A wind field detection method, device, equipment and storage medium

Abstract

The application discloses a kind of wind field detection method, device, equipment and storage medium, applied to laser radar wind measurement field, the method comprises: obtaining the historical aerosol data of target detection area, determines the power parameter of laser radar based on historical aerosol data;Get the echo signal obtained by the detection of target detection area under power parameter by laser radar;Determine the radial velocity of wind field based on preprocessed echo signal, determine signal quality parameter based on radial velocity;Determine power adjustment parameter based on signal quality parameter, update the power parameter of laser radar based on power adjustment parameter;Determine the quality level of radial velocity based on signal quality parameter, inversion is carried out based on the radial velocity of each quality level, and the wind field data of target detection area is obtained.The power of laser radar is adjusted by echo signal in the present application, so that the laser radar can detect accurate wind field data under different meteorological conditions.

Description

Technical Field

[0001] This invention relates to the field of lidar wind measurement, and particularly to a wind field detection method, a wind field detection device, an electronic device, and a computer-readable storage medium. Background Technology

[0002] Doppler lidar has garnered widespread attention due to its high spatiotemporal data resolution and high-precision continuous profile detection capabilities. Based on detection methods, it can be categorized into coherent and incoherent Doppler lidar. A key performance indicator for coherent wind-measuring lidar systems is their ability to detect aerosol particles. Under varying meteorological conditions, the optical thickness of aerosols differs significantly, leading to poor stability in lidar detection range. This is particularly true under conditions of high humidity and salt spray at the air-sea boundary layer, and thin aerosols at high-altitude platforms, where the echo power is difficult to maintain, increasing the difficulty of data analysis and resulting in inaccurate wind field data. Summary of the Invention

[0003] The purpose of this invention is to provide a wind field detection method, a wind field detection device, an electronic device, and a computer-readable storage medium, which are applied to the field of lidar wind measurement. This method adjusts the power of the lidar through the echo signal, enabling the lidar to detect accurate wind field data under different meteorological conditions.

[0004] To solve the above-mentioned technical problems, the present invention provides a wind field detection method, comprising:

[0005] Historical aerosol data of the target detection area is acquired, and the power parameters of the lidar are determined based on the historical aerosol data;

[0006] The echo signal obtained by the lidar from the target detection area under the specified power parameters is acquired.

[0007] The radial velocity of the wind field is determined based on the preprocessed echo signal, and the signal quality parameters are determined based on the radial velocity.

[0008] The power adjustment parameters are determined based on the signal quality parameters, and the power parameters of the lidar are updated based on the power adjustment parameters.

[0009] The quality level of the radial velocity is determined based on the signal quality parameters, and the wind field data of the target detection area is obtained by inversion based on the radial velocity of each quality level.

[0010] Optionally, historical aerosol data of the target detection area is acquired, and the power parameters of the lidar are determined based on the historical aerosol data, including:

[0011] The historical aerosol optical thickness and the percentage of historical aerosol optical thickness in the target detection area are obtained, and the historical average aerosol optical thickness is determined based on the historical aerosol optical thickness.

[0012] High-concentration adjustment terms are determined based on the historical average aerosol optical thickness and the reference aerosol optical thickness; low-concentration adjustment terms are determined based on the historical average aerosol optical thickness.

[0013] The initial pump source power is determined based on the high concentration adjustment term, the low concentration adjustment term, and the reference pump source power.

[0014] The initial local oscillator power is determined based on the power at the historical average aerosol optical thickness, the power at the reference aerosol optical thickness, and the reference local oscillator power.

[0015] Optionally, the radial velocity of the wind field is determined based on the preprocessed echo signal, and signal quality parameters are determined based on the radial velocity, including:

[0016] The echo signal is denoised to remove the background noise and trend term to obtain a denoised echo signal.

[0017] The noise-reduced echo signal is enhanced by performing spectrum enhancement processing on the spectrum enhancement algorithm to obtain the enhanced echo signal.

[0018] The radial velocity of the wind field is obtained based on the enhanced echo signal, and the signal-to-noise ratio, peak quality factor, and spectral width quality factor of the echo signal are determined based on the radial velocity.

[0019] Optionally, determining power adjustment parameters based on the signal quality parameters and updating the power parameters of the lidar based on the power adjustment parameters includes:

[0020] The signal-to-noise ratio (SNR) coefficient is determined based on the SNR of the echo signal in the target detection area and the SNR of the ground echo.

[0021] The power adjustment parameters are determined based on the signal-to-noise ratio scaling factor and the historical aerosol optical thickness ratio.

[0022] The power parameters of the lidar are updated based on the power adjustment parameters.

[0023] Optionally, updating the power parameters of the lidar based on the power adjustment parameters includes:

[0024] Anomaly detection is performed on the power regulation parameters, and abnormal power regulation parameters are removed;

[0025] When the power adjustment parameter is greater than the first power parameter threshold, the pump source power and local oscillator power are reduced.

[0026] When the power adjustment parameter is less than the second power parameter threshold, the pump source power and the local oscillator power are increased.

[0027] Optionally, determining the quality level of the radial velocity based on the signal quality parameters includes:

[0028] When the signal-to-noise ratio is greater than or equal to the first signal-to-noise ratio threshold, and the spectral peak quality factor is greater than or equal to the first spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the first spectral width quality threshold, the quality level of the radial velocity is determined to be high quality.

