Methods, systems, media, and apparatuses for inverting qtf based on multi-source response data

Abstract

The present application relates to the field of offshore floating platform safety assessment, and discloses a method, system, medium and equipment for inverting QTF based on multi-source response data, comprising: collecting multi-source response data of a mooring platform in a pool test; establishing a mooring dynamics model consistent with the pool test, taking the six-degree-of-freedom motion response of the platform as input to solve the mooring dynamics model to obtain the dynamic tension of each mooring cable, checking the mooring dynamics model and calculating the instantaneous generalized force of the mooring system acting on the platform; based on the six-degree-of-freedom time-domain motion equation of the platform, using the platform acceleration response, the instantaneous generalized force and the preset hydrodynamic parameters to invert the environmental load acting on the platform; based on the incident wave height time history and the environmental load, extracting the equivalent low-frequency slow-drift environmental load component, and using the cross-bispectrum analysis method to identify the second-order difference frequency transfer function matrix. The present application can improve the accuracy of mooring floating platform low-frequency slow-drift load identification.

Description

Technical Field

[0001] This invention relates to the field of safety assessment technology for offshore floating platforms, and in particular to a method, system, medium, and equipment for inverting QTF (second-order difference frequency transfer function matrix) based on multi-source response data. Background Technology

[0002] Large moored floating bodies such as floating offshore platforms, semi-submersible platforms, floating wind turbine foundations, and FPSOs are subjected to both first-order wave frequency loads and second-order low-frequency slow-drift loads in irregular wave environments. Among these, the second-order differential frequency slow-drift load easily induces low-frequency large-amplitude movements such as sway, roll, and bow roll on the platform, further affecting platform drift, mooring cable tension, and overall positioning safety. Therefore, accurately obtaining the low-frequency slow-drift wave force of the platform under irregular wave action is a key issue in the hydrodynamic forecasting, mooring system design, and safety assessment of floating platforms.

[0003] Second-order low-frequency slow-drift wave forces are typically described using the second-order difference-frequency transfer function matrix (QTF). The QTF characterizes the difference-frequency slow-drift force generated by the interaction of different frequency components of the incident wave. It is a crucial input parameter for reconstructing low-frequency wave loads in irregular waves, predicting platform slow-drift motion, and calculating mooring system responses. Existing methods for obtaining QTF mainly include numerical calculation methods based on second-order potential flow theory, direct extraction methods based on two-color wave or regular wave pool experiments, and system identification methods based on irregular wave model experimental data. Among these, methods based on potential flow theory have high computational efficiency, but often struggle to fully consider complex factors such as viscous drift, wave-current coupling, higher-order nonlinearities, and mooring coupling. Methods based on two-color wave experiments have clear physical meaning, but require numerous experiments with different frequency combinations, resulting in high experimental costs. Identification methods based on irregular wave experimental data can back-estimate QTF using a smaller number of experimental conditions, showing good potential for engineering applications.

[0004] In actual moored floating platform tank tests, the incident wave height time history, the platform's six-degree-of-freedom motion response, acceleration response, and part of the mooring cable tip tension time history can usually be measured, but it is difficult to directly measure the complete six-degree-of-freedom environmental load. The platform's low-frequency motion response is not only determined by the second-order slow-drift wave force, but is also affected by the platform's inertial force, radiation force, damping force, still water restoring force, mooring restoring force, and the dynamic effects of the mooring cable. If the mooring system forces cannot be accurately separated when inverting the slow-drift wave force from the motion response, the inverted environmental load may contain mooring load components, thus affecting the identification accuracy of the QTF matrix. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to provide a method, system, medium, and device for inverting QTF based on multi-source response data. This method combines incident wave height, platform six-degree-of-freedom motion, acceleration response, and mooring cable tension data. First, it inverts and separates mooring loads using a mooring dynamics model. Then, it inverts environmental loads based on the platform's motion equations, thereby identifying the difference-frequency QTF matrix. This improves the accuracy and engineering applicability of identifying low-frequency slow-drift loads on moored floating platforms.

