A LiDAR Wind Field Inversion Method Based on Filtered Velocity-Volume Processing Algorithm

Abstract

This disclosure provides a lidar wind field inversion method based on a filtered velocity-volume processing algorithm, applicable to the field of lidar technology. The method includes acquiring basic observation data for each voxel in the lidar wind field inversion. This basic data includes the signal-to-noise ratio (SNR) at each observation point within the voxel, used to calculate the average SNR within the voxel. When the average SNR is greater than or equal to a preset SNR switching threshold, the three-dimensional wind vector within the voxel is inverted based on the velocity-volume processing algorithm; otherwise, the three-dimensional wind vector is inverted based on a preset filtered velocity-volume processing algorithm. In this way, the velocity-volume processing algorithm can be used to invert the three-dimensional wind vector from near-field wind field data with a high SNR to improve computational efficiency. In the far-field, when the SNR drops to the preset SNR switching threshold, the three-dimensional wind vector is inverted based on the preset filtered velocity-volume processing algorithm, thereby improving the accuracy of lidar wind field inversion.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and more particularly to the field of lidar technology, specifically to a lidar wind field inversion method based on a filtered velocity-volume processing algorithm. Background Technology

[0002] In the Volume Velocity Processing (VVP) algorithm for retrieving three-dimensional wind fields from radial wind speed data from a single radar, the least squares method is used to solve an overdetermined linear equation set for the data points within the analysis volume element. The goal is to find a set of wind field component values ​​that minimizes the sum of squared residuals between the calculated and actual observed radial data values ​​for all observation points. In other words, the optimization objective function constructed by the least squares method is the sum of squared residuals of the radial wind speed.

[0003] However, numerical simulation analysis shows that the wind field inversion error of the least squares method based on this optimization objective function increases continuously when the signal-to-noise ratio (SNR) is small and continuously decreasing. This is because as the SNR decreases, the narrowband carrier-to-noise ratio (CNR) also decreases. Noise spikes, which are uniformly distributed across the signal power spectrum, are more likely to exceed the peak value of the true Doppler signal (which has a Gaussian spectral shape) and be incorrectly identified as signal peaks by the wind speed estimation algorithm, resulting in an incorrect estimate of the radial wind speed. This incorrect estimate is called a "bad estimate." The smaller the SNR, the higher the proportion of bad estimates of radial wind speed within the analysis voxel, and the larger the wind field error retrieved by the least squares method. This is a shortcoming of the least squares inversion method based on the sum of squared residuals as the optimization objective function. Summary of the Invention

[0004] This disclosure provides a lidar wind field inversion method, apparatus, device, and storage medium based on a filtered velocity-volume processing algorithm.

[0005] According to a first aspect of this disclosure, a lidar wind field inversion method based on a filtered velocity-volume processing algorithm is provided. The method includes:

[0006] Acquire the observation data base data of each volume element for lidar wind field inversion, the observation data base data including the signal-to-noise ratio at each observation point within the volume element, used to calculate the average signal-to-noise ratio within the volume element;

[0007] When the average signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio switching threshold, the three-dimensional wind vector within the volume element is inverted based on the velocity-volume processing algorithm.

[0008] When the average signal-to-noise ratio is less than the preset signal-to-noise ratio switching threshold, the three-dimensional wind vector within the volume element is inverted based on the preset filtering velocity-volume processing algorithm.

[0009] As described above and in any possible implementation, a further implementation is provided, wherein the inversion of the three-dimensional wind vector within the volume element based on the preset filtration velocity-volume processing algorithm includes:

[0010] The three-dimensional wind vector within the volume element is inverted based on the preset filtering velocity volume processing algorithm, and the proportion of bad radial wind speed estimates is calculated simultaneously. The inversion of the wind field in the current radial direction is terminated when the proportion of bad radial wind speed estimates within the volume element is greater than or equal to the preset bad estimate proportion threshold.

[0011] As described above and in any possible implementation, a further implementation is provided in which the optimization objective function constructed in the preset filtering speed volume processing algorithm includes:

[0012]

[0013] Among them, Q FVVO (V) represents the optimization objective function derived within the volume element based on the preset filtering velocity volume processing algorithm, and u represents the optimization variable matrix with 12 components. i This represents the three-dimensional wind vector to be inverted within the volume element. The measured radial wind speed at each observation point within the volume element is represented by s. i σ represents the radial azimuth vector corresponding to each observation point within the voxel. V This represents the standard deviation spectral width of the Doppler signal peak on the power spectrum of the echo signal.

[0014] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the process of determining the preset signal-to-noise ratio switching threshold includes:

[0015] A velocity-volume processing algorithm inversion error model is constructed using numerical simulation algorithms to determine the first preset error threshold.

[0016] Based on the first preset error threshold, a preset signal-to-noise ratio switching threshold is determined;

[0017] The velocity-volume processing algorithm inversion error model includes:

[0018] E1 = f1(SNR, other configuration parameters);

[0019] B1 = f2(SNR, other configuration parameters);

[0020] Where E1 represents the root mean square error of the inverted wind speed in the velocity-volume processing algorithm inversion error model, B1 represents the systematic bias of the inverted wind speed in the velocity-volume processing algorithm inversion error model, and SNR represents the signal-to-noise ratio; other configuration parameters include equipment operating parameters, atmospheric state parameters, and algorithm configuration parameters; the velocity-volume processing algorithm is applied within the volume element to invert the three-dimensional wind vector.

[0021] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the process of determining the preset bad estimation percentage threshold includes:

[0022] A numerical simulation algorithm is used to construct an inversion error model for the filtration velocity-volume processing algorithm, and a second preset error threshold is determined.

[0023] Based on the second preset error threshold, a preset bad estimation percentage threshold is determined;

[0024] The inversion error model of the filtration velocity-volume processing algorithm includes:

[0025] E2 = f3(b, other configuration parameters);

[0026] B2 = f4(b, other configuration parameters);

[0027] Where E2 represents the root mean square error of the inverted wind speed in the inversion error model of the filtration velocity-volume processing algorithm, B2 represents the systematic deviation of the inverted wind speed in the inversion error model of the filtration velocity-volume processing algorithm, and b represents the proportion of bad estimates of radial wind speed within the volume element; other configuration parameters include equipment operating parameters, atmospheric state parameters, and algorithm configuration parameters; the filtration velocity-volume processing algorithm is applied within the volume element to invert the three-dimensional wind vector.

