Wave motion compensation method and system for a floating lidar wind measurement system

Abstract

The application provides a wave motion compensation method and system for a floating laser radar wind measurement system, and relates to the technical field of laser radar wind measurement.The application obtains synchronous wind field, wave field and sea current field data, and constructs an environmental disturbance model by fusion;then, the model identifies the direction and frequency characteristics of the main disturbance force, and accordingly, a damping process is performed on the laser radar sensor head, a first damping force is applied to the target direction high-frequency impact and wind pressure pulsation, and a second damping force is applied to the non-target direction low-frequency rigid body motion;then, the motion measurement data of the damping process are input into a fusion filter, the motion state information of the sensor head and the corresponding weight are analyzed in combination with the environmental disturbance model;finally, based on the information, a robust control algorithm is used to generate a compensation control signal, the wave motion compensation of the measurement data of the floating laser radar wind measurement system is completed, the interference of the wave motion on the floating laser radar wind measurement can be effectively offset, and the wind measurement data precision is improved.

Description

Technical Field

[0001] This application relates to the field of lidar wind measurement technology, and in particular to a wave motion compensation method and system for a floating lidar wind measurement system. Background Technology

[0002] The wave motion compensation method for floating lidar wind measurement systems aims to counteract the interference of ocean wave motion on wind measurement. Compared with traditional fixed wind measurement towers, this technology offers advantages such as flexible deployment, lower cost, and high reusability, making it widely applicable to scenarios such as offshore wind farm site selection and wind resource assessment. With the rapid development of the offshore wind power industry, its market application prospects are very broad.

[0003] Existing wave motion compensation methods are mainly divided into two categories: mechanical compensation strategies, which isolate the floating body's motion and maintain lidar stability through a two-axis stabilization platform, gimbal, or gimbal; and software compensation strategies, including low-pass filtering, second-level data processing, and attitude calibration algorithms based on inertial measurement units. These methods all attempt to reduce the impact of wave motion on wind measurement data through their respective technical logic.

[0004] Mechanical compensation strategies increase the difficulty of equipment installation and subsequent maintenance costs, while software compensation strategies have significant limitations: the effectiveness of low-pass filtering is greatly affected by parameter settings, second-level data processing methods do not fully consider the equipment's own movement speed, and attitude calibration algorithms often have response delays. These shortcomings result in incomplete compensation effects, making it difficult to accurately counteract interference under complex sea conditions such as strong winds and waves. Therefore, existing technologies suffer from insufficient accuracy in wind measurement data from floating lidar systems under complex sea conditions. Summary of the Invention

[0005] The purpose of this application is to provide a wave motion compensation method and system for a floating lidar wind measurement system, so as to solve the problem of insufficient accuracy of floating lidar wind measurement data under complex sea conditions in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a wave motion compensation method for a floating lidar wind measurement system, comprising:

[0007] Acquire synchronized wind field data, wave field data, and ocean current field data, and fuse the wind field data, wave field data, and ocean current field data to construct an environmental disturbance model;

[0008] Using the aforementioned environmental disturbance model, the directional and frequency characteristics of the main disturbance forces are identified;

[0009] Based on the direction and frequency characteristics of the main disturbance forces, the sensor head of the lidar is subjected to damping processing. The damping processing includes applying a first damping force to the high-frequency wave impact and wind pressure pulsation from the target direction, and applying a second damping force to the low-frequency rigid body motion from the non-target direction.

[0010] The motion measurement data generated during the damping process is input into the fusion filter, and the motion measurement data is processed by the fusion filter in combination with the data of the environmental disturbance model to obtain the motion state information of the sensor head. The motion state information includes a first motion component caused by high-frequency local deformation or vibration, a second motion component caused by low-frequency rigid body displacement, and corresponding contribution weights.

[0011] Based on the motion state information, a robust control algorithm is used to generate a wave motion compensation control signal, and the compensation control signal is used to perform motion compensation on the measurement data of the floating lidar wind measurement system.

[0012] Optionally, the step of generating a wave motion compensation control signal using a robust control algorithm based on the motion state information, and using the compensation control signal to perform motion compensation on the measurement data of the floating lidar wind measurement system, includes:

[0013] A motion compensation model for the sensor head is established, wherein the motion compensation model includes the motion parameters of the sensor head and the uncertainty range of the motion parameters;

[0014] The motion state information is input into the motion compensation model;

[0015] In the motion compensation model, the motion state information is processed based on the worst-case performance optimization criterion of robust control to generate a compensation control signal, which is used to counteract the low-frequency rigid body motion caused by wave motion.

[0016] The compensation control signal is used to correct the wind speed and direction data measured by the floating lidar wind measurement system, and the corrected wind speed and direction data is output.

[0017] Optionally, in the motion compensation model, processing the motion state information based on the worst-case performance optimization criterion of robust control to generate a compensation control signal includes:

[0018] Based on the first motion component and its contribution weight in the motion state information, a high-frequency disturbance observation input is constructed.

[0019] Based on the second motion component and its contribution weight in the motion state information, a low-frequency disturbance observation input is constructed.

[0020] In the motion compensation model, the low-frequency disturbance observation input is set as the dominant compensation target, and the uncertainty range of the motion parameters in the motion compensation model and the high-frequency disturbance observation input are jointly set as system disturbance constraints.

[0021] Under the system disturbance constraints, the worst-case performance optimization criterion of robust control is applied to solve the problem, so as to generate a compensation control signal that can achieve optimized compensation effect for the dominant compensation objective.

[0022] Optionally, the damping treatment of the lidar sensor head based on the direction and frequency characteristics of the main disturbance force includes:

[0023] According to the direction of the main disturbance force, the first damping mechanism installed on the lidar sensor head is adjusted to generate a first damping force that is proportional to the speed of the sensor head movement. The direction of the first damping force is consistent with the direction of the main disturbance force.

[0024] Based on the high-frequency component of the frequency characteristics of the main disturbance force, the response frequency of the first damping mechanism is adjusted so that the first damping force can counteract the high-frequency wave impact and wind pressure pulsation from the target direction.

[0025] According to the direction of the main disturbance force, the second damping mechanism installed on the sensor head is adjusted so that it generates a second damping force that is proportional to the displacement of the sensor head from the preset equilibrium position. The direction of the second damping force is perpendicular to the direction of the main disturbance force.

[0026] Based on the low-frequency component of the frequency characteristics of the main disturbance force, the response frequency of the second damping mechanism is adjusted so that the second damping force suppresses low-frequency rigid body motion in non-target directions.

[0027] Optionally, the step of processing the motion measurement data by combining the data from the environmental disturbance model with the fusion filter to obtain the motion state information of the sensor head includes:

[0028] In the fusion filter, the portion of the motion measurement data with a frequency higher than a first threshold is separated to obtain the first motion component caused by the high-frequency local vibration of the sensor head;

[0029] Separate the portion of the motion measurement data whose frequency of change is lower than a second threshold to obtain the second motion component caused by the low-frequency rigid body displacement of the sensor head;

[0030] Based on the direction and frequency distribution ratio of the external force in the environmental disturbance model, the contribution weights of the first motion component and the second motion component are calculated respectively.

[0031] The first motion component, the second motion component, and their respective contribution weights are output as the motion state information of the sensor head.

