A method, equipment and medium for monitoring and early warning of scour of offshore wind turbine foundations

Abstract

This application discloses a method, equipment, and medium for monitoring and early warning of scour of offshore wind turbine foundations, relating to the field of marine monitoring technology. The method includes: controlling a gimbal to rotate a sonar sensor along a preset trajectory; controlling the sonar sensor to emit ultrasonic waves and receive raw echo signals at a preset angle; using an elastic wave vibration signal as a reference signal to adaptively cancel interference in the raw echo signal; calculating the distance between the sonar sensor and the scanning point on the seabed based on the clean echo signal; compensating the original azimuth and pitch angles for wave motion based on the instantaneous attitude angle; converting the distance, compensated azimuth, and compensated pitch angles into Cartesian coordinates to obtain the current elevation of the scanning point; comparing the waveform similarity between the clean echo signal of the scanning point and the reference echo signal to calculate the waveform distortion coefficient. This application achieves over-threshold perception of microstructural changes in the early stages of scour through the above method, significantly advancing the early warning response time.

Description

Technical Field

[0001] This application relates to the field of marine monitoring technology, and in particular to a method, equipment and medium for monitoring and early warning of scour of offshore wind turbine pile foundations. Background Technology

[0002] Offshore wind turbine foundations are prone to localized scour pits due to the long-term effects of tidal currents and waves. This reduces the effective burial depth of the foundation and decreases its load-bearing capacity, potentially leading to turbine overturning accidents. One approach involves periodic inspections using multibeam echo sounders or scanning sonar, which can obtain high-precision underwater topographic data. However, these systems are expensive, complex to maintain, and difficult to implement continuous real-time monitoring. Another approach uses fixed-point sonar or pressure sensors for point-to-point measurements. While this is less expensive, the monitoring range is limited and susceptible to environmental interference such as pile vibration and wave swaying, resulting in insufficient measurement accuracy and reliability. Furthermore, existing technologies rely on triggering alarms only after the scour depth reaches a certain threshold, failing to detect early signs of scour in the initial stages or before significant depth changes, leading to delayed early warning responses.

[0003] Based on the above analysis, the problems and shortcomings of the existing technology are as follows: In existing offshore wind power pile foundation scour monitoring technologies, the measurement accuracy is low due to pile foundation vibration and wave swaying, and it is impossible to detect scour precursors in advance when the scour depth change is not yet significant. Summary of the Invention

[0004] This application provides a method, equipment, and medium for monitoring and early warning of scour of offshore wind power pile foundations. It can solve the problems in the prior art of monitoring scour of offshore wind power pile foundations, such as low measurement accuracy due to pile foundation vibration and wave swaying, and the inability to detect early signs of scour before the change in scour depth is significant.

[0005] Firstly, this application provides a method for monitoring and early warning of scour of offshore wind power pile foundations. The method includes: controlling a gimbal to rotate a sonar sensor along a preset trajectory; controlling the sonar sensor to emit ultrasonic waves and receive raw echo signals at a preset angle; recording the original azimuth and pitch angles of the gimbal, the instantaneous attitude angle output by the attitude sensor, and the elastic wave vibration signal output by the vibration detection array; using the elastic wave vibration signal as a reference signal to adaptively cancel interference in the raw echo signal to obtain a clean echo signal; calculating the distance between the sonar sensor and the scanning point on the seabed based on the clean echo signal, and performing wave motion compensation on the original azimuth and pitch angles based on the instantaneous attitude angle to obtain the compensated azimuth and pitch angles; converting the distance, compensated azimuth, and compensated pitch angles into Cartesian coordinates to generate three-dimensional point cloud data and obtain the current elevation of the scanning point; comparing the waveform similarity between the clean echo signal of the scanning point and the reference echo signal to calculate the waveform distortion coefficient; triggering an early warning when the waveform distortion coefficient exceeds a first threshold and the difference between the current elevation of the scanning point and the reference elevation is less than a second threshold.

[0006] In one implementation of this application, the elastic wave vibration signal is used as a reference signal to adaptively cancel interference in the original echo signal to obtain a clean echo signal. Specifically, this includes: inputting the elastic wave vibration signal into the reference input terminal of an adaptive filter, and inputting the original echo signal into the desired signal input terminal of the adaptive filter; estimating the interference components related to the elastic wave vibration signal in the original echo signal point by point using an iterative update algorithm of the adaptive filter; and subtracting the interference components from the original echo signal to output a clean echo signal.

[0007] In one implementation of this application, the distance between the sonar sensor and the scanning point is calculated based on the pure echo signal, and wave motion compensation is performed on the original azimuth and original pitch angles based on the instantaneous attitude angles to obtain the compensated azimuth and pitch angles. Specifically, this includes: converting the original azimuth and original pitch angles into the initial components of the beam pointing vector in the gimbal's servo coordinate system; using the yaw, pitch, and roll angles in the instantaneous attitude angles as rotation angles, sequentially rotating the initial components around the yaw axis, pitch axis, and roll axis to obtain the compensated components of the beam pointing vector in the fixed coordinate system; and inversely solving for the compensated azimuth and pitch angles based on the compensated components.

[0008] In one implementation of this application, the waveform similarity of the clean echo signal of the scan point and the reference echo signal is compared, and the waveform distortion coefficient is calculated. Specifically, this includes: extracting the clean echo signal sequence of the scan point in the time domain, and reading the reference echo signal sequence stored at the scan point in the reference state; calculating the cross-correlation function between the current echo signal sequence and the reference echo signal sequence, and obtaining the maximum value of the cross-correlation function; and taking the absolute value of the difference between the maximum value and 1 as the waveform distortion coefficient.

