Scouring test method and device for offshore wind power foundation supporting structure

Abstract

The invention relates to the technical field of offshore wind power, and discloses a scouring test method and device for an offshore wind power foundation supporting structure, and the method comprises the steps: building a mapping relation between water flow scouring and tower drum vibration based on a real-time vibration signal and a water flow parameter; performing scouring simulation on the target tower drum based on a mapping relation between water flow scouring and tower drum vibration to obtain an initial scouring depth profile; on the basis of the initial scouring depth profile, tower drum vibration response is simulated through a tower drum rigidity distribution model, modal analysis is conducted on a tower drum vibration response simulation result, and modal frequency is obtained; the initial scouring depth profile and the modal frequency are corrected, and the scouring depth profile and the optimal modal frequency of the tower drum foundation are obtained; and optimizing the target tower drum structure based on the scouring depth profile of the tower drum foundation and the optimal modal frequency. The reliability and practicability of the offshore wind power foundation scouring test method are improved, and effective guarantee is provided for safe operation of offshore wind power facilities.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power, and in particular to a scour testing method and device for an offshore wind power foundation support structure. Background Art

[0002] As a pillar technology in the field of clean energy, offshore wind power has irreplaceable strategic significance for promoting global energy transformation and responding to climate change. The stability of its foundation support structure is directly related to the safe operation and long-term performance of the wind power system. Especially in complex marine environments, scouring has become a key threat that cannot be ignored. Especially near the estuary, the scouring effect of the incoming sea water is particularly significant. Failure to monitor and respond in a timely and accurate manner may lead to a decrease in the foundation bearing capacity and even the risk of structural failure.

[0003] However, the testing of offshore wind turbine foundation scour relies on on-site monitoring using ultrasonic scanning equipment carried on ships, which cannot guarantee the reliability and practicality of the offshore wind turbine foundation scour testing method. Summary of the Invention

[0004] In view of this, the present invention provides a scour testing method and device for an offshore wind power foundation support structure to solve the problem that the offshore wind power foundation scour testing cannot ensure the reliability and practicality of the offshore wind power foundation scour testing method.

[0005] In a first aspect, the present invention provides a method for scour testing an offshore wind power foundation support structure, the method comprising:

[0006] Collect real-time vibration signals at different heights of the target tower, as well as water flow parameters at multiple points around the target tower. Build a mapping relationship between water flow erosion and tower vibration based on the real-time vibration signals and water flow parameters.

[0007] Based on the mapping relationship between water scouring and tower vibration, the scouring of the target tower is simulated to obtain the initial scouring depth profile;

[0008] Based on the initial scour depth profile, the tower vibration response is simulated using the tower stiffness distribution model, and modal analysis is performed on the tower vibration response simulation results to obtain the modal frequency;

[0009] The initial scour depth profile and modal frequency are modified respectively to obtain the scour depth profile and optimal modal frequency of the tower foundation;

[0010] The target tower structure is optimized based on the scour depth profile and optimal modal frequency of the tower foundation.

[0011] This embodiment provides a scour testing method for an offshore wind turbine foundation support structure. The method constructs a mapping relationship between water flow scour and tower vibration through real-time vibration signals and water flow parameters. Based on the mapping relationship between water flow scour and tower vibration, a scour simulation is performed on a target tower to obtain an initial scour depth profile. The tower vibration response is simulated using a tower stiffness distribution model to obtain a modal frequency, thereby achieving quantification of the scour depth and vibration response and establishing a precise quantitative relationship between the vibration response and the scour depth. Furthermore, by correcting the initial scour depth profile and modal frequency, the scour depth profile and optimal modal frequency of the tower foundation are used to optimize the target tower structure, thereby improving the reliability and practicality of the offshore wind turbine foundation scour testing method, realizing real-time monitoring and early warning of offshore wind turbine tower foundation scour, providing effective protection for the safe operation of offshore wind power facilities, and having important engineering application value.

[0012] In an optional embodiment, a mapping relationship between water flow scouring and tower vibration is constructed based on the real-time vibration signal and water flow parameters, including:

[0013] Convert the real-time vibration signal into a frequency domain signal, and determine the vibration amplitude peak and frequency distribution bandwidth based on the frequency domain signal;

[0014] Determine the water flow velocity component and the water flow direction angle based on the water flow parameters;

[0015] Determine the water flow shear force based on the water flow velocity component and the water flow direction angle;

[0016] Based on the changing trends of water flow shear force and vibration amplitude peak, a mapping relationship between water flow scouring and tower vibration is constructed.

[0017] This embodiment provides a scour testing method for an offshore wind turbine foundation support structure. In response to the interference of water velocity components in a dynamic marine environment, the real-time vibration signal is converted into a frequency domain signal to obtain the vibration amplitude peak and frequency distribution bandwidth, which provides an important basis for the structural health monitoring of the tower and helps to timely discover potential safety hazards. In addition, since the influence of water flow characteristics on scour is complex and changeable, especially the vertical and horizontal distribution differences of the water flow profile, which directly affect the accuracy of the relationship between vibration frequency and scour depth, the water flow velocity component and the water flow direction angle are determined by the water flow parameters, and the water flow shear force is determined based on the water flow velocity component and the water flow direction angle, thereby realizing the quantification of water flow characteristics and providing important data support for further analysis of foundation scour.

[0018] In an optional embodiment, a scouring simulation is performed on a target tower based on a mapping relationship between water scouring and tower vibration to obtain an initial scouring depth profile, including:

[0019] Based on the mapping relationship between water flow scouring and tower vibration, the relationship between the frequency distribution bandwidth and the water flow direction angle is fitted to obtain the correlation coefficient matrix;

[0020] The scour simulation of the target tower is performed based on the correlation coefficient matrix to obtain the initial scour depth profile.

[0021] This embodiment provides a scour test method for an offshore wind turbine foundation support structure. By mapping the water flow scour and tower vibration, the relationship between the frequency distribution bandwidth and the water flow direction angle is fitted, and a correlation relationship between the frequency distribution bandwidth and the water flow direction angle is established, thereby realizing the quantification of the influencing factors in the scour simulation process.

[0022] In an optional embodiment, the initial scour depth profile and modal frequency are respectively corrected to obtain the scour depth profile and optimal modal frequency of the tower foundation, including:

[0023] Calculate the deviation between the modal frequency and the peak vibration amplitude;

[0024] The initial scour depth profile is corrected based on the deviation between the modal frequency and the vibration amplitude peak value to obtain a corrected scour depth profile;

[0025] According to the corrected scour depth profile, the distribution trend of stress concentration points is determined using the long short-term memory network model.

[0026] The scour depth profile of the tower foundation is determined based on the quantitative relationship between the distribution trend of stress concentration points and the peak value of vibration amplitude;

[0027] A vibration monitoring strategy that optimizes sensor acquisition frequency and grid density based on the scour depth profile and water flow direction angle of the tower foundation.

[0028] The optimal modal frequency is obtained by using the vibration monitoring strategy corresponding to the optimized sensor acquisition frequency and grid division density.

[0029] This embodiment provides a scour testing method for an offshore wind power foundation support structure. The method corrects the initial scour depth profile by using the deviation between the modal frequency and the vibration amplitude peak value, and then uses a long short-term memory network model to determine the stress concentration point distribution trend. The scour depth profile of the tower foundation is determined based on the quantitative relationship between the stress concentration point distribution trend and the vibration amplitude peak value. By monitoring the tower vibration frequency and combining the water flow profile characteristics, an accurate quantitative relationship between the vibration response and the scour depth is established to ensure that the simulation results of the tower vibration response are highly consistent with the actual characteristic frequency, thereby improving the reliability and practicality of the offshore wind power foundation scour testing method. By correcting the initial scour depth profile and modal frequency, a reliable basis is provided for the subsequent target tower structure design.

[0030] In an optional embodiment, the initial scour depth profile is corrected based on the deviation between the modal frequency and the vibration amplitude peak value to obtain a corrected scour depth profile, including:

[0031] comparing the deviation between the modal frequency and the peak vibration amplitude to a preset deviation threshold;

[0032] If the deviation between the modal frequency and the vibration amplitude peak is greater than the preset deviation threshold, the water velocity component and the frequency distribution bandwidth are fused;

[0033] The correlation coefficient matrix is ​​updated based on the fused water velocity components and frequency distribution bandwidth, and the updated correlation coefficient matrix is ​​used to correct the initial scour depth profile to obtain the corrected scour depth profile.

[0034] In an optional embodiment, optimizing a target tower structure based on the scour depth profile and the optimal modal frequency of the tower foundation includes:

[0035] Obtaining the vibration amplitude and the reference amplitude of the stress concentration point, and calculating the theoretical amplitude ratio based on the vibration amplitude and the reference amplitude of the stress concentration point;

[0036] The target tower structure is optimized based on the theoretical amplitude ratio, the scour depth profile of the tower foundation, and the optimal modal frequency.

