Quality control method based on laser wind finding radar data

Abstract

The invention provides a quality control method based on laser wind-finding radar data, and the method comprises the steps: taking radial data directly obtained by a laser wind-finding radar as basic data, and fusing the signal-to-noise ratio characteristics and spatial distribution characteristics of abnormal data; quality control such as abnormal value elimination and missing value filling is carried out on the radial wind speed data, and abnormal value effective elimination is carried out on the radial wind speed data, so that the false alarm rate of the laser wind finding radar detection signals is greatly reduced. According to the spatial relationship of the wind speed data detected by the laser wind-finding radar in the radial direction and the tangential direction, effective filling of the radial wind speed is realized by adopting a mode based on frequency statistics and median filtering in the moving window, and the integrity of the wind speed measured by the laser wind-finding radar is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of laser radar, and in particular to a quality control method based on laser wind radar data. Background Art

[0002] In recent years, with the development of laser technology, all-fiber coherent laser wind radar has gradually become an important device for three-dimensional wind field detection. Compared with traditional wind measuring equipment, laser wind radar has the advantages of high sensitivity, high temporal and spatial resolution, fine three-dimensional wind field information, flexible detection mode, and strong anti-interference ability. It is widely used in civil aviation, meteorology, wind power and other fields. The basic principle of laser wind radar is that the radar emits laser pulses into the atmosphere, receives the backscattered signals of aerosol particles carrying wind speed information in the atmosphere, and uses coherent heterodyne detection technology to obtain laser Doppler frequency shift information, thereby obtaining radial wind speed information. From this principle, the moving speed of atmospheric aerosol particles is similar to the speed of wind. Raindrops, snow, fog, solid particles, clouds, and the uncertainty of atmospheric wind field within the detection range of laser wind radar cause certain interference to the echo signal received by the radar, resulting in many abnormal points in the radial wind speed data directly obtained by the laser wind radar, which brings large errors to the accurate inversion of the three-dimensional wind field. Summary of the invention

[0003] In order to solve the problems of the prior art, the present invention provides a quality control method based on laser wind radar data, which provides reliability guarantee for inverting the real wind field based on radial wind speed data, greatly reduces the false alarm rate of the laser wind radar detection signal, and ensures the integrity of the wind speed measured by the laser wind radar.

[0004] The present invention provides a quality control method based on laser wind radar data, comprising the following steps: 1) Obtain radial data of the laser wind radar: set the elevation and azimuth ranges of the laser wind radar, perform periodic scanning, and obtain a radial data set within a cycle; 2) Eliminate outliers based on signal-to-noise ratio: 2.1) Based on the obtained radial wind speed data set, determine whether the radial wind speed value is Nan (Nan is a symbol indicating an invalid value of radial wind speed). If it is Nan, the radial wind speed data flag Flag=0, otherwise, the radial wind speed data flag Flag=1; 2.2) Weak echo signal elimination: determine whether the signal data belongs to weak echo signal data. If so, set the radial wind speed flag Flag from the starting range gate Start_gate to the last range gate End_gate of the radial wind speed data set to 0. Otherwise, the radial wind speed flag Flag remains unchanged. 2.3) Eliminate the signal data blocked by the hard target, and determine whether the signal data belongs to the signal data blocked by the hard target. If so, set the radial wind speed flag Flag to 0, otherwise, the radial wind speed flag Flag remains unchanged; 2.4) Eliminate the signal data blocked by obstacles in the range gate, and determine whether the signal data is blocked by obstacles in the range gate. If so, set the radial wind speed flag to 0; otherwise, the radial wind speed flag remains unchanged. 2.5) Eliminate the signal data affected by obstacles in the blind area, and determine whether the signal data is blocked by obstacles in the blind area. If so, set the radial wind speed flag of all range gates of the light beam that generates the signal data to 0, otherwise, the radial wind speed flag remains unchanged; 3) Fill in missing wind speed values ​​based on moving windows: 3.1) Initialize the size of the moving window in the radial wind speed data set to m×n, where m represents the number of radial range gates, n represents the number of tangential beams, the number of intervals divided by the radial speed is number, the minimum interval of the radial wind speed is vbin, the neighborhood threshold P1, the neighborhood threshold P2, the neighborhood threshold P3, and the neighborhood threshold P4, where m=5, n=3, number=10, vbin=0.5, P1=m×n / number, P2=m×n / number×2, P3=m×n×3 / 5, and P4=m×n×2 / 3; 3.2) Radial velocity interval division: For the radial wind speed set V of the center point (i, j) in the moving window, the size is m×n, Vc=V(i,j), where i represents the range gate direction, j represents the beam direction, and the number of valid radial wind speeds in the statistical window is N. The radial wind speed set bin_x is divided into equal speed intervals vbin based on the minimum and maximum values ​​of the radial wind speed. The frequency of number equal speed intervals based on bin_x is x. The radial wind speed set bin_xp=[Vc-vbin / 2,Vc+vbin / 2] is divided into the center point speed interval bin_xp based on the speed interval vbin. The frequency xp of the center point speed interval bin_xp is calculated. The center speed Vp of each speed interval is the average of the upper and lower limits of each speed division interval. The maximum frequency value xmax=max(x,xp) and the center speed Vp_xmax of the interval corresponding to the maximum frequency are calculated. 3.3) Fill in the radial wind speed based on the threshold value. If xp≥P1, the radial wind speed V(i,j)=Vc at this point, and its radial wind speed flag position is 1; otherwise, if xmax≥P2, the radial wind speed V(i,j)=Vp_xmax at this point, and its radial wind speed flag position is 1; otherwise, if (N>P3&&N≤P4)||(N>P4), the radial wind speed V(i,j) at this point = the middle value of the radial wind speed set V in the moving window, and its radial wind speed flag position is 1; otherwise, the radial wind speed and data flag position remain unchanged; 4) Based on wind speed profile filtering, the range gate index set of all beam radial wind speed sets as the valid profile is obtained and the median is obtained as Index, the median value of the range gate index is obtained as Index_med, and the radial wind speed flag Flag of all beams between Index_med and the range gate number m is set to 0; 5) Generate a radial wind speed data set after quality control: Based on the final radial wind speed data flag Flag, if Flag==0, the corresponding radial wind speed is set to empty, otherwise, the corresponding radial wind speed data is retained, and finally a radial wind speed data set after quality control is generated.