[0009] In one implementation of this application, an early warning is triggered when the waveform distortion coefficient exceeds a first threshold and the difference between the current elevation and the reference elevation of the scan point is less than a second threshold. Specifically, this includes: marking the scan point as a suspected precursor point when the waveform distortion coefficient exceeds the first threshold and the difference between the current elevation and the reference elevation is less than the second threshold; determining whether the same scan point is marked as a suspected precursor point for a consecutive preset number of scan cycles; if so, generating a confirmatory early warning and recording the position coordinates of the scan point and the trend of waveform distortion coefficient changes.

[0010] In one implementation of this application, the method further includes: a gimbal with a built-in angular position encoder, controlling the gimbal to rotate to a preset calibration angle so that the beam of the sonar sensor points to the wind turbine pile foundation; acquiring calibration point cloud data of the wind turbine pile foundation, and extracting the measured coordinate values ​​of at least one geometric feature point from the calibration point cloud data; reading the theoretical coordinate values ​​of the pre-stored geometric feature points in the theoretical coordinate system, and calculating the deviation between the measured coordinate values ​​and the theoretical coordinate values; generating azimuth and pitch correction values ​​of the angular position encoder based on the deviation, and superimposing the correction values ​​on the original angle values ​​output in real time by the angular position encoder as the corrected original azimuth and pitch angles.

[0011] In one implementation of this application, the calibration point cloud data of the wind turbine pile foundation is obtained, and the measured coordinate values ​​of at least one geometric feature point are extracted from the calibration point cloud data. Specifically, this includes: filtering the calibration point cloud data to remove discrete noise points; using a point cloud segmentation algorithm to separate the point cloud belonging to the wind turbine pile foundation from the seabed point cloud; identifying at least one feature structure with a geometric shape in the point cloud of the wind turbine pile foundation, and extracting the center point or edge point of the feature structure as a geometric feature point.

[0012] In one implementation of this application, the method further includes: reading historical 3D point cloud data generated in the previous scanning cycle; calculating the elevation change rate between adjacent scanning points based on the elevation values ​​of the scanning points in the historical 3D point cloud data; marking areas where the elevation change rate exceeds a change rate threshold as high change areas and areas where the elevation change rate is below the change rate threshold as low change areas; in high change areas, setting the horizontal angle step size and pitch angle step size of the gimbal to a first step size; in low change areas, setting the horizontal angle step size and pitch angle step size of the gimbal to a second step size, wherein the first step size is smaller than the second step size; and generating a preset trajectory based on the first step size and the second step size.

[0013] Secondly, embodiments of this application also provide an offshore wind power pile foundation scour monitoring and early warning device, the device including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: perform any of the steps of an offshore wind power pile foundation scour monitoring and early warning method.

[0014] Thirdly, this application also provides a non-volatile computer storage medium for monitoring and early warning of scour of offshore wind power pile foundations, which stores computer-executable instructions, and the computer-executable instructions are set to execute any one of the steps of a method for monitoring and early warning of scour of offshore wind power pile foundations.

[0015] This application provides a method for monitoring and early warning of scour in offshore wind power pile foundations. It collects elastic wave vibration signals from the pile foundation using a vibration detection array and performs adaptive interference cancellation on the original echo, effectively eliminating the interference of pile structure vibration on sonar ranging and significantly improving measurement accuracy and signal-to-noise ratio. It uses an attitude sensor to detect the instantaneous attitude angle of the gimbal in real time and performs wave motion compensation on the original azimuth and pitch angles, eliminating beam pointing deviation caused by wave swaying and improving the spatial consistency of point cloud data. While generating three-dimensional point cloud data, it compares the waveform similarity of the clean echo signal with the reference echo signal and calculates the waveform distortion coefficient. When the waveform distortion coefficient exceeds a threshold while the elevation change is still less than the threshold, an early warning is triggered, achieving over-threshold perception of microstructural changes in the early stage of scour and significantly advancing the early warning response time. Furthermore, the gimbal's self-calibration function uses geometric feature points of the pile foundation to correct the accumulated error of the angular position encoder, further ensuring the measurement stability during long-term operation. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1A flowchart illustrating a method for monitoring and early warning of scour of offshore wind turbine foundations provided in this application embodiment; Figure 2 A pan-tilt cross-sectional view of a method for monitoring and early warning of scour of offshore wind power pile foundations provided in this application embodiment; Figure 3 A technical roadmap for a method for monitoring and early warning of scour of offshore wind power pile foundations provided in this application embodiment; Figure 4 This is a schematic diagram of the internal structure of a monitoring and early warning device for scour of offshore wind power pile foundations provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. 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.

[0018] This application provides a method, equipment, and medium for monitoring and early warning of scour of offshore wind power pile foundations. It solves the problems in the prior art of monitoring scour of offshore wind power pile foundations, where the measurement accuracy is low due to pile foundation vibration and wave swaying, and the inability to detect early signs of scour before the scour depth changes significantly.

[0019] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0020] Figure 1 A flowchart illustrating a method for monitoring and early warning of scour of offshore wind turbine foundations, provided as an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for monitoring and early warning of scour of offshore wind turbine pile foundations specifically includes the following steps: Step 10: Control the gimbal to drive the sonar sensor to rotate along a preset trajectory. At a preset angle, control the sonar sensor to emit ultrasonic waves and receive the original echo signal. Record the original azimuth and pitch angles of the gimbal, the instantaneous attitude angle output by the attitude sensor, and the elastic wave vibration signal output by the vibration detection array.