[0037] This embodiment provides a scour testing method for an offshore wind turbine foundation support structure. The method determines the tower stiffness distribution state by calculating the theoretical amplitude ratio of the tower at the stress concentration point. The scour depth profile and optimal modal frequency of the tower foundation are then combined to determine whether the tower stiffness distribution state meets the operational requirements. The target tower structure is optimized based on the judgment result, and the tower is reinforced or adjusted in design to ensure its safe operation.

[0038] In a second aspect, the present invention provides a scour testing device for an offshore wind power foundation support structure, the device comprising:

[0039] A construction module is used to collect real-time vibration signals at different heights of the target tower and water flow parameters at multiple points around the target tower, and to build a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signals and water flow parameters;

[0040] The scour simulation module is used to simulate the scour of the target tower based on the mapping relationship between water scour and tower vibration to obtain the initial scour depth profile;

[0041] The modal analysis module is used to simulate the tower vibration response based on the initial scour depth profile using the tower stiffness distribution model, and perform modal analysis on the tower vibration response simulation results to obtain the modal frequency;

[0042] A correction module is used to correct the initial scour depth profile and modal frequency respectively to obtain the scour depth profile and optimal modal frequency of the tower foundation;

[0043] An optimization module is used to optimize the target tower structure based on the scour depth profile and optimal modal frequency of the tower foundation.

[0044] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the scour test method for the offshore wind power foundation support structure of the first aspect or any corresponding embodiment thereof.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the scour test method for an offshore wind power foundation support structure according to the first aspect or any corresponding embodiment thereof.

[0046] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the scour test method for an offshore wind power foundation support structure according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 1 is a flow chart of a method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention;

[0049] Figure 2 is a flow chart of another method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention;

[0050] Figure 3 1 is a flow chart of another method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention;

[0051] Figure 4 1 is a flow chart of another method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention;

[0052] Figure 51 is a flow chart of a method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention;

[0053] Figure 6 This is a structural block diagram of a scour testing device for an offshore wind power foundation support structure according to an embodiment of the present invention;

[0054] Figure 7 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0056] Scour testing for offshore wind turbine foundations relies on on-site monitoring using ultrasonic scanning equipment mounted on ships. While this method can provide certain scour data, its drawbacks are obvious: it requires personnel to drive ships out to sea, which is labor-intensive and subject to weather and sea conditions. It also involves high economic costs, such as ship rental, fuel consumption, and labor costs. These limitations make it difficult for offshore wind turbine foundation scour testing to meet the needs of efficient, low-cost, and sustainable monitoring.

[0057] In this context, the core challenges of scour testing methods have gradually become prominent. First, how to achieve real-time monitoring of scour depth without relying on manual field operations is a key technical bottleneck for improving efficiency and reducing costs. Second, the impact of water flow characteristics on scour is complex and changeable, especially the differences in the vertical and horizontal distribution of water flow profiles, which directly affect the accuracy of the relationship between vibration frequency and scour depth. If these factors cannot be accurately quantified, the prediction model will be inaccurate. Finally, as an indirect measurement method, the mapping relationship between tower vibration frequency and scour depth is subject to multivariate interference. How to establish a reliable correlation model in a dynamic marine environment is still a difficult problem that needs to be overcome. Unresolved technical factors together constitute a unique obstacle to the transformation of scour monitoring from traditional means to intelligent means.

[0058] Therefore, how to establish an accurate quantitative relationship between vibration response and scour depth by monitoring the tower vibration frequency and combining it with the water flow profile characteristics has become a key issue in improving the reliability and practicality of offshore wind power foundation scour testing methods. To solve this problem, it is necessary not only to overcome the limitations of manual monitoring, but also to refine the analysis of the contribution of water flow distribution to scour, thereby laying the foundation for building an efficient and economical prediction model.

[0059] An embodiment of the present invention provides a scouring test method for an offshore wind turbine foundation support structure. By arranging sensor arrays at different heights of the tower to collect vibration signals, and combining them with an acoustic Doppler current meter to measure water flow parameters, a mapping relationship between water flow scouring and tower vibration is established. The acoustic Doppler current meter (ADCP) is used to measure the velocity components (such as horizontal and vertical flow velocities) and direction angles of the water flow around the tower in real time. The above parameters directly reflect the scouring force of the water flow on the tower foundation (such as water flow shear force). By correlating and analyzing the water flow parameters collected by the ADCP with the vibration signals at different heights of the tower (such as vibration amplitude and frequency), a "water flow scouring intensity-tower vibration characteristic" can be established. For example, the greater the water velocity and the stronger the shear force, the greater the peak value of the tower vibration amplitude may be. By statistically analyzing the changing trends of the two, a mathematical model (such as a correlation coefficient matrix) can be constructed to achieve the purpose of inferring the scouring state from the vibration signal; Fourier transform and wavelet analysis are used to extract vibration characteristics, the tower response is simulated through finite element analysis, and the Kalman filter is used to optimize the scouring depth profile; the method also uses a long short-term memory network to predict the distribution of stress concentration points, and optimizes the monitoring strategy through an adaptive filtering algorithm; it realizes real-time monitoring and early warning of offshore wind turbine tower foundation scouring, provides effective protection for the safe operation of offshore wind power facilities, and has important engineering application value.

[0060] An embodiment of the present invention provides a method for scour testing of an offshore wind power foundation support structure. It should be noted that the execution subject of the scour testing method of an offshore wind power foundation support structure provided by the embodiment of the present invention can be a scour testing device for an offshore wind power foundation support structure. The scour testing device for an offshore wind power foundation support structure can be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. The electronic device can be a server or a terminal. The server in the embodiment of the present application can be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, a wearable device, an intelligent robot, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0061] According to an embodiment of the present invention, an embodiment of a scour testing method for an offshore wind power foundation support structure is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0062] In this embodiment, a method for scour testing of an offshore wind power foundation support structure is provided, which can be used for the above-mentioned electronic equipment. Figure 1FIG. 1 is a flow chart of a method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0063] Step S101 : collecting real-time vibration signals of a target tower at different heights and water flow parameters at multiple points around the target tower, and constructing a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signals and water flow parameters.

[0064] Specifically, a tower is selected as the offshore wind power foundation support structure. A sensor array is installed at different heights of the tower to collect real-time vibration signals. The water flow velocity component and water flow direction angle data are collected at multiple points around the tower through an acoustic Doppler current meter to obtain the distribution of water flow velocity and direction angle, that is, the water flow parameters at multiple points around the target tower.

[0065] Step S102 : performing scouring simulation on the target tower based on the mapping relationship between water scouring and tower vibration to obtain an initial scouring depth profile.

[0066] Step S103 : Based on the initial scour depth profile, the tower vibration response is simulated using the tower stiffness distribution model, and a modal analysis is performed on the tower vibration response simulation results to obtain the modal frequency.

[0067] Step S104: correcting the initial scour depth profile and modal frequency respectively to obtain the scour depth profile and optimal modal frequency of the tower foundation;

[0068] Step S105 : Optimizing the target tower structure based on the scour depth profile and the optimal modal frequency of the tower foundation.

[0069] This embodiment provides a scour testing method for an offshore wind turbine foundation support structure. The method constructs a mapping relationship between water flow scour and tower vibration through real-time vibration signals and water flow parameters. Based on the mapping relationship between water flow scour and tower vibration, a scour simulation is performed on a target tower to obtain an initial scour depth profile. The tower vibration response is simulated using a tower stiffness distribution model to obtain a modal frequency, thereby achieving quantification of the scour depth and vibration response and establishing a precise quantitative relationship between the vibration response and the scour depth. Furthermore, by correcting the initial scour depth profile and modal frequency, the scour depth profile and optimal modal frequency of the tower foundation are used to optimize the target tower structure, thereby improving the reliability and practicality of the offshore wind turbine foundation scour testing method, realizing real-time monitoring and early warning of offshore wind turbine tower foundation scour, providing effective protection for the safe operation of offshore wind power facilities, and having important engineering application value.

[0070] In this embodiment, a method for scour testing of an offshore wind power foundation support structure is provided, which can be used for the above-mentioned electronic equipment. Figure 2FIG. 1 is a flow chart of a method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0071] Step S201 : collecting real-time vibration signals of a target tower at different heights and water flow parameters at multiple points around the target tower, and constructing a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signals and water flow parameters.

[0072] Specifically, the above step S201 includes:

[0073] Step S2011: convert the real-time vibration signal into a frequency domain signal, and determine the vibration amplitude peak value and the frequency distribution bandwidth based on the frequency domain signal.

[0074] Specifically, real-time vibration signals are collected at different heights of the tower through a sensor array, and multi-channel synchronous sampling technology is used to obtain raw vibration data. In order to address the interference of the water velocity component in the raw vibration data, Fourier transform is used to convert the time domain signal into a frequency domain signal to obtain the frequency domain vibration characteristics. The vibration amplitude peak is extracted from the frequency domain vibration characteristics, and the main vibration component is determined by the peak detection algorithm. According to the vibration amplitude peak and frequency domain vibration characteristics, the frequency distribution bandwidth is calculated to obtain the spectral characteristics of the vibration signal.