[0032] Optionally, identifying the direction and frequency characteristics of the main disturbance forces using the environmental disturbance model includes:

[0033] Based on the environmental disturbance model, disturbance components with intensity higher than a preset threshold and change frequency within a predetermined range are extracted;

[0034] Determine the dominant change frequency and the direction of maximum influence for each of the aforementioned perturbation components;

[0035] The directions of the maximum intensity of all disturbance components are statistically analyzed, and the one or more directions that appear most frequently are identified as the directions of the main disturbance forces.

[0036] The dominant change frequencies corresponding to all disturbance components are statistically analyzed, and one or more frequencies that appear most frequently are identified as the frequency characteristics of the main disturbance forces.

[0037] Optionally, the step of fusing the wind field data, wave field data, and ocean current field data to construct an environmental disturbance model includes:

[0038] The wind field data, wave field data, and ocean current field data at each moment are decomposed along three orthogonal spatial directions to obtain component data;

[0039] The component data at the same time and in the same direction are synthesized to obtain the total external force in the corresponding direction, and three-dimensional total force data are generated based on the total external force in the three directions.

[0040] Multiple three-dimensional total force data arranged in time sequence are combined to form a force sequence;

[0041] The force sequence is analyzed to obtain the intensity distribution of the three-dimensional total force data at different periods. Based on the intensity distribution, an environmental disturbance model is constructed to describe the distribution of the direction and frequency of change of the external force.

[0042] Secondly, this application provides a wave motion compensation system for a floating lidar wind measurement system, comprising:

[0043] The module is used to acquire synchronized wind field data, wave field data, and ocean current field data, and to fuse the wind field data, wave field data, and ocean current field data to construct an environmental disturbance model;

[0044] The identification module is used to identify the direction and frequency characteristics of the main disturbance forces using the environmental disturbance model.

[0045] The first processing module is used to perform damping processing on the sensor head of the lidar based on the direction and frequency characteristics of the main disturbance force. The damping processing includes applying a first damping force to the high-frequency wave impact and wind pressure pulsation from the target direction, and applying a second damping force to the low-frequency rigid body motion from the non-target direction.

[0046] The second processing module is used to input the motion measurement data generated during the damping process into the fusion filter, so as to process the motion measurement data by combining the data of the environmental disturbance model through the fusion filter to obtain the motion state information of the sensor head. The motion state information includes a first motion component caused by high-frequency local deformation or vibration, a second motion component caused by low-frequency rigid body displacement, and corresponding contribution weights.

[0047] The compensation module is used to generate a wave motion compensation control signal based on the motion state information using a robust control algorithm, and to use the compensation control signal to perform motion compensation on the measurement data of the floating lidar wind measurement system.

[0048] Thirdly, this application provides an electronic device, comprising:

[0049] Memory, used to store computer programs;

[0050] A processor is configured to execute the computer program to implement the steps of the wave motion compensation method for the floating lidar wind measurement system as described in the first aspect above.

[0051] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the wave motion compensation method for the floating lidar wind measurement system described in the first aspect above.

[0052] The wave motion compensation method for the floating lidar wind measurement system provided in this application acquires synchronous wind field, wave field, and ocean current field data and constructs an environmental disturbance model, providing comprehensive and accurate environmental data support for subsequent disturbance force identification. This model identifies the direction and frequency characteristics of the main disturbance forces, clarifying key interference sources to lay the foundation for targeted processing. Different damping forces are applied to the sensor head according to characteristics, based on direction and frequency, initially suppressing high-frequency impacts and low-frequency rigid body motion, reducing ineffective motion interference. The motion measurement data of the damping process is input into a fusion filter and processed in conjunction with the environmental disturbance model to accurately separate high-frequency deformation vibration and low-frequency rigid body displacement components and their corresponding weights, providing accurate compensation data. A robust control algorithm is used to generate compensation control signals and apply them to the measurement data, effectively offsetting wave motion interference and improving the accuracy of wind measurement data.

[0053] Furthermore, a motion compensation model is established, incorporating sensor head motion parameters and their uncertainty range. After inputting the sensor head motion state information into this model, it is processed based on the worst-case performance optimization criterion of robust control to generate a compensation control signal to offset the low-frequency rigid body motion caused by wave motion. This signal is then used to correct the wind speed and direction data measured by the floating lidar anemometer system, and the corrected results are output. This step, by fully considering the uncertainty of motion parameters and generating the compensation control signal based on the worst-case performance optimization criterion, can accurately offset the impact of low-frequency rigid body motion caused by wave motion on anemometer measurement, effectively avoid compensation deviations caused by parameter fluctuations, significantly improve the reliability of wind speed and direction data correction, and ensure the accuracy and stability of the output data. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating a wave motion compensation method for a floating lidar wind measurement system provided in this application embodiment;

[0056] Figure 2 A flowchart illustrating another wave motion compensation method for a floating lidar wind measurement system provided in this application embodiment;

[0057] Figure 3 This is a schematic diagram of the wave motion compensation system of a floating lidar wind measurement system provided in an embodiment of this application. Detailed Implementation

[0058] Existing wave motion compensation methods for floating lidar wind measurement systems have significant limitations: mechanical compensation strategies increase equipment installation and maintenance costs, while software compensation strategies suffer from incomplete compensation effects due to factors such as low-pass filtering relying on parameter settings, second-level data processing not considering equipment movement speed, and attitude calibration algorithms being prone to response delays. These issues make it difficult to accurately offset interference under complex sea conditions such as strong winds and waves, ultimately affecting the accuracy of wind measurement data.

[0059] To address the aforementioned issues, this application proposes a wave motion compensation method for a floating lidar wind measurement system. The core of this method involves constructing a disturbance model by fusing multi-field environmental data, accurately identifying the direction and frequency characteristics of the disturbance force, applying targeted damping processing to the sensor head by direction and frequency, analyzing the motion state through fusion filtering, and finally generating a compensation signal using a robust control algorithm. This method can effectively address both high-frequency impacts and low-frequency rigid body motion disturbances, dynamically adapting to environmental changes under complex sea conditions. It avoids the high cost of mechanical compensation and overcomes the limitations and lag of traditional software compensation, fundamentally improving the accuracy of wave motion compensation and ensuring the reliability of wind measurement data.

[0060] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] The core of this application is to provide a wave motion compensation method for a floating lidar wind measurement system, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0062] S101. Acquire synchronized wind field data, wave field data, and ocean current field data, and fuse the wind field data, wave field data, and ocean current field data to construct an environmental disturbance model.

[0063] S101 specifically includes:

[0064] S1011. Decompose the wind field data, wave field data, and ocean current field data at each moment along three orthogonal spatial directions to obtain component data.

[0065] Among them, component data refers to the subdivided data obtained by splitting the original data of wind field, wave field and ocean current field according to three mutually perpendicular spatial directions.

[0066] S1012. Synthesize the component data at the same time and in the same direction to obtain the total external force in the corresponding direction, and generate three-dimensional total force data based on the total external force in the three directions.

[0067] Among them, the three-dimensional total force data is formed by synthesizing the component data in three directions at the same moment, which can comprehensively reflect the total force of the environment.

[0068] S1013. Combine the multiple three-dimensional total force data arranged in time sequence to form a force sequence.

[0069] Among them, the force sequence is a continuous data set formed by arranging the three-dimensional total force data at different times in chronological order.

[0070] S1014. Analyze the force sequence to obtain the intensity distribution of the three-dimensional total force data in different periods, and construct an environmental disturbance model based on the intensity distribution.