[0028] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the calculation process for the estimated proportion of radial wind speed damage within the volume element includes:

[0029]

[0030] Where b represents the proportion of radial wind speed damage within the volume element. This represents the measured radial wind speed at each observation point within the volume element. Represents the inverted three-dimensional wind vector Radial wind speeds decomposed along the laser beam direction at each observation point within the volume element. Indicates that the volume element satisfies The number of observation data points under the given conditions, N total σ represents the total number of observation data points within a voxel. V It represents the standard deviation spectral width of the signal peak on the signal power spectrum.

[0031] In addition to the aspects described above and any possible implementations, a further implementation is provided, wherein the method further includes:

[0032] When inverting the wind field within a volume element using the preset filtering velocity-volume processing algorithm, the search interval is defined with the inverted three-dimensional wind vector of the previous distance library in the radial direction as the center, and the three-dimensional wind vector within the volume element is inverted only within the search interval; the width of the search area is determined by calculating the difference values ​​of the inverted three-dimensional wind vector components of all adjacent distance libraries within the radial distance of the current observation point to be inverted, and then multiplying the maximum value of its absolute value by the magnification factor.

[0033] According to a second aspect of this disclosure, a lidar wind field inversion device based on a filtered velocity-volume processing algorithm is provided. The device includes:

[0034] The acquisition module is used to acquire the observation data base data of each volume element in the lidar wind field inversion. The observation data base data includes the signal-to-noise ratio at each observation point within the volume element, and is used to calculate the average signal-to-noise ratio within the volume element.

[0035] The inversion module is used to invert the three-dimensional wind vector within the volume element based on the velocity-volume processing algorithm when the average signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio switching threshold.

[0036] The inversion module is also used to invert the three-dimensional wind vector within the volume element based on a preset filtering velocity-volume processing algorithm when the average signal-to-noise ratio is less than the preset signal-to-noise ratio switching threshold.

[0037] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0038] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described above.

[0039] This application provides a lidar wind field inversion method based on a filtered velocity-volume processing algorithm. This method acquires the observational data of each volume element within the lidar wind field inversion. The observational data includes the signal-to-noise ratio (SNR) at each observation point within the volume element, used to calculate the average SNR within the volume element. When the average SNR is greater than or equal to a preset SNR switching threshold, the three-dimensional wind vector within the volume element is inverted based on the velocity-volume processing algorithm. When the average SNR is less than the preset SNR switching threshold, the three-dimensional wind vector within the volume element is inverted based on a preset filtered velocity-volume processing algorithm. Therefore, for near-field wind field data with high SNR, the velocity-volume processing algorithm based on the least squares method can be used to invert the near-field three-dimensional wind vector to improve computational efficiency. In the far field, when the SNR drops to the preset SNR switching threshold, the far-field three-dimensional wind vector is inverted based on the preset filtered velocity-volume processing algorithm. This solves the core problem of low SNR wind field inversion and improves the accuracy of lidar wind field inversion.

[0040] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0041] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0042] Figure 1 A flowchart of a lidar wind field inversion method based on a filtered velocity-volume processing algorithm according to an embodiment of the present disclosure is shown;

[0043] Figure 2 A schematic diagram of the data point distribution within a planar sector analysis volume element according to an embodiment of the present disclosure is shown;

[0044] Figure 3 A schematic diagram illustrating the dynamic calculation of the local search space in a filtering velocity volume processing algorithm according to an embodiment of the present disclosure is shown.

[0045] Figure 4 A block diagram of a lidar wind field inversion apparatus based on a filtered velocity-volume processing algorithm according to an embodiment of the present disclosure is shown.

[0046] Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0048] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0049] In this disclosure, near-field three-dimensional wind vectors can be inverted using a velocity-volume processing algorithm based on the least squares method to improve computational efficiency for near-field wind field data with high signal-to-noise ratio. In the far field, when the signal-to-noise ratio drops to a preset signal-to-noise ratio switching threshold, far-field three-dimensional wind vectors are inverted based on a preset filtered velocity-volume processing algorithm, thereby solving the core pain point of low signal-to-noise ratio wind field inversion and improving the accuracy of lidar wind field inversion.

[0050] It should be noted that this disclosure also specifically relates to the fields of atmospheric detection and wind field measurement technology, and to data processing methods for coherent Doppler wind lidar, particularly the three-dimensional wind vector inversion algorithm, which improves upon the shortcomings of the VVP algorithm under low signal-to-noise ratio conditions.

[0051] Figure 1 A flowchart of a lidar wind field inversion method 100 based on a filtered velocity-volume processing algorithm according to an embodiment of the present disclosure is shown.

[0052] In box 110, the observation data of each volume element in the lidar wind field inversion is obtained. The observation data includes the signal-to-noise ratio at each observation point within the volume element, which is used to calculate the average signal-to-noise ratio within the volume element.

[0053] In some embodiments, the observational base data for lidar wind field inversion may include a common database and radial data blocks. The common data blocks are used to provide common information such as data station information and task configuration, while the radial data blocks are used to store lidar detection data and include three sub-blocks: radial head, radial data head, and radial data.

[0054] In some embodiments, each volume element in the lidar wind field inversion, i.e. the analysis volume element (or surface element) of the lidar wind field inversion, includes position, shape and size, and can be divided according to user needs to obtain the defined three-dimensional wind field inversion analysis volume element.

[0055] For example, by elevation angle The azimuth angle θ and radial distance R determine the position of the center point of the analysis volume element; in addition, the azimuth angle range Δθ and the pitch angle range of the volume element are also included. The size and shape of the analyte are determined by six parameters in total, including the radial shape adjustment factor 'a'. These parameters are then combined with the radial distance resolution δR, the azimuth resolution of the baseline data δθ, and the elevation resolution of the baseline data. These three parameters determine the distribution location and number of observation data points within the analysis voxel. The analysis voxels defined above can be represented as follows: Figure 2 As shown.

