performs noise reduction and optimization at the level of the original power spectrum data, thus obtaining reliable 3D wind field inversion results even under extreme conditions of low signal-to-noise ratio. This overcomes the physical limitations of convergence without initial values ​​and detection of extremely weak signals, achieving stable inversion under low signal-to-noise ratio conditions. This provides a more reliable wind measurement method for fields such as weather forecasting, aviation safety, and emergency operations. Furthermore, it deeply couples Fourier interpolation with the cumulative power spectrum function construction, enabling the subsequent optimizer to more easily and accurately lock the global maximum value, thereby improving the accuracy of the final 3D wind field inversion results.

[0017] 2. The purpose of interpolating the original power spectrum data in this invention is to improve the resolution of the spectrum, thereby enabling more accurate positioning of the Doppler frequency shift and, consequently, more accurate estimation of the radial velocity. .

[0018] 3. This invention utilizes the cumulative power spectral function. For non-continuous problems with multiple local extrema, direct gradient optimization will fail. Therefore, an optimizer that does not require gradient information and has global search capabilities is needed. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm inspired by the collective behavior of biological groups such as flocks of birds or schools of fish. It is simple in concept, has few parameters, and converges quickly, making it very suitable for handling complex global optimization problems in continuous spaces. In addition, PSO can efficiently and reliably find the global optimal solution representing the real wind field under complex objective function "terrain", ensuring both extreme detection capabilities and the reliability and authenticity of the inversion results. Attached Figure Description

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Figure 1 This is a flowchart of the three-dimensional wind field inversion method based on coherent Doppler wind lidar of the present invention; Figure 2 This is a flowchart of S1 of the present invention; Figure 3 This is a schematic diagram of the scanning mode of the coherent Doppler wind lidar of the present invention; Figure 4 This is a flowchart of S2 of the present invention; Figure 5 This is a flowchart of the three-dimensional wind speed spatial search process of the present invention. Detailed Implementation

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0023] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0024] like Figures 1 to 5 The diagram shows the preferred embodiment of the present invention. The method for inverting a three-dimensional wind field based on a coherent Doppler wind lidar in this embodiment includes the following steps: S1. Obtain the original power spectrum data of the wind measurement lidar to be inverted in each direction, and perform interpolation processing on the original power spectrum data of each direction in sequence to obtain the interpolated power spectrum data of each direction. S2, Set the vector three-dimensional wind field And utilize this vector three-dimensional wind field The cumulative power spectrum function is calculated by combining the interpolated power spectrum data from each direction in S1. ; S3. Utilize the cumulative power spectrum function in S2 The desired three-dimensional vector wind field is optimized to calculate the cumulative power spectral function. The maximum function value, corresponding to which the three-dimensional wind field is found, is the optimal three-dimensional wind field. Therefore, the original power spectrum data of all azimuth angles are directly interpolated and used as the overall input, and a vector three-dimensional wind field is set. All spectra are coherently summed to obtain the cumulative power spectral function. Then, for the cumulative power spectrum function This method, which seeks to obtain the optimal 3D wind field, is superior to existing inversion methods based on velocity and azimuth display using a single radar cone scan. It effectively averages out noise and highlights the signal, bypassing the unavoidable intermediate product of "bad pixels." It directly performs noise reduction and optimization at the level of the original power spectrum data, thus obtaining reliable 3D wind field inversion results even under extreme conditions of low signal-to-noise ratio. This overcomes the physical limitations of convergence without initial values ​​and detection of extremely weak signals, achieving stable inversion under low signal-to-noise ratio conditions. This provides a more reliable wind measurement method for fields such as weather forecasting, aviation safety, and emergency operations. Furthermore, it deeply couples Fourier interpolation with the cumulative power spectrum function construction, enabling the subsequent optimizer to more easily and accurately lock the global maximum value, thereby improving the accuracy of the final 3D wind field inversion results.