[0071] Among them, the environmental disturbance model is used to describe the direction and frequency distribution of external forces acting on the floating lidar in the marine environment. Here, external forces refer to the forces generated by wind, waves, and ocean currents.

[0072] In one specific implementation, wind field data, wave field data, and ocean current field data are first collected synchronously by a measurement unit set on the same floating platform to ensure that the three types of data are completely synchronized and matched in the time dimension, thus avoiding deviations in subsequent data fusion due to asynchronous collection.

[0073] In step S1011, the wind field data, wave field data, and ocean current field data collected at each moment are decomposed along three orthogonal spatial directions. For example, the wind speed data at a certain moment is decomposed into wind speed components in the X-axis direction, the Y-axis direction, and the Z-axis direction; similarly, the wave force data and ocean current impact force data at the same moment are also decomposed in the same direction, finally obtaining the detailed component data of the three types of data in the three orthogonal directions at each moment.

[0074] Subsequently, in step S1012, the wind field component data, wave field component data, and ocean current field component data at the same time and in the same orthogonal spatial direction are superimposed and calculated to obtain the total external force in that direction; then the total external force calculation in the Y-axis and Z-axis directions is completed in sequence, and finally the total external force in the three orthogonal directions is integrated to generate the three-dimensional total force data at that time.

[0075] Next, in step S1013, multiple time-series arranged three-dimensional total force data are systematically combined to form a continuous force sequence. This sequence fully records the dynamic changes of environmental external forces over time, such as the changes in wave force strength and the adjustment of force direction caused by wind changes within a certain period, providing continuous and complete data support for subsequent analysis of the changing patterns of external forces and extraction of frequency characteristics.

[0076] Finally, the constructed force sequence is processed using data analysis algorithms, such as Fourier transform and power spectrum analysis, to obtain the intensity distribution of the three-dimensional total force data across different time periods. This includes identifying which periods have the strongest force and which have negligible influence. Based on these intensity distribution characteristics, an environmental disturbance model is constructed. This model clearly indicates the main directions, core frequencies, and corresponding intensities of various external forces in the marine environment. For example, the X-axis is dominated by a low-frequency force with a period of 10 seconds and an intensity of 5 N; the Z-axis is dominated by a high-frequency force with a period of 0.5 seconds and an intensity of 3 N. This provides clear and reliable data for accurately identifying the direction and frequency characteristics of the main disturbance forces.

[0077] S102. Using the environmental disturbance model, identify the direction and frequency characteristics of the main disturbance forces.

[0078] S102 specifically includes:

[0079] S1021. Based on the environmental disturbance model, extract disturbance components with intensity higher than a preset threshold and change frequency within a predetermined range.

[0080] The preset threshold is a pre-defined critical value for disturbance intensity, used to filter out disturbance components that significantly affect lidar wind measurement and exclude weak, invalid disturbances. The predetermined interval is a pre-defined range of disturbance variation frequencies, focusing on common frequency ranges of core disturbances such as ocean waves and wind fields, filtering out irrelevant frequency disturbances outside this range.

[0081] S1022. Determine the dominant change frequency and the direction of maximum effect intensity for each of the disturbance components.

[0082] In this model, a disturbance component refers to a single interference unit with a specific frequency of change and direction of action, extracted from the environmental disturbance model. Each disturbance component corresponds to a specific environmental disturbance effect. The dominant frequency of change is the frequency of change that occurs most frequently and has the highest intensity proportion in a single disturbance component, reflecting the core fluctuation pattern of that disturbance component. The direction of maximum influence intensity is the spatial orthogonal direction of the strongest force in a single disturbance component, reflecting the main direction of the disturbance's effect on the floating platform and lidar.

[0083] S1023. Statistically determine the direction of the maximum effect intensity corresponding to all disturbance components, and identify one or more directions that appear most frequently as the direction of the main disturbance force.

[0084] S1024. Statistically analyze the dominant change frequencies corresponding to all disturbance components, and determine one or more frequencies that appear most frequently as the frequency characteristics of the main disturbance force.

[0085] In one specific implementation, the process generally follows the steps of screening effective perturbation components, extracting key features of single components, and statistically analyzing core features to achieve accurate identification of the main perturbation direction and frequency characteristics. The specific steps are as follows:

[0086] First, in step S1021, based on threshold filtering and frequency range filtering algorithms, disturbance components with intensity higher than a preset threshold and frequency within a predetermined range are extracted from the environmental disturbance model. Specifically, the preset threshold can be set according to the intensity of common marine disturbances, such as 3N, and the predetermined frequency range is set to 0.1~10Hz, covering the core frequency range of wave and wind pressure pulsations. The algorithm will verify the intensity and frequency of each disturbance in the model one by one, retaining the disturbance components that meet the conditions as the analysis objects.

[0087] Secondly, step S1022 processes each screened disturbance component using frequency analysis algorithms, such as power spectral density analysis, to extract its frequency distribution characteristics. The frequency with the highest proportion in the distribution, i.e., the highest frequency, is determined as the dominant change frequency of the component. Simultaneously, through directional intensity analysis, the force intensity of the component in three orthogonal directions is compared, and the direction corresponding to the maximum intensity is determined as the direction of maximum force intensity. For example, if a disturbance component is wave impact interference, and its frequency distribution is 45% at 0.5Hz and 20% at 1Hz, then the dominant change frequency is 0.5Hz; its intensity in the X-axis direction is 4.2N, in the Y-axis direction is 2.1N, and in the Z-axis direction is 3.5N, then the direction of maximum force intensity is the X-axis.

[0088] Subsequently, in step S1023, the frequency statistics method is used to statistically analyze the directions of maximum influence of all effective disturbance components, record the frequency of occurrence of each direction, and identify the direction with the most occurrences or the multiple directions with the most ties as the main disturbance force. Simultaneously, in step S1024, the dominant change frequencies of all components are processed using the same statistical method to determine the frequency with the most occurrences or the multiple frequencies with the most ties, which are then used as the frequency characteristics of the main disturbance force.

[0089] To illustrate the statistical process more clearly, the following is a statistical example in a specific scenario:

[0090] Disturbance component number 1: dominant change frequency 0.5Hz, maximum influence direction is X-axis, disturbance intensity 5.71N;

[0091] Disturbance component number 2: dominant change frequency 1.0Hz, maximum influence direction is X-axis, disturbance intensity 5.71N;

[0092] Disturbance component number 3: dominant change frequency 0.5Hz, maximum influence direction is Z-axis, disturbance intensity 5.71N;

[0093] Disturbance component number 4: dominant change frequency 0.5Hz, maximum influence direction is X-axis, disturbance intensity 5.71N;

[0094] Disturbance component number 5: dominant change frequency 2.0Hz, maximum influence direction is X-axis, disturbance intensity 60.00N;

[0095] Disturbance component number 6: dominant change frequency 1.0 Hz, maximum influence direction is Y-axis, disturbance intensity 5.71 N;

[0096] Disturbance component number 7: dominant change frequency 0.5Hz, maximum influence direction is Z-axis, disturbance intensity 5.71N;

[0097] Disturbance component number 8: dominant change frequency 0.5Hz, maximum influence direction is X-axis, disturbance intensity 5.71N.