[0075] Furthermore, the specific calculation process of the peak detection algorithm to determine the main vibration components includes: setting the amplitude threshold: based on historical data or experience, setting the amplitude threshold higher than the ambient noise (such as twice the average amplitude); identifying peak points: traversing the frequency domain signal, extracting frequency points with amplitudes exceeding the threshold, and marking them as candidate vibration components; screening valid peaks: excluding harmonic interference (such as points whose frequencies are integer multiples of the fundamental frequency) and retaining independent frequency components; sorting and prioritizing: sorting from high to low by amplitude, and selecting the top three significant frequencies as the main vibration components.

[0076] Furthermore, based on the vibration amplitude peak value and the frequency domain vibration characteristics, the calculation steps for calculating the frequency distribution bandwidth include: determining the significant frequency range: extracting the frequency values ​​corresponding to all valid peaks and sorting them from small to large; calculating the bandwidth: bandwidth = highest significant frequency - lowest significant frequency.

[0077] Furthermore, if the frequency distribution bandwidth exceeds the preset threshold, the frequency domain vibration characteristics are denoised through wavelet transform to obtain optimized spectrum data. For the optimized spectrum data, power spectrum density analysis is used to determine the energy distribution pattern of the vibration signal. The correspondence between the energy distribution pattern and the tower height is used to judge the degree of influence of the dynamic ocean environment on the vibration signal.

[0078] Furthermore, the specific steps of power spectral density analysis include: signal preprocessing: de-averaging and filtering the time domain signal (such as sliding average filtering); FFT conversion: converting the preprocessed signal into the frequency domain and calculating the square of the amplitude (power) of each frequency component; normalization processing: dividing the power value by the signal duration to obtain the power value per unit frequency (i.e., power spectral density, PSD for short); energy distribution calculation: accumulating the PSD values ​​of each frequency interval to determine the frequency range where the energy is concentrated.

[0079] Furthermore, the bottom of the tower is directly impacted by the water flow, so the low-frequency vibration energy (such as 0-10Hz) accounts for a higher proportion; the top is affected by the high-order mode, and the high-frequency component (such as 10-20Hz) may be more significant (for example, the bottom vibration amplitude is 2m / s 2 , top 5m / s 2 , reflecting height-related energy attenuation). Therefore, sensors are arranged at different heights of the tower (such as the bottom, middle, and top) to obtain PSD data of each layer and calculate the proportion of low-frequency (<10Hz) energy at each height. If the proportion at the bottom exceeds 30% of the middle / top, it is judged to be an effective signal dominated by water impact; if the high-frequency energy at the top increases abnormally, it may indicate a change in structural stiffness.

[0080] Step S2012: determining the water flow velocity component and the water flow direction angle based on the water flow parameters.

[0081] Specifically, the water flow velocity component and water flow direction angle data are collected at multiple points around the tower by using an acoustic Doppler flow meter to obtain the distribution of water flow velocity and direction angle.

[0082] Step S2013: determining the water flow shear force based on the water flow velocity component and the water flow direction angle.

[0083] Specifically, vertical and horizontal differences are calculated based on the distribution of water flow velocity and direction angle, the spatial variation characteristics of water flow distribution are determined, and the spatial variation characteristics of water flow distribution are used to calculate the shear force to obtain the distribution results of water flow shear force.

[0084] Furthermore, the initial scour slope is calculated based on the shear force distribution results to obtain the spatial distribution of the initial slope; based on the distribution of the initial slope and the water flow velocity, the contribution vector is calculated to determine the action vector of the water flow on the foundation scour; if the value of the contribution vector exceeds the preset threshold, the scour degree is classified using the support vector machine algorithm to determine the scour risk level; the scour risk level is used to adjust the direction of the contribution vector in combination with the direction angle to obtain the final directional distribution of the scour impact.

[0085] Step S2014: constructing a mapping relationship between water flow scouring and tower vibration based on the changing trends of the water flow shear force and the vibration amplitude peak value.

[0086] Step S202 : performing scouring simulation on the target tower based on the mapping relationship between water scouring and tower vibration to obtain an initial scouring depth profile.

[0087] Specifically, the above step S202 includes:

[0088] Step S2021 : Based on the mapping relationship between water flow scouring and tower vibration, the relationship between the frequency distribution bandwidth and the water flow direction angle is fitted to obtain a correlation coefficient matrix.

[0089] Specifically, a correlation coefficient matrix between the frequency distribution bandwidth and the water flow direction angle is fitted through regression analysis to obtain a preliminary scour depth profile. The specific steps include: calculating the change trend through the water flow shear force and the vibration amplitude peak value, constructing an initial mapping relationship, and obtaining a first data set; based on the first data set, using regression analysis to fit the relationship between the frequency distribution bandwidth and the water flow direction angle to determine the correlation coefficient matrix.

[0090] Furthermore, the water shear force (τ) and the vibration amplitude peak (A) are normalized to the interval [0, 1]. The relationship between τ′ and A′ is fitted using the least squares method to obtain the initial mapping function. Then, regression analysis is used to fit the relationship between the frequency distribution bandwidth and the water flow direction angle to determine the correlation coefficient matrix. The calculation formula of the correlation coefficient matrix is:

[0091]

[0092] Among them, ρ ij is the frequency component f i and direction angle θ j Cov is the covariance, and σ is the standard deviation.

[0093] Step S2022: Perform scour simulation on the target tower based on the correlation coefficient matrix to obtain an initial scour depth profile.

[0094] Specifically, for the correlation coefficient matrix, the matrix eigenvalues ​​are obtained, and the correlation strength between the frequency distribution bandwidth and the water flow direction angle is determined to obtain the second data set; the preliminary scour depth is extracted from the second data set to generate the first profile data; through the first profile data, the data correlation in the profile generation process is analyzed to obtain the scour depth distribution characteristics; according to the scour depth distribution characteristics, the support vector machine algorithm is used to optimize the fitting process to determine the final scour depth profile (i.e., the initial scour depth profile).

[0095] Furthermore, if the deviation between the final scour depth profile and the initial mapping relationship exceeds a preset threshold, the scour depth profile is recalculated by adjusting the water flow direction angle to obtain a corrected scour depth profile.

[0096] Step S203: Based on the initial scour depth profile, the tower stiffness distribution model is used to simulate the tower vibration response, and modal analysis is performed on the tower vibration response simulation results to obtain the modal frequency. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0097] Step S204: Correct the initial scour depth profile and modal frequency respectively to obtain the scour depth profile and optimal modal frequency of the tower foundation. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0098] Step S205: Optimize the target tower structure based on the scour depth profile and optimal modal frequency of the tower foundation. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0099] This embodiment provides a scour testing method for an offshore wind turbine foundation support structure. This method converts real-time vibration signals into frequency domain signals to address the interference of water velocity components in a dynamic marine environment. The peak vibration amplitude and frequency distribution bandwidth are obtained, providing an important basis for structural health monitoring of the tower and facilitating the timely detection of potential safety hazards. Furthermore, since the effects of water flow characteristics on scour are complex and variable, especially the differences in vertical and horizontal distribution of water flow profiles, which directly affect the accuracy of the relationship between vibration frequency and scour depth, the water flow velocity components and water flow direction angles are determined using water flow parameters, and the water flow shear force is determined based on the water flow velocity components and water flow direction angles. This quantifies the water flow characteristics and provides important data support for further analysis of foundation scour. Furthermore, through the mapping relationship between water flow scour and tower vibration, the relationship between the frequency distribution bandwidth and the water flow direction angle is fitted, and a correlation between the frequency distribution bandwidth and the water flow direction angle is established, thereby quantifying the influencing factors in the scour simulation process.

[0100] In this embodiment, a method for scour testing of an offshore wind power foundation support structure is provided, which can be used for the above-mentioned electronic equipment. Figure 3 FIG. 1 is a flow chart of a method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0101] Step S301: collect real-time vibration signals of the target tower at different heights and water flow parameters at multiple points around the target tower, and build a mapping relationship between water flow erosion and tower vibration based on the real-time vibration signals and water flow parameters. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0102] Step S302: Based on the mapping relationship between water scouring and tower vibration, scouring simulation is performed on the target tower to obtain the initial scouring depth profile. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0103] Step S303: Based on the initial scour depth profile, the tower stiffness distribution model is used to simulate the tower vibration response, and modal analysis is performed on the tower vibration response simulation results to obtain the modal frequency. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0104] Step S304 : Correcting the initial scour depth profile and modal frequency respectively to obtain the scour depth profile and optimal modal frequency of the tower foundation.

[0105] Specifically, the above step S304 includes:

[0106] Step S3041: Calculate the deviation between the modal frequency and the peak value of the vibration amplitude.