[0005] Further improvement, step 1) the data set includes data subsets such as radial wind speed and signal-to-noise ratio, and each data subset has a dimension of M×N, where M represents the number of range gates, representing different radial distances, and N represents the number of scanning beams, representing different tangential orientations.

[0006] As a further improvement, the weak echo signal data judgment process described in step 2.2) is as follows: for any light beam within a cycle, find the starting range gate Start_gate whose signal-to-noise ratio SNR of 2×Count consecutive range gates is lower than the signal-to-noise ratio threshold SNR_Th1, where SNR_Th1=0.5dB, Count represents the number of search range gates, which is dynamically adjusted according to the equipment parameters of the laser wind radar; if Count=round (laser pulse width×0.15 / adjacent range gate spacing) is satisfied, the light beam is considered to belong to weak echo signal data.

[0007] As a further improvement, the signal data judgment process blocked by the hard target in step 2.3) is as follows: for any light beam within a period, the range gate position Loc with SNR greater than the threshold value SNR_Th2 and the maximum point and its corresponding signal-to-noise ratio value SNR_Loc are screened out, SNR_Th2=25dB, and considering the spatial relationship between the blocked signal and other range gates, the signal data that satisfies both the absolute value of the difference between SNR and SNR_Loc in the left range [Loc—Count, Loc] and the right range [Loc, Loc+Count] is screened out, SNR_Th3=10dB, and is recorded as the signal data blocked by the hard target.

[0008] As a further improvement, the signal data judgment process blocked by obstacles in the range gate in step 2.4) is as follows: for any light beam within a period, the range gate position Loc and its corresponding signal-to-noise ratio value SNR_Loc whose SNR is greater than the threshold value SNR_Th4 and is the maximum point are screened out, SNR_Th4=4dB, and considering the spatial relationship between the blocked signal and other range gates, the signal data that satisfies both the absolute value of the difference between SNR and SNR_Loc in the left range [Loc—Count, Loc] and the right range [Loc, Loc+Count] is screened out, SNR_Th5=min(10,SNR_Loc×0.5)dB, which is recorded as the signal data blocked by obstacles in the range gate.

[0009] As a further improvement, the signal data judgment process affected by obstacles in the blind spot in step 2.5) is as follows: for any light beam in a cycle, if the maximum signal-to-noise ratio of all range gates is less than SNR_Th6, SNR_Th6=4dB, it is recorded as signal data affected by obstacles in the blind spot.