[0098] The statistical results above show that the X-axis appears 5 times in the direction of maximum force intensity, accounting for the highest proportion, therefore the main disturbance direction is the X-axis; 0.5Hz appears 5 times in the dominant frequency change, accounting for the highest proportion, therefore the main disturbance frequency characteristic is 0.5Hz. In the distribution of external force intensity, low-frequency disturbances (≤1Hz) have 7 components, each with an intensity of 5.71N, and a total intensity of 7 × 5.71 ≈ 40N, accounting for 40% of the total external force intensity of 100N; high-frequency disturbances (>1Hz) have 1 component with an intensity of 60N, accounting for 60% of the total external force intensity. This intensity distribution data will be simultaneously entered into the environmental disturbance model to provide a basis for subsequent contribution weight calculations. The above example is only one example of this application. In practical applications, the preset threshold and predetermined frequency range can be adjusted according to the marine environment and equipment characteristics, and the number of statistical objects can also be increased or decreased as needed. This application does not limit these aspects.

[0099] In another specific implementation, a weighted statistical algorithm can be introduced to replace simple frequency statistics. That is, different weights are assigned according to the intensity of the disturbance component, with higher weights for greater intensity. The weighted scores for each direction and frequency are calculated, and the highest score is used as the main feature. This is suitable for scenarios where the impact of disturbances of different intensities on the system varies greatly, further improving the accuracy of identification.

[0100] Through the above steps, this application can accurately identify the direction and frequency characteristics of the main disturbance forces from complex marine environmental disturbances, clarify the core objectives of subsequent damping treatment, provide a reliable basis for targeted damping design by direction and frequency, effectively improve the targeting and effectiveness of subsequent disturbance suppression, and lay the foundation for ultimately improving the accuracy of wind measurement data.

[0101] S103. Based on the direction and frequency characteristics of the main disturbance force, damping treatment is applied to the sensor head of the lidar.

[0102] The damping treatment includes applying a first damping force to the high-frequency wave impact and wind pressure pulsation from the target direction, and applying a second damping force to the low-frequency rigid body motion from the non-target direction.

[0103] S103 specifically includes:

[0104] S1031. According to the direction of the main disturbance force, adjust the first damping mechanism installed on the lidar sensor head to generate a first damping force proportional to the movement speed of the sensor head.

[0105] The first damping mechanism is a damping actuator installed at the lidar sensor head, used to generate a first damping force proportional to the speed of the sensor head movement, specifically designed to counteract high-frequency disturbances.

[0106] S1032. Based on the high-frequency component of the frequency characteristics of the main disturbance force, adjust the response frequency of the first damping mechanism so that the first damping force can counteract the high-frequency wave impact and wind pressure pulsation from the target direction.

[0107] The target direction refers to the direction of action of the main disturbance force, which is the main source direction of the high-frequency disturbance. The first damping force is a damping force adapted to the high-frequency disturbance. Its magnitude changes with the speed of the sensor head movement, and its direction is consistent with the main disturbance force to achieve a cancellation effect.

[0108] S1033. According to the direction of the main disturbance force, adjust the second damping mechanism installed on the sensor head so that it generates a second damping force proportional to the displacement of the sensor head from the preset equilibrium position.

[0109] The second damping mechanism is also installed on the sensor head to generate a second damping force that is proportional to the displacement of the sensor head from the preset equilibrium position. Its core function is to suppress low-frequency rigid body motion.

[0110] S1034. Based on the low-frequency component of the frequency characteristics of the main disturbance force, adjust the response frequency of the second damping mechanism so that the second damping force suppresses the low-frequency rigid body motion in the non-target direction.

[0111] The non-target direction refers to the spatial direction perpendicular to the direction of the main disturbance force, and low-frequency rigid body motion is mostly affected in this direction. The second damping force is a damping force adapted to low-frequency disturbances. Its magnitude changes with the displacement of the sensor head from the equilibrium position, and its direction is perpendicular to the main disturbance force to suppress low-frequency rigid body motion.

[0112] In one specific implementation, based on the main disturbance direction and frequency characteristics identified above, the overall process follows a coherent flow: adjusting the parameters of the first damping mechanism to adapt to high-frequency disturbances, and then adjusting the parameters of the second damping mechanism to adapt to low-frequency disturbances. Through precise adjustments of each mechanism and frequency, high-frequency disturbances in the target direction and low-frequency disturbances in non-target directions are addressed respectively, ultimately completing the targeted damping treatment of the sensor head. The specific steps are as follows:

[0113] First, step S1031, based on the damping parameter adjustment algorithm and combined with the identified main disturbance force direction, adjusts the damping coefficient of the first damping mechanism so that the mechanism generates a first damping force proportional to the speed of the sensor head movement, and the direction of the damping force is consistent with the direction of the main disturbance force, thus laying the foundation for offsetting high-frequency disturbances.

[0114] Subsequently, in step S1032, the response frequency of the first damping mechanism is adjusted according to the high-frequency component of the main disturbance frequency characteristics to match the high-frequency disturbance frequency, ensuring that the first damping force can accurately offset the high-frequency wave impact and wind pressure pulsation in the target direction.

[0115] Next, step S1033 is also based on the damping parameter adjustment algorithm. Combined with the direction of the main disturbance force, the stiffness parameter of the second damping mechanism is adjusted so that the mechanism generates a second damping force that is proportional to the displacement of the sensor head from the preset equilibrium position. The direction of the damping force is perpendicular to the direction of the main disturbance force, which is suitable for the suppression requirements of low-frequency rigid body motion.

[0116] Finally, in step S1034, the response frequency of the second damping mechanism is adjusted according to the low-frequency component of the main disturbance frequency characteristics to match the low-frequency disturbance frequency, thereby effectively suppressing the low-frequency rigid body motion in the non-target direction.

[0117] The first damping force is a viscous damping force, and its calculation formula is shown in equation (1) below:

[0118] (1)

[0119] In the formula, The first damping force, in N; The damping coefficient of the first damping mechanism, in N. s / m; The velocity of the sensor head in the direction of the target is expressed in m / s.

[0120] The second damping force is a displacement-dependent damping force, and its calculation formula is shown in equation (2) below:

[0121] (2)

[0122] In the formula, This is the second damping force, in N. The stiffness coefficient of the second damping mechanism is expressed in N / m. The displacement of the sensor head from the preset equilibrium position in a non-target direction, in meters (m).

[0123] As an example, based on the identification results above: the main disturbance direction is the X-axis, and the frequency characteristics include a high frequency of 5Hz and a low frequency of 0.5Hz, the adjustment process of the damping mechanism is as follows:

[0124] Adjust the first damping mechanism: Based on the target direction X-axis, adjust the damping coefficient of the first damping mechanism. Adjust to 50N When the sensor head generates a velocity of 2 m / s along the X-axis due to the impact of high-frequency waves, substituting into equation (1) yields: N, the damping force is along the X-axis and is consistent with the direction of the main disturbance force; then the response frequency of the first damping mechanism is adjusted to 5Hz to match the high-frequency disturbance frequency, so as to ensure that the high-frequency wave impact and wind pressure pulsation of 5Hz in the X-axis direction can be offset in time.