[0029] When the signal-to-noise ratio is greater than or equal to the second signal-to-noise ratio threshold and less than the first signal-to-noise ratio threshold, and the peak quality factor is greater than or equal to the second peak quality threshold, and the width quality factor is greater than or equal to the second width quality threshold, then the quality level of the radial velocity is determined to be medium quality.

[0030] When the signal-to-noise ratio is less than the second signal-to-noise ratio threshold, or the peak quality factor is less than the third peak quality threshold, or the width quality factor is less than the third width quality threshold, the quality level of the radial velocity is determined to be low quality.

[0031] Optionally, wind field data for the target detection area is obtained by inversion based on the radial velocity of each of the aforementioned quality levels, including:

[0032] A radial velocity matrix is ​​constructed based on the radial velocity, a weight matrix is ​​constructed based on the quality level, and an angle projection matrix is ​​constructed based on the fixed elevation angle and beam azimuth angle of the lidar.

[0033] The wind field data of the target detection area are obtained by solving the wind field vector based on the radial velocity matrix, the weight matrix and the angular projection matrix.

[0034] To solve the above-mentioned technical problems, the present invention provides a wind field detection device, comprising:

[0035] The first module is used to acquire historical aerosol data of the target detection area and determine the power parameters of the lidar based on the historical aerosol data.

[0036] The second module is used to acquire the echo signal obtained by the lidar from detecting the target detection area under the power parameters;

[0037] The third module is used to determine the radial velocity of the wind field based on the preprocessed echo signal, and to determine the signal quality parameters based on the radial velocity.

[0038] The fourth module is used to determine the power adjustment parameters based on the signal quality parameters, and update the power parameters of the lidar based on the power adjustment parameters;

[0039] The fifth module is used to determine the quality level of the radial velocity based on the signal quality parameters, and to perform inversion based on the radial velocity of each quality level to obtain the wind field data of the target detection area.

[0040] To solve the above-mentioned technical problems, the present invention provides an electronic device, comprising:

[0041] Memory, used to store computer programs;

[0042] A processor is used to implement the wind field detection method described above when executing the computer program.

[0043] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned wind field detection method.

[0044] As can be seen, this invention acquires historical aerosol data of the target detection area and determines the power parameters of the lidar based on this data; acquires the echo signal obtained by the lidar under the specified power parameters when detecting the target detection area; determines the radial velocity of the wind field based on the pre-processed echo signal, and determines the signal quality parameters based on the radial velocity; determines the power adjustment parameters based on the signal quality parameters, and updates the lidar's power parameters based on these parameters; determines the quality level of the radial velocity based on the signal quality parameters, and performs inversion based on the radial velocity at each quality level to obtain the wind field data of the target detection area. By adjusting the lidar power using the echo signal, the lidar can obtain accurate wind field data under different meteorological conditions. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 A flowchart of a wind field detection method provided in an embodiment of the present invention;

[0047] Figure 2 This is a structural block diagram of a wind field detection device provided in an embodiment of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Doppler lidar has garnered widespread attention due to its high spatiotemporal data resolution and high-precision continuous profile detection capabilities. Based on detection methods, it can be categorized into coherent and incoherent Doppler lidar; based on the characteristics of the emitted laser, it can be divided into pulsed and continuous-wave lidar. Compared to incoherent Doppler lidar, coherent Doppler lidar is smaller, lighter, and has higher spatial resolution, making it easier to implement in operational observation and applications. Compared to continuous-wave lidar, pulsed Doppler lidar has a larger measurement range, stable spatial resolution, and the ability to detect vertical profiles at the same time, resulting in a wider range of applications. This invention employs a coherent wind-measuring lidar. The key performance indicator of a coherent wind-measuring lidar system is its ability to detect aerosol particle targets. Under different meteorological conditions, the optical thickness of aerosols varies significantly, leading to poor stability of the lidar detection range. This is especially true under conditions such as high humidity and salt fog in the air-sea boundary layer, and thin aerosols on high-altitude platforms, where the echo power is difficult to maintain stability, increasing the difficulty of lidar data analysis.

[0050] To enhance the detection capability of wind-measuring lidar, the main focus in equipment development is increasing laser power; to improve data analysis capabilities, the main approach in inversion algorithms is spectral analysis. Currently, research on improving the detection capability of coherent wind-measuring lidar primarily concentrates on the aforementioned hardware development (mainly power enhancement) and algorithm inversion (mainly spectral analysis), and is mainly applied to ground-based lidar equipment, with the primary goal of increasing the usable detection range. However, there is limited research on integrated solutions for stable detection, dynamic adjustment, and data analysis of lidar under different meteorological environments, and environmental parameters, equipment specifications, and inversion algorithms are not organically combined. This invention proposes a complete technical approach for lidar applications in various meteorological environments, such as high humidity and salt fog in the air-sea boundary layer and thin aerosols on high-altitude platforms. This approach combines inversion of key aerosol optical thickness parameters, dynamic adjustment of lidar beam energy, spectral analysis of lidar echo signals, and wind field inversion considering quality parameters to stably obtain high-precision atmospheric dynamic parameters.