[0010] Further improvement, the wind speed profile filtering process in step 4) is as follows: 4.1) Based on the radial wind speed data set V, radial wind speed data flag Flag, and search range gate number Count obtained in steps 1) to 3), for any beam j within a cycle, obtain the range gate index ind1 with Flag==1 and the range gate index ind2 with Flag==0. If the first element value of ind1 is greater than the last element value of ind2, then Index(j)=ind1(1); otherwise, if the number of elements in ind1 is greater than or equal to 2×Count, then Index(j)=ind1(2×Count); otherwise, Index(j)=range gate number m; loop through each beam in turn, and obtain the range gate index set with valid profiles for all beam radial wind speed sets as Index; 4.2) Median filtering step 4.1) Get the range gate index set Index of all beams, get the middle value Index_med of the range gate index, and set the radial wind speed flag Flag of all beams between Index_med and the range gate number m to 0.

[0011] The present invention also provides a device for executing a quality control method based on laser wind radar data, which at least includes a processor and a memory, wherein the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory, including data input, data processing, and data output modules, to execute the above-mentioned quality control method based on laser wind radar data.

[0012] The present invention also provides a computer-readable storage medium storing a computer program or instruction. When the computer program or instruction is executed, the above-mentioned quality control method based on laser wind radar data is implemented.

[0013] The beneficial effects of the present invention are: 1) The radial data directly obtained from the laser wind radar is used as the basic data in the present invention. The signal-to-noise ratio characteristics and spatial distribution characteristics of the abnormal data are integrated to perform quality control such as removing abnormal values ​​and filling missing values ​​on the radial wind speed data, thus providing reliability guarantee for inverting the real wind field based on the radial wind speed data.

[0014] 2) The present invention combines the typical characteristics of weak echo signals detected by the laser wind radar, signals blocked by hard targets, signals affected by obstacles, etc., and effectively eliminates abnormal values ​​in the radial wind speed data, greatly reducing the false alarm rate of the laser wind radar detection signal.

[0015] 3) According to the spatial relationship between the wind speed data detected by the laser wind radar in the radial direction and the tangential direction, the present invention adopts a method based on frequency statistics and median filtering in a moving window to achieve effective filling of the radial wind speed and ensure the integrity of the wind speed measured by the laser wind radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is a schematic diagram of laser wind radar scanning; Figure 2 This is the data before radial wind speed quality control; Figure 3This is the radial wind speed data after quality control. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] A specific implementation of the present invention is as follows, comprising the following steps: Step 1: Obtain radial data of the laser wind radar: 1) Set the elevation and azimuth ranges of the laser wind radar, perform periodic scanning, and obtain a radial data set within a cycle, including radial wind speed, signal-to-noise ratio and other data subsets. The dimension of each data subset is M×N, where M represents the number of range gates, representing different radial distances, and N represents the number of scanning beams, representing different tangential azimuths. The scanning schematic diagram is shown in the figure. Figure 1 shown.

[0020] Step 2: Eliminate outliers based on signal-to-noise ratio: 1) Based on the obtained radial wind speed data set, determine whether the radial wind speed value is Nan. If it is Nan, the radial wind speed data flag Flag=0, otherwise, the radial wind speed data flag Flag=1; 2) Weak echo signal elimination: For any beam within a cycle, find the starting range gate Start_gate whose signal-to-noise ratio (SNR) of 2×Count consecutive range gates is lower than the signal-to-noise ratio threshold SNR_Th1, where SNR_Th1=0.5dB, and Count represents the number of search range gates, which can be dynamically adjusted according to the equipment parameters of the laser wind radar.