[0125] Adjusting the second damping mechanism: Based on the X-axis direction of the main disturbance force, adjust the stiffness coefficient of the second damping mechanism. Adjusted to 200 N / m, when the sensor head deviates from its equilibrium position by 0.3 m in the non-target direction Y-axis due to low-frequency rigid body motion, substituting into equation (2) yields: N, the damping force is along the Y-axis and perpendicular to the X-axis; then the response frequency of the second damping mechanism is adjusted to 0.5Hz to match the low-frequency disturbance frequency, effectively suppressing the low-frequency rigid body motion of 0.5Hz in the Y-axis direction.

[0126] The above example is only one example of this application. In practical applications, the damping coefficient, stiffness coefficient and response frequency can be flexibly adjusted according to the intensity of sea disturbance and the characteristics of the sensor head. This application does not limit these parameters.

[0127] In another specific implementation, the first damping mechanism can adopt an electromagnetic damping structure, which can precisely control the damping coefficient by adjusting the current, resulting in a faster response speed and adaptability to rapid changes in high-frequency disturbances; the second damping mechanism can adopt a hydraulic damping structure, which can control the stiffness coefficient by adjusting the hydraulic oil flow rate, resulting in stronger load-bearing capacity and suitability for suppressing low-frequency rigid body motion in high wind and wave scenarios, further improving the adaptability and stability of damping treatment.

[0128] Through the above steps, this application achieves directional and targeted damping of high-frequency and low-frequency disturbances, effectively suppressing the impact of the two types of core disturbances on the sensor head, reducing invalid motion of the sensor head, providing a stable foundation for subsequent accurate identification of motion state and efficient compensation, and improving the reliability of the overall compensation scheme.

[0129] S104. The motion measurement data generated during the damping process is input to the fusion filter, so that the motion measurement data is processed by the fusion filter in combination with the data of the environmental disturbance model to obtain the motion state information of the sensor head.

[0130] The motion state information includes a first motion component caused by high-frequency local deformation or vibration, a second motion component caused by low-frequency rigid body displacement, and corresponding contribution weights.

[0131] S104 specifically includes:

[0132] S1041. In the fusion filter, the portion of the motion measurement data with a frequency higher than a first threshold is separated to obtain the first motion component caused by the high-frequency local vibration of the sensor head.

[0133] The fusion filter is a signal processing component used to fuse motion measurement data and environmental disturbance model data. Its core function is to separate motion components, calculate contribution weights, and output accurate motion state information. Motion measurement data, recorded by the motion measurement unit on the sensor head during damping processing, includes displacement, velocity, and other information, reflecting the real-time motion of the sensor head. The first motion component is the motion generated by high-frequency local deformation or vibration of the sensor head, corresponding to motion caused by high-frequency disturbances. The first threshold is the frequency threshold that distinguishes high-frequency from mid-frequency motion, used to separate the high-frequency motion component.

[0134] S1042. Separate the portion of the motion measurement data whose frequency of change is lower than the second threshold to obtain the second motion component caused by the low-frequency rigid body displacement of the sensing head.

[0135] The second motion component is the motion generated by the sensor head due to low-frequency rigid body displacement, corresponding to the motion caused by low-frequency disturbances. The second threshold is the frequency threshold that distinguishes between mid-frequency and low-frequency motion, used to separate the low-frequency motion component, and the first threshold is greater than the second threshold.

[0136] S1043. Based on the direction and frequency distribution ratio of the external force in the environmental disturbance model, calculate the contribution weights for the first motion component and the second motion component respectively.

[0137] Among them, the contribution weight is a parameter that measures the degree of influence of the first and second motion components on the overall motion of the sensor head. Its value is determined by the proportion of the direction and frequency distribution of the external force in the environmental disturbance model.

[0138] S1044. Output the first motion component, the second motion component, and their respective contribution weights as motion state information of the sensor head.

[0139] In one specific implementation, the overall process follows a coherent flow: first, acquiring two types of multi-source data—motion measurement data and environmental disturbance model data—and inputting them together into a fusion filter for integration. Then, a frequency separation algorithm is used to separate the high-frequency first motion component and the low-frequency second motion component. Next, the contribution weight of each component is calculated based on the external force direction and frequency distribution characteristics of the environmental disturbance model. Finally, all processing results are integrated to output complete motion state information, systematically achieving accurate identification of the sensor head's motion state. The specific steps are as follows:

[0140] First, the motion measurement unit on the sensor head collects motion measurement data such as displacement, velocity, and acceleration of the sensor head in real time during the damping process, comprehensively capturing the real-time motion trajectory of the sensor head. At the same time, the environmental disturbance model data constructed earlier is retrieved. This data includes the direction, frequency, and intensity distribution characteristics of external forces at sea, which can provide environmental background support for subsequent data processing. After aligning and integrating the two types of data along the time dimension, they are input together into the trained fusion filter to ensure that the data source is complete and matched, laying the foundation for accurate processing in the future.

[0141] Next, in step S1041, a frequency separation algorithm combining high-pass and low-pass filtering is used within the fusion filter to decompose the motion measurement data into components. First, based on a preset first threshold, low-frequency and mid-frequency signals in the motion measurement data are filtered out by high-pass filtering, retaining only motion data with frequencies higher than the first threshold, thereby obtaining the first motion component caused by high-frequency local deformation or vibration. Then, in step S1042, based on a preset second threshold, high-frequency and mid-frequency signals in the data are filtered out by low-pass filtering, retaining motion data with frequencies lower than the second threshold, thereby obtaining the second motion component caused by low-frequency rigid body displacement, achieving accurate separation of high and low frequency motion components.

[0142] Then, in step S1043, the data on the proportion of external force direction and frequency distribution recorded in the environmental disturbance model are called, and the contribution weight of the two types of motion components is calculated using a weight calculation algorithm. The core logic is: the proportion of external force frequency distribution directly reflects the strength of disturbances at different frequencies. The higher the proportion of high-frequency external force, the greater the impact of the first motion component on the overall motion of the sensor head, and the higher its contribution weight; conversely, the higher the proportion of low-frequency external force, the higher the contribution weight of the second motion component. The priority of the two types of components can be clearly defined through quantitative calculation.

[0143] Finally, step S1044 integrates the separated first motion component and second motion component with their respective calculated contribution weights to form complete sensor head motion state information. This information clearly marks the specific characteristics and influence of different types of sensor head motion, and is finally output to the subsequent compensation control module, providing accurate and comprehensive data support for the robust control algorithm to generate compensation signals in a targeted manner.

[0144] The fusion filter in this application adopts an improved model based on Kalman filtering. Its core is to fuse multi-source data through state equations and observation equations to improve the anti-interference capability of data processing. The training process of the filter is as follows: using motion measurement data and environmental disturbance model data under historical marine conditions as training samples, the true motion components and weights in the samples are labeled, and the state transition matrix and observation matrix of the filter are optimized through gradient descent algorithm to minimize the error between the filter output and the true value. After training is completed, it can be used for real-time data processing.

[0145] The contribution weight is calculated based on the frequency distribution ratio of external forces in the environmental disturbance model, and the calculation formula is shown in equations (3) and (4):

[0146] (3)

[0147] (4)

[0148] In the formula, The contribution weight of the first motion component; The contribution weight of the second motion component; This represents the proportion of the total intensity of high-frequency external forces in the environmental disturbance model. The proportion of low-frequency external forces in the total intensity of the environmental disturbance model; and satisfying .

[0149] As an example, considering the scenario described earlier: the main disturbance direction is the X-axis, with a high frequency of 5Hz and a low frequency of 0.5Hz; in the environmental disturbance model, the total intensity of high-frequency external forces accounts for 60%, and the total intensity of low-frequency external forces accounts for 40%.