[0056] In some embodiments, based on the predefined volume elements, the observation data of each volume element for lidar wind field inversion can be obtained by inputting the observation data base data from the plan position indicator (PPI) of the wind-measuring lidar.

[0057] The baseline data for each volume element may include radial wind speed V recorded in polar coordinates at different elevation and azimuth angles. r Parameters such as spectral width, spectral intensity, and signal-to-noise ratio (SNR).

[0058] In some embodiments, the average signal-to-noise ratio can also be calculated based on the observation data of each voxel, such as by averaging the signal-to-noise ratio data of all observation points within the voxel.

[0059] In some embodiments, when performing lidar wind field inversion, a specific strategy can be used to search for each volume element starting from the near field. Based on an adaptive algorithm switching mechanism, the inversion algorithm corresponding to each volume element is selected to invert the three-dimensional wind vector within each volume element according to the average signal-to-noise ratio within the volume element and a preset signal-to-noise ratio switching threshold.

[0060] In box 120, when the average signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio switching threshold, the three-dimensional wind vector within the volume element is inverted based on the velocity-volume processing algorithm.

[0061] In some embodiments, the VVP algorithm still uses the least squares method, and its optimization objective function is the sum of squared residuals of radial wind speed. The least squares method aims to minimize the value of the sum of squared residuals as the optimization objective function.

[0062] In some embodiments, the VVP algorithm is used to invert the three-dimensional wind vector within the volume element, which is specifically designed for near-field wind field data with a high signal-to-noise ratio, thereby improving computational efficiency.

[0063] In some embodiments, the optimization objective function used by the VVP algorithm includes:

[0064]

[0065] Among them, Q VVP (V) represents the objective function of the VVP algorithm inversion, where V represents the three-dimensional wind vector to be inverted within the volume element. The measured radial wind speed at each observation point within the volume element is represented by s. i This represents the radial azimuth vector corresponding to each observation point within the voxel. This represents the optimal estimate of the three-dimensional wind vector inverted within the voxel based on the VVP algorithm.

[0066] In some embodiments, based on optimal estimation Requirements must be met The solution to its optimization problem can be

[0067] In some embodiments, the above-mentioned three-dimensional wind vector inversion results based on the least squares method can be converted into finding the inverse matrix of the matrix, which can be solved by an efficient and mature numerical method.

[0068] In box 130, when the average signal-to-noise ratio is less than the preset signal-to-noise ratio switching threshold, the three-dimensional wind vector within the volume element is inverted based on the preset filtering velocity volume processing algorithm.

[0069] In some embodiments, a preset filtered volume velocity processing (FVVP) algorithm can be constructed by optimizing the objective function reconstruction.

[0070] In some embodiments, the optimization objective function constructed in the above-mentioned preset filtration velocity-volume processing algorithm includes:

[0071]

[0072] In the formula, u≡[u0,v0,w0,u′] x ,u′ y ,u′ z ,v x ′,v y ′,v z ′,w x ′,w y ′,w z ′] T The optimization variable matrix has 12 components, where V0≡[u0,v0,w0]. T Let u′ be the wind vector at the center point (x0, y0, z0) of the volume element. x ,u′ y The nine parameters, including , ..., are shear parameters, defined as follows: …; The radial azimuth vector is the corresponding radial direction vector for each observation point within the voxel. θ represents the angle of elevation. i Indicates azimuth; σ represents the measured radial wind speed at each observation point within the volume element; V V is the spectral width of the Doppler signal peak standard deviation on the power spectrum of the echo signal; i Let be the wind field vector to be inverted at each observation point within the volume element. Under the linear wind field assumption, its expression is:

[0073]

[0074] In the formula, (x i ,y i ,z i ) represents the coordinates of each observation data point within the voxel.

[0075] Wind field inversion aims to find the optimal estimate. Requirements must be met: This is a nonlinear optimization problem with 12 variables.

[0076] When a uniform wind field assumption is adopted, all shear parameters are zero, and the optimization objective function simplifies to: In the formula, the optimization variable V≡[u,v,w] T Let be the average wind vector within the volume element. The wind field inversion problem simplifies to the following three-variable nonlinear optimization problem:

[0077] In other words, under the general form (i.e., non-uniform wind field), the optimization objective function for a uniform wind field can be as follows:

[0078]

[0079] Among them, Q FVVP (V) represents the optimization objective function of the preset filtration velocity-volume processing algorithm inversion; V represents the three-dimensional wind vector to be inverted within the volume element, V=[u,v,w] T ; This represents the measured radial wind speed at each observation point within the volume element; s i This represents the radial azimuth vector corresponding to each observation point within the voxel. θ represents the angle of elevation. i Indicates azimuth angle; σ V This represents the spectral width of the Doppler signal peak standard deviation on the power spectrum of the echo signal; This represents the optimal estimate of the three-dimensional wind vector within the volume element, derived from a preset filtering velocity-volume processing algorithm.

[0080] It should be noted that the novel inversion method disclosed herein, namely the FVVP algorithm, does not use the sum of squared residuals as its objective function, but rather a sum of Gaussian functions. This objective function is derived from the maximum likelihood estimation of the wind field vector using a Gaussian probability model under low signal-to-noise ratio conditions. In other words, the objective function is reconstructed from a sum of squared residuals to a sum of squared Gaussian functions based on maximum likelihood estimation, which is the core breakthrough of the FVVP algorithm. During wind field inversion, the search range of the three wind field components is first determined, then the values ​​of each component of the objective function are calculated separately. Finally, the combination of wind field components corresponding to the maximum value of the objective function is the inversion result of this method. This differs from the aforementioned least squares method, which aims to minimize the sum of squared residuals. The inversion method based on this new objective function is called the FVVP algorithm. Because it uses Gaussian functions, the FVVP algorithm is highly sensitive to the residuals at each data point, making the influence of poorly estimated data points on the objective function extremely small. This is a natural bad data filtering function. Therefore, this makes the FVVP algorithm extremely tolerant of the proportion of bad estimates. When the signal-to-noise ratio is very low and the proportion of bad estimates is high, the FVVP algorithm can still invert the wind field vector with high accuracy, while the aforementioned least squares inversion error has long become too large to be used.