[0051] Current common technologies use fixed-power lidar or lidar with power adjustments within a certain range. However, lidar detection range stability is poor under extreme atmospheric conditions. Specifically, the aerosol optical thickness varies significantly across different regions, times, and altitudes, especially under conditions of high humidity and salt spray in the air-sea boundary layer, and thin aerosols at high-altitude platforms. These conditions severely impact lidar detection performance, causing unstable echo signal strength and resulting in large variations in detection range. This invention aims to achieve adaptive and stable lidar detection under various meteorological environments, including the air-sea boundary layer and thin aerosols at high altitudes, to meet the requirements for stable wind field detection at different altitudes.

[0052] In weak echo signals, spectral analysis and wind field inversion exhibit significant errors. Specifically, when the effective signal is strong, the radial velocity data stored directly within the lidar device can be used to obtain the three-dimensional atmospheric wind field through wind field inversion algorithms. However, under poor atmospheric conditions, such as in air-sea boundary layer dynamics studies and upper-level atmospheric wind environment observations, lidar echo signals are weak, leading to significant errors in the radial velocity obtained by the internal algorithm and resulting in incorrect wind field inversion results. This invention aims to improve the accuracy of atmospheric wind field inversion under extreme atmospheric conditions by accurately acquiring dynamic parameters under weak signal conditions through laser selection and power adjustment.

[0053] The following combination Figure 1 , Figure 1 A flowchart of a wind field detection method provided in an embodiment of the present invention, the method may include:

[0054] S101: Acquire historical aerosol data of the target detection area and determine the power parameters of the lidar based on the historical aerosol data.

[0055] This embodiment first constructs a historical aerosol database to obtain historical aerosol data for different regions, times, and altitudes, such as historical aerosol optical thickness and the percentage of historical aerosol optical thickness.

[0056] This embodiment can obtain aerosol optical depth (AOD) and aerosol backscattering ratio (BSR), specifically from satellite remote sensing data sources. By utilizing a satellite remote sensing data to obtain an aerosol database as prior knowledge, this embodiment allows for reasonable preset of system power. It also enables the pre-design of pump sources with dynamic ranges capable of covering the required power for the target area's atmospheric environment, thus expanding the applicable environmental range of the lidar system.

[0057] Aerosol optical thickness refers to the total extinction effect of aerosol particles on light of a specific wavelength within a unit cross-sectional area of ​​an air column in the vertical direction; aerosol backscattering ratio refers to the ratio of the aerosol backscattering coefficient to the molecular backscattering coefficient, indicating the ability of aerosols to backscatter light relative to the ability of air molecules to backscatter light.

[0058] Aerosol optical thickness can be obtained from satellite remote sensing data, such as through MODIS (Moderate-resolution Imaging Spectroradiometer).

[0059] Aerosol backscattering ratio: This can be obtained from satellite remote sensing data. Taking CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) as an example, it is defined as:

[0060] ;

[0061] In the formula, BSR is the aerosol backscattering ratio. The aerosol backscattering coefficient is... This is the backscattering coefficient of atmospheric molecules.

[0062] Based on the aerosol backscattering ratio, the aerosol extinction coefficient at different heights can be calculated using the lidar equation inversion:

[0063] ;

[0064] In the formula, P(z) is the power received at height z, P0 is the transmitted power, c is the speed of light, β(z) is the total backscattering coefficient, T(z) is the atmospheric transmittance, and N... B It's background noise.

[0065] Atmospheric transmittance is related to the total extinction coefficient, as shown in the formula:

[0066] ;

[0067] In the formula, σ(z) is the total extinction coefficient at height z, and dz is the differential of z.

[0068] The aerosol extinction coefficient can be calculated from the aerosol backscattering ratio and the extinction backscattering ratio, using the following formula:

[0069] ;

[0070] In the formula, σ a(z) is the aerosol extinction coefficient at height z, S a The extinction backscattering ratio is usually assumed to be a typical value, β. m (z) is the molecular backscattering coefficient, which can be calculated using an atmospheric model (such as the American Standard Atmospheric Model). BSR(z) is the aerosol backscattering ratio at height z.

[0071] By solving the lidar equations through iteration or the Fernald method, the aerosol extinction coefficients at different altitudes can be obtained.

[0072] Based on the aerosol extinction coefficient, the historical aerosol optical thickness at different regions, times, and altitudes is calculated as an absolute parameter of aerosol optical thickness; based on the historical aerosol optical thickness, the aerosol optical thickness at different altitudes is calculated as a relative parameter of aerosol optical thickness.

[0073] For a given height layer (z1, z2), the historical aerosol optical thickness of the region is obtained by integrating the aerosol extinction coefficient of the height layer within the region:

[0074] ;

[0075] In the formula, AOD(z1,z2) is the aerosol extinction coefficient in the region from height z1 to z2.

[0076] Historical aerosol optical thickness is defined as the ratio of the historical aerosol optical thickness of this layer to the total aerosol optical thickness. In other words, the formula for the historical aerosol optical thickness percentage is:

[0077] ;

[0078] In the formula, P AOD (z1, z2) represents the percentage of historical aerosol optical thickness within the region from height z1 to z2.

[0079] The global or target detection area is divided into grids or classified by geographical region; data is divided according to the four seasons (spring, summer, autumn, and winter) or months; multiple altitude layers are set, such as dividing by 1km units. During the statistical analysis, the historical aerosol optical thickness and the percentage of historical aerosol optical thickness are calculated for each region, each time period, and each altitude layer to form a historical aerosol database, as shown in Table 1.