[0006] To achieve the above objectives, in a first aspect, the technical solution adopted by the present invention is as follows: a method for inverting QTF based on multi-source response data, comprising: acquiring multi-source response data of a mooring platform in a water tank test and preprocessing it; wherein, the multi-source response data includes the incident wave height time history, the platform's six-degree-of-freedom motion response, the platform's acceleration response, and the tension time history of multiple mooring cables; establishing a mooring dynamics model consistent with the water tank test layout, using the platform's six-degree-of-freedom motion response as input, and solving the mooring dynamics model to obtain the dynamic tension of each mooring cable and The spatial morphology was determined, and the mooring dynamics model was verified using tension time history. The verified mooring dynamics model was then used to calculate the instantaneous generalized force exerted by the mooring system on the platform. Based on the constructed six-degree-of-freedom time-domain motion equations of the platform, the environmental loads on the platform were inverted using the platform acceleration response, instantaneous generalized force, and preset hydrodynamic parameters. The environmental loads were frequency-separated to extract the equivalent low-frequency slow-drift environmental load components. Based on the incident wave high time history and the equivalent low-frequency slow-drift environmental load components, the second-order difference frequency transfer function matrix was identified using bispectral analysis.

[0007] Furthermore, the mooring dynamics model was verified using the tension time history, including: The calculated dynamic tension of the mooring cable is compared with the measured tension time history of the mooring cable. If the comparison result exceeds the preset error range, the parameters of the mooring dynamic model are corrected to make the mooring dynamic model closer to the actual test state. The parameters include the axial stiffness of the cable, damping parameters, drag coefficient, anchor position, pretension and connection point coordinates. The comparison indicators include at least one of the following: average tension, root mean square tension, peak tension, tension time history correlation coefficient, tension power spectrum, low-frequency tension component, or wave frequency tension component.

[0008] Furthermore, the environmental loads experienced by the platform were obtained through inversion: ; in, The platform mass and moment of inertia matrix; For the additional mass matrix; For still water restoring force; It is either a viscous damping force or an empirical damping force; For instantaneous generalized force; The environmental load to be inverted; For a moment; For historical time integration variables; Acceleration for the platform's six degrees of freedom; For the platform's six degrees of freedom speed; This is the impulse response function.

[0009] Furthermore, frequency separation is performed on the environmental load to extract the equivalent low-frequency slow-drift environmental load component, specifically: Low-pass or band-pass filters are used to process the environmental load to filter out first-order frequency load components and high-frequency noise, while retaining components within the difference frequency drift range as equivalent low-frequency drift environmental load components.

[0010] Furthermore, the second-order difference-frequency transfer function matrix was identified using the bispectral analysis method as follows:

[0011] in, For two incident wave frequencies and mutual bispectral; It is frequency Wave spectrum at that location.

[0012] Furthermore, after identifying the second-order difference frequency transfer function matrix, the process also includes steps for second-order difference frequency wave force reconstruction verification and platform motion response reconstruction verification. Second-order difference frequency wave force reconstruction verification: The second-order difference frequency wave force is reconstructed using the identified second-order difference frequency transfer function matrix and incident wave height time history, and the reconstructed second-order difference frequency wave force is compared and verified with the extracted equivalent low-frequency slow drift environmental load components. Platform motion response reconstruction verification: The reconstructed second-order difference frequency wave force is input into the platform's six-degree-of-freedom time-domain motion equation to calculate the reconstructed platform six-degree-of-freedom motion response, and the reconstructed platform six-degree-of-freedom motion response is compared and verified with the acquired platform six-degree-of-freedom motion response.

[0013] Furthermore, the verification method is as follows: If the reconstructed six-degree-of-freedom motion response of the platform matches the experimentally acquired six-degree-of-freedom motion response within the error range in terms of time history, power spectrum, RMS, peak value, phase characteristics, and low-frequency dominant frequency, then the identified QTF matrix has high reliability.

[0014] Secondly, the technical solution adopted by this invention is as follows: a system for inverting QTF based on multi-source response data, comprising: a data acquisition module, which acquires multi-source response data of a mooring platform in a water tank test and performs preprocessing; wherein, the multi-source response data includes the incident wave height time history, the platform's six-degree-of-freedom motion response, the platform's acceleration response, and the tension time history of multiple mooring cables; and a mooring force verification and correction module, which establishes a mooring dynamics model consistent with the water tank test layout, takes the platform's six-degree-of-freedom motion response as input, solves the mooring dynamics model to obtain the dynamic tension and spatial morphology of each mooring cable, and utilizes the tension... The time history is used to verify the mooring dynamics model, and then the verified mooring dynamics model is used to calculate the instantaneous generalized force acting on the platform by the mooring system; the environment inversion module, based on the constructed six-degree-of-freedom time-domain motion equations of the platform, uses the platform acceleration response, instantaneous generalized force and preset hydrodynamic parameters to invert the environmental loads on the platform; the equivalent low-frequency slow-drift environmental load component extraction module performs frequency separation on the environmental loads and extracts the equivalent low-frequency slow-drift environmental load components; the identification module, based on the incident wave high time history and the equivalent low-frequency slow-drift environmental load components, uses the bispectral analysis method to identify the second-order difference frequency transfer function matrix.

