Attitude compensation method, system and equipment of vehicle-mounted wind measurement laser radar and medium

Abstract

The invention provides an attitude compensation method, system and device for a vehicle-mounted wind measurement laser radar and a medium. The attitude compensation method comprises the steps of obtaining attitude information of a vehicle, installation position information and scanning parameters of the vehicle-mounted three-dimensional scanning wind measurement laser radar and collected atmosphere detection data; according to the attitude information, the installation position information and the scanning parameters of the vehicle, attitude correction information of the laser radar is determined; performing attitude compensation on the atmosphere detection data according to the attitude correction information to obtain compensated atmosphere detection data; performing timestamp alignment on the compensated atmospheric detection data, the positioning information of the vehicle and the mileage counting information to obtain fused atmospheric detection data, and performing attitude compensation on the laser radar according to the fused atmospheric detection data; the method can achieve the precise compensation of the attitude of the vehicle-mounted three-dimensional scanning wind measurement laser radar, eliminates the interference of the vehicle attitude, the installation position and the scanning parameters, guarantees the accuracy of atmospheric detection data in a moving state, and improves the measurement precision of an atmospheric wind field.

Description

Technical Field

[0001] This invention relates to the field of attitude control technology for vehicle-mounted lidar, specifically to an attitude compensation method, system, device, and medium for vehicle-mounted wind-measuring lidar. Background Technology

[0002] Currently, with the industrialization of fields such as real-time road weather monitoring and mobile wind field mapping, vehicle-mounted 3D scanning wind-measuring lidar has become a core device for acquiring dynamic wind field data due to its ability to detect atmospheric wind fields while in motion. Equipped with an adjustable azimuth and elevation scanning head, this radar can accurately collect atmospheric echo intensity and atmospheric radial velocity at various detection distances through scanning modes such as PPI (Plane Position Indicator), RHI (Range and Altitude Indicator), and DBS (Doppler Beam Sharpening), providing crucial data support for the spatial distribution inversion and real-time analysis of atmospheric wind fields.

[0003] However, in real-world mobile applications, the dynamic attitude changes of vehicles during operation become a key interference factor affecting the detection accuracy of vehicle-mounted 3D scanning wind lidar. For example, vehicles experience pitch shifts when traversing slopes, roll changes when turning, and even momentary heading jitters when driving on bumpy roads. These attitude changes directly cause the radar body and scanning head to deviate from the preset installation reference. Because existing technologies do not effectively correct for the interference of vehicle attitude changes on the scanning head's detection direction, the actual detection trajectory of the scanning head deviates from the preset PPI loop path, RHI vertical profile, or DBS beam direction. This results in a misalignment between the spatial location associated with the collected atmospheric detection data (atmospheric backlight intensity, atmospheric radial velocity) and the target location in the actual atmospheric environment. For example, wind field data from the left side of the road may be mismatched to the right side, or radial velocity data from a certain high-altitude layer may be incorrectly associated with a lower altitude. Consequently, the spatial distribution measurement results of the atmospheric wind field are distorted and cannot accurately reflect the actual wind field conditions. Summary of the Invention

[0004] To address the issue that existing attitude correction methods for vehicle-mounted 3D scanning wind-measuring lidar fail to effectively resolve the issue of scanning head detection direction deviation caused by attitude changes such as pitch, roll, and yaw during vehicle movement, resulting in misalignment between atmospheric detection data and the actual atmospheric spatial location, ultimately affecting the accuracy of atmospheric wind field measurements and failing to meet the application requirements for precise wind measurement in mobile scenarios, this invention proposes an attitude compensation method for vehicle-mounted wind-measuring lidar, comprising:

[0005] Acquire vehicle attitude information, installation location information and scanning parameters of the vehicle-mounted 3D scanning wind measurement lidar, and atmospheric detection data collected by the vehicle-mounted 3D scanning wind measurement lidar;

[0006] Based on the vehicle's attitude information, the installation location information, and the scanning parameters, the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar is determined;

[0007] The attitude correction information is used to perform attitude compensation on the atmospheric sounding data to obtain compensated atmospheric sounding data.

[0008] The compensated atmospheric sounding data, the vehicle's positioning information, and mileage count information are timestamped to obtain fused atmospheric sounding data, and attitude compensation is performed on the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the fused atmospheric sounding data.

[0009] Optionally, determining the attitude correction information of the vehicle-mounted 3D scanning wind-measuring lidar based on the vehicle's attitude information, the installation location information, and the scanning parameters includes:

[0010] The vehicle's attitude information is converted into a vehicle coordinate system rotation matrix;

[0011] Based on the installation location information, the translation vector of the vehicle-mounted three-dimensional scanning wind measurement lidar relative to the vehicle coordinate system and the angle adjustment reference vector of the scanning head of the vehicle-mounted three-dimensional scanning wind measurement lidar relative to the vehicle-mounted three-dimensional scanning wind measurement lidar are determined.

[0012] Based on the vehicle coordinate system rotation matrix, the translation vector, and the angle adjustment reference vector, and in conjunction with the scanning parameters, a three-level attitude transformation matrix is ​​established between the vehicle coordinate system, the radar coordinate system, and the scanning head detection coordinate system.

[0013] The attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar is extracted from the three-level attitude transformation matrix.

[0014] Optionally, the step of performing attitude compensation on the atmospheric sounding data based on the attitude correction information to obtain compensated atmospheric sounding data includes:

[0015] The attitude correction information and the atmospheric sounding data are matched according to the scanning trajectory to obtain the spatial coordinate mapping relationship between the attitude correction information and the atmospheric sounding data of each sounding point;

[0016] Based on the spatial coordinate mapping relationship, the atmospheric sounding data is rotated and compensated to obtain rotated and compensated atmospheric sounding data;

[0017] Based on the spatial coordinate mapping relationship, spatial translation compensation is performed on the rotation-compensated atmospheric sounding data to obtain spatially compensated atmospheric sounding data;

[0018] The spatially compensated atmospheric sounding data is time-synchronized and normalized according to the scanning cycle to obtain compensated atmospheric sounding data.

[0019] Optionally, the calculation formula corresponding to the spatial translation compensation is as follows:

[0020] ;

[0021] in, This represents the spatial coordinates corresponding to the spatially compensated atmospheric sounding data. This represents the inverse of the rotation matrix corresponding to the rotation compensation applied to atmospheric sounding data; This represents the spatial coordinate data of the detection point corresponding to the current detection direction of the scanning head; This represents the translation parameter.