[0080] Table 1: Examples of Aerosol Databases

[0081]

[0082] This embodiment can acquire information such as the geographical coordinates, standard time, and altitude of the target detection area, and determine the historical aerosol data of the target detection area based on the historical aerosol database, and determine the power parameters of the lidar based on the historical aerosol data.

[0083] This embodiment can obtain the historical aerosol optical thickness and the percentage of historical aerosol optical thickness in the target detection area, and determine the historical average aerosol optical thickness based on the historical aerosol optical thickness; determine a high-concentration adjustment term based on the historical average aerosol optical thickness and the reference aerosol optical thickness; determine a low-concentration adjustment term based on the historical average aerosol optical thickness; determine the initial pump source power based on the high-concentration adjustment term, the low-concentration adjustment term, and the reference pump source power; and determine the initial local oscillator power based on the power under the historical average aerosol optical thickness, the power under the reference aerosol optical thickness, and the reference local oscillator power.

[0084] For example, the formula for calculating the initial pump source power of the system is:

[0085] ;

[0086] In the formula, The initial pump source power, As the reference pump source power, The first system's overall gain coefficient. For high concentration adjustment, This is a low-concentration adjustment term.

[0087] The high-concentration adjustment term can be calculated as follows:

[0088] ;

[0089] In the formula, m is the parameter of the first high-concentration adjustment term, and p is the parameter of the second high-concentration adjustment term. The historical average aerosol optical thickness (statistical aerosol optical thickness within the target detection range). The reference aerosol optical thickness (default is medium aerosol optical thickness) is used, and max is the maximum value function.

[0090] The low-concentration adjustment term can be calculated as follows:

[0091] ;

[0092] In the formula, n is the parameter of the first low concentration adjustment term, and ε is the parameter of the second high concentration adjustment term.

[0093] The formula for calculating the initial local oscillator power can be:

[0094] ;

[0095] In the formula, This represents the initial local oscillator power. As the reference local oscillator power, This represents the overall gain coefficient of the second system. Power at historical average aerosol optical thickness Power at reference aerosol optical thickness.

[0096] S102: Acquire the echo signal obtained by the lidar in detecting the target detection area under the power parameters.

[0097] S103: Determine the radial velocity of the wind field based on the preprocessed echo signal, and determine the signal quality parameters based on the radial velocity.

[0098] This embodiment can acquire the echo signal obtained by the lidar in detecting the target detection area under power parameters, determine the radial velocity of the wind field based on the preprocessed echo signal, and determine the signal quality parameters based on the radial velocity.

[0099] This embodiment does not limit the specific method of preprocessing the echo signal. Generally, the echo signal can be denoised to remove the background noise and trend term to obtain a denoised echo signal. The denoised echo signal can then be spectrally enhanced using a spectrum enhancement algorithm to obtain an enhanced echo signal.

[0100] In the background noise removal section, the invalid signals from the last few range gates in the lidar echo signal are used as the noise floor, and the formula is:

[0101] ;

[0102] In the formula, The spectral signal of the echo model after removing the background noise. The original echo signal spectrum, where f is the frequency, RGp is ​​the p-th range gate, and RGq is the q-th range gate.

[0103] In the trend removal section, taking the nonlinear least squares polynomial algorithm for fitting as an example, the formula is:

[0104] ;

[0105] In the formula, For trend items, Here, is the polynomial coefficient, k is the polynomial order, and the maximum order of the polynomial is K.

[0106] Construct the optimization objective function with regularization constraints, as shown in the formula:

[0107] ;

[0108] In the formula, J(x) is the objective function, w(f) is the adaptive weight function, ω is the regularization parameter, R(x) is the regularization term, min is the minimum value function, and x is the polynomial coefficient vector to be solved.

[0109] The optimal polynomial coefficients are obtained by solving the iterative reweighted least squares algorithm to obtain the spectral noise baseline. The fitted noise baseline is then removed from the original spectrum, as shown in the following formula:

[0110] ;

[0111] In the formula, This is the spectral signal of the noise-reduced echo signal.

[0112] The spectral peaks of a signal exhibit distinct minima in its higher-order derivatives, while the amplitudes of the higher-order derivatives of noise are relatively small. This characteristic can be used to enhance the signal-to-noise ratio and to solve for the radial velocity of the spectrum under weak signals. Taking the discrete second derivative as an example, its calculation formula is as follows:

[0113] ;

[0114] In the formula, The second derivative of the frequency spectrum of the echo signal. This refers to the frequency resolution.

[0115] The enhanced echo signal is obtained by enhancing the spectrum based on the derivative result, as shown in the formula:

[0116] ;

[0117] In the formula, γ is a weighting coefficient used to enhance the spectral signal of the echo signal.

[0118] Furthermore, this embodiment can determine the radial velocity of the wind field based on the enhanced echo signal, and determine the signal quality parameters, such as the signal-to-noise ratio, peak quality factor, and spectral width quality factor of the echo signal, based on the radial velocity.

[0119] The formula for calculating radial velocity is as follows:

[0120] ;

[0121] In the formula, Let λ be the radial velocity of the wind field, and λ be the laser wavelength. This represents the offset of the spectral peak value relative to the transmission frequency.

[0122] After determining the radial velocity, this embodiment can solve for the signal-to-noise ratio, peak quality factor, and spectral width quality factor based on the original echo signal.

[0123] The formula for calculating the signal-to-noise ratio is as follows:

[0124] ;

[0125] In the formula, The signal-to-noise ratio of the echo signal in the target detection area. The signal power is calculated using the peak intensity of the spectrum. It is noise power, calculated from the average power of the no-signal frequency band.