[0150] First, the motion measurement unit acquires displacement, velocity, and acceleration data of the sensor head in the X, Y, and Z axes in real time during the damping process, including rigid body displacement caused by low frequencies of 0.5Hz / 1.0Hz and local vibration caused by high frequencies of 2.0Hz. Simultaneously, it retrieves environmental disturbance model data, including information such as the direction of external forces, frequency distribution, and intensity ratio of 40% / 60%. The two types of data are aligned and integrated according to the time dimension and input together into the trained fusion filter, providing a complete and synchronous data source for subsequent processing.

[0151] Next, based on the frequency characteristics of scene disturbances, frequency segmentation thresholds are set: a first threshold of 1.5Hz and a second threshold of 1.2Hz, with the first threshold being greater than the second. Within the fusion filter, a high-pass filtering algorithm is used to filter out signals with frequencies ≤1.5Hz from the motion measurement data, retaining only motion data with frequencies >1.5Hz. This yields the first motion component caused by high-frequency 2.0Hz local deformation or vibration. Subsequently, a low-pass filtering algorithm is used to filter out signals with frequencies ≥1.2Hz from the data, retaining motion data with frequencies <1.2Hz. This yields the second motion component caused by low-frequency 0.5Hz or 1.0Hz rigid body displacement, achieving precise separation of high and low frequency motion components and avoiding confusion between the two types of motion signals.

[0152] Then, the high-frequency and low-frequency external force intensity ratio data recorded in the environmental disturbance model are called, with the high-frequency external force intensity accounting for 60% and the low-frequency external force intensity accounting for 40%, and substituted into equations (3) and (4) to carry out the calculation:

[0153] Given that the total intensity ratio of high-frequency external forces is... The proportion of low-frequency external force in total intensity ,but: , That is, the contribution weight of the first motion component is 0.6, and the contribution weight of the second motion component is 0.4. The weighting results intuitively reflect that the high-frequency disturbance has a greater impact on the overall motion of the sensor head.

[0154] Finally, the first motion component corresponding to the 2.0Hz high-frequency vibration and the second motion component corresponding to the 0.5Hz / 1.0Hz low-frequency displacement are separated and integrated with the calculated contribution weights to form complete sensor head motion state information. This information clearly marks the specific characteristics and influence priorities of motion at different frequencies and is finally output to the subsequent compensation control module, providing accurate data support for the robust control algorithm to generate high-frequency and low-frequency compensation signals.

[0155] The above example is only one example of this application. In practical applications, the first threshold and the second threshold can be flexibly adjusted according to the characteristics of the disturbance frequency, and the weight calculation can also be further optimized by introducing the proportion of the external force direction. This application does not limit this.

[0156] Through the above steps, this application achieves deep fusion of motion measurement data and environmental disturbance model data, accurately separates high-frequency and low-frequency motion components and clarifies their influence weights, and outputs accurate and reliable motion state information, providing core data support for the subsequent robust control algorithm to generate targeted compensation signals and ensuring the accuracy of subsequent motion compensation.

[0157] S105. Based on the motion state information, a robust control algorithm is used to generate a compensation control signal for wave motion, and the compensation control signal is used to perform motion compensation on the measurement data of the floating lidar wind measurement system.

[0158] S105 specifically includes:

[0159] S1051. Establish a motion compensation model for the sensor head.

[0160] Among them, the motion compensation model is a mathematical model used to simulate the relationship between the motion of the sensor head and the compensation. It includes the motion parameters of the sensor head and the uncertainty range of the motion parameters, such as displacement, velocity, acceleration, and the parameter deviation range caused by environmental disturbances and equipment measurement errors. The core is used to carry the calculation of the robust control algorithm and generate accurate compensation control signals.

[0161] S1052. Input the motion state information into the motion compensation model.

[0162] S1053. In the motion compensation model, the motion state information is processed based on the worst-case performance optimization criterion of robust control to generate a compensation control signal.

[0163] S1053 specifically includes:

[0164] Based on the first motion component and its contribution weight in the motion state information, a high-frequency disturbance observation input is constructed; based on the second motion component and its contribution weight in the motion state information, a low-frequency disturbance observation input is constructed; in the motion compensation model, the low-frequency disturbance observation input is set as the dominant compensation target, and the uncertainty range of the motion parameters in the motion compensation model and the high-frequency disturbance observation input are jointly set as system disturbance constraints; under the system disturbance constraints, the worst-case performance optimization criterion of robust control is applied to solve the problem, so as to generate a compensation control signal that can achieve optimized compensation effect for the dominant compensation target.

[0165] The worst-case performance optimization criterion is the core optimization rule in robust control. It aims to achieve the optimal compensation effect within the limits of system disturbance constraints, ensuring effective cancellation of the target disturbance even under the worst disturbance scenarios. The high-frequency disturbance observation input is a signal constructed based on the first motion component and its contribution weight, used to characterize the impact of high-frequency disturbances on the sensor head motion. The low-frequency disturbance observation input is a signal constructed based on the second motion component and its contribution weight, used to characterize the impact of low-frequency disturbances on the sensor head motion. System disturbance constraints are the constraints in robust control, consisting of the uncertainty range of motion parameters in the motion compensation model and the high-frequency disturbance observation input, defining the disturbance boundary of the algorithm's operation. The compensation control signal is the output signal of the robust control algorithm, specifically used to cancel low-frequency rigid body motion caused by wave motion, reducing its interference with wind measurement data.

[0166] S1054. Using the compensation control signal, correct the wind speed and direction data measured by the floating lidar wind measurement system, and output the corrected wind speed and direction data.

[0167] In one specific implementation, the overall process follows this procedure: first, a motion compensation model containing sensor head motion parameters and uncertainty range is established and trained / optimized. Then, the motion state information output earlier is input into the model. Next, a robust control algorithm is used to construct high- and low-frequency disturbance observation inputs, set the dominant compensation target and system disturbance constraints, and generate a targeted compensation control signal by solving the worst-case performance optimization criterion. Finally, this compensation control signal is used to correct the measured wind speed and direction data of the floating lidar wind measurement system, thus completing the coherent process of motion compensation for wind measurement data. Specifically, as follows... Figure 2 As shown:

[0168] First, a motion compensation model for the sensor head is constructed through step S1051. The core of the model includes the key motion parameters of the sensor head and the uncertainty range of these parameters. Then, model training is conducted, selecting motion parameters, uncertainty range data, and corresponding wind measurement error data under different sea areas and wind / wave conditions as training samples. The motion parameters and uncertainty range are used as inputs, and the ideal compensation amount is used as the output label. The model parameter matrix is ​​optimized using a gradient descent algorithm to minimize the error between the model output and the ideal compensation amount of the samples, improving the model's fitting accuracy of the mapping relationship between motion state and compensation amount, and ensuring that the model is adaptable to complex disturbance scenarios at sea.

[0169] Next, in step S1052, complete motion state information is retrieved, including the first motion component, the second motion component, and the contribution weights of each component. This information is aligned with the model's input format along the time dimension, and after removing invalid and redundant data, it is synchronously input into the trained motion compensation model, providing core data support for subsequent perturbation analysis and compensation signal generation.