[0022] Optionally, the step of aligning the compensated atmospheric sounding data, the vehicle's location information, and mileage counting information with timestamps to obtain fused atmospheric sounding data includes:

[0023] The compensated atmospheric detection data, the vehicle's positioning information, and the mileage count information are time-stamped according to the scanning frames of the scanning head of the vehicle-mounted three-dimensional scanning wind-measuring lidar to obtain time-synchronized atmospheric detection data, positioning information, and mileage count information.

[0024] Based on the time-synchronized positioning information and mileage count information, and combined with the scanning trajectory of the scanning head, preliminary spatial registration is performed on the compensated atmospheric sounding data to obtain preliminary registered atmospheric sounding data.

[0025] The improved iterative nearest point algorithm is used to iteratively register the initially registered atmospheric sounding data to obtain fused atmospheric sounding data.

[0026] Optionally, the step of iteratively registering the initially registered atmospheric sounding data using an improved iterative nearest-point algorithm to obtain fused atmospheric sounding data includes:

[0027] Step S1: Obtain the reference atmospheric sounding dataset and use the preliminary registration atmospheric sounding data as the input atmospheric sounding dataset to initialize the number of iterations;

[0028] Step S2: For each detection point in the input atmospheric sounding dataset, find detection points with the same spatial location and the same scanning angle in the reference atmospheric sounding dataset according to the scanning trajectory of the detection point, and form a valid detection point pair;

[0029] Step S3: Construct a deviation vector matrix based on the valid detection point pairs corresponding to each detection point in the input atmospheric sounding dataset;

[0030] Step S4: Update the input atmospheric sounding dataset according to the deviation vector matrix to obtain the updated atmospheric sounding dataset;

[0031] Step S5: Based on the updated atmospheric sounding dataset, obtain the updated effective sounding point pairs; and determine whether the average spatial distance between the updated effective sounding point pairs is greater than a preset distance threshold, or whether the average atmospheric radial movement velocity difference is greater than a preset velocity threshold, or whether the number of iterations is less than the maximum value; if any of the judgment conditions are met, proceed to step S2; otherwise, proceed to step S6.

[0032] Step S6: Use the updated atmospheric sounding dataset as fused atmospheric sounding data.

[0033] Optionally, updating the input atmospheric sounding dataset based on the deviation vector matrix to obtain the updated atmospheric sounding dataset includes:

[0034] By solving the deviation vector matrix, the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters between the reference atmospheric sounding dataset and the input atmospheric sounding dataset are obtained;

[0035] The optimal transformation matrix is ​​obtained based on the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters.

[0036] Based on the optimal transformation matrix, the spatial position and atmospheric radial velocity of the input atmospheric sounding dataset are updated synchronously to obtain the updated atmospheric sounding dataset.

[0037] Based on the same inventive concept, the present invention also provides an attitude compensation system for a vehicle-mounted lidar, comprising:

[0038] The information acquisition module is used to acquire the vehicle's attitude information, the installation location information and scanning parameters of the vehicle-mounted three-dimensional scanning wind measurement lidar, and the atmospheric detection data collected by the vehicle-mounted three-dimensional scanning wind measurement lidar.

[0039] An attitude correction module is used to determine the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the vehicle's attitude information, the installation position information, and the scanning parameters.

[0040] The data compensation module is used to perform attitude compensation on the atmospheric sounding data according to the attitude correction information to obtain compensated atmospheric sounding data.

[0041] The attitude compensation module is used to timestamp-align the compensated atmospheric detection data, the vehicle's positioning information, and mileage counting information to obtain fused atmospheric detection data, and to perform attitude compensation on the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the fused atmospheric detection data.

[0042] Optionally, the attitude correction module includes:

[0043] The information conversion submodule is used to convert the vehicle's attitude information into a vehicle coordinate system rotation matrix;

[0044] An angle adjustment submodule is used to determine the translation vector of the vehicle-mounted 3D scanning wind measurement lidar relative to the vehicle coordinate system and the angle adjustment reference vector of the scanning head of the vehicle-mounted 3D scanning wind measurement lidar relative to the vehicle-mounted 3D scanning wind measurement lidar based on the installation position information.

[0045] The attitude transformation submodule is used to establish a three-level attitude transformation matrix between the vehicle coordinate system, the radar coordinate system, and the scanning head detection coordinate system based on the vehicle coordinate system rotation matrix, the translation vector, and the angle adjustment reference vector, combined with the scanning parameters.

[0046] The information extraction submodule is used to extract the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar from the three-level attitude transformation matrix.

[0047] Optionally, the data compensation module includes:

[0048] The information matching submodule is used to match the attitude correction information and the atmospheric sounding data according to the scanning trajectory to obtain the spatial coordinate mapping relationship between the attitude correction information and the atmospheric sounding data of each sounding point;

[0049] The rotation compensation submodule is used to perform rotation compensation on the atmospheric sounding data according to the spatial coordinate mapping relationship to obtain the rotation-compensated atmospheric sounding data;

[0050] The spatial compensation submodule is used to perform spatial translation compensation on the rotationally compensated atmospheric sounding data according to the spatial coordinate mapping relationship, so as to obtain spatially compensated atmospheric sounding data.

[0051] The time synchronization submodule is used to perform time synchronization and normalization processing on the spatially compensated atmospheric sounding data according to the scanning cycle to obtain compensated atmospheric sounding data.

[0052] Optionally, the calculation formula corresponding to the spatial translation compensation is as follows:

[0053] ;

[0054] in, This represents the spatial coordinates corresponding to the spatially compensated atmospheric sounding data. This represents the inverse of the rotation matrix corresponding to the rotation compensation applied to atmospheric sounding data; This represents the spatial coordinate data of the detection point corresponding to the current detection direction of the scanning head; This represents the translation parameter.

[0055] Optionally, the attitude compensation module includes:

[0056] The time alignment submodule is used to align the compensated atmospheric sounding data, the vehicle's positioning information, and the mileage counting information with the timestamps of the scanning frames of the scanning head of the vehicle-mounted three-dimensional scanning wind-measuring lidar, so as to obtain time-synchronized atmospheric sounding data, positioning information, and mileage counting information.