[0107] Specifically, for the scour depth profile, finite element analysis is used to simulate the tower vibration response in a pre-established tower stiffness distribution model. The modal frequency set is calculated through boundary constraints to determine the deviation between the displacement distribution and the actual characteristic frequency.

[0108] Furthermore, the characteristic frequency is extracted from the peak value of the vibration amplitude, and the low-frequency and high-frequency components are separated by wavelet transform. In view of the non-stationary characteristics caused by the water flow direction angle, time-frequency analysis is used to determine the changing trend of the vibration frequency with the time window length.

[0109] Furthermore, profile data is obtained through scour depth profiling, and key characteristic frequencies are extracted using data preprocessing methods to obtain an initial input set. Finite element analysis is used to generate the tower vibration response from the initial input set, and the simulation response results are determined by combining the stiffness distribution and distribution model. Boundary constraints are used to process the simulation response results, calculate the modal frequency set, and obtain frequency distribution characteristics. Displacement distribution is extracted from the frequency distribution characteristics, and the degree of matching with the characteristic frequency is judged through numerical comparison methods to obtain a preliminary deviation value.

[0110] Furthermore, the data preprocessing method extracts key characteristic frequencies. The specific steps to obtain the initial input set include denoising: median filtering the scour depth profile data to remove outliers; dimensionality reduction: extracting the first two principal components through principal component analysis (PCA) to retain more than 80% of the information; and normalization: scaling the data to the [0,1] interval for easy input to the finite element model.

[0111] Furthermore, finite element analysis is used to generate the tower vibration response from the initial input set. Combined with the stiffness distribution and distribution model, the steps to determine the simulation response results include: stiffness modeling and meshing: based on the geometric parameters after corrosion thinning (such as wall thickness changes), the stiffness distribution of each part of the tower (elastic modulus, section inertia moment, etc.) is established; the finite element mesh is divided, and the mesh is denser in the severely corroded area to ensure accurate simulation of the stiffness change (such as the mesh at the bottom of the tower is refined to 2.5 meters); boundary condition setting: the bottom is fixed (simulating pile foundation connection), and the top is free or elastically supported to match the actual installation state; modal analysis is used to calculate the vibration response: solve the natural frequency and mode shape, specifically, Through modal analysis, the first six modal frequencies are calculated as 0.278 Hz, 0.269 Hz, 1.305 Hz, 1.31 Hz, 2.05 Hz, and 2.15 Hz, respectively, and the displacement distribution of each order is obtained (e.g., the top displacement is the largest); the simulated frequency (e.g., 0.278 Hz) is compared with the measured frequency (e.g., 0.29 Hz), and the deviation (e.g., 4%) is calculated to check whether the vibration mode displacement conforms to the rule that "the bottom displacement is greater than the top"; parameter iterative optimization: If the deviation exceeds the threshold (e.g., 5%), adjust the mesh density or material stiffness (e.g., elastic modulus ±5%), recalculate until the deviation meets the standard (e.g., <5%), and output the final modal frequency set and vibration mode data.

[0112] Furthermore, the boundary conditions include the tower bottom being fixed (fully constrained, DOF=0) and the top being free (DOF=6), simulating the actual pile foundation fixing scenario.

[0113] Furthermore, the specific steps for calculating the modal frequency set include: meshing: dividing the tower into tetrahedral units (such as 10,000 units in the document, with a size of ≤5m); modal analysis: using the BlockLanczos algorithm (a numerical method for solving the eigenvalues ​​and eigenvectors of a symmetric matrix) to calculate the first 6 modal frequencies (such as 0.278Hz, 0.269Hz, 1.305Hz, 1.31Hz, 2.05Hz and 2.15Hz in the document), and extracting the displacement distribution (such as the maximum displacement at the top is 1.5m).

[0114] Furthermore, the steps of judging the degree of matching with the characteristic frequency by the numerical comparison method include: data preparation: measured characteristic frequency: extracted from the vibration signal (such as 0.278Hz, 1.305Hz obtained by Fourier transform), simulated characteristic frequency: finite element modal analysis calculation (such as the first three order frequencies 0.269Hz, 0.278Hz, 1.305Hz); sorting and screening: sorting by frequency from small to large, selecting the first few key frequencies (such as the first three orders) for comparison, and ignoring high-order secondary frequencies; calculating deviation: absolute deviation: (measured frequency - simulated frequency) frequency) (e.g., the deviation between 0.278Hz and 0.269Hz is 0.009Hz), relative deviation: deviation / measured frequency × 100% (e.g., 3.2%); threshold setting: 5%-10% is commonly used as the relative deviation threshold in engineering (e.g., setting 5%); matching judgment: single frequency: if the deviation is less than the threshold (e.g., 5%), then it matches; otherwise, it does not match; overall matching: the proportion of matching frequencies is statistically analyzed (e.g., ≥80% is qualified); result processing: qualified: the model is usable; unqualified: adjust the model parameters (e.g., stiffness, boundary conditions) and recalculate until it meets the standards.

[0115] Furthermore, if the preliminary deviation value exceeds the preset threshold, the simulation response is optimized by adjusting the stiffness distribution parameters, the modal frequency set and displacement distribution are recalculated, and the optimized deviation value is determined; based on the optimized deviation value, the support vector machine algorithm is used to classify the deviation trend, judge the degree of fit between the displacement distribution and the characteristic frequency, and obtain the final consistency result; the consistency result is used to generate a corrected model of the tower vibration response, and the linear regression algorithm is used to predict the long-term impact of the scour depth profile on the vibration to obtain the predicted distribution.

[0116] Furthermore, if the modal frequency deviation is greater than 5%, the material properties (such as elastic modulus ±5%) or mesh density (enhanced to 2.5 m) are adjusted and the modal analysis is repeated until the deviation is less than 5%, generating a revised stiffness distribution model.

[0117] Step S3042: Correct the initial scour depth profile based on the deviation between the modal frequency and the vibration amplitude peak value to obtain a corrected scour depth profile.

[0118] In some optional implementations, step S3042 includes:

[0119] Step a1: Compare the deviation between the modal frequency and the vibration amplitude peak with a preset deviation threshold.

[0120] Step a2: If the deviation between the modal frequency and the vibration amplitude peak value is greater than a preset deviation threshold, the water flow velocity component and the frequency distribution bandwidth are fused.

[0121] Specifically, if the deviation between the modal frequency and the vibration amplitude peak value exceeds the preset threshold, the modal frequency set and vibration amplitude peak value data are obtained through signal processing to determine the deviation state; the water flow velocity component and frequency distribution bandwidth data are fused through Kalman filtering to obtain an updated feature data set.

[0122] Step a3: updating the correlation coefficient matrix based on the fused water velocity components and the frequency distribution bandwidth, and using the updated correlation coefficient matrix to correct the initial scour depth profile to obtain a corrected scour depth profile.

[0123] Specifically, the correlation coefficient calculation method is used to process the feature data set to determine the initial parameter values ​​in the correlation coefficient matrix; if the deviation between the initial parameter value and the historical parameter value exceeds the preset range, the matrix parameters are adjusted through the matrix update algorithm to obtain the optimized parameter set; the scour depth value is calculated according to the optimized parameter set to obtain preliminary scour depth profile data; the preliminary scour depth profile data is processed through the interpolation algorithm to obtain a smoothed corrected profile; the consistency check is performed on the corrected profile and the water velocity component to determine the integrity of the final scour depth profile.

[0124] Furthermore, the characteristic data includes the water velocity component (v x ,v y ,v z ), frequency distribution bandwidth (Bandwidth) and vibration amplitude peak (A peak ); the initial parameters are the initial values ​​of the correlation coefficient matrix (such as initial α = 0.75, β = 0.25), which are set based on historical data or experience; the matrix parameters are the correlation coefficients after Kalman filtering update (such as α = 0.8, β = 0.2), which are used to correct the scour depth calculation model.

[0125] Step S3043: Determine the distribution trend of stress concentration points using the long short-term memory network model based on the corrected scour depth profile.

[0126] Specifically, a long short-term memory network is used to obtain the water shear force and displacement distribution from historical data, and the training model is used to obtain the time series change characteristics; the input boundary of the training model is determined based on the corrected scour depth profile and the initial scour slope, and a time series change sequence is output; based on the time series change sequence, the interaction between the displacement distribution and the water shear force is analyzed to determine the preliminary distribution of stress concentration points; after obtaining the preliminary distribution, the long short-term memory network is used to predict the distribution trend of stress concentration points in combination with the length of the future time window; through the distribution trend, the dynamic changes in the scour depth profile are extracted to obtain the adjustment parameters of the initial scour slope; based on the adjustment parameters, the training model is updated to determine the degree of match between the prediction results and the historical data, and the optimized distribution trend is output; the optimized distribution trend is used to determine the distribution of stress concentration points in the future time window to generate the final prediction result.

[0127] Step S3044: determining the scour depth profile of the tower foundation based on the quantitative relationship between the stress concentration point distribution trend and the vibration amplitude peak value.