[0021] In this step, the two-degree-of-freedom gimbal is first fixedly installed on the offshore wind turbine foundation. The gimbal, as... Figure 2As shown, the gimbal has a built-in angular position encoder for real-time output of its horizontal azimuth and vertical pitch angles. A sonar sensor, attitude sensor, and vibration detection array are rigidly fixed to the gimbal. The attitude sensor detects the instantaneous attitude angles of the gimbal under wave action, including roll, pitch, and yaw angles. The vibration detection array detects the elastic wave vibration signals generated by the pile foundation during wave impact and structural vibration. This array preferably consists of multiple accelerometers arranged in different directions to detect the radial, tangential, and axial elastic wave vibration components of the pile foundation. Simultaneously with transmission and reception, the system records three data streams: the first stream is the raw azimuth and pitch angles output by the gimbal's angular position encoder; the second stream is the instantaneous roll, pitch, and yaw angles output by the attitude sensor; and the third stream is the elastic wave vibration signal output by the vibration detection array.

[0022] Step 20: Using the elastic wave vibration signal as a reference signal, adaptive interference cancellation is performed on the original echo signal to obtain a pure echo signal.

[0023] In this step, the correlation between the synchronously recorded elastic wave vibration signal and the original echo signal is analyzed. Adaptive filtering technology is used to remove structural vibration interference components introduced by the propagation of the pile foundation elastic wave to the sonar sensor from the original echo signal, thereby obtaining a pure echo signal that only reflects the seabed reflection characteristics, so as to improve the accuracy of subsequent ranging and waveform analysis.

[0024] As an optional embodiment, the elastic wave vibration signal is used as a reference signal to adaptively cancel the interference of the original echo signal to obtain a pure echo signal. Specifically, it may include: Step 201: Input the elastic wave vibration signal into the reference input terminal of the adaptive filter and input the original echo signal into the desired signal input terminal of the adaptive filter.

[0025] In this step, the elastic wave vibration signal output by the vibration detection array is used as a reference signal and connected to the reference input terminal of the adaptive filter. The original echo signal received by the sonar sensor is used as the desired signal and connected to the desired signal input terminal of the adaptive filter. The adaptive filter can adopt a transverse filter structure, and the tap coefficients can be dynamically adjusted.

[0026] Step 202: The interference components related to the elastic wave vibration signal in the original echo signal are estimated point by point through the iterative update algorithm of the adaptive filter.

[0027] In this step, the adaptive filter incorporates an iterative update algorithm, preferably using the least mean square (LMS) algorithm or the normalized least mean square (NMS) algorithm. At each sampling point, the algorithm calculates the filtered output of the reference signal based on the current filter coefficients. This output is an estimate of the component related to the elastic wave vibration in the original echo signal. Simultaneously, the algorithm calculates the instantaneous error between the desired signal and the filter output, and updates the filter coefficients according to a preset step size factor based on this error, gradually approximating the interference component in the desired signal. Through point-by-point iteration, the filter can adaptively track the time-varying coupling relationship between the elastic wave vibration signal and the original echo, thereby accurately estimating the interference component.

[0028] Step 203: Subtract the interference components from the original echo signal to output a clean echo signal.

[0029] In this step, the interference components are subtracted point by point from the original echo signal to obtain the residual signal after cancellation, which is the pure echo signal. This has significantly attenuated the elastic wave interference components coupled to the sonar sensor due to the vibration of the pile foundation structure, while retaining the main characteristics of the seabed reflected echo.

[0030] Step 30: Based on the pure echo signal, calculate the distance between the sonar sensor and the scanning point on the seabed, and perform wave motion compensation on the original azimuth and pitch angles based on the instantaneous attitude angles to obtain the compensated azimuth and pitch angles.

[0031] This step includes two parallel processing branches. The first branch calculates the straight-line distance between the sonar sensor and each scanning point on the seabed based on the pure echo signal. The second branch uses the instantaneous attitude angle to perform wave motion compensation on the original azimuth and original pitch angles to eliminate the beam pointing deviation caused by the swaying of the gimbal with the pile foundation. The outputs of the two branches will be used for coordinate transformation in subsequent steps.

[0032] In the distance calculation branch, the first significant peak of the clean echo signal is extracted as the arrival time of the seabed echo. The time difference between the transmitted pulse and the received echo is recorded. Combined with the pre-calibrated sound speed value at the current water temperature, the straight-line distance between the sensor and the seabed scanning point is calculated according to the distance calculation formula: distance = sound speed × time difference / 2. In the wave motion compensation branch, since the gimbal is rigidly fixed to the pile foundation, the pile foundation will generate six degrees of freedom of motion under the action of waves, resulting in a dynamic deviation between the actual pointing of the gimbal and the original azimuth and pitch angles output by the angular position encoder. To eliminate the deviation, the instantaneous roll, pitch, and yaw angles output in real time by the attitude sensor are used to compensate for the original angles.

[0033] As an optional embodiment, the distance between the sonar sensor and the scanning point is calculated based on the pure echo signal, and wave motion compensation is performed on the original azimuth angle and the original pitch angle based on the instantaneous attitude angle to obtain the compensated azimuth angle and the compensated pitch angle. Specifically, it may include: Step 301: Convert the original azimuth angle and the original pitch angle into the initial components of the beam pointing vector in the gimbal follow-up coordinate system.