[0057] The spatial registration submodule is used to perform preliminary spatial registration on the compensated atmospheric sounding data based on the time-synchronized positioning information and odometer count information, combined with the scanning trajectory of the scanning head, to obtain preliminary registered atmospheric sounding data.

[0058] The data fusion submodule is used to iteratively register the initially registered atmospheric sounding data using an improved iterative nearest-point algorithm to obtain fused atmospheric sounding data.

[0059] Optionally, the data fusion submodule is specifically used for:

[0060] Step S1: Obtain the reference atmospheric sounding dataset and use the preliminary registration atmospheric sounding data as the input atmospheric sounding dataset to initialize the number of iterations;

[0061] Step S2: For each detection point in the input atmospheric sounding dataset, find detection points with the same spatial location and the same scanning angle in the reference atmospheric sounding dataset according to the scanning trajectory of the detection point, and form a valid detection point pair;

[0062] Step S3: Construct a deviation vector matrix based on the valid detection point pairs corresponding to each detection point in the input atmospheric sounding dataset;

[0063] Step S4: Update the input atmospheric sounding dataset according to the deviation vector matrix to obtain the updated atmospheric sounding dataset;

[0064] Step S5: Based on the updated atmospheric sounding dataset, obtain the updated effective sounding point pairs; and determine whether the average spatial distance between the updated effective sounding point pairs is greater than a preset distance threshold, or whether the average atmospheric radial movement velocity difference is greater than a preset velocity threshold, or whether the number of iterations is less than the maximum value; if any of the judgment conditions are met, proceed to step S2; otherwise, proceed to step S6.

[0065] Step S6: Use the updated atmospheric sounding dataset as fused atmospheric sounding data.

[0066] Optionally, the data fusion submodule includes: a dataset update unit, specifically including:

[0067] The parameter extraction subunit is used to obtain the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters between the reference atmospheric sounding dataset and the input atmospheric sounding dataset by solving the deviation vector matrix.

[0068] The matrix generation subunit is used to obtain the optimal transformation matrix based on the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters;

[0069] The synchronous update subunit is used to synchronously update the spatial position and atmospheric radial velocity of the input atmospheric sounding dataset according to the optimal transformation matrix, so as to obtain the updated atmospheric sounding dataset.

[0070] In another aspect, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0071] The memory is used to store one or more programs;

[0072] When the one or more programs are executed by the at least one processor, the attitude compensation method of a vehicle-mounted wind-measuring lidar as described above is implemented.

[0073] In another aspect, the present invention also provides a computer device readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the attitude compensation method of a vehicle-mounted wind-measuring lidar as described above.

[0074] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0075] This invention provides an attitude compensation method, system, device, and medium for a vehicle-mounted wind-measuring lidar, comprising: acquiring vehicle attitude information, installation location information and scanning parameters of the vehicle-mounted 3D scanning wind-measuring lidar, and atmospheric detection data collected by the vehicle-mounted 3D scanning wind-measuring lidar; determining attitude correction information of the vehicle-mounted 3D scanning wind-measuring lidar based on the vehicle attitude information, the installation location information, and the scanning parameters; performing attitude compensation on the atmospheric detection data based on the attitude correction information to obtain compensated atmospheric detection data; aligning the compensated atmospheric detection data, the vehicle's positioning information, and mileage count information with timestamps to obtain fused atmospheric detection data, and performing attitude compensation on the vehicle-mounted 3D scanning wind-measuring lidar based on the fused atmospheric detection data. This invention utilizes a three-dimensional scanning wind-measuring lidar for attitude compensation. By fusing multi-source information to determine attitude correction information, the correction information can compensate for attitude interference such as vehicle pitch and roll, and adapt to radar installation offset characteristics and different scanning trajectory requirements, thereby improving the accuracy of attitude correction. Based on the attitude correction information, directional compensation is applied to atmospheric detection data, which can directly correct the detection direction offset caused by changes in vehicle attitude. This helps ensure that the spatial position corresponding to the detection data is consistent with the real atmospheric environment, guaranteeing the authenticity of wind field inversion data from the source. Therefore, the method of this invention can achieve high-precision and stable measurement of the spatial distribution and radial velocity of atmospheric wind fields under moving conditions, meeting the stringent requirements for wind measurement accuracy in scenarios such as road meteorological monitoring and mobile wind field mapping. Attached Figure Description

[0076] Figure 1 A flowchart illustrating the attitude compensation method for a vehicle-mounted wind-measuring lidar provided by the present invention;

[0077] Figure 2 This is a schematic diagram of the attitude compensation framework in the attitude compensation method of a vehicle-mounted wind-measuring lidar provided by the present invention.

[0078] Figure 3 A schematic diagram of the framework for iterative configuration using the iterative nearest point algorithm in the attitude compensation method of a vehicle-mounted wind-measuring lidar provided by the present invention;

[0079] Figure 4 A schematic diagram of the structural composition of an attitude compensation system for a vehicle-mounted wind-measuring lidar provided by the present invention;

[0080] Figure 5 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0081] This invention proposes an attitude compensation method, system, device, and medium for vehicle-mounted wind-measuring lidar. The specific embodiments of this invention will be further described in detail below with reference to the accompanying drawings.

[0082] Example 1:

[0083] This invention provides an attitude compensation method for a vehicle-mounted wind-measuring lidar, the flowchart of which is shown below. Figure 1 As shown, it includes:

[0084] Step 1: Acquire the vehicle's attitude information, the installation location information and scanning parameters of the vehicle-mounted 3D scanning wind measurement lidar, and the atmospheric detection data collected by the vehicle-mounted 3D scanning wind measurement lidar;

[0085] Step 2: Determine the attitude correction information of the vehicle-mounted 3D scanning wind-measuring lidar based on the vehicle's attitude information, the installation location information, and the scanning parameters;

[0086] Step 3: Perform attitude compensation on the atmospheric sounding data based on the attitude correction information to obtain compensated atmospheric sounding data;

[0087] Step 4: Timestamp-align the compensated atmospheric detection data, the vehicle's positioning information, and mileage count information to obtain fused atmospheric detection data, and perform attitude compensation on the vehicle-mounted three-dimensional scanning wind measurement lidar based on the fused atmospheric detection data.