[0170] Then, in step S1053, a series of processes are carried out within the motion compensation model based on the robust control algorithm. First, a high-frequency disturbance observation input is constructed by combining the first motion component and its weight, and a low-frequency disturbance observation input is constructed by combining the second motion component and its weight, accurately characterizing the influence of the two types of disturbances on the sensor head motion. Next, the low-frequency disturbance observation input is set as the dominant compensation target, which is to offset the low-frequency rigid body displacement caused by waves. The uncertainty range of the motion parameters and the high-frequency disturbance observation input are set together as the system disturbance constraint to define the disturbance boundary of the algorithm operation. Finally, within this constraint range, the worst-case performance optimization criterion is applied to solve the problem. First, the compensation signal variables are fixed to find the disturbance case that makes the compensation performance index the worst. Then, the variables are adjusted to make the compensation effect under the worst case optimal, and finally, a targeted compensation control signal is generated.

[0171] Finally, in step S1054, the generated compensation control signal is input into the floating lidar wind measurement system. Combined with the system's measured wind speed and direction data, a signal correction algorithm is used to offset the measurement deviation caused by disturbances. That is, based on the amplitude and phase of the compensation control signal, the deviation components in the measured data are corrected in reverse. After correction, the data is validated, outliers are removed, and finally, accurate and stable wind speed and direction data are output, providing reliable support for applications such as wind resource assessment.

[0172] The core solution formula for the worst-case performance optimization criterion is shown in equation (5) below:

[0173] (5)

[0174] In the formula, u is the compensation control signal, i.e. the variable to be solved; d is the system disturbance, including uncertainties in motion parameters and high-frequency disturbances; D is the system disturbance constraint range. The compensation performance index function is used to evaluate the effect of the compensation control signal. The smaller the J value, the better the compensation effect. Its expression is shown in equation (6):

[0175] (6)

[0176] In the formula, y represents the measured wind speed and direction data from the lidar. The wind speed and direction data are corrected by the compensation control signal; It is the square of the L2 norm, used to quantify the deviation between the measured data and the corrected data.

[0177] In the solution process, the compensation control signal u is first fixed, and the performance index function is found within the system disturbance constraint range D. The maximum disturbance d represents the worst-case scenario; u is then adjusted to minimize this maximum J value, ultimately yielding the optimal compensation control signal. This ensures that optimal compensation can still be achieved even under the worst-case disturbance conditions.

[0178] As an example, considering the scenario described earlier: the main disturbance direction is the X-axis; the first motion component corresponds to a 2.0Hz high-frequency vibration, contributing a weight of 0.6; the second motion component corresponds to a 0.5Hz / 1.0Hz low-frequency rigid body displacement, contributing a weight of 0.4; and the known uncertainty range of the motion parameters is ±5%.

[0179] First, a motion compensation model is established, which includes motion parameters such as sensor head displacement and velocity, and an uncertainty range of ±5%. The motion state information consisting of the first motion component (2.0Hz high-frequency vibration), the second motion component (0.5Hz / 1.0Hz low-frequency displacement), and the corresponding weights of 0.6 and 0.4 is input into the trained motion compensation model.

[0180] Next, based on the first motion component (amplitude 0.2m) and weight 0.6, a high-frequency disturbance observation input is constructed. , where t is time; based on the second motion component (amplitude 0.5m) and weight 0.4, a low-frequency disturbance observation input is constructed. Input low-frequency disturbance observations Set as the primary compensation target, the core compensates for low-frequency rigid body displacement, and the uncertainty range of the motion parameters is ±5% compared with the high-frequency disturbance observation input. They are collectively set as system disturbance constraint D.

[0181] Within the system disturbance constraint D, the worst-case performance optimization criterion is applied to solve for the compensation control signal. Assume that at a certain time t=1s, the high-frequency disturbance observation input... Low-frequency disturbance observation input In the worst-case perturbation scenario, the motion parameters are taken as a +5% deviation, at which point the performance index function... The maximum value is obtained by solving the optimization formula. Its frequency is consistent with the low-frequency disturbance, and its amplitude is adapted to the disturbance intensity, which can accurately cancel the low-frequency rigid body displacement.

[0182] Finally, the lidar measured a wind speed of 8.5 m / s at a certain moment, with a wind direction of 30° in the positive X-axis direction. Due to the influence of low-frequency rigid body displacement, the measured data had a deviation: wind speed deviation +0.3 m / s and wind direction deviation +2°. A compensation control signal was input into the wind measurement system to correct the measured data: the wind speed correction was 8.5 - 0.3 = 8.2 m / s, and the wind direction correction was 30° - 2° = 28°. The final output was a corrected wind speed of 8.2 m / s and a wind direction of 28°, effectively canceling out the interference from low-frequency rigid body motion.

[0183] The above example is only one example of this application. In practical applications, the uncertainty range of motion parameters, the construction method of disturbance observation input, and the solution accuracy of compensation control signals can be adjusted according to the marine environment and equipment performance. This application does not limit these aspects.

[0184] In another specific implementation, a sliding mode robust control algorithm can be used instead of the traditional robust control algorithm. This algorithm has a faster response speed, can track low-frequency disturbance changes more quickly, and generate real-time compensation control signals. It is suitable for sea areas with drastic wind and wave changes and large fluctuations in disturbance frequency, further improving the real-time performance and effectiveness of compensation.

[0185] Through the above steps, this application generates targeted compensation control signals based on a robust control algorithm, accurately counteracts the interference of low-frequency rigid body motion on wind measurement data, effectively corrects wind speed and direction measurement deviations, improves the accuracy and stability of floating lidar wind measurement data, and provides reliable data support for wind resource assessment in fields such as offshore wind power.

[0186] Figure 3 This is a schematic diagram illustrating a specific implementation of a floating lidar wind measurement system wave motion compensation system provided in this application embodiment. (Refer to...) Figure 3 The system may include:

[0187] Module 31 is used to acquire synchronized wind field data, wave field data and ocean current field data, and to fuse the wind field data, wave field data and ocean current field data to construct an environmental disturbance model;

[0188] The identification module 32 is used to identify the direction and frequency characteristics of the main disturbance forces using the environmental disturbance model.

[0189] The first processing module 33 is used to perform damping processing on the sensor head of the lidar according to the direction and frequency characteristics of the main disturbance force. The damping processing includes applying a first damping force to the high-frequency wave impact and wind pressure pulsation from the target direction, and applying a second damping force to the low-frequency rigid body motion from the non-target direction.

[0190] The second processing module 34 is used to input the motion measurement data generated during the damping process to the fusion filter, so as to process the motion measurement data by combining the data of the environmental disturbance model through the fusion filter to obtain the motion state information of the sensor head. The motion state information includes a first motion component caused by high-frequency local deformation or vibration, a second motion component caused by low-frequency rigid body displacement, and corresponding contribution weights.

[0191] The compensation module 35 is used to generate a wave motion compensation control signal based on the motion state information using a robust control algorithm, and to use the compensation control signal to perform motion compensation on the measurement data of the floating lidar wind measurement system.

[0192] The wave motion compensation system of the floating lidar wind measurement system in this application embodiment is used to implement the aforementioned wave motion compensation method of the floating lidar wind measurement system. Therefore, the specific implementation of the wave motion compensation system of the floating lidar wind measurement system can be found in the embodiment section of the floating lidar wind measurement system wave motion compensation method above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0193] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the wave motion compensation method for any of the above-described floating lidar wind measurement systems.