[0034] In this step, specifically, a follower coordinate system is established with the rotation center of the gimbal as the origin. This coordinate system is fixed to the gimbal and moves with it. The original azimuth angle θraw and the original pitch angle φraw are converted into the initial components of the unit beam pointing vector in this follower coordinate system. The expressions for the initial components are: Xs = cos(φraw)·cos(θraw), Ys =cos(φraw)·sin(θraw), Zs = sin(φraw).

[0035] Step 302: Using the bow angle, pitch angle and roll angle in the instantaneous attitude angle as rotation angles, the initial components are rotated around the bow axis, pitch axis and roll axis in sequence to obtain the compensated components of the beam pointing vector in the fixed coordinate system.

[0036] In this step, the instantaneous roll angle γ is used as the rotation angle, and the system rotates around the roll axis Z of the follower coordinate system to obtain the component after the first rotation. Next, the instantaneous pitch angle β is used as the rotation angle, and the system rotates around the pitch axis Y to obtain the component after the second rotation. Finally, the instantaneous roll angle α is used as the rotation angle, and the system rotates around the roll axis X to obtain the finally compensated component in the fixed coordinate system. The rotation order can be ZYX Euler angle order, and the rotation transformation can be implemented using a direction cosine matrix or quaternions.

[0037] Step 303: Solve the compensated azimuth and elevation angles based on the compensated components.

[0038] In this step, the compensated azimuth angle θcomp and the compensated elevation angle φcomp are solved from the obtained compensated components (Xc, Yc, Zc). The inverse solution formulas are: θcomp = atan2(Yc, Xc), φcomp = asin(Zc), where atan2 is the arctangent function in the four quadrants and asin is the arcsine function. The compensated azimuth angle and elevation angle are the true beam pointing angles after eliminating the influence of wave swaying, which are used in subsequent steps to convert to Cartesian coordinates together with the straight-line distance.

[0039] Step 40: Convert the distance, compensated azimuth angle, and compensated elevation angle into Cartesian coordinates to generate 3D point cloud data and obtain the current elevation of the scanned point.

[0040] In this step, the precise position of each seabed scanning point in three-dimensional space is obtained. A fixed Cartesian coordinate system is established with the gimbal rotation center as the origin, where the X and Y axes lie in the horizontal plane and the Z axis points vertically upward. For each scanning point, its Cartesian coordinates are calculated according to the following transformation formula: X=d·cos(φcomp)·cos(θcomp); Y=d·cos(φcomp)·sin(θcomp); Z = d·sin(φcomp); Where d is the straight-line distance, θcomp is the compensated azimuth angle, φcomp is the compensated pitch angle, and the transformed (X, Y, Z) is the three-dimensional spatial coordinate of the scanning point in the fixed coordinate system.

[0041] The three-dimensional spatial coordinates of all scanned points are organized into a point cloud dataset according to the scanning order or spatial location to generate three-dimensional point cloud data, which reflects the spatial distribution of the seabed topography around the wind turbine pile foundation. The current elevation value is extracted from the Z coordinate of each scanned point and used to compare with the benchmark elevation in subsequent steps to determine the change in scour depth.

[0042] Step 50: Compare the waveform similarity between the clean echo signal at the scan point and the reference echo signal, and calculate the waveform distortion coefficient; As an optional embodiment, the waveform similarity of the clean echo signal of the scan point and the reference echo signal is compared and the waveform distortion coefficient is calculated. Specifically, it may include: Step 501: Extract the clean echo signal sequence of the scan point in the time domain and read the reference echo signal sequence stored at the scan point in the reference state.

[0043] In this step, a signal amplitude sequence within a fixed time window centered on the arrival time of the seabed echo is extracted from the pure echo signal as the current echo signal sequence. At the same time, based on the spatial coordinates of the current scan point, the reference echo signal sequence corresponding to the scan point in the reference state is read from the storage medium. The two sequences have the same length and the same sampling rate.

[0044] Step 502: Calculate the cross-correlation function between the current echo signal sequence and the reference echo signal sequence, and obtain the maximum value of the cross-correlation function.

[0045] In this step, cross-correlation is performed on the current echo signal sequence x(n) and the reference echo signal sequence y(n), and the cross-correlation function Rxy(m) = Σx(n)·y(n+m) is calculated. The maximum value is searched in the cross-correlation function result. When the two sequence waveforms are exactly the same, the maximum value of the cross-correlation function appears at the zero delay position and takes the value of 1. When the waveform is distorted, the maximum value will be less than 1, reflecting the degree of similarity between the two waveforms.

[0046] Step 503: Use the absolute value of the difference between the maximum value and 1 as the waveform distortion coefficient.

[0047] In this step, the waveform distortion coefficient δ is calculated using the formula: δ = |Rmax - 1|, where Rmax is the maximum value of the obtained cross-correlation function. When the waveforms are perfectly identical, Rmax = 1, and δ = 0; the more severe the waveform distortion, the smaller Rmax becomes, and the closer δ is to 1. Therefore, the waveform distortion coefficient is a dimensionless parameter between 0 and 1; a larger value indicates a greater degree of distortion of the current echo waveform relative to the reference waveform. This coefficient will be used in conjunction with the elevation change in subsequent steps to trigger early warning.

[0048] Step 60: When the waveform distortion coefficient exceeds the first threshold and the difference between the current elevation and the reference elevation of the scan point is less than the second threshold, an early warning is triggered.