[0021] Count = round up (laser pulse width × 0.15 / adjacent distance gate spacing) If the above conditions are met, it belongs to weak echo signal data, then the radial wind speed flag Flag from the starting range gate Start_gate to the last range gate End_gate of the radial wind speed data set is set to 0, otherwise, the radial wind speed flag Flag remains unchanged; 3) Eliminate the signal data blocked by the hard target. For any beam in one cycle, select the range gate position Loc and its corresponding signal-to-noise ratio value SNR_Loc with SNR greater than the threshold SNR_Th2 and the maximum point, SNR_Th2=25dB. Considering the spatial relationship between the blocked signal and other range gates, select the signal data that satisfies the absolute value of the difference between SNR and SNR_Loc in the left range [Loc-Count,Loc] and the right range [Loc,Loc+Count] greater than SNR_Th3, SNR_Th3=10dB, and set the radial wind speed flag Flag to 0. Otherwise, the radial wind speed flag Flag remains unchanged. 4) Eliminate the signal data of obstacles (rain, snow, fog, etc.) within the range gate. For any beam within a cycle, select the range gate position Loc and its corresponding signal-to-noise ratio value SNR_Loc with SNR greater than the threshold SNR_Th4 and the maximum point, SNR_Th4=4dB. Considering the spatial relationship between the blocked signal and other range gates, select the signal data that satisfies the absolute value of the difference between SNR and SNR_Loc within the left range [Loc-Count,Loc] and the right range [Loc,Loc+Count] greater than SNR_Th5, SNR_Th5=min(10,SNR_Loc×0.5)dB, and set the radial wind speed flag Flag to 0. Otherwise, the radial wind speed flag Flag remains unchanged. 5) Eliminate the signal data affected by obstacles in the blind area. For any beam within a cycle, if the maximum signal-to-noise ratio of all range gates is less than SNR_Th6, SNR_Th6=4dB, then set the radial wind speed flag of all range gates of the beam to 0, otherwise, the radial wind speed flag remains unchanged.

[0022] Step 3: Fill missing values ​​of wind speed based on moving window: 1) Initialize the size of the moving window in the radial wind speed data set to m×n, where m represents the number of radial range gates, n represents the number of tangential beams, the number of intervals for radial speed division is number, the minimum interval for radial wind speed is vbin, the neighborhood threshold P1, neighborhood threshold P2, neighborhood threshold P3, and neighborhood threshold P4, where m=5, n=3, number=10, vbin=0.5, P1=m×n / number, P2=m×n / number×2, P3=m×n×3 / 5, and P4=m×n×2 / 3; 2) Radial velocity interval division: For the radial wind speed set V of the center point (i, j) in the moving window, the size is m×n, Vc=V(i,j), where i represents the range gate direction and j represents the beam direction. The number of valid radial wind speeds in the statistical window is N. The radial wind speed set bin_x is divided into equal speed intervals vbin based on the minimum and maximum values ​​of the radial wind speed. The frequency of number equal speed intervals based on bin_x is x. The radial wind speed set bin_xp=[Vc-vbin / 2,Vc+vbin / 2] is divided into the center point speed interval bin_xp based on the speed interval vbin. The frequency xp of the center point speed interval bin_xp is calculated. The center speed Vp of each speed interval is the average of the upper and lower limits of each speed division interval. The maximum frequency value xmax=max(x,xp) and the center speed Vp_xmax of the interval corresponding to the maximum frequency are calculated.

[0023] 3) Fill the radial wind speed based on the threshold. If xp≥P1, the radial wind speed V(i,j) of the point is Vc, and its radial wind speed flag is 1; otherwise, if xmax≥P2, the radial wind speed V(i,j) of the point is Vp_xmax, and its radial wind speed flag is 1; otherwise, if (N>P3&&N≤P4)||(N>P4), the radial wind speed V(i,j) of the point is the middle value of the radial wind speed set V in the moving window, and its radial wind speed flag is 1; otherwise, the radial wind speed and data flag remain unchanged.

[0024] Step 4: Filter based on wind speed profile: 1) Based on the radial wind speed data set V that has completed the above steps, the radial wind speed data flag Flag, and the number of search range gates Count, for any beam j within a cycle, obtain the range gate index ind1 with Flag==1 and the range gate index ind2 with Flag==0. If the first element value of ind1 is greater than the last element value of ind2, then Index(j)=ind1(1); otherwise, if the number of elements in ind1 is greater than or equal to 2×Count, then Index(j)=ind1(2×Count); otherwise, Index(j)=range gate number m. Loop through each beam in turn to obtain the range gate index set Index for which the radial wind speed sets of all beams are valid profiles.

[0025] 2) Perform median filtering on the range gate index set Index of all the light beams obtained above to obtain the middle value Index_med of the range gate index, and set the radial wind speed flag Flag of all light beams between Index_med and the range gate number m to 0.