[0126] The formula for calculating the peak quality factor is as follows:

[0127] ;

[0128] In the formula, Let be the spectral peak quality factor of the i-th distance gate. Let be the maximum power value of the spectral peak of the i-th distance gate. Let be the average power value across the entire frequency band for the i-th distance gate. Let be the full width at half maximum (FWHM) of the spectral peak of the i-th distance gate. The maximum half-height and full width.

[0129] The formula for calculating the spectral width quality factor is as follows:

[0130] ;

[0131] In the formula, Let be the spectral width quality factor of the i-th distance gate. The current spectral width measurement value for the i-th distance gate. This is the nominal spectral width value. This represents the range of spectral width variation.

[0132] S104: Determine the power adjustment parameters based on the signal quality parameters, and update the power parameters of the lidar based on the power adjustment parameters.

[0133] This embodiment can determine the power adjustment parameters based on the signal quality parameters, and update the power parameters of the lidar based on the power adjustment parameters.

[0134] Specifically, in this embodiment, the signal-to-noise ratio (SNR) coefficient can be determined based on the SNR of the echo signal in the target detection area and the SNR of the ground echo. The calculation method is as follows:

[0135] ;

[0136] In the formula, This is the signal-to-noise ratio scaling factor. Signal-to-noise ratio of ground echo signals.

[0137] The power adjustment parameter is determined based on the signal-to-noise ratio scaling factor and the prior aerosol concentration ratio. The power adjustment parameter is defined as the ratio of the signal-to-noise ratio scaling factor of the echo signal from the target detection area to the historical aerosol optical thickness ratio of the corresponding target detection area, expressed by the following formula:

[0138] ;

[0139] In the formula, These are power regulation parameters. This is the signal-to-noise ratio scaling factor. The percentage of historical aerosol optical thickness in the target detection area.

[0140] This embodiment does not limit the specific method of updating the lidar's power parameters based on power adjustment parameters. In the system's dynamic power adjustment section, the signal-to-noise ratio scaling factor is only for reference and is applied only when the power adjustment parameters... Adjustments are made when changes reach a certain magnitude. This is to prevent the lidar from detecting clouds or other phenomena that could cause localized increases or decreases in echo signals. After the system responds to and adjusts the power for this localized area, power imbalances would occur in the majority of the target detection area. This embodiment proposes a power adjustment factor that combines aerosol parameters and signal-to-noise ratio (SNR) parameters, rather than adjusting hardware parameters solely based on SNR errors. Furthermore, a delay time is incorporated to effectively avoid direct feedback adjustments caused by changes in echo signals from localized clouds, thereby enhancing the overall stability of the system's detection data.

[0141] Therefore, in each adjustment process, abnormal power adjustment parameters can be detected and removed, and power adjustment can be performed only based on normal power adjustment parameters.

[0142] When the power regulation parameter is greater than the first power parameter threshold , that is > If the signal quality is excessive, the pump source power and local oscillator power will be reduced.

[0143] When the power regulation parameter is less than the second power parameter threshold , that is, < If the signal quality is insufficient, increase the pump source power and the local oscillator power.

[0144] No power adjustment is required when the power regulation parameter is between the first power parameter threshold and the second power parameter threshold.

[0145] Furthermore, in addition to anomaly detection, this embodiment can also calculate the average power regulation parameter after removing outliers over a continuous time period t. Power is adjusted based on the average value of the power adjustment parameters, and the adjustment method is the same as that of the power adjustment parameters.

[0146] This embodiment dynamically adjusts the current pump source power and local oscillator power of the lidar system based on the echo signal-to-noise ratio error parameter. Note that this part needs to be adjusted according to the detection requirements and system characteristics; the following formula is only one method for reference.

[0147] Taking the adjustment of pump source power in exponential response mode as an example, the formula is:

[0148] ;

[0149] In the formula, To adjust the new pump source power, α represents the current pump source power, and α is the pump source adjustment intensity coefficient.

[0150] Example of adjusting the local oscillator power using logarithmic response mode, the formula is:

[0151] ;

[0152] In the formula, To adjust the new local oscillator power, β represents the current local oscillator power, and β is the local oscillator adjustment sensitivity coefficient.

[0153] Furthermore, during power regulation, it is necessary to ensure that the power is within a safe range:

[0154] ;

[0155] ;

[0156] In the formula, For pump source power, For minimum pump source power, For maximum pump source power, For local oscillator power, To minimize the local oscillator power, This represents the maximum local oscillator power.

[0157] Furthermore, this embodiment can also limit the rate of power change to prevent system oscillation, as shown in the formula:

[0158] ;

[0159] In the formula, The adjusted power parameters, The power parameters before adjustment The time difference before and after power adjustment. This represents the maximum power change rate.

[0160] This embodiment allows for power adjustment based on the echo signal after each detection, following the aforementioned process. This embodiment adjusts not only the pump source power but also the local oscillator power, enabling amplification of the heterodyne signal, enhancing the system's ability to detect weak signals, and strengthening the lidar system's signal detection capabilities under extreme atmospheric conditions.

[0161] S105: Determine the quality level of radial velocity based on signal quality parameters, and perform inversion based on radial velocity at each quality level to obtain wind field data for the target detection area.