[0049] As an optional embodiment, when the waveform distortion coefficient exceeds a first threshold and the difference between the current elevation and the reference elevation of the scan point is less than a second threshold, an early warning is triggered. Specifically, this may include: Step 601: When the waveform distortion coefficient exceeds the first threshold and the difference between the current elevation and the reference elevation is less than the second threshold, the scan point is marked as a suspected precursor point; Step 602: Determine whether the same scan point is marked as a suspected precursor point for a consecutive preset number of scan cycles; Step 603: If so, a confirmatory early warning is generated, and the position coordinates of the scan point and the trend of waveform distortion coefficient change are recorded.

[0050] In this step, the early warning triggering conditions include two criteria that must be met simultaneously. The first criterion is that the waveform distortion coefficient exceeds a preset first threshold, indicating that the microstructure of the seabed surface has undergone significant changes and there may be precursors to scour. The second criterion is that the difference between the current elevation and the reference elevation is less than a preset second threshold, indicating that the scour depth has not yet reached the alarm threshold of traditional methods. When both criteria are met simultaneously, it means that the system has detected changes in the seabed microstructure before the scour depth change is significant, thus triggering an early warning. This overcomes the technical deficiency of traditional monitoring methods that rely solely on depth thresholds, leading to delayed warnings.

[0051] It is understood that the first and second thresholds can be calibrated based on the type of seabed sediments, hydrodynamic conditions, and historical scour data of the sea area where the pile foundation is located. The first threshold is usually set as the statistical critical value of the waveform distortion coefficient, and the second threshold is usually set as several times the sonar ranging accuracy.

[0052] As an optional embodiment, the method may further include: the gimbal has a built-in angular position encoder to control the gimbal to rotate to a preset calibration angle so that the beam of the sonar sensor is pointed at the wind turbine pile foundation.

[0053] In this step, because the gimbal operates continuously underwater for extended periods, the built-in angular position encoder may experience angular output deviations due to mechanical wear, temperature drift, or accumulated pulse counting errors. If not corrected in time, these deviations will lead to inaccurate measurements of the original azimuth and pitch angles, thereby affecting the spatial consistency of point cloud data and the accuracy of scour monitoring. To address these issues, this embodiment utilizes the offshore wind turbine foundation itself as a fixed reference benchmark. Sonar scanning identifies geometric feature points on the foundation, allowing for the reverse calculation of encoder angular deviations and automatic compensation.

[0054] Obtain calibration point cloud data of wind turbine pile foundations, and extract the measured coordinate values ​​of at least one geometric feature point from the calibration point cloud data.

[0055] In this step, the pre-stored calibration parameters are first read, including the theoretical azimuth and pitch angles of the pile foundation relative to the gimbal installation position. The control module drives the gimbal to rotate to the theoretical calibration angle to ensure that the main lobe of the sonar sensor beam points to the surface of the wind turbine pile foundation, rather than to the seabed topography. Since the pile foundation has a definite spatial position and geometry, its surface echo characteristics are significantly different from the seabed echo, which facilitates the subsequent separation of the pile foundation point cloud from the point cloud data.

[0056] Read the pre-stored theoretical coordinate values ​​of geometric feature points in the theoretical coordinate system, and calculate the deviation between the measured coordinate values ​​and the theoretical coordinate values.

[0057] In this step, after the gimbal is at the calibration angle, the sonar sensor is controlled to emit ultrasonic waves and receive echoes. Following the same data processing flow as conventional scanning, including adaptive interference cancellation, wave motion compensation, and coordinate transformation, three-dimensional point cloud data of the pile foundation surface is generated, i.e., calibration point cloud data. The calibration point cloud data only covers the pile foundation body and its adjacent area. The measured coordinate values ​​of at least one geometric feature point are extracted from the calibration point cloud data. Geometric feature points refer to structural parts on the pile foundation with a definite geometric shape that are easily identifiable in the point cloud, such as: weld joints of the pile foundation cylinder, the edges of flange connection surfaces, stepped cross-sections with abrupt diameter changes, reflective targets preset on the pile foundation surface, or feature contour points at the junction of the pile foundation and the seabed. Extraction methods can employ algorithms such as point cloud segmentation, edge detection, or template matching. Taking weld joints as an example, the measured coordinate values ​​of the center point of the ring-shaped structure can be calculated by identifying a continuous set of points forming a ring in the point cloud.

[0058] The azimuth and pitch correction values ​​of the angular position encoder are generated based on the deviation, and the original angle values ​​output by the angular position encoder in real time are superimposed with the correction values ​​to form the corrected original azimuth and pitch angles.

[0059] In this step, based on the positional deviation between the measured coordinates and the theoretical coordinates, and combined with the geometric relationship between the gimbal and the pile foundation, the positional deviation is converted into an angular deviation through trigonometric transformation, resulting in azimuth and pitch correction values. The correction values ​​can be positive or negative and are used to compensate for the systematic errors of the encoder. After generating the correction values, the system stores them in the non-volatile memory of the industrial control computer. During subsequent normal scanning, for each set of original azimuth and pitch angles output in real time by the angular position encoder, the corresponding correction values ​​are superimposed on them to obtain the corrected original azimuth and pitch angles. The corrected original angles then enter the wave motion compensation process described in step 30, thereby ensuring the angle measurement accuracy of the entire monitoring system during long-term operation.

[0060] If the system detects that the measured deviation exceeds the preset alarm threshold, for example, if the angle error corresponding to the deviation is several times greater than the gimbal positioning accuracy, a calibration failure alarm will be triggered at the same time, prompting maintenance personnel to check the gimbal mechanical structure or encoder hardware.

[0061] As an optional embodiment, the calibration point cloud data of the wind power pile foundation is obtained, and the measured coordinate values ​​of at least one geometric feature point are extracted from the calibration point cloud data. Specifically, this may include filtering the calibration point cloud data to remove discrete noise points.