[0026] Step 5: Generate a quality-controlled radial wind speed dataset: Based on the final generated radial wind speed data flag Flag, if Flag==0, the corresponding radial wind speed is set to empty, otherwise, the corresponding radial wind speed data is retained, and finally a radial wind speed data set after quality control is generated. The comparison results before and after data quality control are as follows: Figure 2 and Figure 3 As shown, the black solid points in the figure represent the radial wind speed data points measured by the laser wind measurement lidar.

[0027] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, the above is only a preferred embodiment of the present invention. Since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with the technical field is within the technical scope disclosed by the present invention. For ordinary technicians in the technical field, changes or replacements that can be easily thought of should be covered within the protection scope of the present invention without departing from the principle of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A quality control method based on laser wind radar data, characterized in that The following steps are involved: 1) Obtain radial data of the laser wind radar: set the elevation and azimuth ranges of the laser wind radar, perform periodic scanning, and obtain a radial data set within a cycle; 2) Eliminate outliers based on signal-to-noise ratio: 2.1) Based on the obtained radial wind speed data set, determine whether the radial wind speed value is Nan. If it is Nan, the radial wind speed data flag Flag=0, otherwise, the radial wind speed data flag Flag=1; 2.2) Weak echo signal elimination: determine whether the signal data belongs to weak echo signal data. If so, set the radial wind speed flag Flag from the starting range gate Start_gate to the last range gate End_gate of the radial wind speed data set to 0. Otherwise, the radial wind speed flag Flag remains unchanged. 2.3) Eliminate the signal data blocked by the hard target, and determine whether the signal data belongs to the signal data blocked by the hard target. If so, set the radial wind speed flag Flag to 0, otherwise, the radial wind speed flag Flag remains unchanged; 2.4) Eliminate the signal data blocked by obstacles in the range gate, and determine whether the signal data is blocked by obstacles in the range gate. If so, set the radial wind speed flag to 0; otherwise, the radial wind speed flag remains unchanged. 2.5) Eliminate the signal data affected by obstacles in the blind area, and determine whether the signal data is blocked by obstacles in the blind area. If so, set the radial wind speed flag of all range gates of the light beam that generates the signal data to 0, otherwise, the radial wind speed flag remains unchanged; 3) Fill in missing wind speed values ​​based on moving windows: 3.1) Initialize the size of the moving window in the radial wind speed data set to m×n, where m represents the number of radial range gates, n represents the number of tangential beams, the number of intervals divided by the radial speed is number, the minimum interval of the radial wind speed is vbin, the neighborhood threshold P1, the neighborhood threshold P2, the neighborhood threshold P3, and the neighborhood threshold P4, where m=5, n=3, number=10, vbin=0.5, P1=m×n / number, P2=m×n / number×2, P3=m×n×3 / 5, and P4=m×n×2 / 3; 3.2) Radial velocity interval division: For the radial wind speed set V of the center point (i, j) in the moving window, the size is m×n, Vc=V(i,j), where i represents the range gate direction, j represents the beam direction, and the number of valid radial wind speeds in the statistical window is N. The radial wind speed set bin_x is divided into equal speed intervals vbin based on the minimum and maximum values ​​of the radial wind speed. The frequency of number equal speed intervals based on bin_x is x. The radial wind speed set bin_xp=[Vc-vbin / 2,Vc+vbin / 2] is divided into the center point speed interval bin_xp based on the speed interval vbin. The frequency xp of the center point speed interval bin_xp is calculated. The center speed Vp of each speed interval is the average of the upper and lower limits of each speed division interval. The maximum frequency value xmax=max(x,xp) and the center speed Vp_xmax of the interval corresponding to the maximum frequency are calculated. 3.3) Fill in the radial wind speed based on the threshold. If xp≥P1, the radial wind speed V(i,j)=Vc at the point, and its radial wind speed flag position is 1; otherwise, if xmax≥P2, the radial wind speed V(i,j)=Vp_xmax at the point, and its radial wind speed flag position is 1; otherwise, if (N>P3 && N≤P4)||(N>P4), the radial wind speed V(i,j) at the point = the middle value of the radial wind speed set V in the moving window, and its radial wind speed flag position is 1; otherwise, the radial wind speed and data flag positions remain unchanged; 4) Based on wind speed profile filtering, the range gate index set of all beam radial wind speed sets as the valid profile is obtained and the median is obtained as Index, the median value of the range gate index is obtained as Index_med, and the radial wind speed flag Flag of all beams between Index_med and the range gate number m is set to 0; 5) Generate a radial wind speed data set after quality control: Based on the final radial wind speed data flag Flag, if Flag==0, the corresponding radial wind speed is set to empty, otherwise, the corresponding radial wind speed data is retained, and finally a radial wind speed data set after quality control is generated.