[0162] This embodiment can determine the quality level of radial velocity based on signal quality parameters, and perform inversion based on the radial velocity of each quality level to obtain wind field data of the target detection area.

[0163] This embodiment does not limit the specific method for determining the quality level of radial velocity. It can be implemented based on actual applications. The following is only an example.

[0164] For example, if the signal-to-noise ratio is greater than or equal to the first signal-to-noise ratio threshold, and the spectral peak quality factor is greater than or equal to the first spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the first spectral width quality threshold, then the quality level of the radial velocity is determined to be high.

[0165] When the signal-to-noise ratio is greater than or equal to the second signal-to-noise ratio threshold and less than the first signal-to-noise ratio threshold, and the spectral peak quality factor is greater than or equal to the second spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the second spectral width quality threshold, the radial velocity quality level is determined to be medium quality.

[0166] If the signal-to-noise ratio is less than the second signal-to-noise ratio threshold, or the peak quality factor is less than the third peak quality threshold, or the width quality factor is less than the third width quality threshold, then the radial velocity quality level is determined to be low quality.

[0167] Specifically, when ,and ,and The characterization data is completely reliable, participates in the inversion and is given high weight, and the radial velocity is of high quality; among them, Let be the signal-to-noise ratio of the i-th distance gate. The first signal-to-noise ratio threshold, Let be the spectral peak quality factor of the i-th distance gate. The mass threshold for the first spectral peak. The spectral width quality factor of the i-th distance gate This is the first spectral width quality threshold.

[0168] when ,and ,and The characterization data is generally reliable, participates in the inversion but is assigned a low weight, and the radial velocity is of medium mass; among them, This is the second signal-to-noise ratio threshold. This is the mass threshold for the second spectral peak. This is the second spectral width quality threshold.

[0169] when ,or ,or The characterization data is unreliable and does not participate in wind field inversion; the radial velocity is of low quality. The mass threshold for the third spectral peak. This is the third spectral width quality threshold.

[0170] This embodiment does not limit the specific method of wind field inversion. Generally, after weighting according to the radial velocity mass level, atmospheric three-dimensional wind field inversion is carried out. Taking the N-beam DBS (Doppler Beam Swing) observation mode as an example, an angle projection matrix is ​​constructed based on the fixed elevation angle and beam azimuth angle of the lidar:

[0171] ;

[0172] In the formula, A is the angle projection matrix, and θ is the fixed elevation angle in the DBS beam observation mode of the lidar. Let be the azimuth angle of the i-th beam, where i is at most N.

[0173] Construct a radial velocity matrix based on radial velocity:

[0174] ;

[0175] In the formula, The radial velocity matrix is... Let be the radial velocity of the i-th beam, where i is at most N, and T is the matrix transpose.

[0176] Construct a weight matrix based on quality level:

[0177] ;

[0178] In the formula, W is the weight matrix, and diag is the diagonal matrix function. Let be the weight of the i-th beam, where i is at most N.

[0179] Furthermore, wind field vectors are solved based on the radial velocity matrix, weight matrix, and angular projection matrix to obtain wind field data for the target detection area:

[0180] ;

[0181] In the formula, u, v, and w are the wind speed components in the east-west, north-north, and vertical directions of the coordinate system, respectively.

[0182] This embodiment combines hardware system adjustments and algorithm processing methods to not only obtain data with a relatively stable signal-to-noise ratio, but also improves the accuracy of atmospheric wind field inversion of lidar system detection data under extreme weather conditions by using weak signal spectrum analysis methods and atmospheric wind field inversion algorithms combined with weight matrices.

[0183] This embodiment provides a complete implementation approach for dynamic, adaptive lidar wind measurement technology and algorithms applicable to various environmental meteorological parameters. It includes setting two key power indicators for the hardware system, covering the calculation of major data products in the algorithm stage, and adds a transmission feedback and real-time adjustment mechanism between the signal end and the hardware end, realizing an intelligent lidar integrated detection and inversion system. Compared to existing technologies, this embodiment is not limited to hardware systems or inversion algorithms, but realizes a complete chain of "hardware adjustment - echo detection - signal analysis - algorithm inversion".

[0184] Based on the above embodiments, the present invention adjusts the power of the lidar by using echo signals, enabling the lidar to detect accurate wind field data under different meteorological conditions.

[0185] Figure 2 This is a structural block diagram of a wind field detection device provided in an embodiment of the present invention. The device may include:

[0186] The first module 100 is used to acquire historical aerosol data of the target detection area and determine the power parameters of the lidar based on the historical aerosol data.

[0187] The second module 200 is used to acquire the echo signal obtained by the lidar from the target detection area under the power parameters;

[0188] The third module 300 is used to determine the radial velocity of the wind field based on the preprocessed echo signal, and to determine the signal quality parameters based on the radial velocity.

[0189] The fourth module 400 is used to determine the power adjustment parameters based on the signal quality parameters, and to update the power parameters of the lidar based on the power adjustment parameters.

[0190] The fifth module 500 is used to determine the quality level of the radial velocity based on the signal quality parameters, and to perform inversion based on the radial velocity of each quality level to obtain the wind field data of the target detection area.

[0191] Based on the above embodiments, the present invention adjusts the power of the lidar by using echo signals, enabling the lidar to detect accurate wind field data under different meteorological conditions.

[0192] Based on the above embodiments, the first module 100 may include:

[0193] The first unit is used to obtain the historical aerosol optical thickness and the percentage of historical aerosol optical thickness in the target detection area, and to determine the historical average aerosol optical thickness based on the historical aerosol optical thickness.