[0062] In this step, due to the complexity of the underwater environment, the calibration point cloud data obtained by sonar scanning often contains discrete noise points caused by suspended particles, marine organisms, or multiple echoes. These noise points are isolated or sparse in spatial distribution compared to the real point cloud on the pile foundation surface. This step uses a statistical or density-based filtering method to preprocess the calibration point cloud data.

[0063] Specifically, for each point in the calibration point cloud, the number of neighboring points within the neighborhood is calculated. If the number of neighboring points of a point is lower than the preset density threshold, it is determined to be a discrete noise point and removed from the point cloud. Alternatively, the average distance from each point to its nearest neighboring points is calculated. If the statistical distribution of the average distance deviates from the overall mean by more than a preset standard deviation, the point is determined to be a noise point and filtered out. The filtered calibration point cloud data retains the true structural points of the pile foundation surface and seabed topography.

[0064] A point cloud segmentation algorithm is used to separate the point cloud belonging to the wind turbine pile foundation from the seabed point cloud.

[0065] In this step, the filtered calibration point cloud data contains both the point cloud of the wind turbine pile foundation and the point cloud of the seabed topography around the pile. Since there are significant differences in spatial position between the pile foundation and the seabed, the pile foundation is a vertical or slightly tapered cylindrical structure extending upward from the seabed, while the seabed topography is a horizontal or gently sloping continuous curved surface. This step uses a point cloud segmentation algorithm to separate the two.

[0066] Specifically, the filtered calibration point cloud is first spatially analyzed to identify continuous point clusters with vertical extension characteristics. One possible implementation is to use a region-growing-based segmentation algorithm: select a seed point, such as the point with the largest Z-coordinate, corresponding to the top of the pile foundation, and grow outwards according to the consistency of the normal vector direction and spatial proximity, gradually aggregating points with similar geometric properties to form pile foundation point cloud clusters. Another implementation is to use a random sampling consensus algorithm to fit a vertical cylindrical model or a vertical plane model, classifying point clouds that match the model's points as pile foundation point clouds and the remaining point clouds as seabed point clouds. After segmentation, separate pile foundation point cloud datasets and seabed point cloud datasets are output.

[0067] In the point cloud of wind turbine pile foundation, identify at least one feature structure with a geometric shape, and extract the center point or edge point of the feature structure as a geometric feature point.

[0068] In this step, one or more feature structures with a defined geometry that are easy to locate in the point cloud are identified in the separated pile foundation point cloud. The feature structures may be: circumferential welds on the pile foundation cylinder, the edge of the flange connection plate, a stepped section with a sudden change in diameter, a reflective target preset on the outer surface of the pile foundation, or the end face edge of the pile foundation base.

[0069] Taking a circumferential weld as an example, the weld appears in the pile foundation point cloud as a continuous annular protrusion or a zone of increased point cloud density distributed along the circumference of the pile foundation. The identification method is as follows: slice the pile foundation point cloud vertically, perform circle fitting or cylindrical fitting on the point cloud in each slice, and calculate the radius and center of the fitted circle; when the fitting radius of two adjacent slices changes abruptly or the fitting residual shows a peak, the location is the layer where the weld is located, and extract the average center coordinates of all points in the layer or the center coordinates of the fitted circle as the measured coordinate values ​​of the weld feature points.

[0070] Taking a flange connection as an example, the flange edge appears as a complete or partial circular point set with a specific radius in the point cloud. The identification method is as follows: extract a subset of the point cloud within a preset height range from the pile foundation point cloud, and use the RANSAC algorithm to fit a circle. If the radius of the fitted circle matches the flange design radius within a preset error range, then the coordinates of the center of the circle are used as the measured coordinate values ​​of the flange feature points.

[0071] Taking a pre-defined reflective target as an example, the target typically uses a corner reflector or a planar structure with high reflectivity, which appears in the point cloud as a local high-density cluster of points or a subset of point clouds with a specific geometric shape. The identification method is as follows: search for a subset of point clouds in the pile foundation point cloud that matches the geometric template of the target, and calculate the centroid or center point of the subset as the measured coordinate value of the target feature point.

[0072] After extraction, the measured 3D coordinates of at least one geometric feature point are output for subsequent deviation calculations between the measured and theoretical coordinates. To improve calibration accuracy, multiple geometric feature points can be extracted, and the deviation between the measured and theoretical coordinates of each feature point can be calculated and averaged to eliminate the influence of single-point measurement errors.

[0073] Because the development speed and morphological changes of scour pits around offshore wind turbine foundations vary in different areas, areas with severe scour require higher spatial sampling density to capture subtle topographic changes, while areas with stable scour can use lower sampling density to save scanning time and energy. Therefore, this embodiment proposes an adaptive trajectory planning method based on historical scour patterns, which uses point cloud data from the previous scanning cycle to identify highly variable areas and dynamically adjusts the gimbal angle step size for the current scan.

[0074] As an optional embodiment, the method may further include: reading historical 3D point cloud data generated in the previous scanning cycle, and calculating the elevation change rate between adjacent scanning points based on the elevation values ​​of the scanning points in the historical 3D point cloud data.