[0128] Specifically, the vibration amplitude is acquired by collecting sensor data, and the real-time collected data is processed by Fourier transform to obtain frequency domain characteristics; the vibration amplitude peak is extracted according to the frequency domain characteristics, and the stress concentration point distribution trend is calculated through a pre-established stress distribution model; by comparing the stress concentration point distribution trend with the vibration amplitude peak, if the deviation between the two is less than a preset threshold, the real-time vibration amplitude peak and the predicted stress concentration point distribution are paired by time / position, and a correlation coefficient matrix is ​​calculated; the correlation coefficient matrix is ​​used to analyze the quantitative relationship to obtain a preliminary estimate of the scour depth; based on the preliminary estimate and the geometric parameters of the tower foundation, the scour depth profile distribution is determined; the profile distribution data is obtained, and the scour depth profile is optimized by the least squares method to obtain the final quantitative result; for the final quantitative result, the accuracy of the profile distribution is verified using historical data to determine the tower foundation status, that is, the scour depth profile of the tower foundation.

[0129] Furthermore, vibration amplitude data is obtained through sensors, and the peak detection method is used to extract characteristic frequencies; wavelet transform is applied to the characteristic frequency data to decompose the low-frequency and high-frequency components; non-stationary characteristic parameters are calculated for the water flow direction data to determine their impact on vibration; time-frequency analysis is used to process the non-stationary characteristic data to obtain the distribution of vibration frequency over the time window length; the distribution data is analyzed by sliding the time window length to obtain the frequency change trend; if the change trend exceeds a preset threshold, the low-frequency and high-frequency components are classified by support vector machine to determine the source of the abnormal frequency; the time window length is adjusted according to the classification result to determine the final vibration frequency change trend.

[0130] Furthermore, the time derivative (rate of change) and standard deviation (amplitude of fluctuation) of the water flow direction angle were calculated as non-stationary characteristic parameters; the rate of change of the direction angle and the vibration frequency drift (such as the change value of the characteristic frequency over time) were subjected to Pearson correlation analysis (a statistical method used to evaluate the strength and direction of the linear relationship between two continuous variables), and parameters with a correlation coefficient > 0.7 were screened out as key influencing factors; among them, the time rate of change of the water flow direction angle reflects the degree of mutation of the water flow direction, the standard deviation of the direction angle describes the severity of the direction fluctuation, and the Reynolds number (dimensionless) is the ratio of the water flow velocity to the characteristic length, which indirectly reflects the turbulence intensity (a typical parameter of water flow non-stationarity).

[0131] Furthermore, the preliminary estimate of the scour depth h est The calculation formula is as follows:

[0132]

[0133] Among them, ρ is the correlation coefficient, σ h is the standard deviation of scour depth, is the historical average scour depth, A peak is the peak value of vibration amplitude, is the average value of the peak value of the vibration amplitude, σ A is the standard deviation of the peak vibration amplitude.

[0134] Furthermore, the specific steps for the scour depth profile distribution are: combining the tower foundation diameter (D) and the scour pit slope (k), using the cylindrical coordinate system to fit the scour depth profile:

[0135]

[0136] Where r is the horizontal distance from the center of the tower.

[0137] Furthermore, by adjusting the fitting parameters (such as the slope k), the mean square error between the predicted scour depth and the measured scour depth is minimized to obtain the final quantitative result.

[0138] Step S3045 : Optimizing the vibration monitoring strategy corresponding to the sensor acquisition frequency and grid division density based on the scour depth profile and water flow direction angle of the tower foundation.

[0139] For local monomers, the convolutional neural network is used to extract the water velocity characteristics through the scouring depth profile data to obtain the velocity distribution set; based on the velocity distribution set and the water flow direction angle data, the distribution characteristic pattern is obtained to determine the dynamic adjustment parameters; for the dynamic adjustment parameters, the adaptive filtering algorithm is used to process the sensor acquisition signal to obtain the optimized frequency sequence; the sensor acquisition frequency is adjusted by optimizing the frequency sequence to obtain the initial vibration monitoring data and generate the preliminary modal frequency set; based on the preliminary modal frequency set and the grid division density, the k-means clustering algorithm is used to optimize the grid division scheme to obtain the adjusted monitoring data; for the adjusted monitoring data, the vibration monitoring update results are obtained to determine the continuously optimized modal frequency set; through the continuously optimized modal frequency set and the distribution characteristic pattern, the adjustment direction of the monitoring strategy is judged to obtain the final frequency optimization scheme.

[0140] Step S3046: Obtain the optimal modal frequency using the vibration monitoring strategy corresponding to the optimized sensor acquisition frequency and grid division density.

[0141] Step S305: Optimize the target tower structure based on the scour depth profile and optimal modal frequency of the tower foundation. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0142] This embodiment provides a scour testing method for an offshore wind power foundation support structure. The method corrects the initial scour depth profile by using the deviation between the modal frequency and the vibration amplitude peak value, and then uses a long short-term memory network model to determine the stress concentration point distribution trend. The scour depth profile of the tower foundation is determined based on the quantitative relationship between the stress concentration point distribution trend and the vibration amplitude peak value. By monitoring the tower vibration frequency and combining the water flow profile characteristics, an accurate quantitative relationship between the vibration response and the scour depth is established to ensure that the simulation results of the tower vibration response are highly consistent with the actual characteristic frequency, thereby improving the reliability and practicality of the offshore wind power foundation scour testing method. By correcting the initial scour depth profile and modal frequency, a reliable basis is provided for the subsequent target tower structure design.

[0143] In this embodiment, a method for scour testing of an offshore wind power foundation support structure is provided, which can be used for the above-mentioned electronic equipment. Figure 4 FIG. 1 is a flow chart of a method for scour testing an offshore wind power foundation support structure according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0144] Step S401: collect real-time vibration signals of the target tower at different heights and water flow parameters at multiple points around the target tower, and build a mapping relationship between water flow erosion and tower vibration based on the real-time vibration signals and water flow parameters. Figure 3 Step S301 of the illustrated embodiment will not be described in detail here.

[0145] Step S402: Based on the mapping relationship between water scouring and tower vibration, scouring simulation is performed on the target tower to obtain the initial scouring depth profile. Figure 3 Step S302 of the illustrated embodiment will not be described in detail here.

[0146] Step S403: Based on the initial scour depth profile, the tower stiffness distribution model is used to simulate the tower vibration response, and modal analysis is performed on the tower vibration response simulation results to obtain the modal frequency. Figure 3 Step S303 of the illustrated embodiment will not be described in detail here.

[0147] Step S404: Correct the initial scour depth profile and modal frequency to obtain the scour depth profile and optimal modal frequency of the tower foundation. Figure 3 Step S304 of the illustrated embodiment will not be described in detail here.

[0148] Step S405 : Optimizing the target tower structure based on the scour depth profile and the optimal modal frequency of the tower foundation.

[0149] Specifically, the above step S405 includes:

[0150] Step S4051: Obtain the vibration amplitude and reference amplitude of the stress concentration point, and calculate the theoretical amplitude ratio based on the vibration amplitude and reference amplitude of the stress concentration point.

[0151] Specifically, the σ of the stress concentration point is obtained by the finite element model max and A stress ; Retrieve A from the design document ref and ε ref ; Substitute into the formula to calculate the amplitude ratio. If the result is less than the safety threshold, it is determined that the stiffness does not meet the requirements. The calculation formula for the theoretical amplitude ratio is:

[0152]

[0153] Among them, A stress is the vibration amplitude of the stress concentration point, A ref is the reference amplitude (amplitude without scouring), σ max is the maximum stress at the stress concentration point (e.g. 120 MPa), L is the characteristic length of the tower (e.g. height), E is the elastic modulus of the material (210 GPa), ε ref is the reference strain (strain value within the safety threshold).

[0154] Step S4052: Optimize the target tower structure based on the theoretical amplitude ratio, the scour depth profile of the tower foundation, and the optimal modal frequency.

[0155] Specifically, an increase in the scouring depth will lead to a decrease in the supporting force of the soil around the tower foundation, exacerbating the stress concentration at the bottom of the tower or in the weak links; the change in modal frequency reflects the change in the tower stiffness distribution. A decrease in stiffness (such as loosening of the foundation due to scouring) will reduce the modal frequency, thereby affecting the distribution of the vibration amplitude at the stress concentration point; the theoretical amplitude ratio is combined with the scouring depth and modal frequency to evaluate the abnormality of the vibration amplitude at the stress concentration point and determine whether the tower stiffness meets the safety requirements.

[0156] Furthermore, by quantifying the vibration amplitude ratio of stress concentration points, the weak areas of the tower structure are located, providing clear targets for structural optimization (such as increasing wall thickness or reinforcing supports in areas where the theoretical amplitude ratio exceeds the safety threshold), ensuring the structural safety of the tower in scouring environments.