[0088] Generally, existing technologies for attitude compensation of vehicle-mounted 3D scanning wind-measuring lidar mostly rely solely on the radar's own attitude data provided by the inertial measurement unit (IMU) or vehicle positioning data from the Global Navigation Satellite System (GNSS). Some may supplement with a small amount of basic radar parameters, but they generally fail to incorporate the vehicle's dynamic attitude information during movement, the radar's specific installation position relative to the vehicle, and the radar's actual scanning parameters into the compensation logic. Furthermore, they do not coordinate these data with the radar's real-time acquisition data. This limitation in data acquisition prevents existing attitude compensation methods from accurately quantifying the differentiated interference of vehicle attitude changes on radars at different installation positions, and also makes it difficult to adapt to the characteristics of radar detection trajectories under different scanning modes. The demand for this technology leads to a disconnect between attitude correction information and the actual working state of the radar. This not only fails to effectively correct the scanning head's detection direction deviation caused by vehicle pitch and roll, but also causes the spatial position corresponding to the atmospheric detection data to be misaligned with the real atmospheric environment. Ultimately, this affects the accuracy and reliability of atmospheric wind field measurement, making it difficult to meet the stringent requirements for data accuracy in mobile wind measurement scenarios. To solve the above problems, this invention considers the targeted introduction of vehicle attitude information, installation position information and scanning parameters of the vehicle-mounted 3D scanning wind measurement lidar, as well as the atmospheric detection data collected by the radar, when performing attitude compensation on the vehicle-mounted 3D scanning wind measurement lidar. Through the coordinated collection and comprehensive utilization of these key data, high-precision measurement of the atmospheric wind field can be achieved.

[0089] For example, the vehicle attitude information in step 1 above may include pitch changes and roll changes;

[0090] The installation location information may include: front-to-back offset and height deviation;

[0091] The scanning parameters may include: angle step size in PPI mode, scanning range in RHI mode, and beam pointing in DBS mode;

[0092] The atmospheric sounding data may include: atmospheric backlight intensity and atmospheric radial movement velocity, etc.

[0093] After considering the specific composition of vehicle attitude, installation position, scanning parameters, and atmospheric detection data in the above steps, we can further consider establishing geometric and kinematic relationships between these data. For example, we can quantify the interference of pitch / roll changes on the radar, the spatial impact of front / rear / altitude offsets, and the beam characteristics under different scanning modes, in order to accurately deduce the direction and magnitude of radar attitude correction. Specifically:

[0094] In one implementation, the process of determining the attitude correction information of the vehicle-mounted 3D scanning wind-measuring lidar in step 2 above, based on the vehicle's attitude information, the installation location information, and the scanning parameters, may include:

[0095] The vehicle's attitude information is converted into a vehicle coordinate system rotation matrix;

[0096] Based on the installation location information, determine the translation vector of the vehicle-mounted 3D scanning wind measurement lidar relative to the vehicle coordinate system and the angle adjustment reference vector of the scanning head of the vehicle-mounted 3D scanning wind measurement lidar relative to the vehicle-mounted 3D scanning wind measurement lidar (for example, it may include azimuth reference and pitch reference).

[0097] Based on the vehicle coordinate system rotation matrix, the translation vector, and the angle adjustment reference vector, and in conjunction with the scanning parameters, a three-level attitude transformation matrix is ​​established between the vehicle coordinate system, the radar coordinate system, and the scanning head detection coordinate system.

[0098] The attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar is extracted from the three-level attitude transformation matrix (for example, it may include vehicle attitude interference compensation amount and scanning head azimuth / pitch angle correction amount).

[0099] In this implementation, by converting vehicle attitude information into a vehicle coordinate system rotation matrix, the interference of vehicle pitch, roll, and other dynamic attitudes on the radar can be accurately quantified, avoiding the correction ambiguity caused by unquantified attitude interference. Simultaneously, based on the installation position information, the radar's translation vector relative to the vehicle and the scanning head's angle adjustment reference vector relative to the radar are determined, ensuring that vehicle attitude correlation adapts to the radar's actual installation offset characteristics and the scanning head's independent adjustment attributes. Furthermore, a three-level attitude transformation matrix is ​​established, combining scanning parameters for vehicle, radar, and scanning head detection. This matrix synchronously incorporates the angle step size, beam pointing, and other characteristics of scanning modes such as PPI / RHI / DBS, ensuring deep coupling between attitude correlation and the radar's actual operating state, preventing the correction direction from deviating from detection requirements due to separation from scanning parameters. Finally, the attitude correction information extracted from the three-level matrix integrates vehicle attitude compensation, installation offset adaptation, and scanning mode adaptation, eliminating the cumulative errors easily generated by multi-coordinate system step-by-step transformation. This ensures that the correction information not only compensates for basic attitude deviations but also conforms to the radar's characteristics throughout the entire process from installation to operation, guaranteeing a high degree of match between the radar's attitude correction accuracy and detection requirements under different installation positions and scanning modes.

[0100] The attitude correction information extracted from the three-level attitude transformation matrix through the above steps can accurately quantify the combined interference of vehicle attitude, installation offset, and scanning mode on radar detection. This quantification result needs to be further applied to atmospheric detection data. For example, a spatial correlation can be established between the correction information and the atmospheric backlight intensity and radial movement velocity at each detection point to eliminate detection data misalignment caused by the scanner's attitude offset. Specifically:

[0101] In one implementation, such as Figure 2 As shown, step 3 above involves performing attitude compensation on the atmospheric sounding data based on the attitude correction information to obtain compensated atmospheric sounding data, which may include:

[0102] The attitude correction information and the atmospheric sounding data are matched according to the scanning trajectory (e.g., it may include PPI circular trajectory / RHI vertical profile trajectory / DBS beam trajectory) to obtain the spatial coordinate mapping relationship between the attitude correction information and the atmospheric sounding data of each detection point (corresponding to the atmospheric sounding position under the current azimuth / pitch angle of the scanning head).

[0103] Based on the spatial coordinate mapping relationship, the atmospheric detection data is rotated (to compensate for the offset of the scanning head detection direction by vehicle pitch / roll) to obtain rotated-compensated atmospheric detection data.

[0104] Based on the spatial coordinate mapping relationship, spatial translation compensation is performed on the rotationally compensated atmospheric detection data (to compensate for the deviation of vehicle displacement from the spatial position of the detection point) to obtain spatially compensated atmospheric detection data.