[0025] In this embodiment, step S1 includes the following steps: S1-1. Obtain the raw power spectrum data of the wind measurement lidar in eight directions to be inverted; S1-2, Perform inverse Fourier transform on the original power spectrum data in eight directions obtained from S1-1 respectively; S1-3. Perform time-domain zero-padding on the power spectrum data in eight directions after inverse Fourier transform according to the target interpolation factor. S1-4. Perform a Fourier transform on the power spectrum data in eight directions after time-domain zero-padding to obtain the interpolated power spectrum data in eight directions. Therefore, the purpose of interpolating the original power spectrum data is to improve the spectral resolution, thereby enabling more accurate location of the Doppler frequency shift and, consequently, more accurate estimation of the radial velocity. .

[0026] It should be noted that: such as Figure 3As shown, the coherent Doppler wind lidar measures the three-dimensional wind field by sequentially changing the direction of the laser beam. In a complete scanning cycle, the laser beam first points at a fixed angle (zenith angle) deviating from the vertical direction to eight evenly distributed directions: north, northeast, east, southeast, south, southwest, west, and northwest. At each azimuth point, the beam pauses briefly to complete a radial wind speed measurement. The system quickly and point-by-point completes the measurements in these eight directions, and then integrates the power spectrum data measured in these eight directions to accurately calculate the horizontal wind speed, wind direction, and vertical airflow velocity at the measurement height. The entire process is repeated continuously, thereby achieving continuous monitoring of the wind field.

[0027] Specifically, interpolation can also be replaced by algorithms such as wavelet transform and higher-order spectrum estimation, which can also obtain a high-resolution spectrum that accurately reflects the Doppler frequency shift.

[0028] In this embodiment, step S2 includes the following steps: S2-1, Setting the Vector Three-Dimensional Wind Field , Scan azimuth To calculate the radial velocity ; S2-2, Radial velocity Converted to Doppler frequency shift And obtain the Doppler frequency shift Corresponding frequency index ; S2-3. Set M azimuth angles and find the frequency index of the original power spectrum data for each azimuth angle. Corresponding spectral intensity value M azimuth angles correspond to M spectral intensity values Accumulate the sums to obtain the cumulative power spectral function. ; in: Indicates the first Azimuth angle Indicates the number of azimuth angles; In S2-1, the radial velocity The expression is: ; in: This represents a unit vector along the direction of the laser beam. Indicates the elevation angle measured by the lidar; In S2-2, the Doppler frequency shift The expression is: ; Frequency Index The expression is: ; in: This indicates the laser emission wavelength of the wind-measuring lidar to be retrieved. This represents the index of the spectrum after rounding. This indicates the intermediate frequency of the wind-measuring lidar to be retrieved. Indicates the spectral resolution after interpolation; In S2-3, the cumulative power spectral function The expression is: .

[0029] Specifically, construct the cumulative objective function. It can also be replaced by an equivalent form of monotonic function transformation of the power spectrum data, which does not change the position of the extreme points after the transformation.