[0081] Of course, the FVVP algorithm also has drawbacks compared to the aforementioned least squares method. For example, at high signal-to-noise ratios, both algorithms can achieve the same results, but the aforementioned least squares method, in solving overdetermined linear equations, ultimately reduces the computation to solving the inverse of the coefficient matrix, for which there are mature and efficient numerical algorithms to improve computational efficiency. However, due to the nonlinearity of the objective function being optimized, the FVVP algorithm can only use a search algorithm to find the optimal solution, resulting in low computational efficiency and high time consumption.

[0082] Based on this, in this disclosure, the VVP algorithm based on the least squares method is used to improve the computational efficiency for inverting near-field wind field data with high signal-to-noise ratio; in the far field, when the SNR drops to a certain threshold, i.e., the preset signal-to-noise ratio switching threshold, the FVVP algorithm is used to invert the three-dimensional wind field.

[0083] According to the embodiments of this disclosure, the following technical effects are achieved:

[0084] This system can acquire the observational data of each volume element in lidar wind field inversion. The observational data includes the signal-to-noise ratio (SNR) at each observation point within the volume element, used to calculate the average SNR within the volume element. When the average SNR is greater than or equal to a preset SNR switching threshold, the three-dimensional wind vector within the volume element is inverted based on a velocity-volume processing algorithm. When the average SNR is less than the preset SNR switching threshold, the three-dimensional wind vector within the volume element is inverted based on a preset filtered velocity-volume processing algorithm. Based on this, near-field wind field data with high SNR can be inverted using a velocity-volume processing algorithm based on the least squares method to improve computational efficiency. In the far field, when the SNR drops to the preset SNR switching threshold, the far-field three-dimensional wind vector is inverted based on a preset filtered velocity-volume processing algorithm. This solves the core problem of low SNR wind field inversion, improves the accuracy of lidar wind field inversion, and ultimately increases the maximum detection range of the lidar.

[0085] In some embodiments, the process of determining the preset signal-to-noise ratio switching threshold includes:

[0086] A VVP algorithm inversion error model based on the least squares method is constructed using numerical simulation algorithms to determine the first preset error threshold.

[0087] Based on the first preset error threshold, a preset signal-to-noise ratio switching threshold is determined;

[0088] The least squares inversion error model includes:

[0089] E1 = f1(SNR, other configuration parameters);

[0090] B1 = f2(SNR, other configuration parameters);

[0091] Where E1 represents the root mean square error (RMSE) of the inverted wind speed in the VVP algorithm inversion error model based on the least squares method, in m / s, including both systematic bias and random error; B1 represents the systematic bias of the inverted wind speed in the VVP algorithm inversion error model based on the least squares method, in m / s; SNR represents the signal-to-noise ratio; other configuration parameters include equipment operating parameters, atmospheric state parameters, and algorithm configuration parameters, such as laser wavelength λ and elevation angle. Azimuth θ, radial distance R, radial distance resolution δR, azimuth resolution of basic data δθ, elevation resolution of basic data Volume element azimuth range Δθ, volume element pitch range Radial shape adjustment factor a, pulse accumulation count N c Sampling frequency f s FFT point count M, spectral bandwidth B v Horizontal wind speed search range Sx, Sy; vertical airflow velocity search range Sz; signal spectrum standard deviation width σ vWait; the VVP algorithm based on the least squares method is applied within the volume element to invert the three-dimensional wind vector.

[0092] In some embodiments, the preset signal-to-noise ratio switching threshold can be determined according to the user's actual needs.

[0093] In some embodiments, a preset signal-to-noise ratio switching threshold can be used as a switching node for the adaptive algorithm switching mechanism in a lidar wind field inversion method based on a filtered velocity-volume processing algorithm.

[0094] In some embodiments, the first preset error threshold can be determined according to the user's actual needs, such as setting RMSE < 1m / s, thereby determining the preset signal-to-noise ratio (SNR) switching threshold based on the first preset error threshold. th .

[0095] It should be noted that in the lidar wind field inversion method based on the filtered velocity-volume processing algorithm, the SNR threshold for algorithm switching, i.e., SNR... th Determining the correct SNR is crucial. Therefore, it is necessary to study the relationship between the inversion accuracy and the changes in SNR and other parameters of the VVP algorithm inversion method based on the least squares method, so as to determine the SNR for switching this algorithm based on the inversion error requirement. th While the adaptive algorithm switching mechanism is clear and simple to operate, obtaining the relationship between the inversion error and SNR of the least squares-based VVP algorithm is technically challenging. There is no simple analytical expression between the two; only numerical simulation algorithms, such as the Monte Carlo method, can yield the statistical numerical relationship. Furthermore, a considerable number of other configuration parameters are involved. The signal-to-noise ratio (SNR) is singled out because it is measurable and can serve as a characteristic parameter representing changes in the inversion error.

[0096] In some embodiments, after constructing the VVP algorithm inversion error model based on the least squares method, the relationship between error and SNR can be obtained through numerical simulation algorithms so that the algorithm switching threshold can be determined by studying the error-SNR relationship in the adaptive switching mechanism.

[0097] To facilitate understanding, the following will briefly describe the process of establishing the inversion error model of the VVP algorithm based on the least squares method and solving the relationship between inversion error and SNR.