[0105] The spatially compensated atmospheric sounding data is time-synchronized and normalized according to the scanning cycle (to eliminate time deviations in different scanning modes) to obtain the compensated atmospheric sounding data.

[0106] In this implementation, attitude correction information is matched with atmospheric sounding data according to specific scanning trajectories (such as PPI circular trajectory, RHI vertical profile trajectory, and DBS beam trajectory). This ensures that the two are precisely mapped to each detection point along the actual scanning path. The resulting spatial coordinate mapping directly relates to the atmospheric sounding position of the scanning head at the current azimuth / pitch angle, avoiding the problem of misalignment between correction information and detection points due to deviation from the scanning trajectory matching. Based on the obtained mapping relationship, rotational compensation is performed to address the scanning head's detection direction offset caused by vehicle pitch and roll. This is not a simple rotation process, but rather a targeted elimination of the interference of attitude changes on the detection direction, thereby ensuring that the detection angle corresponding to the atmospheric sounding data is consistent with the preset scanning direction. Based on the mapping relationship, the spatial position deviation of the detection points caused by vehicle displacement is compensated, so that the atmospheric backlight intensity, radial movement speed and other data of each detection point can be accurately associated with the position in the real atmosphere, and the data position will not be distorted due to vehicle movement. Finally, the spatially compensated atmospheric detection data is time-synchronized and normalized according to the scanning cycle, specifically to eliminate the time deviation caused by the difference in the acquisition cycle of different scanning modes such as PPI, RHI and DBS. Therefore, the method of this invention can not only accurately match the real atmospheric environment in the spatial dimension, but also maintain consistency in the temporal dimension, and at the same time make the compensation effect adaptable to the working characteristics of different scanning modes, without the compensation accuracy decreasing due to mode differences.

[0107] For example, the calculation formula corresponding to the spatial translation compensation can be as follows:

[0108] ;

[0109] in, This represents the spatial coordinates corresponding to the spatially compensated atmospheric sounding data. This represents the inverse of the rotation matrix corresponding to the rotation compensation of atmospheric sounding data (for example, it can include the rotation relationship after the scanning head azimuth / elevation angle is corrected). This represents the spatial coordinates of the detection point corresponding to the current detection direction of the scanning head (e.g., it can be calculated based on the PPI / RHI / DBS scanning trajectory). This represents translation parameters (e.g., vehicle displacement compensation and the translation of the scanner head relative to the radar body). In this example, by introducing a rotation matrix inverse with scanner head azimuth / elevation correction, not only can the angular correlation error that may remain from the previous rotation compensation be offset, but the attitude deviation between the scanner head and the radar body during independent adjustment can also be specifically corrected. This avoids the problem that a conventional inverse matrix based solely on vehicle attitude cannot cover scanner head-specific interference. At the same time, the spatial coordinate data of the detection point calculated based on the PPI / RHI / DBS scanning trajectory is used as the basis for calculation, allowing the detection point coordinates to accurately match the actual path of the current scanning mode, preventing coordinate reference misalignment due to differences in scanning trajectory. Combined with translation parameters that include vehicle displacement compensation and the translation of the scanner head relative to the radar body, the overall offset of the detection point caused by vehicle movement and the positional deviation between the scanner head and the radar body during installation can be eliminated simultaneously. Therefore, this example can accurately calibrate multi-dimensional interference such as vehicle attitude, scanner head adjustment, scanning mode, and installation offset through mathematical coupling, significantly reducing the deviation between the spatially compensated atmospheric detection data coordinates and the actual atmospheric detection position, thereby ensuring the consistency of spatial correlation of detection points under different scanning modes.

[0110] After performing rotation, translation compensation, and time normalization on the atmospheric sounding data through the above steps, spatial misalignment of single-source data can be corrected. However, the sounding data still needs to be coordinated with the real-time motion status of the vehicle. Therefore, it is possible to consider introducing vehicle positioning information to reflect the current spatial position and odometer information to record the motion trajectory. Aligning these three information according to the scan frames and completing spatial registration will achieve precise matching between the compensated sounding data and the vehicle's motion status. Specifically:

[0111] In one implementation, step 4 above, which involves aligning the compensated atmospheric sounding data, the vehicle's location information, and mileage counting information with timestamps to obtain fused atmospheric sounding data, may include:

[0112] The compensated atmospheric sounding data, the vehicle's positioning information, and the mileage count information are time-stamped according to the scanning frames (single frame scanning cycle of PPI / RHI / DBS) of the scanning head of the vehicle-mounted three-dimensional scanning wind-measuring lidar to obtain time-synchronized atmospheric sounding data, positioning information, and mileage count information.

[0113] Based on the time-synchronized positioning information and odometer information, and combined with the scanning trajectory of the scanning head (PPI ring / RHI vertical / DBS beam), preliminary spatial registration is performed on the compensated atmospheric sounding data to obtain preliminary registered atmospheric sounding data.

[0114] An improved iterative nearest-point algorithm (adapted to the characteristics of atmospheric sounding data and constrained by the consistency of atmospheric radial movement velocity) is used to iteratively register the initially registered atmospheric sounding data to obtain fused atmospheric sounding data.

[0115] In this implementation, such as Figure 3 As shown, the process of iteratively registering the initially registered atmospheric sounding data using the improved iterative nearest-point algorithm to obtain fused atmospheric sounding data may include:

[0116] Step S1: Obtain the reference atmospheric sounding dataset (stable atmospheric wind field data based on historical scan frames), and use the preliminary registered atmospheric sounding data as the input atmospheric sounding dataset to initialize the number of iterations;

[0117] Step S2: For each detection point in the input atmospheric sounding dataset, find detection points with the same spatial location and the same scanning angle in the reference atmospheric sounding dataset according to the scanning trajectory of the detection point, and form a valid detection point pair;

[0118] Step S3: Construct a deviation vector matrix (which may include spatial position deviation and atmospheric radial movement velocity deviation) based on the effective detection point pairs corresponding to each detection point in the input atmospheric detection dataset.

[0119] Step S4: Update the input atmospheric sounding dataset according to the deviation vector matrix to obtain the updated atmospheric sounding dataset;

[0120] Step S5: Based on the updated atmospheric sounding dataset, obtain the updated effective sounding point pairs; and determine whether the average spatial distance between the updated effective sounding point pairs is greater than a preset distance threshold, or whether the average atmospheric radial movement velocity difference is greater than a preset velocity threshold, or whether the number of iterations is less than the maximum value; if any of the judgment conditions are met, proceed to step S2; otherwise, proceed to step S6.