[0157] Furthermore, the tower stiffness distribution characteristics are extracted from the amplitude ratio data to determine the trend of stiffness distribution changes. Based on the trend of stiffness distribution changes, the stress concentration distribution of the tower structure under the influence of scouring depth is obtained to determine whether the amplitude at the concentration point is abnormal. The stress concentration distribution is compared with a preset threshold to determine whether the tower stiffness distribution meets the operation requirements and obtain a preliminary judgment result. The support vector machine algorithm is used to classify the preliminary judgment results to obtain the degree of matching between the stiffness distribution and safe operation. For areas with a low degree of matching, the optimized theoretical amplitude ratio is calculated through frequency correction and depth profile adjustment to determine the final stiffness distribution state.

[0158] Furthermore, if the final stiffness distribution state exceeds the safe operation requirements, the modal frequency set is iteratively adjusted to obtain tower structural parameters that meet the conditions.

[0159] This embodiment provides a scour testing method for an offshore wind turbine foundation support structure. The method determines the tower stiffness distribution state by calculating the theoretical amplitude ratio of the tower at the stress concentration point. The scour depth profile and optimal modal frequency of the tower foundation are then combined to determine whether the tower stiffness distribution state meets the operational requirements. The target tower structure is optimized based on the judgment result, and the tower is reinforced or adjusted in design to ensure its safe operation.

[0160] The following describes the specific steps of a scour test method for an offshore wind power foundation support structure through a specific embodiment.

[0161] like Figure 5 As shown, a scour test method for an offshore wind power foundation support structure includes the following specific steps:

[0162] Step 1: Use a sensor array to collect real-time vibration signals at different heights of the tower. In response to the interference of water velocity components in the dynamic ocean environment, use Fourier transform to convert the time domain signal into a frequency domain signal to obtain the vibration amplitude peak and frequency distribution bandwidth.

[0163] Assuming that the sensor sampling frequency is 100 Hz, a 10-second time domain signal is collected, with a total of 1000 data points. In order to eliminate the interference of the water velocity component, the signal is first preprocessed using a sliding average filter with a window size of 50 data points to remove high-frequency noise.

[0164] Fast Fourier Transform (FFT) was used to convert the time domain signal into a frequency domain signal. The FFT algorithm converted 1,000 data points into 500 frequency components with a frequency resolution of 1 Hz. In the frequency domain, the vibration amplitude peak was observed to occur at 5 Hz, with an amplitude of 8 m / s2 and a frequency distribution bandwidth of 5 Hz to 0 Hz. Further analysis found that the 5 Hz peak coincided with the natural frequency of the tower, indicating that the tower resonated at this frequency.

[0165] To verify this conclusion, modal analysis was used to calculate the tower's natural frequency to be 52 Hz, which is consistent with the FFT result. By comparing sensor data at different heights, it was found that the vibration amplitude at the bottom of the tower was the largest, at 2 m / s². As the height increased, the vibration amplitude gradually decreased, and the vibration amplitude at the top was 5 m / s². The above data provides an important basis for the structural health monitoring of the tower and helps to promptly detect potential safety hazards.

[0166] Step 2: Extract the characteristic frequency from the peak value of the vibration amplitude, separate the low-frequency and high-frequency components through wavelet transform, and use time-frequency analysis to determine the changing trend of the vibration frequency with the time window length based on the non-stationary characteristics caused by the water flow direction angle.

[0167] In vibration amplitude peak analysis, the characteristic frequencies are first extracted through fast Fourier transform (FFT). For example, in a certain mechanical system, FFT analysis shows that the main vibration frequencies are 50 Hz and 120 Hz. Next, the discrete wavelet transform (DWT) is used to decompose the signal. Using the Daubechies4 wavelet basis function (a type of wavelet function), the signal is decomposed into low-frequency and high-frequency components. The low-frequency component mainly contains 50 Hz vibrations, while the high-frequency component contains 120 Hz vibrations. To address the non-stationary characteristics caused by the water flow direction angle, the short-time Fourier transform (STFT) is used for time-frequency analysis. The time window length is set to 1 second and the overlap rate is 50%. The STFT can be used to observe the change trend of the vibration frequency over time.

[0168] For example, from 0 to 5 seconds, the vibration amplitude of 50 Hz gradually increases, while from 5 to 1 second, the vibration amplitude of 120 Hz gradually decreases; through the above time-frequency analysis method, the dynamic changes of vibration frequency can be captured more accurately, providing strong support for subsequent fault diagnosis and prediction.

[0169] Step 3: Use an acoustic Doppler flowmeter to collect water velocity components and water direction angles at multiple points around the tower. Based on the vertical and horizontal differences in water flow distribution, calculate the water shear force and initial scour slope to obtain the contribution vector of water flow to foundation scour.

[0170] Assume that acoustic Doppler current meters are placed around the tower, collecting water velocity components and water direction angles at a frequency of 10 times per second, ensuring data accuracy to the millimeter level. For example, in the vertical direction, a measuring point is set every 5 meters, for a total of 10 measuring points, and in the horizontal direction, a measuring point is set every 1 meter, for a total of 5 measuring points. Using the collected velocity components and direction angles, a three-dimensional vector decomposition algorithm is used to calculate the water velocity vector at each measuring point. Assuming that at a certain measuring point, the horizontal velocity component is 8 meters per second, the vertical velocity component is 3 meters per second, and the direction angle is 45 degrees, the water velocity vector is (8cos45°, 8sin45°, 3). Based on this data, a gradient calculation method is used to analyze the vertical and horizontal differences in water flow distribution.

[0171] In the vertical direction, the velocity gradient is 1 m / s per meter, and in the horizontal direction it is 5 m / s per meter. Based on the above gradient values, the shear force of the water flow is calculated using Newton's law of internal friction. Assuming that the dynamic viscosity of water is 0.01 Pascal seconds, the shear force is 0.01 multiplied by the velocity gradient, that is, the vertical shear force is 0.001 Pa and the horizontal shear force is 0.0005 Pa.

[0172] Furthermore, the scour initial slope formula is used to calculate the scour initial slope. Assuming that the critical shear stress of the soil is 0.1 Pa, the scour initial slope is 0.001 divided by 0.1, that is, 0.1 radians.

[0173] Finally, the water flow shear force and the initial scour slope are combined through the vector synthesis method to obtain the contribution vector of the water flow to the foundation scour; for example, the vertical contribution component is 0001 Pa, the horizontal contribution component is 00005 Pa, and the direction angle is 45 degrees, then the contribution vector is (0001cos45°, 0001sin45°, 00005).

[0174] The above calculation results provide important data support for further analysis of foundation scour.

[0175] Step 4: Based on the changing trends of the water shear force and the vibration amplitude peak, an initial mapping relationship is constructed. The correlation coefficient matrix between the frequency distribution bandwidth and the water flow direction angle is fitted through regression analysis to obtain a preliminary scour depth profile.

[0176] Based on the measured data, the shear force and vibration amplitude peaks were first normalized. The relationship between the two was fitted using the least squares method, resulting in an initial mapping function y = 85x + 12, where x is the normalized shear force and y is the normalized vibration amplitude. A correlation coefficient matrix was constructed using multiple linear regression analysis to determine the relationship between the frequency distribution bandwidth and the flow direction angle. The main diagonal elements are 92, 85, and 78, respectively, representing the degree of correlation between different frequency components and the direction angle. Based on these analysis results, the scour process was simulated using the finite element method, with a grid size of 1m × 1m and a time step of 0.1s. A preliminary scour depth profile was obtained through iterative calculations.

[0177] During the simulation process, the Reynolds Stress Models (RSM) was introduced to describe the turbulent characteristics. The initial flow velocity was set to 5 m / s, the median sediment particle size was set to 15 mm, and the maximum scour depth was calculated to be 2 m. The scour area was distributed in an elliptical shape, with the major axis direction consistent with the mainstream direction.

[0178] To further optimize the results, adaptive grid encryption technology was used to densify the grid to 0.5m×0.5m in the severe scour area. After recalculation, a more accurate scour depth distribution was obtained. The maximum scour depth was adjusted to 35m, and the scour range was slightly expanded.

[0179] Finally, the calculated results were compared with the field measured data and it was found that the degree of agreement between the simulation results and the measured data reached more than 85%, which verified the reliability of the model.

[0180] Step 5: Based on the scour depth profile, finite element analysis is used to simulate the tower vibration response in the pre-established tower stiffness distribution model. The modal frequency set is calculated based on boundary constraints to determine the deviation between the displacement distribution and the actual characteristic frequency.

[0181] In the pre-established tower stiffness distribution model, when simulating the tower vibration response using finite element analysis, it is first necessary to define the tower material properties, such as the elastic modulus of 210 GPa, the Poisson's ratio of 3, and the density of 7850 kg / m 3Finite element software such as ANSYS was used to divide the tower into 10,000 units, meshed using tetrahedral units to ensure that each unit did not exceed 5 meters in size. Within the boundary constraints, the tower base was fixed to simulate actual installation conditions. Modal analysis was used to calculate the first six modal frequencies, which were 0.278Hz, 0.269Hz, 1.305Hz, 1.31Hz, 2.05Hz, and 2.15Hz, respectively. Displacement distribution analysis was used to extract the displacement distribution of each mode. For example, the maximum displacement of the first-order mode occurred at the top of the tower, with a displacement value of 2 meters. The accuracy of the simulation results was determined by comparing the measured characteristic frequencies. For example, the actual frequency of the first-order mode was 0.29Hz, which deviated by 4% from the simulated result of 0.278Hz.