[0098] The input parameters for establishing the VVP algorithm inversion error model based on the least squares method can include: signal-to-noise ratio (SNR), laser wavelength (λ), and elevation angle. Azimuth θ, radial distance R, radial distance resolution δR, azimuth resolution of basic data δθ, elevation resolution of basic data Volume element azimuth range Δθ, volume element pitch range Radial shape adjustment factor a, pulse accumulation count N c Sampling frequency f s FFT point count M, spectral bandwidth B v Horizontal wind speed search range Sx, Sy; vertical airflow velocity search range Sz; signal spectrum standard deviation width σ V And the true value of the simulated three-dimensional wind field [u t ,v t ,w t ]etc;

[0099] Step 1: Define the volume elements, i.e., their position, shape, and size;

[0100] Step 2: Decompose the true value of the simulated 3D wind field into the radial velocity of each data point within the volume element to obtain the true radial wind velocity at the observed data point, i.e.

[0101] Step 3: Determine the theoretical power spectrum ensemble average at each data point within the voxel;

[0102] Among them, the noise floor normalized Gaussian power spectrum model is adopted:

[0103] Wherein, CNR represents the narrowband carrier-to-noise ratio, and its relationship with SNR is: ΔV is the spectral interval expressed as velocity. k is the spectrum library number, and its value can be k = 0, ..., n bins n bins Let n be the number of spectrum libraries, satisfying n bins =B V / ΔV, σ V The width of the standard deviation of the signal power spectrum expressed in terms of velocity;

[0104] Step 4: Generate random power spectra at each data point using gamma-distributed random numbers;

[0105] Among them, the power spectrum of the additive Gaussian random signal follows a gamma distribution. By using the ensemble average spectrum from step three and the corresponding random number generator, the random spectrum of the power spectrum of the simulated real atmospheric echo signal can be obtained.

[0106] Step 5: For the random spectrum at each data point, use the Levin maximum likelihood estimation method (standard method) to obtain the radial wind speed estimate and SNR estimate. The radial wind speed estimate obtained here is the simulation value used to simulate the radial wind speed baseline data of the lidar.

[0107] Step 6: Apply the VVP algorithm within the voxel to invert the 3D wind field and obtain its estimate.

[0108] Step 7: Repeat steps 4 to 6 n times to obtain n sets of independent wind field estimation samples [u i v i w i ] Calculate the corresponding root mean square error E and systematic deviation B;

[0109] Specifically, the root mean square error of horizontal wind speed Horizontal wind speed system deviation Root mean square error of vertical airflow velocity Vertical airflow velocity system deviation

[0110] In some embodiments, the above-mentioned inversion of the three-dimensional wind vector within the volume element based on the preset filtration velocity volume processing algorithm includes:

[0111] The three-dimensional wind vector within the volume element is inverted based on the preset filtering velocity volume processing algorithm, and the proportion of bad radial wind speed estimates is calculated simultaneously. The inversion of the wind field in the current radial direction is terminated when the proportion of bad radial wind speed estimates within the volume element is greater than or equal to the preset bad estimate proportion threshold.

[0112] It should be noted that at low SNR, the FVVP algorithm significantly improves the accuracy of wind field inversion and increases the detection range. However, determining the maximum detection range is a technical challenge. Therefore, it is necessary to study the relationship between wind speed inversion error and a certain measurable characteristic parameter during FVVP inversion, and to determine the threshold of this characteristic parameter based on the inversion error requirements. At high signal-to-noise ratios, SNR can be chosen as this characteristic parameter, which relates to the aforementioned determination of the SNR threshold for algorithm switching. However, at extremely low signal-to-noise ratios, as the proportion of bad estimates (b) increases, the estimated SNR also becomes incorrect. Therefore, when determining the maximum inversion distance or height for the FVVP algorithm, SNR is not an ideal parameter because it cannot be measured correctly. The proportion of bad estimates (b) is a suitable parameter, which can be used to evaluate whether each radial wind speed data point is a good or bad estimate using the FVVP inversion results, thereby calculating the proportion.

[0113] Therefore, by studying the relationship between the wind field inversion error of the FVVP algorithm, the proportion of bad estimates (b), and other parameters through numerical simulation, a threshold for the proportion of bad estimates (b) can be determined based on the inversion error requirements. th This allows control over the maximum distance or maximum height of FVVP inversion.

[0114] In some embodiments, the process of determining the aforementioned preset bad estimation percentage threshold includes:

[0115] A numerical simulation algorithm is used to construct an inversion error model for the filtration velocity-volume processing method, and a second preset error threshold is determined.

[0116] Based on the second preset error threshold, determine the preset bad estimation percentage threshold;

[0117] The inversion error model for the filtration velocity-volume processing method includes:

[0118] E2 = f3(b, other configuration parameters);

[0119] B2 = f4(b, other configuration parameters);

[0120] Where E2 represents the root mean square error of the inverted wind speed in the FVVP algorithm inversion error model, B2 represents the systematic bias of the inverted wind speed in the FVVP algorithm inversion error model, and b represents the proportion of bad estimates of radial wind speed within the volume element; other configuration parameters may include equipment operating parameters, atmospheric state parameters, and algorithm configuration parameters, such as signal-to-noise ratio (SNR), laser wavelength (λ), and elevation angle. Azimuth θ, radial distance R, radial distance resolution δR, azimuth resolution of basic data δθ, elevation resolution of basic data Volume element azimuth range Δθ, volume element pitch range Radial shape adjustment factor a, pulse accumulation count N c Sampling frequency f s FFT point count M, spectral bandwidth B v Horizontal wind speed search range Sx, Sy; vertical airflow velocity search range Sz; signal power spectrum standard deviation width σ V And the true value of the simulated three-dimensional wind field [u t ,v t ,w t [etc.; the FVVP algorithm is applied within the volume element to invert the three-dimensional wind vector.]

[0121] In some embodiments, the preset bad estimation percentage threshold can be determined based on the user's actual needs.

[0122] In some embodiments, the second preset error threshold can be determined according to the user's actual needs, such as setting it to 2 m / s, thereby determining the preset bad estimation ratio threshold b based on the second preset error threshold. th .

[0123] In some embodiments, the criterion for determining the proportion of radial wind speed failure estimation can be set as follows: in, This represents the measured radial wind speed at each observation point within the volume element. Represents the inverted three-dimensional wind vector The radial wind speed decomposed along the laser beam direction at each observation point within the volume element, σ V This indicates the width of the standard deviation of the signal power spectrum.