[0121] Step S6: Use the updated atmospheric sounding dataset as fused atmospheric sounding data;

[0122] In this implementation, the reference atmospheric sounding dataset uses stable atmospheric wind field data based on historical scan frames, which ensures the reliability of the registration benchmark better than randomly selecting reference data. Valid detection point pairs are selected by ensuring the same spatial location and scanning angle, eliminating false matching points caused by differences in scanning angles. The deviation vector matrix simultaneously includes spatial location deviation and atmospheric radial velocity deviation, allowing for simultaneous correction of these two dimensions when updating the input dataset, avoiding the problem of correcting only the position while ignoring the continuity of velocity data. When determining whether the iteration terminates, an average atmospheric radial velocity difference threshold is added, ensuring that the registered sounding data not only has accurate spatial location but also maintains the continuity of atmospheric radial velocity, meeting the spatiotemporal accuracy requirements of mobile wind measurement scenarios. Therefore, this implementation, through progressive optimization in time, space, and iteration, eliminates the problems of asynchronous and spatially misaligned multi-source data in the fused atmospheric sounding data. The final fused data accurately reflects the spatial distribution and radial velocity changes of the atmospheric wind field under moving conditions, thus meeting the requirements of high precision and high continuity of wind measurement data in scenarios such as road meteorological monitoring.

[0123] In one implementation, the process of updating the input atmospheric sounding dataset based on the deviation vector matrix to obtain the updated atmospheric sounding dataset may include:

[0124] By solving the deviation vector matrix (e.g., minimizing the weighted sum of spatial position deviation and atmospheric radial movement velocity deviation), the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters between the reference atmospheric sounding dataset and the input atmospheric sounding dataset are obtained.

[0125] Based on the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters, the optimal transformation matrix is ​​obtained (for example, it may include the dynamic adjustment relationship of the scanning head azimuth / elevation angle).

[0126] Based on the optimal transformation matrix, the spatial position and atmospheric radial velocity of the input atmospheric sounding dataset are updated synchronously to obtain the updated atmospheric sounding dataset.

[0127] In this implementation, the deviation vector matrix is ​​solved by minimizing the weighted sum of spatial position deviation and atmospheric radial velocity deviation. This ensures that spatial position accuracy and velocity data consistency are coordinated, avoiding the position-velocity mismatch problem during wind field inversion caused by only correcting position and ignoring velocity deviation. During the solution process, optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters are obtained simultaneously. Combined with the scanning head angle correction parameters, the attitude shift of the scanning head caused by angle adjustment under different scanning modes (PPI / RHI / DBS) is specifically compensated. The optimal transformation matrix is ​​constructed based on these three parameters. Because it includes the dynamic adjustment relationship of the scanning head's azimuth / elevation angle, it is not a fixed static matrix. It can adapt to the angle changes of the scanning head as the scanning mode switches in real time, ensuring that the transformation matrix always matches the current working state of the radar. Finally, the spatial position and atmospheric radial velocity of the input atmospheric sounding dataset are updated synchronously according to the matrix. This can avoid the timing difference or data misalignment that may occur when updating separately. The updated dataset can accurately correspond to the real atmospheric position in space and maintain the continuity of atmospheric radial velocity. It is especially suitable for the core requirements of dynamic adjustment of the scanning head and real-time continuous wind field data in mobile wind measurement scenarios.

[0128] In summary, this invention addresses the problem that existing attitude correction methods for vehicle-mounted 3D scanning wind-measuring lidar fail to effectively solve the issue of scanning head detection direction offset caused by attitude changes such as pitch, roll, and heading during vehicle movement. This results in misalignment between atmospheric detection data and the actual atmospheric spatial location, ultimately affecting the accuracy of atmospheric wind field measurement and failing to meet the application requirements for precise wind measurement in mobile scenarios. Therefore, this invention proposes an attitude compensation method for vehicle-mounted wind-measuring lidar. First, it acquires vehicle attitude information, the installation location information of the vehicle-mounted 3D scanning wind-measuring lidar, PPI / RHI / DBS scanning parameters, and atmospheric detection data collected by the lidar. Then, it transforms the vehicle attitude information into a vehicle coordinate system rotation matrix, combined with the installation location... The information determines the radar's translation vector relative to the vehicle and the scanning head's angle adjustment reference vector relative to the radar. These are then incorporated into a three-level attitude transformation matrix (vehicle-radar-scanning head detection) based on scanning parameters. Attitude correction information adaptable to multi-factor coupling is extracted from this matrix. Based on this correction information, atmospheric sounding data is matched according to the scanning trajectory, and rotation compensation, spatial translation compensation, and time synchronization normalization are sequentially performed to obtain compensated atmospheric sounding data. Finally, this data is aligned with vehicle positioning and odometer information by scanning frame timestamp. After initial spatial registration, iterative registration is completed using an improved iterative nearest-point algorithm with atmospheric radial velocity consistency constraints, resulting in fused atmospheric sounding data and secondary radar attitude compensation. The method of this invention forms a closed loop from data acquisition to compensation fusion. This not only enables attitude correction to accurately adapt to the actual working state of the radar and effectively eliminates the interference of vehicle dynamic attitude on the detection direction, but also, through the spatial and velocity dual deviation constraints in multi-source data time synchronization and iterative registration, ensures that the compensated data accurately corresponds to the real atmospheric environment in the spatial dimension, avoiding misalignment of detection points, and maintains continuity in the temporal dimension. At the same time, the improved iterative registration algorithm overcomes the shortcomings of existing methods that only focus on spatial position and ignore the consistency of atmospheric radial velocity. Ultimately, it achieves high-precision and stable measurement of the spatial distribution of atmospheric wind field and radial movement velocity in the moving state, providing wind measurement data support with both spatiotemporal accuracy for scenarios such as real-time road weather monitoring and mobile wind field mapping, and fully meeting the wind measurement accuracy requirements of vehicle-mounted 3D scanning wind measurement lidar in dynamic environments.