[0194] The second unit is used to determine high-concentration adjustment terms based on historical average aerosol optical thickness and reference aerosol optical thickness; and to determine low-concentration adjustment terms based on historical average aerosol optical thickness.

[0195] The third unit is used to determine the initial pump source power based on the high concentration adjustment term, the low concentration adjustment term, and the reference pump source power.

[0196] The fourth unit is used to determine the initial local oscillator power based on the power at the historical average aerosol optical thickness, the power at the reference aerosol optical thickness, and the reference local oscillator power.

[0197] Based on the above embodiments, the third module 300 may include:

[0198] The fifth unit is used to perform noise reduction processing on the echo signal, removing the background noise and trend term from the echo signal to obtain the noise-reduced echo signal;

[0199] The sixth unit is used to perform spectrum enhancement processing on the denoised echo signal based on the spectrum enhancement algorithm to obtain the enhanced echo signal;

[0200] Unit 7 is used to determine the radial velocity of the wind field based on the enhanced echo signal, and to determine the signal-to-noise ratio, peak quality factor, and spectral width quality factor of the echo signal based on the radial velocity.

[0201] Based on the above embodiments, the fourth module 400 may include:

[0202] Unit 8 is used to determine the signal-to-noise ratio scaling factor based on the signal-to-noise ratio of the echo signal in the target detection area and the signal-to-noise ratio of the ground echo.

[0203] The ninth unit is used to determine the power adjustment parameters based on the signal-to-noise ratio scaling factor and the historical aerosol optical thickness ratio.

[0204] Unit 10 is used to update the power parameters of the lidar based on the power adjustment parameters.

[0205] Based on the above embodiments, the tenth unit may include:

[0206] The first subunit is used to detect anomalies in the power regulation parameters and remove abnormal power regulation parameters.

[0207] The second subunit is used to reduce the pump source power and local oscillator power when the power adjustment parameter is greater than the first power parameter threshold.

[0208] The third subunit is used to increase the pump source power and local oscillator power when the power adjustment parameter is less than the second power parameter threshold.

[0209] Based on the above embodiments, the fifth module 500 may include:

[0210] Unit 11 is used to determine the quality level of radial velocity as high quality when the signal-to-noise ratio is greater than or equal to the first signal-to-noise ratio threshold, the spectral peak quality factor is greater than or equal to the first spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the first spectral width quality threshold.

[0211] The twelfth unit is used to determine the quality level of the radial velocity as medium quality when the signal-to-noise ratio is greater than or equal to the second signal-to-noise ratio threshold and less than the first signal-to-noise ratio threshold, the spectral peak quality factor is greater than or equal to the second spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the second spectral width quality threshold.

[0212] The thirteenth unit is used to determine the quality level of radial velocity as low quality when the signal-to-noise ratio is less than the second signal-to-noise ratio threshold, or the spectral peak quality factor is less than the third spectral peak quality threshold, or the spectral width quality factor is less than the third spectral width quality threshold.

[0213] Based on the above embodiments, the fifth module 500 may include:

[0214] The fourteenth unit is used to construct a radial velocity matrix based on radial velocity, a weight matrix based on quality level, and an angle projection matrix based on the fixed elevation angle and beam azimuth angle of the lidar.

[0215] Unit 15 is used to solve for the wind field vector based on the radial velocity matrix, weight matrix and angular projection matrix, and obtain the wind field data of the target detection area.

[0216] Based on the above embodiments, the present invention also provides an electronic device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the device may also include various necessary network interfaces, a power supply, and other components.

[0217] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an execution terminal or processor, can implement the method provided in the embodiments of the present invention; the storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0218] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A wind field detection method, characterized in that, include: Historical aerosol data of the target detection area is acquired, and the power parameters of the lidar are determined based on the historical aerosol data; The echo signal obtained by the lidar from the target detection area under the specified power parameters is acquired. The radial velocity of the wind field is determined based on the preprocessed echo signal, and the signal quality parameters are determined based on the radial velocity. The power adjustment parameters are determined based on the signal quality parameters, and the power parameters of the lidar are updated based on the power adjustment parameters. The quality level of the radial velocity is determined based on the signal quality parameters, and the wind field data of the target detection area is obtained by inversion based on the radial velocity of each quality level. This includes acquiring historical aerosol data of the target detection area and determining the power parameters of the lidar based on the historical aerosol data, including: The historical aerosol optical thickness and the percentage of historical aerosol optical thickness in the target detection area are obtained, and the historical average aerosol optical thickness is determined based on the historical aerosol optical thickness. High-concentration adjustment terms are determined based on the historical average aerosol optical thickness and the reference aerosol optical thickness; low-concentration adjustment terms are determined based on the historical average aerosol optical thickness. The initial pump source power is determined based on the high concentration adjustment term, the low concentration adjustment term, and the reference pump source power. The initial local oscillator power is determined based on the power at the historical average aerosol optical thickness, the power at the reference aerosol optical thickness, and the reference local oscillator power. Determining the quality level of the radial velocity based on the signal quality parameters includes: When the signal-to-noise ratio is greater than or equal to the first signal-to-noise ratio threshold, and the spectral peak quality factor is greater than or equal to the first spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the first spectral width quality threshold, the quality level of the radial velocity is determined to be high quality. When the signal-to-noise ratio is greater than or equal to the second signal-to-noise ratio threshold and less than the first signal-to-noise ratio threshold, and the peak quality factor is greater than or equal to the second peak quality threshold, and the width quality factor is greater than or equal to the second width quality threshold, then the quality level of the radial velocity is determined to be medium quality. When the signal-to-noise ratio is less than the second signal-to-noise ratio threshold, or the peak quality factor is less than the third peak quality threshold, or the width quality factor is less than the third width quality threshold, the quality level of the radial velocity is determined to be low quality. Based on the radial velocity of each of the aforementioned quality levels, wind field data for the target detection area is obtained through inversion, including: A radial velocity matrix is ​​constructed based on the radial velocity, a weight matrix is ​​constructed based on the quality level, and an angle projection matrix is ​​constructed based on the fixed elevation angle and beam azimuth angle of the lidar. The wind field data of the target detection area are obtained by solving the wind field vector based on the radial velocity matrix, the weight matrix and the angular projection matrix.