2. The quality control method based on laser wind radar data according to claim 1 is characterized in that: Step 1) The data set includes data subsets such as radial wind speed and signal-to-noise ratio, and each data subset has a dimension of M×N, where M represents the number of range gates, representing different radial distances, and N represents the number of scanning beams, representing different tangential orientations.

3. The quality control method based on laser wind radar data according to claim 1 is characterized in that: The weak echo signal data judgment process described in step 2.2) is specifically as follows: for any light beam within a cycle, find the starting range gate Start_gate whose signal-to-noise ratio SNR of 2×Count consecutive range gates is lower than the signal-to-noise ratio threshold SNR_Th1, where SNR_Th1=0.5dB, Count represents the number of search range gates, and is dynamically adjusted according to the equipment parameters of the laser wind radar; if Count=round (laser pulse width×0.15 / adjacent range gate spacing) is satisfied, the light beam is considered to belong to weak echo signal data.

4. The quality control method based on laser wind radar data according to claim 1 is characterized in that: The signal data judgment process blocked by the hard target in step 2.3) is as follows: for any light beam within a period, the range gate position Loc with SNR greater than the threshold SNR_Th2 and the maximum point and its corresponding signal-to-noise ratio value SNR_Loc are screened out, SNR_Th2=25dB, and considering the spatial relationship between the blocked signal and other range gates, the signal data that satisfies both the absolute value of the difference between SNR and SNR_Loc in the left range [Loc-Count, Loc] and the right range [Loc, Loc+Count] is screened out, SNR_Th3=10dB, and is recorded as the signal data blocked by the hard target.

5. The quality control method based on laser wind radar data according to claim 1 is characterized in that: Step 2.4) The signal data judgment process blocked by obstacles in the range gate is as follows: for any light beam within a period, the range gate position Loc and its corresponding signal-to-noise ratio value SNR_Loc whose SNR is greater than the threshold SNR_Th4 and is the maximum point are screened out, SNR_Th4=4dB, and considering the spatial relationship between the blocked signal and other range gates, the signal data that satisfies both the absolute value of the difference between SNR and SNR_Loc in the left range [Loc-Count,Loc] and the right range [Loc,Loc+Count] is screened out, SNR_Th5=min(10,SNR_Loc×0.5)dB, which is recorded as the signal data blocked by obstacles in the range gate.

6. The quality control method based on laser wind radar data according to claim 1 is characterized in that: The signal data judgment process of step 2.5) affected by obstacles in the blind spot is as follows: for any light beam in a cycle, if the maximum signal-to-noise ratio of all range gates is less than SNR_Th6, SNR_Th6=4dB, it is recorded as signal data affected by obstacles in the blind spot.

7. The quality control method based on laser wind radar data according to claim 1 is characterized in that: Step 4) The wind speed profile filtering process is as follows: 4.1) Based on the radial wind speed data set V, radial wind speed data flag Flag, and search range gate number Count obtained in steps 1) to 3), for any beam j within a cycle, obtain the range gate index ind1 with Flag==1 and the range gate index ind2 with Flag==0. If the first element value of ind1 is greater than the last element value of ind2, then Index(j)=ind1(1); otherwise, if the number of elements in ind1 is greater than or equal to 2×Count, then Index(j)=ind1(2×Count); otherwise, Index(j)=range gate number m; loop through each beam in turn, and obtain the range gate index set with valid profiles for all beam radial wind speed sets as Index; 4.2) Median filtering step 4.1) Get the range gate index set Index of all beams, get the middle value Index_med of the range gate index, and set the radial wind speed flag Flag of all beams between Index_med and the range gate number m to 0.

8. A device for performing a quality control method based on laser wind radar data, characterized in that: The system comprises at least a processor and a memory, wherein the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory, and comprises data input, data processing, and data output modules, and executes the quality control method based on laser wind radar data as described in claim 1.

9. A computer-readable storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is executed, the quality control method based on laser wind radar data as claimed in claim 1 is implemented.

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