[0030] In this embodiment, in S3, the optimization process refers to: In three-dimensional wind speed space The search is performed using the cumulative power spectrum function. The maximum function value obtained is the three-dimensional wind field corresponding to the maximum function value, which is the desired optimal three-dimensional wind field. Optimal wind speed vector Corresponding cumulative power spectral function value The expression is: ; In three-dimensional wind speed space The process of conducting a search includes the following steps: S3-1. Initialization: In the search space of the three-dimensional wind field, a group of particles is randomly generated to obtain the initial position of each particle. Initial velocity Calculate the initial position of each particle Corresponding objective function value Each particle's current position is initialized to its own historical best position. Then the global optimal position of the entire group Initialized to the objective function value among all particles The position of the particle corresponding to the maximum value; S3-2, Iterative Update: In each iteration, each particle... The updated velocity is obtained by using the velocity update formula. The updated position is obtained by using the position update formula. ; S3-3, Update and Optimal Value Evaluation: Calculate the value for each particle. Updated location Corresponding objective function value If the updated objective function value Greater than the individual's historical best position Corresponding objective function value Then update the individual optimal value; if the updated objective function value... Greater than the global historical best position The corresponding objective function value Then the global optimal position of the entire group will be determined. Updated to the updated location ; S3-4. Repeat S3-2 and S3-3 until the global optimum position of the entire population is reached. objective function value Convergence occurs when the global optimum of the entire population is reached. That is, the globally optimal wind speed vector ; in: This represents the individual's historical best position. Represents the globally optimal position of the entire group. In S3-2, the expression for the velocity update formula is: ; The expression for the position update formula is: ; in: Represents particles The next moment (i.e.) The speed (in time, after update) This represents the inertial weight (the inertial weight determines the extent to which a particle maintains its current direction of motion). Represents particles Current time (i.e.) The velocity at the moment (initial) It represents the individual (cognitive) learning factor (representing the strength of the tendency of a particle to fly toward its own historical best). , Represents two random numbers uniformly distributed between [0, 1] (introducing randomness into the exploration process to prevent all particles from taking the exact same path). It represents the group (social) learning factor (representing the strength of the tendency of particles to fly towards the global optimum of the group). This represents the optimal position of an individual. This represents the globally optimal position of the entire group. Represents particles The next moment (i.e.) The location (at the time of update) Represents particles Current time (i.e.) The position (time, initial). Therefore, due to the cumulative power spectral function... For non-continuous problems with multiple local extrema, direct gradient optimization will fail. Therefore, an optimizer that does not require gradient information and has global search capabilities is needed. Particle Swarm Optimization (PSO) is a swarm intelligence optimization algorithm inspired by the collective behavior of biological groups such as flocks of birds or schools of fish. It is simple in concept, has few parameters, and converges quickly, making it very suitable for handling complex global optimization problems in continuous spaces. In addition, PSO can efficiently and reliably find the global optimal solution representing the real wind field under complex objective function "terrain", ensuring both extreme detection capabilities and the reliability and authenticity of the inversion results.

[0031] Specifically, in S3-1, due to the random generation of a group of particles, the initial position of each particle is... Initial velocity All are random.

[0032] In summary, this invention directly interpolates the raw power spectrum data of all azimuth angles and uses it as the overall input, and sets a vector three-dimensional wind field. All spectra are coherently summed to obtain the cumulative power spectral function. Then, for the cumulative power spectrum function This method, which seeks to obtain the optimal 3D wind field, is superior to existing inversion methods based on velocity and azimuth display using a single radar cone scan. It effectively averages out noise and highlights the signal, bypassing the unavoidable intermediate product of "bad pixels." It directly performs noise reduction and optimization at the level of the original power spectrum data, thus obtaining reliable 3D wind field inversion results even under extreme conditions of low signal-to-noise ratio. This overcomes the physical limitations of convergence without initial values ​​and detection of extremely weak signals, achieving stable inversion under low signal-to-noise ratio conditions. This provides a more reliable wind measurement method for fields such as weather forecasting, aviation safety, and emergency operations. Furthermore, it deeply couples Fourier interpolation with the cumulative power spectrum function construction, enabling the subsequent optimizer to more easily and accurately lock the global maximum value, thereby improving the accuracy of the final 3D wind field inversion results.

[0033] The above description is based on the preferred embodiments of the present invention. Through the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the contents of the specification, but must be determined by the scope of the claims.

Claims

1. A method for inverting three-dimensional wind fields based on coherent Doppler wind lidar, characterized in that, Includes the following steps: S1. Obtain the original power spectrum data of the wind measurement lidar to be inverted in each direction, and perform interpolation processing on the original power spectrum data of each direction in sequence to obtain the interpolated power spectrum data of each direction. S2, Set the vector three-dimensional wind field And utilize this vector three-dimensional wind field The cumulative power spectrum function is calculated by combining the interpolated power spectrum data from each direction in S1. ; S3. Utilize the cumulative power spectrum function in S2 The desired three-dimensional vector wind field is optimized to calculate the cumulative power spectral function. The maximum function value, and the three-dimensional wind field corresponding to the maximum function value, is the desired optimal three-dimensional wind field.

2. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 1, characterized in that, S1 includes the following steps: S1-1. Obtain the raw power spectrum data of the wind measurement lidar in eight directions to be inverted; S1-2, Perform inverse Fourier transform on the original power spectrum data in eight directions obtained from S1-1 respectively; S1-3. Perform time-domain zero-padding on the power spectrum data in eight directions after inverse Fourier transform according to the target interpolation factor. S1-4. Perform Fourier transform on the power spectrum data in eight directions after time-domain zero-padding to obtain the interpolated power spectrum data in eight directions.

3. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 1, characterized in that, S2 includes the following steps: S2-1, Setting the Vector Three-Dimensional Wind Field , Scan azimuth To calculate the radial velocity ; S2-2, Radial velocity Converted to Doppler frequency shift And obtain the Doppler frequency shift Corresponding frequency index ; S2-3. Set M azimuth angles and find the frequency index of the original power spectrum data for each azimuth angle. Corresponding spectral intensity value M azimuth angles correspond to M spectral intensity values Accumulate the sums to obtain the cumulative power spectral function. ; in: Indicates the first Azimuth angle Indicates the number of azimuth angles.

4. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 3, characterized in that, In S2-1, the radial velocity The expression is: ； in: This represents a unit vector along the direction of the laser beam. This indicates the elevation angle measured by the lidar.

5. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 3, characterized in that, In S2-2, the Doppler frequency shift The expression is: ； Frequency Index The expression is: ； in: This indicates the laser emission wavelength of the wind-measuring lidar to be retrieved. This represents the index of the spectrum after rounding. This indicates the intermediate frequency of the wind-measuring lidar to be retrieved. This indicates the spectral resolution after interpolation.

6. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 3, characterized in that, In S2-3, the cumulative power spectral function The expression is: 。 7. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 1, characterized in that, In S3, the optimization process refers to: In three-dimensional wind speed space The search is performed using the cumulative power spectrum function. The maximum function value obtained is the three-dimensional wind field corresponding to the maximum function value, which is the desired optimal three-dimensional wind field.

8. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 7, characterized in that, In S3, the optimal wind speed vector Corresponding cumulative power spectral function value The expression is: 。 9. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 7, characterized in that, In S3, in the three-dimensional wind speed space The process of conducting a search includes the following steps: S3-1. Initialization: In the search space of the three-dimensional wind field, a group of particles is randomly generated to obtain the initial position of each particle. Initial velocity Calculate the initial position of each particle Corresponding objective function value Each particle's current position is initialized to its own historical best position. Then the global optimal position of the entire group Initialized to the objective function value among all particles The position of the particle corresponding to the maximum value; S3-2, Iterative Update: In each iteration, each particle... The updated velocity is obtained by using the velocity update formula. The updated position is obtained by using the position update formula. ; S3-3, Update and Optimal Value Evaluation: Calculate the value for each particle. Updated location Corresponding objective function value If the updated objective function value Greater than the individual's historical best position Corresponding objective function value Then update the individual optimal value; if the updated objective function value... Greater than the global historical best position The corresponding objective function value Then the global optimal position of the entire group will be determined. Updated to the updated location ; S3-4. Repeat S3-2 and S3-3 until the global optimum position of the entire population is reached. objective function value Convergence occurs when the global optimum of the entire population is reached. That is, the globally optimal wind speed vector ; in: This represents the individual's historical best position. This represents the globally optimal position of the entire group.

10. The method for inverting three-dimensional wind fields based on coherent Doppler wind lidar according to claim 9, characterized in that, In S3-2, the expression for the velocity update formula is: ； The expression for the position update formula is: ； in: Represents particles The speed in the next moment, Indicates inertia weight, Represents particles The speed at the current moment, Represents individual learning factors. , Represents two random numbers uniformly distributed between [0, 1]. Represents the group learning factor. This represents the optimal position of an individual. This represents the globally optimal position of the entire group. Represents particles The position at the next moment, Represents particles Current position.

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