2. The wind field detection method according to claim 1, characterized in that, The radial velocity of the wind field is determined based on the preprocessed echo signal, and signal quality parameters are determined based on the radial velocity, including: The echo signal is denoised to remove the background noise and trend term to obtain a denoised echo signal. The noise-reduced echo signal is enhanced by performing spectrum enhancement processing on the spectrum enhancement algorithm to obtain the enhanced echo signal. The radial velocity of the wind field is obtained based on the enhanced echo signal, and the signal-to-noise ratio, peak quality factor, and spectral width quality factor of the echo signal are determined based on the radial velocity.

3. The wind field detection method according to claim 1, characterized in that, Determining power adjustment parameters based on the signal quality parameters, and updating the power parameters of the lidar based on the power adjustment parameters, includes: The signal-to-noise ratio (SNR) coefficient is determined based on the SNR of the echo signal in the target detection area and the SNR of the ground echo. The power adjustment parameters are determined based on the signal-to-noise ratio scaling factor and the historical aerosol optical thickness ratio. The power parameters of the lidar are updated based on the power adjustment parameters.

4. The wind field detection method according to claim 3, characterized in that, Updating the power parameters of the lidar based on the power adjustment parameters includes: Anomaly detection is performed on the power regulation parameters, and abnormal power regulation parameters are removed; When the power adjustment parameter is greater than the first power parameter threshold, the pump source power and local oscillator power are reduced. When the power adjustment parameter is less than the second power parameter threshold, the pump source power and the local oscillator power are increased.

5. A wind field detection device, characterized in that, include: The first module is used to acquire historical aerosol data of the target detection area and determine the power parameters of the lidar based on the historical aerosol data. The second module is used to acquire the echo signal obtained by the lidar from detecting the target detection area under the power parameters; The third module is used to determine the radial velocity of the wind field based on the preprocessed echo signal, and to determine the signal quality parameters based on the radial velocity. The fourth module is used to determine the power adjustment parameters based on the signal quality parameters, and update the power parameters of the lidar based on the power adjustment parameters; The fifth module is used to determine the quality level of the radial velocity based on the signal quality parameters, and to perform inversion based on the radial velocity of each quality level to obtain the wind field data of the target detection area; This includes acquiring historical aerosol data of the target detection area and determining the power parameters of the lidar based on the historical aerosol data, including: The historical aerosol optical thickness and the percentage of historical aerosol optical thickness in the target detection area are obtained, and the historical average aerosol optical thickness is determined based on the historical aerosol optical thickness. High-concentration adjustment terms are determined based on the historical average aerosol optical thickness and the reference aerosol optical thickness; low-concentration adjustment terms are determined based on the historical average aerosol optical thickness. The initial pump source power is determined based on the high concentration adjustment term, the low concentration adjustment term, and the reference pump source power. The initial local oscillator power is determined based on the power at the historical average aerosol optical thickness, the power at the reference aerosol optical thickness, and the reference local oscillator power. Determining the quality level of the radial velocity based on the signal quality parameters includes: When the signal-to-noise ratio is greater than or equal to the first signal-to-noise ratio threshold, and the spectral peak quality factor is greater than or equal to the first spectral peak quality threshold, and the spectral width quality factor is greater than or equal to the first spectral width quality threshold, the quality level of the radial velocity is determined to be high quality. When the signal-to-noise ratio is greater than or equal to the second signal-to-noise ratio threshold and less than the first signal-to-noise ratio threshold, and the peak quality factor is greater than or equal to the second peak quality threshold, and the width quality factor is greater than or equal to the second width quality threshold, then the quality level of the radial velocity is determined to be medium quality. When the signal-to-noise ratio is less than the second signal-to-noise ratio threshold, or the peak quality factor is less than the third peak quality threshold, or the width quality factor is less than the third width quality threshold, the quality level of the radial velocity is determined to be low quality. Based on the radial velocity of each of the aforementioned quality levels, wind field data for the target detection area is obtained through inversion, including: A radial velocity matrix is ​​constructed based on the radial velocity, a weight matrix is ​​constructed based on the quality level, and an angle projection matrix is ​​constructed based on the fixed elevation angle and beam azimuth angle of the lidar. The wind field data of the target detection area are obtained by solving the wind field vector based on the radial velocity matrix, the weight matrix and the angular projection matrix.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the wind field detection method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the wind field detection method as described in any one of claims 1 to 4.

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