[0129] Example 2:

[0130] Based on the same inventive concept, this invention also provides an attitude compensation system for a vehicle-mounted wind-measuring lidar, the structural composition of which is shown in the schematic diagram below. Figure 4 As shown, it includes:

[0131] The information acquisition module is used to acquire the vehicle's attitude information, the installation location information and scanning parameters of the vehicle-mounted three-dimensional scanning wind measurement lidar, and the atmospheric detection data collected by the vehicle-mounted three-dimensional scanning wind measurement lidar.

[0132] An attitude correction module is used to determine the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the vehicle's attitude information, the installation position information, and the scanning parameters.

[0133] The data compensation module is used to perform attitude compensation on the atmospheric sounding data according to the attitude correction information to obtain compensated atmospheric sounding data.

[0134] The attitude compensation module is used to timestamp-align the compensated atmospheric detection data, the vehicle's positioning information, and mileage counting information to obtain fused atmospheric detection data, and to perform attitude compensation on the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the fused atmospheric detection data.

[0135] In one implementation, the attitude correction module may include:

[0136] The information conversion submodule is used to convert the vehicle's attitude information into a vehicle coordinate system rotation matrix;

[0137] An angle adjustment submodule is used to determine the translation vector of the vehicle-mounted 3D scanning wind measurement lidar relative to the vehicle coordinate system and the angle adjustment reference vector of the scanning head of the vehicle-mounted 3D scanning wind measurement lidar relative to the vehicle-mounted 3D scanning wind measurement lidar based on the installation position information.

[0138] The attitude transformation submodule is used to establish a three-level attitude transformation matrix between the vehicle coordinate system, the radar coordinate system, and the scanning head detection coordinate system based on the vehicle coordinate system rotation matrix, the translation vector, and the angle adjustment reference vector, combined with the scanning parameters.

[0139] The information extraction submodule is used to extract the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar from the three-level attitude transformation matrix.

[0140] In one implementation, the data compensation module may include:

[0141] The information matching submodule is used to match the attitude correction information and the atmospheric sounding data according to the scanning trajectory to obtain the spatial coordinate mapping relationship between the attitude correction information and the atmospheric sounding data of each sounding point;

[0142] The rotation compensation submodule is used to perform rotation compensation on the atmospheric sounding data according to the spatial coordinate mapping relationship to obtain the rotation-compensated atmospheric sounding data;

[0143] The spatial compensation submodule is used to perform spatial translation compensation on the rotationally compensated atmospheric sounding data according to the spatial coordinate mapping relationship, so as to obtain spatially compensated atmospheric sounding data.

[0144] The time synchronization submodule is used to perform time synchronization and normalization processing on the spatially compensated atmospheric sounding data according to the scanning cycle to obtain compensated atmospheric sounding data.

[0145] For example, the calculation formula corresponding to the spatial translation compensation can be as follows:

[0146] ;

[0147] in, This represents the spatial coordinates corresponding to the spatially compensated atmospheric sounding data. This represents the inverse of the rotation matrix corresponding to the rotation compensation applied to atmospheric sounding data; This represents the spatial coordinate data of the detection point corresponding to the current detection direction of the scanning head; This represents the translation parameter.

[0148] In one implementation, the attitude compensation module may include:

[0149] The time alignment submodule is used to align the compensated atmospheric sounding data, the vehicle's positioning information, and the mileage counting information with the timestamps of the scanning frames of the scanning head of the vehicle-mounted three-dimensional scanning wind-measuring lidar, so as to obtain time-synchronized atmospheric sounding data, positioning information, and mileage counting information.

[0150] The spatial registration submodule is used to perform preliminary spatial registration on the compensated atmospheric sounding data based on the time-synchronized positioning information and odometer count information, combined with the scanning trajectory of the scanning head, to obtain preliminary registered atmospheric sounding data.

[0151] The data fusion submodule is used to iteratively register the initially registered atmospheric sounding data using an improved iterative nearest-point algorithm to obtain fused atmospheric sounding data.

[0152] In this implementation, the data fusion submodule can specifically be used for:

[0153] Step S1: Obtain the reference atmospheric sounding dataset and use the preliminary registration atmospheric sounding data as the input atmospheric sounding dataset to initialize the number of iterations;

[0154] Step S2: For each detection point in the input atmospheric sounding dataset, find detection points with the same spatial location and the same scanning angle in the reference atmospheric sounding dataset according to the scanning trajectory of the detection point, and form a valid detection point pair;

[0155] Step S3: Construct a deviation vector matrix based on the valid detection point pairs corresponding to each detection point in the input atmospheric sounding dataset;

[0156] Step S4: Update the input atmospheric sounding dataset according to the deviation vector matrix to obtain the updated atmospheric sounding dataset;

[0157] Step S5: Based on the updated atmospheric sounding dataset, obtain the updated effective sounding point pairs; and determine whether the average spatial distance between the updated effective sounding point pairs is greater than a preset distance threshold, or whether the average atmospheric radial movement velocity difference is greater than a preset velocity threshold, or whether the number of iterations is less than the maximum value; if any of the judgment conditions are met, proceed to step S2; otherwise, proceed to step S6.

[0158] Step S6: Use the updated atmospheric sounding dataset as fused atmospheric sounding data.

[0159] In this implementation, the data fusion submodule may include: a dataset update unit, specifically including:

[0160] The parameter extraction subunit is used to obtain the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters between the reference atmospheric sounding dataset and the input atmospheric sounding dataset by solving the deviation vector matrix.

[0161] The matrix generation subunit is used to obtain the optimal transformation matrix based on the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters;

[0162] The synchronous update subunit is used to synchronously update the spatial position and atmospheric radial velocity of the input atmospheric sounding dataset according to the optimal transformation matrix, so as to obtain the updated atmospheric sounding dataset.

[0163] Example 3:

[0164] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0165] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the attitude compensation method of a vehicle-mounted wind-measuring lidar in the above embodiments.

[0166] Example 4:

[0167] Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the attitude compensation method for a vehicle-mounted wind-measuring lidar in the above embodiments.

[0168] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0170] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. An attitude compensation method for a vehicle-mounted wind-measuring lidar, characterized in that, include: Acquire vehicle attitude information, installation location information and scanning parameters of the vehicle-mounted 3D scanning wind measurement lidar, and atmospheric detection data collected by the vehicle-mounted 3D scanning wind measurement lidar; Based on the vehicle's attitude information, the installation location information, and the scanning parameters, the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar is determined; The attitude correction information is used to perform attitude compensation on the atmospheric sounding data to obtain compensated atmospheric sounding data. The compensated atmospheric sounding data, the vehicle's positioning information, and mileage count information are timestamped to obtain fused atmospheric sounding data, and attitude compensation is performed on the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the fused atmospheric sounding data.