[0182] If the deviation exceeds 5%, the model parameters need to be adjusted, such as increasing the number of units or optimizing material properties, and the modal analysis needs to be repeated until the deviation meets the requirements. Through this iterative optimization, the simulation results of the tower vibration response are ensured to be highly consistent with the actual characteristic frequency, providing a reliable basis for subsequent structural design.

[0183] Step 6: If the deviation between the modal frequency set and the vibration amplitude peak exceeds the preset threshold, the water velocity component and frequency distribution bandwidth data are fused through Kalman filtering, the parameters in the correlation coefficient matrix are updated, and the corrected scour depth profile is obtained.

[0184] When the deviation between the modal frequency set and the vibration amplitude peak exceeds a preset threshold (for example, the deviation exceeds 5%), the system will automatically start the Kalman filter algorithm to fuse the water velocity component and the frequency distribution bandwidth data. The specific steps are as follows: first, collect the current water velocity component data (such as the flow velocity is 2m / s), and combine it with the frequency distribution bandwidth data (such as the bandwidth is 8Hz), and perform data fusion through the state update equation of the Kalman filter. In this process, the parameters in the correlation coefficient matrix are dynamically adjusted according to the fused data; the state update equation of the Kalman filter is:

[0185] x_k=F_k*x_{k-1}+B_k*u_k+w_k (5)

[0186] Where x_k is the current state estimate, F_k is the state transfer matrix, B_k is the control input matrix, u_k is the control input vector, and w_k is the process noise.

[0187] For example, the parameters α and β in the correlation coefficient matrix are updated from 75 and 25 to 8 and 2 respectively. The scour depth profile is recalculated using the updated correlation coefficient matrix. The specific calculation process is as follows:

[0188] h=α*v+β*f (6)

[0189] Where h is the scour depth, v is the water velocity, and f is the frequency distribution bandwidth.

[0190] Finally, a corrected scour depth profile is generated, for example, the corrected scour depth is 5m.

[0191] Throughout the entire process, real-time data acquisition, Kalman filter fusion, and parameter updates are used to ensure the accuracy and reliability of the scour depth profile, providing a scientific basis for subsequent engineering decisions.

[0192] Step 7: Based on the corrected scour depth profile, a long short-term memory network is used to train the historical water flow shear force and displacement distribution data. Based on the temporal changes in the initial scour slope, the distribution trend of stress concentration points within the future time window is predicted.

[0193] First, based on the corrected scour depth profile, the finite element method was used to mesh the river cross-section, setting the grid size to 1 meter and generating a total of 5,000 units. A two-dimensional hydrodynamic model was established and solved using the Saint-Venant equations (a set of partial differential equations in fluid mechanics) to obtain historical water flow shear force data. The time step was set to 1 second, and the simulation period was 72 hours. At the same time, the modal analysis method was used to calculate the displacement distribution, and the first five modes were extracted, with modal frequencies of 5 Hz, 2 Hz, 0 Hz, 1 Hz, and 5 Hz, respectively.

[0194] The above data were used as input to construct a long short-term memory network model, with the number of hidden layer units set to 128, the learning rate to 0.01, and the batch size to 32. A sliding window method was used to extract features based on the temporal changes in the initial slope of the scour, with a window length of 24 hours and a step size of 1 hour. The trained model was used to predict the distribution trend of stress concentration points within the next 48 hours. By calculating the von Mises stress field, areas with stress values ​​exceeding 5 MPa were identified and marked as potential danger areas.

[0195] Finally, the Kriging interpolation method is used to visualize the prediction results and generate a stress concentration point distribution map to provide decision support for river protection projects.

[0196] Step 8: By comparing the predicted stress concentration point distribution trend with the real-time collected vibration amplitude peak value, if the deviation between the predicted value and the actual value is less than the preset threshold, the quantitative relationship of the current correlation coefficient matrix is ​​output to determine the scour depth profile of the tower foundation.

[0197] In the tower foundation scour depth profile analysis, the distribution trend of stress concentration points is first predicted using finite element analysis. Assuming that in a certain area of ​​the tower foundation, the predicted stress concentration point is located at coordinates (5, 8), and the predicted maximum stress value is 120 MPa. Subsequently, the peak vibration amplitude of this area is collected in real time using a vibration sensor, and the actual maximum vibration amplitude is 15 mm. Based on the preset deviation threshold of 5%, the deviation between the predicted and actual values ​​is calculated to be (120-115) / 120=17%, which is less than the threshold. At this point, the quantitative relationship of the current correlation coefficient matrix is ​​output. Assuming the correlation coefficient matrix is ​​[[1, 85], [85, 1]], it indicates a strong correlation between stress concentration points and vibration amplitude. Based on this quantitative relationship, the scour depth profile of the tower foundation is determined. By analyzing the relationship between scour depth and stress concentration points, it is assumed that the scour depth and stress value have a linear relationship, that is, scour depth = 01*stress value + 5, and the calculated scour depth is 7 m. This process is automatically processed by information technology to ensure the accuracy and reliability of the analysis results.

[0198] Step 9: Based on the determined scour depth profile and water flow direction angle distribution characteristics, an adaptive filtering algorithm is used to optimize the sensor acquisition frequency. Based on the dynamics of the water flow velocity component, the vibration monitoring strategy under the grid division density is adjusted to obtain a continuously optimized modal frequency set.

[0199] When determining the scour depth profile, riverbed topography data was acquired through 3D laser scanning technology with a resolution of 1 meter. Combined with the water depth data collected by the multi-beam echo sounder, the Kriging interpolation algorithm was used to generate a high-precision scour depth distribution map. Based on the distribution characteristics of the water flow direction angle, the ADCP (Acoustic Doppler Current Profiler) was used to collect water velocity data with a sampling frequency of 1 Hz. The Fourier transform algorithm was used to analyze the changing pattern of the water flow direction angle, and the main water flow direction angle was extracted to range from 30° to 60°.

[0200] To optimize the sensor acquisition frequency, an adaptive filtering algorithm based on Kalman filtering was used to adjust the acquisition frequency based on the dynamics of the water velocity components. When the standard deviation of the water velocity exceeded 2 meters per second, the acquisition frequency was increased from 1 Hz to 5 Hz to capture more subtle changes. Regarding mesh density, finite element analysis was used to dynamically adjust the mesh size from the initial 2 meters by 2 meters to 5 meters by 5 meters based on the water velocity gradient and scour depth distribution to improve vibration monitoring accuracy.

[0201] Through the above method, combined with the modal analysis algorithm, the modal frequency set of the bridge structure was extracted. The optimized first three modal frequencies were 1 Hz, 3 Hz, and 7 Hz, respectively, providing reliable data support for subsequent structural health monitoring.

[0202] Step 10: Calculate the theoretical amplitude ratio of the tower at the stress concentration point using the corrected modal frequency set and scour depth profile to determine whether the tower stiffness distribution meets the requirements for safe operation.

[0203] First, the tower is subjected to modal analysis using finite element analysis software to obtain a revised set of modal frequencies. For example, through modal analysis, the first six modal frequencies are calculated to be 0.278 Hz, 0.269 Hz, 1.305 Hz, 1.31 Hz, 2.05 Hz, and 2.15 Hz, respectively.

[0204] Next, based on the scour depth profile data, assuming that the scour depth is 5 meters at the bottom of the tower and 5 meters at the top, the scour depth of each section of the tower is calculated using the linear interpolation method.

[0205] Then, using the location information of the stress concentration point, it is assumed that the stress concentration point is located at one-third of the tower height, that is, 10 meters from the bottom. According to the scour depth profile, the scour depth of this point is 5 meters.

[0206] Next, the theoretical amplitude ratio calculation formula is used. Assuming that the elastic modulus of the tower is 210 GPa and the density is 7850 kg per cubic meter, the theoretical amplitude ratio at the stress concentration point is calculated. Through calculation, the theoretical amplitude ratio is 85.

[0207] Finally, the theoretical amplitude ratio is compared with the safe operation standard. Assuming the safe operation standard is 9, since 85 is less than 9, it is judged that the tower stiffness distribution does not meet the safe operation requirements. Therefore, the tower needs to be reinforced or adjusted to ensure its safe operation.