[0075] In this step, the 3D point cloud data generated in the previous complete scanning cycle is read, including the spatial coordinates of each scanning point in a fixed Cartesian coordinate system. The elevation value of each scanning point is the Z-coordinate. For adjacent scanning points on the gimbal scanning path, the elevation change rate between them is calculated. The formula for calculating the elevation change rate is: Change rate = |Zi - Zj| / L, where Zi and Zj are the elevation values ​​of two adjacent points, and L is the horizontal or spatial distance between the two points, reflecting the degree of slope or undulation of the seabed topography in the horizontal direction. The elevation change rate at the edge of the scour pit is significantly higher than that in the flat seabed area.

[0076] Regions with elevation change rates exceeding the threshold are marked as high-change regions, and regions with elevation change rates below the threshold are marked as low-change regions. In high-change regions, the horizontal and pitch angle steps of the gimbal are set to the first step size. In low-change regions, the horizontal and pitch angle steps of the gimbal are set to the second step size, with the first step size being smaller than the second step size. A preset trajectory is generated based on the first and second step sizes.

[0077] The preset trajectory consists of a series of continuous gimbal target angle positions. In areas of high variation, the target angles are densely arranged according to the first step length; in areas of low variation, the target angles are sparsely arranged according to the second step length. After the trajectory is generated, the gimbal will be driven to rotate to each target angle sequentially according to the preset trajectory, triggering the sonar sensor to collect data. In this way, the utilization efficiency of limited computing and storage resources is improved while keeping the total number of sampling points constant. As the scour pit evolves, the location and range of the high-variable area may shift.

[0078] Specifically, in practical implementation, such as Figure 3 As shown, this application adopts a combination of dual motors + single-point sonar + industrial control computer, which is fixed to the wind turbine pile by a cantilever beam. It is small in size, simple in structure, and has a low failure rate. When used, it is required that the liquid is non-corrosive to stainless steel and can effectively conduct ultrasonic waves, such as seawater or fresh water. It is also required that the solid is non-corrosive to stainless steel and can effectively conduct ultrasonic waves, and the solid is required to be dense and free of air bubbles.

[0079] Table 1 Specific parameters of the sonar sensor

[0080] The dual-degree-of-freedom gimbal is a high-precision mechanical rotating mechanism used to adjust the scanning angle of underwater ranging sonar. It achieves dual-axis motion control (horizontal (azimuth) and vertical (pitch)) through two servo motors, equipped with dedicated watertight connectors and sealing ring assemblies. Suitable for marine environments, it can work with sonar sensors to complete large-area, multi-angle underwater terrain scanning. Dual servo motor drive: Utilizing internal rotor torque motors (model PH14) from Eyou Technology, both motors have a rated torque of 18nm. The horizontal axis motor rotates continuously for 360°, while the vertical axis motor is adjustable from -30° to +90°. It employs a 19-bit absolute encoder. Sealing system: Equipped with multiple O-ring watertight connectors; conforms to IP68 standards and can withstand 10MPa hydrostatic pressure. Structural optimization: 7075-T6 aluminum alloy main frame with hard anodized surface treatment for seawater corrosion resistance.

[0081] Table 2 Specific parameters of the gimbal

[0082] The gimbal mounting bracket is a specialized accessory device that provides stable support for the underwater dual-degree-of-freedom gimbal system. It primarily ensures the positioning accuracy and motion stability of the gimbal in complex underwater environments, and is connected to the fan via bolts. The mechanical design employs a modular truss structure with an overall stiffness ≥500 N / mm, tensile strength ≥310 MPa, yield strength ≥276 MPa, and an annual seawater corrosion rate <0.02 mm / a. It exhibits strong motion adaptability, supporting 360° continuous rotation of the gimbal.

[0083] Table 3 Specific parameters of the fixed bracket

[0084] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a monitoring and early warning device for scour of offshore wind turbine foundations, the structure of which is as follows: Figure 4 As shown.

[0085] Figure 4 This is a schematic diagram of the internal structure of a monitoring and early warning device for scour of offshore wind power pile foundations, provided as an embodiment of this application. Figure 4 As shown, the device includes: At least one processor 401; And a memory 402 that is communicatively connected to at least one processor; The memory 402 stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor 401 to enable at least one processor 401 to: perform any one of the steps of a method for monitoring and early warning of scour of offshore wind power pile foundations.

[0086] Some embodiments of this application provide corresponding to Figure 1A non-volatile computer storage medium for monitoring and early warning of scour of offshore wind power pile foundations, storing computer-executable instructions, wherein the computer-executable instructions are configured to execute any one of the steps of a method for monitoring and early warning of scour of offshore wind power pile foundations.

[0087] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0088] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0089] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] 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.

[0092] 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.

[0093] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0094] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0095] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0097] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for monitoring and early warning of scour of offshore wind turbine foundation piles, characterized in that, The method includes: The gimbal is controlled to rotate the sonar sensor along a preset trajectory. The sonar sensor is controlled to emit ultrasonic waves and receive the original echo signal at a preset angle. The original azimuth and pitch angles of the gimbal, the instantaneous attitude angle output by the attitude sensor, and the elastic wave vibration signal output by the vibration detection array are recorded. Using the elastic wave vibration signal as a reference signal, adaptive interference cancellation is performed on the original echo signal to obtain a pure echo signal; Based on the pure echo signal, the distance between the sonar sensor and the scanning point on the seabed is calculated, and wave motion compensation is performed on the original azimuth angle and the original pitch angle based on the instantaneous attitude angle to obtain the compensated azimuth angle and the compensated pitch angle. The distance, compensated azimuth angle, and compensated elevation angle are converted into Cartesian coordinates to generate three-dimensional point cloud data, thus obtaining the current elevation of the scanned point. The waveform similarity of the clean echo signal at the scan point with the reference echo signal is compared, and the waveform distortion coefficient is calculated. An early warning is triggered when the waveform distortion coefficient exceeds a first threshold and the difference between the current elevation and the reference elevation of the scan point is less than a second threshold.