[0124] In some embodiments, the calculation process for the estimated proportion of radial wind speed damage within the above-mentioned volume element includes:

[0125]

[0126] Where b represents the proportion of radial wind speed damage within the volume element. This represents the measured radial wind speed at each observation point within the volume element. Represents the inverted three-dimensional wind vector The radial wind speed decomposed along the laser beam direction at each observation point within the volume element, i.e. The superscript "cal" means "calculated". Indicates that the volume element satisfies The number of observation data points under the given conditions, i.e., the number of bad estimates, N total σ represents the total number of observation data points within a voxel. V This indicates the width of the standard deviation of the signal power spectrum.

[0127] In some embodiments, after constructing the FVVP algorithm inversion error model, the relationship between the FVVP algorithm inversion error and the bad estimation ratio b can be obtained through numerical simulation algorithm, so as to use the bad point ratio as a new criterion, that is, when the bad estimation ratio exceeds the set bad estimation ratio threshold, the inversion is terminated, thus solving the problem that SNR cannot be accurately measured when the SNR is extremely low.

[0128] In some embodiments, establishing the relationship between the inversion error of the FVVP algorithm and the proportion of bad estimates is particularly important. The operation steps are basically the same as the construction and solution of the VVP algorithm inversion error model based on the least squares method described above, except that step six is ​​to replace the VVP algorithm with the FVVP algorithm.

[0129] To facilitate understanding, the following will briefly describe the establishment of the inversion error model for the filtration velocity-volume treatment method and the solution process for the inversion error-b relationship.

[0130] The input parameters for establishing the inversion error model of the filtration velocity-volume processing method can include: signal-to-noise ratio (SNR), laser wavelength (λ), and elevation angle. Azimuth θ, radial distance R, radial distance resolution δR, azimuth resolution of basic data δθ, elevation resolution of basic data Volume element azimuth range Δθ, volume element pitch range Radial shape adjustment factor a, pulse accumulation count N c Sampling frequency f s FFT point count M, spectral bandwidth B v Horizontal wind speed search range Sx, Sy; vertical airflow velocity search range Sz; signal spectrum standard deviation width σ v And the true value of the simulated three-dimensional wind field [u t ,vt ,w t ]etc;

[0131] Step 1: Define the volume elements, i.e., their position, shape, and size;

[0132] Step 2: Decompose the true value of the simulated 3D wind field into the radial velocity of each data point within the volume element to obtain the true radial wind velocity at the observed data point, i.e.

[0133] Step 3: Determine the theoretical power spectrum ensemble average at each data point within the voxel;

[0134] The following noise-base normalized Gaussian power spectrum model is adopted:

[0135] Wherein, CNR represents the narrowband carrier-to-noise ratio, and its relationship with SNR is: ΔV is the spectral interval expressed as velocity. k is the spectrum library number, and its value can be k = 0, ..., n bins n bins Let n be the number of spectrum libraries, satisfying n bins =B V / ΔV, σ V The width of the standard deviation of the signal power spectrum expressed in terms of velocity;

[0136] Step 4: Generate random power spectra at each data point using gamma-distributed random numbers;

[0137] Among them, the power spectrum of the additive Gaussian random signal follows a gamma distribution. By using the ensemble average spectrum from step three and the corresponding random number generator, the random spectrum of the power spectrum of the simulated real atmospheric echo signal can be obtained.

[0138] Step 5: For the random spectrum at each data point, use the Levin maximum likelihood estimation method (standard method) to obtain the radial wind speed estimate and SNR estimate. The radial wind speed estimate obtained here is the simulation value used to simulate the radial wind speed baseline data of the lidar.

[0139] Step 6: Apply the FVVP algorithm within the voxel to invert the 3D wind field and obtain its estimate. And calculate the estimated proportion of radial wind speed damage within the volume element.

[0140] Step 7: Repeat steps 4 to 6 n times to obtain n sets of independent wind field estimation samples [u i v i w i ] and the proportion of bad estimates in sample b i Calculate the corresponding root mean square error E and systematic deviation B;

[0141] Specifically, the root mean square error of horizontal wind speed Horizontal wind speed system deviation Root mean square error of vertical airflow velocity Vertical airflow velocity system deviation

[0142] In some embodiments, the above method further includes:

[0143] In some embodiments, to further improve the performance of the FVVP algorithm, the global search FVVP algorithm can be modified into a dynamic local search FVVP algorithm to improve computational efficiency. This requires prior wind field information from adjacent high signal-to-noise ratio (SNR) endpoints, i.e., dynamic local search optimization, which uses prior wind field information to narrow the search range and improve efficiency. Therefore, this method not only improves the computational efficiency of FVVP but also eliminates the influence of noise peaks outside the computational search interval, further enhancing FVVP performance.

[0144] For example, wind-measuring lidar services require the ability to measure horizontal wind speeds from -50 m / s to +50 m / s and vertical airflow velocities from -20 m / s to +20 m / s. However, lidar is not actually used to measure strong convection, so we set the vertical airflow velocity search range to -3 m / s to +3 m / s. With an inversion velocity resolution of 0.1 m / s, if a global search algorithm were used, the objective function Q would need to be calculated over 100 * 100 * 6 = 60,000 velocity points. FVVP This is quite time-consuming, but using a dynamic local search algorithm to compress the search interval will greatly improve computational efficiency.

[0145] In some embodiments, when inverting the wind field within a volume element using the preset filtering velocity-volume processing algorithm, a search interval is defined centered on the inverted three-dimensional wind vector of the preceding distance library in the radial direction, and the three-dimensional wind vector within the volume element is inverted only within the search interval; the width of the search area is determined by calculating the difference values ​​of the inverted three-dimensional wind vector components of all adjacent distance libraries within the radial distance of the observation point to be inverted, and then multiplying the maximum absolute value by the magnification factor.