[0208] This embodiment also provides a scour test device for an offshore wind turbine foundation support structure. This device is used to implement the above-mentioned embodiments and preferred embodiments, and the details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0209] This embodiment provides a scour test device for an offshore wind power foundation support structure. Figure 6 As shown, including:

[0210] Construction module 601 is used to collect real-time vibration signals of the target tower at different heights and water flow parameters at multiple points around the target tower, and to construct a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signals and water flow parameters;

[0211] The scour simulation module 602 is used to perform scour simulation on the target tower based on the mapping relationship between water scour and tower vibration to obtain an initial scour depth profile;

[0212] The modal analysis module 603 is used to simulate the tower vibration response based on the initial scour depth profile using the tower stiffness distribution model, and perform modal analysis on the tower vibration response simulation results to obtain the modal frequency;

[0213] A correction module 604 is used to correct the initial scour depth profile and modal frequency respectively to obtain the scour depth profile and optimal modal frequency of the tower foundation;

[0214] The optimization module 605 is used to optimize the target tower structure based on the scour depth profile and the optimal modal frequency of the tower foundation.

[0215] In some optional implementations, the building block 601 includes:

[0216] a first determining unit, configured to convert the real-time vibration signal into a frequency domain signal, and determine a vibration amplitude peak value and a frequency distribution bandwidth based on the frequency domain signal;

[0217] a second determining unit, configured to determine a water flow velocity component and a water flow direction angle based on the water flow parameter;

[0218] a third determining unit, configured to determine the water flow shear force based on the water flow velocity component and the water flow direction angle;

[0219] The construction unit is used to construct a mapping relationship between water flow scouring and tower vibration based on the changing trends of water flow shear force and vibration amplitude peak value.

[0220] In some optional embodiments, the flushing simulation module 602 includes:

[0221] A fitting unit is used to fit the relationship between the frequency distribution bandwidth and the water flow direction angle based on the mapping relationship between water flow scouring and tower vibration, and obtain a correlation coefficient matrix;

[0222] The simulation unit is used to perform scour simulation on the target tower based on the correlation coefficient matrix to obtain the initial scour depth profile.

[0223] In some optional implementations, the modal analysis module 603 includes:

[0224] a calculation unit for calculating a deviation between a modal frequency and a peak value of a vibration amplitude;

[0225] a correction unit, configured to correct the initial scour depth profile based on a deviation between the modal frequency and the vibration amplitude peak value to obtain a corrected scour depth profile;

[0226] a fourth determining unit, configured to determine a distribution trend of stress concentration points using a long short-term memory network model according to the corrected scour depth profile;

[0227] a fifth determining unit, configured to determine a scour depth profile of the tower foundation based on a quantitative relationship between a distribution trend of stress concentration points and a peak value of vibration amplitude;

[0228] The first optimization unit is used to optimize the vibration monitoring strategy corresponding to the sensor acquisition frequency and grid division density based on the scour depth profile and water flow direction angle of the tower foundation;

[0229] The acquisition unit is used to obtain the optimal modal frequency by using the vibration monitoring strategy corresponding to the optimized sensor acquisition frequency and grid division density.

[0230] In some optional embodiments, the correction unit includes:

[0231] a comparison subunit, configured to compare a deviation between the modal frequency and the vibration amplitude peak with a preset deviation threshold;

[0232] a fusion subunit for fusing the water velocity component and the frequency distribution bandwidth if the deviation between the modal frequency and the vibration amplitude peak is greater than a preset deviation threshold;

[0233] The updating subunit is used to update the correlation coefficient matrix based on the fused water velocity components and the frequency distribution bandwidth, and to correct the initial scour depth profile using the updated correlation coefficient matrix to obtain a corrected scour depth profile.

[0234] In some optional implementations, the optimization module 605 includes:

[0235] a sixth determining unit, configured to obtain a vibration amplitude and a reference amplitude at the stress concentration point, and calculate a theoretical amplitude ratio based on the vibration amplitude and the reference amplitude at the stress concentration point;

[0236] The second optimization unit is used to optimize the target tower structure based on the theoretical amplitude ratio, the scour depth profile of the tower foundation and the optimal modal frequency.

[0237] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0238] In this embodiment, a scour test device for an offshore wind power foundation support structure is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0239] The embodiment of the present invention also provides a computer device having the above Figure 6 A scour testing device for an offshore wind power foundation support structure is shown.

[0240] See also Figure 7 , Figure 7 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 7 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of a GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0241] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0242] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0243] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0244] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0245] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0246] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0247] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0248] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A scour test method for an offshore wind power foundation support structure, characterized in that: The method comprises: Collecting real-time vibration signals of the target tower at different heights and water flow parameters at multiple points around the target tower, and building a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signals and the water flow parameters; Based on the mapping relationship between the water flow scouring and the tower vibration, a scouring simulation is performed on the target tower to obtain an initial scouring depth profile; Based on the initial scour depth profile, a tower stiffness distribution model is used to simulate the tower vibration response, and a modal analysis is performed on the tower vibration response simulation results to obtain the modal frequency; Correcting the initial scour depth profile and the modal frequency respectively to obtain the scour depth profile and the optimal modal frequency of the tower foundation; The target tower structure is optimized based on the scour depth profile of the tower foundation and the optimal modal frequency.

2. The method according to claim 1, characterized in that The step of constructing a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signal and the water flow parameter includes: Converting the real-time vibration signal into a frequency domain signal, and determining a vibration amplitude peak and a frequency distribution bandwidth based on the frequency domain signal; determining a water flow velocity component and a water flow direction angle based on the water flow parameters; determining a water flow shear force based on the water flow velocity component and the water flow direction angle; Based on the change trends of the water flow shear force and the vibration amplitude peak value, a mapping relationship between the water flow scouring and the tower vibration is constructed.

3. The method according to claim 2, characterized in that The scouring simulation of the target tower is performed based on the mapping relationship between the water flow scouring and the tower vibration to obtain an initial scouring depth profile, including: Based on the mapping relationship between the water flow scouring and the tower vibration, the relationship between the frequency distribution bandwidth and the water flow direction angle is fitted to obtain a correlation coefficient matrix; A scour simulation is performed on the target tower based on the correlation coefficient matrix to obtain the initial scour depth profile.

4. The method according to claim 3, characterized in that The respectively correcting the initial scour depth profile and the modal frequency to obtain the scour depth profile and the optimal modal frequency of the tower foundation includes: calculating a deviation between the modal frequency and the peak value of the vibration amplitude; Correcting the initial scour depth profile based on a deviation between the modal frequency and the vibration amplitude peak value to obtain the corrected scour depth profile; According to the corrected scour depth profile, the distribution trend of stress concentration points is determined using the long short-term memory network model. determining a scour depth profile of the tower foundation based on a quantitative relationship between the stress concentration point distribution trend and the vibration amplitude peak value; Optimizing the vibration monitoring strategy corresponding to the sensor acquisition frequency and grid division density based on the scour depth profile of the tower foundation and the water flow direction angle; The optimal modal frequency is obtained by utilizing a vibration monitoring strategy corresponding to the optimized sensor acquisition frequency and grid division density.

5. The method according to claim 4, characterized in that The correcting the initial scour depth profile based on the deviation between the modal frequency and the vibration amplitude peak value to obtain the corrected scour depth profile includes: comparing the deviation between the modal frequency and the peak value of the vibration amplitude with a preset deviation threshold; If the deviation between the modal frequency and the vibration amplitude peak value is greater than the preset deviation threshold, fusing the water flow velocity component and the frequency distribution bandwidth; The correlation coefficient matrix is ​​updated based on the fused water velocity component and the frequency distribution bandwidth, and the updated correlation coefficient matrix is ​​used to correct the initial scour depth profile to obtain the corrected scour depth profile.

6. The method according to claim 1, characterized in that The optimizing the target tower structure based on the scour depth profile of the tower foundation and the optimal modal frequency includes: Acquiring a vibration amplitude and a reference amplitude at a stress concentration point, and calculating a theoretical amplitude ratio based on the vibration amplitude and the reference amplitude at the stress concentration point; The target tower structure is optimized based on the theoretical amplitude ratio, the scour depth profile of the tower foundation, and the optimal modal frequency.

7. A scour test device for an offshore wind power foundation support structure, characterized in that: The device comprises: A construction module is used to collect real-time vibration signals of the target tower at different heights and water flow parameters at multiple points around the target tower, and to construct a mapping relationship between water flow scouring and tower vibration based on the real-time vibration signals and the water flow parameters; A scour simulation module is used to perform scour simulation on the target tower based on the mapping relationship between the water flow scour and the tower vibration to obtain an initial scour depth profile; a modal analysis module for simulating the tower vibration response based on the initial scour depth profile using a tower stiffness distribution model, and performing modal analysis on the tower vibration response simulation results to obtain modal frequencies; A correction module, configured to correct the initial scour depth profile and the modal frequency respectively to obtain the scour depth profile and the optimal modal frequency of the tower foundation; An optimization module is used to optimize a target tower structure based on the scour depth profile of the tower foundation and the optimal modal frequency.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the scour test method for an offshore wind power foundation support structure according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the scour test method for an offshore wind power foundation support structure according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the scour test method for an offshore wind power foundation support structure according to any one of claims 1 to 6.

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