2. The method for monitoring and early warning of scour of offshore wind turbine foundation piles according to claim 1, characterized in that, The step of using the elastic wave vibration signal as a reference signal to perform adaptive interference cancellation on the original echo signal to obtain a clean echo signal specifically includes: The elastic wave vibration signal is input to the reference input terminal of the adaptive filter, and the original echo signal is input to the desired signal input terminal of the adaptive filter. The interference components related to the elastic wave vibration signal in the original echo signal are estimated point by point through the iterative update algorithm of the adaptive filter. The interference component is subtracted from the original echo signal to output the pure echo signal.

3. The method for monitoring and early warning of scour of offshore wind turbine foundation piles according to claim 1, characterized in that, The process of calculating the distance between the sonar sensor and the scanning point based on the pure echo signal, and performing wave motion compensation on the original azimuth and original pitch angles based on the instantaneous attitude angles to obtain the compensated azimuth and pitch angles, specifically includes: The original azimuth and original elevation angles are converted into the initial components of the beam pointing vector in the gimbal servo coordinate system. Using the bow angle, pitch angle, and roll angle in the instantaneous attitude angles as rotation angles, the initial components are sequentially rotated around the bow axis, pitch axis, and roll axis to obtain the compensated components of the beam pointing vector in the fixed coordinate system. The compensated azimuth and elevation angles are derived from the compensated components.

4. The method for monitoring and early warning of scour of offshore wind turbine foundation piles according to claim 1, characterized in that, The step of comparing the waveform similarity between the clean echo signal at the scan point and the reference echo signal, and calculating the waveform distortion coefficient, specifically includes: Extract the pure echo signal sequence of the scan point in the time domain. In the pure echo signal, extract the signal amplitude sequence within a fixed time window centered on the arrival time of the seabed echo as the current echo signal sequence. Read the reference echo signal sequence stored at the scan point in the reference state. Calculate the cross-correlation function between the current echo signal sequence and the reference echo signal sequence, and obtain the maximum value of the cross-correlation function; The absolute value of the difference between the maximum value and 1 is used as the waveform distortion coefficient.

5. A method for monitoring and early warning of scour of offshore wind turbine foundation piles according to claim 1, characterized in that, When the waveform distortion coefficient exceeds a first threshold and the difference between the current elevation and the reference elevation of the scan point is less than a second threshold, an early warning is triggered, specifically including: When the waveform distortion coefficient exceeds the first threshold and the difference between the current elevation and the reference elevation is less than the second threshold, the scan point is marked as a suspected precursor point. Determine whether all consecutive preset number of scan cycles are marked as suspected precursor points at the same scan point; If so, generate a confirmatory early warning and record the location coordinates of the scan point and the trend of waveform distortion coefficient changes.

6. The method for monitoring and early warning of scour of offshore wind turbine foundation piles according to claim 1, characterized in that, The method further includes: The gimbal has a built-in angular position encoder to control the gimbal to rotate to a preset calibration angle so that the beam of the sonar sensor is pointed at the wind turbine pile foundation. Obtain the calibration point cloud data of the wind power pile foundation, and extract the measured coordinate values ​​of at least one geometric feature point from the calibration point cloud data; Read the pre-stored theoretical coordinate values ​​of the geometric feature points in the theoretical coordinate system, and calculate the deviation between the measured coordinate values ​​and the theoretical coordinate values; The azimuth and pitch correction values ​​of the angular position encoder are generated based on the deviation, and the original angle values ​​output by the angular position encoder in real time are superimposed with the correction values ​​to obtain the corrected original azimuth and pitch angles.

7. A method for monitoring and early warning of scour of offshore wind turbine foundation piles according to claim 6, characterized in that, The process of acquiring the calibration point cloud data of the wind turbine pile foundation and extracting the measured coordinate values ​​of at least one geometric feature point from the calibration point cloud data specifically includes: The calibration point cloud data is filtered to remove discrete noise points; A point cloud segmentation algorithm is used to separate the point cloud belonging to the wind turbine pile foundation from the seabed point cloud; In the point cloud of the wind turbine pile foundation, at least one feature structure with a geometric shape is identified, and the center point or edge point of the feature structure is extracted as the geometric feature point.

8. A method for monitoring and early warning of scour of offshore wind turbine foundations according to claim 6, characterized in that, The method further includes: Read the historical 3D point cloud data generated in the previous scanning cycle, and calculate the elevation change rate between adjacent scanning points based on the elevation values ​​of the scanning points in the historical 3D point cloud data; Regions where the elevation change rate exceeds the change rate threshold are marked as high change regions, and regions where the elevation change rate is below the change rate threshold are marked as low change regions. In the highly variable region, the horizontal angle step and pitch angle step of the gimbal are set to the first step. In the low-change region, the horizontal angle step and the pitch angle step of the gimbal are set as a second step, wherein the first step is smaller than the second step. The preset trajectory is generated based on the first step length and the second step length.

9. A monitoring and early warning device for scour of offshore wind turbine foundations, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Perform as claimed in claim 1 8. The steps of any one of the methods for monitoring and early warning of scour of offshore wind power pile foundations.

10. A non-volatile computer storage medium for monitoring and early warning of scour of offshore wind turbine foundations, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: Perform as claimed in claim 1 8. The steps of any one of the methods for monitoring and early warning of scour of offshore wind power pile foundations.

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