[0146] like Figure 3 As shown, the wind field retrieved from the first radial distance reservoir is: [u1,v1,w1] T ;

[0147] The second radial distance reservoir retrieves the wind field: [u2,v2,w2] T ;

[0148] The difference between the first and second radial distances inverted wind field: [u2-u1,v2-v1,w2-w1] T ;

[0149] The third radial distance reservoir retrieves the wind field: [u3,v3,w3] T ;

[0150] The third and second radial distances are used to retrieve the wind field difference: [u3-u2,v3-v2,w3-w2] T ;

[0151] ...

[0152] The inverted wind field at radial distance i-1: [u i-1 ,v i-1 ,w i-1 ] T ;

[0153] Difference between the (i-1)th and (i-2)th radial distances inverted wind field: [u i-1 -u i-2 ,v i-1 -v i-2 ,w i-1 -w i-2 ] T ;

[0154] If the FVVP algorithm is applied to the i-th radial distance library, then its wind speed search interval is: {[u i-1 -Sx,u i-1 +Sx],[v i-1 -Sy,v i-1 +Sy],[w i-1 -Sz,w i-1 +Sz]}, where Sx=ax×max{|u j+1 -u j |,j=1,…,i-2},Sy=ay×max{|v j+1 -v j |,j=1,…,i-2},Sz=az×max{|w j+1 -w j |,j=1,…,i-2};ax,ay,az are amplification factors.

[0155] In some embodiments, the preset proximity range value can be determined according to the user's actual needs.

[0156] In some embodiments, this disclosure also proposes another lidar wind field inversion method based on a filtered velocity-volume processing algorithm, including:

[0157] Input the basic data of wind-measuring lidar PPI observation data, which includes radial wind speed Vr and signal-to-noise ratio SNR;

[0158] Divide the three-dimensional wind field inversion analysis into volumetric elements, that is, determine the location, shape, and size of the analysis volumetric elements;

[0159] Calculate the average signal-to-noise ratio (SNR) for each analysis voxel. av

[0160] Wind field inversion starting from the near field, when the SNR of the current analysis volume element... av ≥SNR th At that time, the VVP algorithm is executed on the analysis voxel to invert the three-dimensional wind vector; when the SNR of the current analysis voxel... av <SNR th At that time, the FVVP algorithm is executed on the analysis voxel to invert the three-dimensional wind vector, and the proportion of radial wind speed bad estimation b within the analysis voxel is calculated simultaneously. When b > b th At this point, the inversion of the analytical volume element in the radial direction ends.

[0161] In summary, the SNR in this disclosure th and b th The determination process is also technically demanding, requiring numerical simulation and cannot be arbitrarily specified; based on SNR th and b th When the signal-to-noise ratio (SNR) decreases (far field), the FVVP algorithm can provide wind field inversion results with higher accuracy than the VVP algorithm. When the VVP algorithm is unusable due to excessive errors, combining it with the FVVP algorithm can further enhance the equipment's detection capability and allow for measurements at greater distances. This disclosure solves the core pain points of low SNR wind field inversion through three breakthroughs: innovative objective function, adaptive architecture design, and multi-threshold collaborative control.

[0162] It is important to emphasize that the key to this disclosure lies in the fact that the optimization objective function constructed using the Gaussian function inherently possesses the ability to filter bad estimation data, a capability lacking in the least squares method employed by the VVP algorithm. However, other approaches can be adopted to identify and eliminate this bad data. For instance, when estimating radial wind speed using spectral data, since noise spikes may be mistakenly identified as signals at low signal-to-noise ratios (SNR), instead of searching for the highest point across the entire spectral width as the signal peak location, a suitable spectral analysis interval is defined using the radial wind speed estimation results at high SNR in the near field. Signal identification and estimation are then performed within this local spectral interval, filtering out false signals outside the analysis interval, but not those inside. Alternatively, the different statistical characteristics of signal peaks and noise spikes can be used to filter out false signals. However, these methods all require direct processing of the echo signal power spectrum. Due to the massive amount of power spectrum data, it is generally not stored; only equipment manufacturers can ensure real-time performance by performing this process locally on the equipment. The FVVP algorithm disclosed herein processes the radial wind speed generated after the equipment's spectrum processing, rather than directly dealing with the power spectrum. It can be used by equipment manufacturers and meteorological departments, making it more widely applicable.

[0163] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0164] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0165] Figure 4 A block diagram of a lidar wind field inversion device 400 based on a filtered velocity-volume processing algorithm according to an embodiment of the present disclosure is shown. Figure 4 As shown, the device 400 includes:

[0166] The acquisition module 410 is used to acquire the observation data base data of each volume element in the lidar wind field inversion. The observation data base data includes the signal-to-noise ratio at each observation point within the volume element, which is used to calculate the average signal-to-noise ratio within the volume element.

[0167] Inversion module 420 is used to invert the three-dimensional wind vector within the volume element based on the velocity-volume processing algorithm when the average signal-to-noise ratio is greater than or equal to a preset signal-to-noise ratio switching threshold.

[0168] The inversion module 420 is also used to invert the three-dimensional wind vector within the volume element based on a preset filtering velocity volume processing algorithm when the average signal-to-noise ratio is less than a preset signal-to-noise ratio switching threshold.

[0169] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0170] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0171] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0172] Figure 5A block diagram of an exemplary electronic device 500 capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0173] Electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in ROM 502 or a computer program loaded into RAM 503 from storage unit 508. RAM 503 can also store various programs and data required for the operation of electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O interface 505 is also connected to bus 504.

[0174] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0175] Computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 501 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508.