2. The method as described in claim 1, characterized in that, The step of determining the attitude correction information of the vehicle-mounted 3D scanning wind-measuring lidar based on the vehicle's attitude information, the installation location information, and the scanning parameters includes: The vehicle's attitude information is converted into a vehicle coordinate system rotation matrix; Based on the installation location information, the translation vector of the vehicle-mounted three-dimensional scanning wind measurement lidar relative to the vehicle coordinate system and the angle adjustment reference vector of the scanning head of the vehicle-mounted three-dimensional scanning wind measurement lidar relative to the vehicle-mounted three-dimensional scanning wind measurement lidar are determined. Based on the vehicle coordinate system rotation matrix, the translation vector, and the angle adjustment reference vector, and in conjunction with the scanning parameters, a three-level attitude transformation matrix is ​​established between the vehicle coordinate system, the radar coordinate system, and the scanning head detection coordinate system. The attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar is extracted from the three-level attitude transformation matrix.

3. The method as described in claim 1, characterized in that, The step of performing attitude compensation on the atmospheric sounding data based on the attitude correction information to obtain compensated atmospheric sounding data includes: The attitude correction information and the atmospheric sounding data are matched according to the scanning trajectory to obtain the spatial coordinate mapping relationship between the attitude correction information and the atmospheric sounding data of each sounding point; Based on the spatial coordinate mapping relationship, the atmospheric sounding data is rotated and compensated to obtain rotated and compensated atmospheric sounding data; Based on the spatial coordinate mapping relationship, spatial translation compensation is performed on the rotation-compensated atmospheric sounding data to obtain spatially compensated atmospheric sounding data; The spatially compensated atmospheric sounding data is time-synchronized and normalized according to the scanning cycle to obtain compensated atmospheric sounding data.

4. The method as described in claim 3, characterized in that, The calculation formula for the spatial translation compensation is as follows: ； in, This represents the spatial coordinates corresponding to the spatially compensated atmospheric sounding data. This represents the inverse of the rotation matrix corresponding to the rotation compensation applied to atmospheric sounding data; This represents the spatial coordinate data of the detection point corresponding to the current detection direction of the scanning head; This represents the translation parameter.

5. The method as described in claim 1, characterized in that, The step of aligning the compensated atmospheric sounding data, the vehicle's location information, and mileage count information with timestamps to obtain fused atmospheric sounding data includes: The compensated atmospheric detection data, the vehicle's positioning information, and the mileage count information are time-stamped according to the scanning frames of the scanning head of the vehicle-mounted three-dimensional scanning wind-measuring lidar to obtain time-synchronized atmospheric detection data, positioning information, and mileage count information. Based on the time-synchronized positioning information and mileage count information, and combined with the scanning trajectory of the scanning head, preliminary spatial registration is performed on the compensated atmospheric sounding data to obtain preliminary registered atmospheric sounding data. The improved iterative nearest point algorithm is used to iteratively register the initially registered atmospheric sounding data to obtain fused atmospheric sounding data.

6. The method as described in claim 5, characterized in that, The improved iterative nearest-point algorithm is used to iteratively register the initially registered atmospheric sounding data to obtain fused atmospheric sounding data, including: Step S1: Obtain the reference atmospheric sounding dataset and use the preliminary registration atmospheric sounding data as the input atmospheric sounding dataset to initialize the number of iterations; Step S2: For each detection point in the input atmospheric sounding dataset, find detection points with the same spatial location and the same scanning angle in the reference atmospheric sounding dataset according to the scanning trajectory of the detection point, and form a valid detection point pair; Step S3: Construct a deviation vector matrix based on the valid detection point pairs corresponding to each detection point in the input atmospheric sounding dataset; Step S4: Update the input atmospheric sounding dataset according to the deviation vector matrix to obtain the updated atmospheric sounding dataset; Step S5: Based on the updated atmospheric sounding dataset, obtain the updated effective sounding point pairs; and determine whether the average spatial distance between the updated effective sounding point pairs is greater than a preset distance threshold, or whether the average atmospheric radial movement velocity difference is greater than a preset velocity threshold, or whether the number of iterations is less than the maximum value; if any of the judgment conditions are met, proceed to step S2; otherwise, proceed to step S6. Step S6: Use the updated atmospheric sounding dataset as fused atmospheric sounding data.

7. The method as described in claim 6, characterized in that, The step of updating the input atmospheric sounding dataset based on the deviation vector matrix to obtain the updated atmospheric sounding dataset includes: By solving the deviation vector matrix, the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters between the reference atmospheric sounding dataset and the input atmospheric sounding dataset are obtained; The optimal transformation matrix is ​​obtained based on the optimal rotation parameters, optimal translation parameters, and optimal scanning head angle correction parameters. Based on the optimal transformation matrix, the spatial position and atmospheric radial velocity of the input atmospheric sounding dataset are updated synchronously to obtain the updated atmospheric sounding dataset.

8. An attitude compensation system for a vehicle-mounted wind-measuring lidar, characterized in that, include: The information acquisition module is used to acquire the vehicle's attitude information, the installation location information and scanning parameters of the vehicle-mounted three-dimensional scanning wind measurement lidar, and the atmospheric detection data collected by the vehicle-mounted three-dimensional scanning wind measurement lidar. An attitude correction module is used to determine the attitude correction information of the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the vehicle's attitude information, the installation position information, and the scanning parameters. The data compensation module is used to perform attitude compensation on the atmospheric sounding data according to the attitude correction information to obtain compensated atmospheric sounding data. The attitude compensation module is used to timestamp-align the compensated atmospheric detection data, the vehicle's positioning information, and mileage counting information to obtain fused atmospheric detection data, and to perform attitude compensation on the vehicle-mounted three-dimensional scanning wind-measuring lidar based on the fused atmospheric detection data.

9. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an attitude compensation method for a vehicle-mounted wind-measuring lidar as described in any one of claims 1 to 7 is implemented.

10. A computing device readable storage medium, characterized in that, It contains an execution program, which, when executed, implements an attitude compensation method for a vehicle-mounted wind-measuring lidar as described in any one of claims 1 to 7.

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