[0194] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the wave motion compensation method for any of the above-described floating lidar wind measurement systems.

[0195] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0196] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the wave motion compensation method for a floating lidar wind measurement system.

[0197] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0198] The wave motion compensation method and system for a floating lidar wind measurement system provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A wave motion compensation method for a floating lidar wind measurement system, characterized in that, include: Acquire synchronized wind field data, wave field data, and ocean current field data, and fuse the wind field data, wave field data, and ocean current field data to construct an environmental disturbance model; Using the aforementioned environmental disturbance model, the directional and frequency characteristics of the main disturbance forces are identified; Based on the direction and frequency characteristics of the main disturbance forces, the sensor head of the lidar is subjected to damping processing. The damping processing includes applying a first damping force to the high-frequency wave impact and wind pressure pulsation from the target direction, and applying a second damping force to the low-frequency rigid body motion from the non-target direction. The motion measurement data generated during the damping process is input into the fusion filter, and the motion measurement data is processed by the fusion filter in combination with the data of the environmental disturbance model to obtain the motion state information of the sensor head. The motion state information includes a first motion component caused by high-frequency local deformation or vibration, a second motion component caused by low-frequency rigid body displacement, and corresponding contribution weights. Based on the motion state information, a robust control algorithm is used to generate a wave motion compensation control signal, and the compensation control signal is used to perform motion compensation on the measurement data of the floating lidar wind measurement system.

2. The method according to claim 1, characterized in that, The step involves generating a wave motion compensation control signal using a robust control algorithm based on the motion state information, and then using the compensation control signal to perform motion compensation on the measurement data of the floating lidar wind measurement system, including: A motion compensation model for the sensor head is established, wherein the motion compensation model includes the motion parameters of the sensor head and the uncertainty range of the motion parameters; The motion state information is input into the motion compensation model; In the motion compensation model, the motion state information is processed based on the worst-case performance optimization criterion of robust control to generate a compensation control signal, which is used to counteract the low-frequency rigid body motion caused by wave motion. The compensation control signal is used to correct the wind speed and direction data measured by the floating lidar wind measurement system, and the corrected wind speed and direction data is output.

3. The method according to claim 2, characterized in that, In the motion compensation model, the motion state information is processed based on the worst-case performance optimization criterion of robust control to generate a compensation control signal, including: Based on the first motion component and its contribution weight in the motion state information, a high-frequency disturbance observation input is constructed. Based on the second motion component and its contribution weight in the motion state information, a low-frequency disturbance observation input is constructed. In the motion compensation model, the low-frequency disturbance observation input is set as the dominant compensation target, and the uncertainty range of the motion parameters in the motion compensation model and the high-frequency disturbance observation input are jointly set as system disturbance constraints. Under the system disturbance constraints, the worst-case performance optimization criterion of robust control is applied to solve the problem, so as to generate a compensation control signal that can achieve optimized compensation effect for the dominant compensation objective.

4. The method according to claim 1, characterized in that, The damping treatment of the lidar sensor head based on the direction and frequency characteristics of the main disturbance force includes: According to the direction of the main disturbance force, the first damping mechanism installed on the lidar sensor head is adjusted to generate a first damping force that is proportional to the speed of the sensor head movement. The direction of the first damping force is consistent with the direction of the main disturbance force. Based on the high-frequency component of the frequency characteristics of the main disturbance force, the response frequency of the first damping mechanism is adjusted so that the first damping force can counteract the high-frequency wave impact and wind pressure pulsation from the target direction. According to the direction of the main disturbance force, the second damping mechanism installed on the sensor head is adjusted so that it generates a second damping force that is proportional to the displacement of the sensor head from the preset equilibrium position. The direction of the second damping force is perpendicular to the direction of the main disturbance force. Based on the low-frequency component of the frequency characteristics of the main disturbance force, the response frequency of the second damping mechanism is adjusted so that the second damping force suppresses low-frequency rigid body motion in non-target directions.

5. The method according to claim 1, characterized in that, The process of processing the motion measurement data using the fusion filter and the data from the environmental disturbance model to obtain the motion state information of the sensor head includes: In the fusion filter, the portion of the motion measurement data with a frequency higher than a first threshold is separated to obtain the first motion component caused by the high-frequency local vibration of the sensor head; Separate the portion of the motion measurement data whose frequency of change is lower than a second threshold to obtain the second motion component caused by the low-frequency rigid body displacement of the sensor head; Based on the direction and frequency distribution ratio of the external force in the environmental disturbance model, the contribution weights of the first motion component and the second motion component are calculated respectively. The first motion component, the second motion component, and their respective contribution weights are output as the motion state information of the sensor head.

6. The method according to claim 1, characterized in that, The process of identifying the direction and frequency characteristics of the main disturbance forces using the environmental disturbance model includes: Based on the environmental disturbance model, disturbance components with intensity higher than a preset threshold and change frequency within a predetermined range are extracted; Determine the dominant change frequency and the direction of maximum influence for each of the aforementioned perturbation components; The directions of the maximum intensity of all disturbance components are statistically analyzed, and the one or more directions that appear most frequently are identified as the directions of the main disturbance forces. The dominant change frequencies corresponding to all disturbance components are statistically analyzed, and one or more frequencies that appear most frequently are identified as the frequency characteristics of the main disturbance forces.

7. The method according to claim 1, characterized in that, The construction of the environmental disturbance model by integrating the wind field data, wave field data, and ocean current field data includes: The wind field data, wave field data, and ocean current field data at each moment are decomposed along three orthogonal spatial directions to obtain component data; The component data at the same time and in the same direction are synthesized to obtain the total external force in the corresponding direction, and three-dimensional total force data are generated based on the total external force in the three directions. Multiple three-dimensional total force data arranged in time sequence are combined to form a force sequence; The force sequence is analyzed to obtain the intensity distribution of the three-dimensional total force data at different periods. Based on the intensity distribution, an environmental disturbance model is constructed to describe the distribution of the direction and frequency of change of the external force.

8. A wave motion compensation system for a floating lidar wind measurement system, characterized in that, include: The module is used to acquire synchronized wind field data, wave field data, and ocean current field data, and to fuse the wind field data, wave field data, and ocean current field data to construct an environmental disturbance model; The identification module is used to identify the direction and frequency characteristics of the main disturbance forces using the environmental disturbance model. The first processing module is used to perform damping processing on the sensor head of the lidar based on the direction and frequency characteristics of the main disturbance force. The damping processing includes applying a first damping force to the high-frequency wave impact and wind pressure pulsation from the target direction, and applying a second damping force to the low-frequency rigid body motion from the non-target direction. The second processing module is used to input the motion measurement data generated during the damping process into the fusion filter, so as to process the motion measurement data by combining the data of the environmental disturbance model through the fusion filter to obtain the motion state information of the sensor head. The motion state information includes a first motion component caused by high-frequency local deformation or vibration, a second motion component caused by low-frequency rigid body displacement, and corresponding contribution weights. The compensation module is used to generate a wave motion compensation control signal based on the motion state information using a robust control algorithm, and to use the compensation control signal to perform motion compensation on the measurement data of the floating lidar wind measurement system.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the wave motion compensation method for a floating lidar wind measurement system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the wave motion compensation method for the floating lidar wind measurement system as described in any one of claims 1 to 7.

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