[0176] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, computing unit 501 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0177] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0178] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0179] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0180] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0181] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0182] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0183] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0184] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A lidar wind field inversion method based on a filtered velocity-volume processing algorithm, characterized in that, include: Acquire the observation data base data of each volume element for lidar wind field inversion, the observation data base data including the signal-to-noise ratio at each observation point within the volume element, used to calculate the average signal-to-noise ratio within the volume element; When the average signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio switching threshold, the three-dimensional wind vector within the volume element is inverted based on the velocity-volume processing algorithm. When the average signal-to-noise ratio is less than the preset signal-to-noise ratio switching threshold, the three-dimensional wind vector within the volume element is inverted based on the preset filtering velocity-volume processing algorithm. The method of inverting the three-dimensional wind vector within the volume element based on the preset filtering velocity volume processing algorithm includes: inverting the three-dimensional wind vector within the volume element based on the preset filtering velocity volume processing algorithm, and simultaneously calculating the proportion of bad radial wind speed estimates, until the proportion of bad radial wind speed estimates within the volume element is greater than or equal to the preset bad estimate proportion threshold, at which point the inversion of the wind field in the current radial direction is terminated. The optimization objective function constructed in the preset filtering speed-volume processing algorithm includes: in, This represents the optimization objective function derived within the voxel based on a preset filtering velocity-volume processing algorithm. This represents the optimization variable matrix with 12 components. This represents the three-dimensional wind vector to be inverted within the volume element. This represents the measured radial wind speed at each observation point within the volume element. This represents the radial azimuth vector corresponding to each observation point within the voxel. This represents the spectral width of the Doppler signal peak standard deviation on the power spectrum of the echo signal; The calculation process for the estimated proportion of radial wind speed damage within the volume element includes: in, This indicates the estimated percentage of radial wind speed damage within the volume element. This represents the measured radial wind speed at each observation point within the volume element. Represents the inverted three-dimensional wind vector The radial wind speed, N, decomposed along the laser beam direction at each observation point within the volume element. Indicates that the volume element satisfies The number of observation data points under the given conditions, N total This represents the total number of observation data points within a voxel. It represents the standard deviation spectral width of the signal peak on the signal power spectrum.

2. The method according to claim 1, characterized in that, The process of determining the preset signal-to-noise ratio switching threshold includes: A velocity-volume processing algorithm inversion error model is constructed using numerical simulation algorithms to determine the first preset error threshold. Based on the first preset error threshold, a preset signal-to-noise ratio switching threshold is determined; The velocity-volume processing algorithm inversion error model includes: E1=f1(SNR, other configuration parameters); B1=f2(SNR, other configuration parameters); Where E1 represents the root mean square error of the inverted wind speed in the velocity-volume processing algorithm inversion error model, B1 represents the systematic bias of the inverted wind speed in the velocity-volume processing algorithm inversion error model, and SNR represents the signal-to-noise ratio; other configuration parameters include equipment operating parameters, atmospheric state parameters, and algorithm configuration parameters; the velocity-volume processing algorithm is applied within the volume element to invert the three-dimensional wind vector.

3. The method according to claim 1, characterized in that, The process of determining the preset bad estimation percentage threshold includes: A numerical simulation algorithm is used to construct an inversion error model for the filtration velocity-volume processing algorithm, and a second preset error threshold is determined. Based on the second preset error threshold, a preset bad estimation percentage threshold is determined; The inversion error model of the filtration velocity-volume processing algorithm includes: E2=f3(b, other configuration parameters); B2=f4(b, other configuration parameters); Where E2 represents the root mean square error of the inverted wind speed in the inversion error model of the filtration velocity-volume processing algorithm, and B2 represents the systematic bias of the inverted wind speed in the inversion error model of the filtration velocity-volume processing algorithm. This indicates the percentage of radial wind speed inaccurate estimation within the volume element; other configuration parameters include equipment operating parameters, atmospheric state parameters, and algorithm configuration parameters; the filtration velocity-volume processing algorithm is applied within the volume element to invert the three-dimensional wind vector.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: When inverting the wind field within a volume element using the preset filtering velocity-volume processing algorithm, a search interval is defined centered on the inverted three-dimensional wind vector of the previous distance library in the radial direction. The three-dimensional wind vector within the volume element is inverted only within the search interval. The width of the search interval is determined by calculating the difference values ​​of the inverted three-dimensional wind vector components of all adjacent distance libraries within the radial distance of the observation point to be inverted, finding the maximum absolute value, and then multiplying it by the magnification factor.

5. A lidar wind field inversion device based on a filtered velocity-volume processing algorithm, characterized in that, include: The acquisition module is used to acquire the observation data base data of each volume element in the lidar wind field inversion. The observation data base data includes the signal-to-noise ratio at each observation point within the volume element, and is used to calculate the average signal-to-noise ratio within the volume element. The inversion module is used to invert the three-dimensional wind vector within the volume element based on the velocity-volume processing algorithm when the average signal-to-noise ratio is greater than or equal to the preset signal-to-noise ratio switching threshold. The inversion module is also used to invert the three-dimensional wind vector within the volume element based on a preset filtering velocity-volume processing algorithm when the average signal-to-noise ratio is less than the preset signal-to-noise ratio switching threshold. The inversion module is also specifically used to invert the three-dimensional wind vector within the volume element based on a preset filtering velocity volume processing algorithm, and simultaneously calculate the proportion of bad radial wind speed estimates, until the proportion of bad radial wind speed estimates within the volume element is greater than or equal to a preset bad estimate proportion threshold, at which point the inversion of the wind field in the current radial direction is terminated. The optimization objective function constructed in the preset filtering speed-volume processing algorithm includes: in, This represents the optimization objective function derived within the voxel based on a preset filtering velocity-volume processing algorithm. This represents the optimization variable matrix with 12 components. This represents the three-dimensional wind vector to be inverted within the volume element. This represents the measured radial wind speed at each observation point within the volume element. This represents the radial azimuth vector corresponding to each observation point within the voxel. This represents the spectral width of the Doppler signal peak standard deviation on the power spectrum of the echo signal; The calculation process for the estimated proportion of radial wind speed damage within the volume element includes: in, This indicates the estimated percentage of radial wind speed damage within the volume element. This represents the measured radial wind speed at each observation point within the volume element. Represents the inverted three-dimensional wind vector The radial wind speed, N, decomposed along the laser beam direction at each observation point within the volume element. Indicates that the volume element satisfies The number of observation data points under the given conditions, N total This represents the total number of observation data points within a voxel. It represents the standard deviation spectral width of the signal peak on the signal power spectrum.

6. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

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