[0015] Thirdly, the technical solution adopted by the present invention is: a computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods described above.

[0016] Fourthly, the technical solution adopted by the present invention is: a computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described above.

[0017] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention improves the efficiency of QTF matrix acquisition. This invention utilizes irregular wave pool test data to reverse-identify the difference frequency QTF matrix, avoiding the need to conduct separate two-color wave tests for a large number of frequency combinations. This reduces the number of test conditions, lowers the cost of pool testing, and improves QTF acquisition efficiency.

[0018] 2. This invention improves the engineering applicability of slow-drift load identification. Based on measured incident wave height, platform six-degree-of-freedom motion, acceleration response, and mooring cable tension data, this invention inverts environmental loads, extracts equivalent low-frequency slow-drift environmental load components, and further identifies the empirical QTF matrix. Compared to QTF calculated solely based on potential flow theory, this invention can better reflect the combined effects of the platform's true structural characteristics, mooring status, viscous influence, and wave-current coupling within the scope of experimental data.

[0019] 3. This invention can reduce the interference of nonlinear mooring loads on QTF identification. This invention inputs the measured six-degree-of-freedom motion of the platform into the mooring dynamics model, and verifies the mooring model in conjunction with measured cable tension. It then calculates and separates the instantaneous six-degree-of-freedom generalized forces acting on the platform by the mooring system, thereby avoiding the mixing of mooring restoring force, cable tension nonlinearity, and cable dynamic effects into the low-frequency slow-drift environmental load components, thus reducing their interference with the QTF identification results.

[0020] 4. This invention improves the reliability of the identification results. This invention establishes a closed-loop verification process including mooring cable tension verification, second-order difference frequency wave force reconstruction verification, and platform low-frequency motion reconstruction verification, thereby improving the stability and reliability of the QTF matrix identification results. Attached Figure Description

[0021] Figure 1 This is a flowchart of the method for inverting QTF based on multi-source response data in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] In one embodiment of the present invention, a method for inverting the QTF based on multi-source response data from a water tank test is provided, which can fully utilize the QTF inverse identification method of multi-source data from platform water tank tests. Using the incident wave height time history, platform six-degree-of-freedom motion response, platform acceleration response, and tension data of multiple mooring cables measured in the water tank test, combined with the mooring dynamics model and the platform six-degree-of-freedom motion equations, the instantaneous generalized forces acting on the platform by the mooring system are first calculated and separated. Then, the environmental loads on the platform are inverted, and the equivalent low-frequency slow-drift environmental load components are extracted. Finally, the difference-frequency QTF matrix is ​​identified through a bispectral analysis inversion method. In this embodiment, as shown... Figure 1 As shown, the method includes the following steps: 1) Collect multi-source response data of the mooring platform in the pool test and preprocess it; the multi-source response data includes incident wave height time history, platform six-degree-of-freedom motion response, platform acceleration response and tension time history of multiple mooring cables.

[0025] 2) Based on the pool test setup, establish a mooring dynamics model consistent with the pool test. Use the platform's six-degree-of-freedom motion response as input to solve the mooring dynamics model to obtain the dynamic tension and spatial shape of each mooring cable. Then, use the tension time history to verify the mooring dynamics model and use the verified mooring dynamics model to calculate the instantaneous generalized force of the mooring system acting on the platform.

[0026] 3) Based on the constructed six-degree-of-freedom time-domain motion equations of the platform, the environmental loads on the platform are obtained by inversion using the platform's acceleration response, instantaneous generalized force, and preset hydrodynamic parameters.

[0027] 4) Perform frequency separation on the environmental load to extract the equivalent low-frequency slow-drift environmental load component.

[0028] 5) Based on the incident wave high time history and the equivalent low-frequency slow-drift environmental load component, the second-order difference frequency transfer function matrix is ​​identified using the bispectral analysis method.

[0029] In step 1) above, during the platform pool test, the following multi-source response data of the moored platform during the pool test were collected simultaneously: (1) incident wave high time history at time .

[0030] (2) Moment-time platform six-degree-of-freedom motion response .in, These are respectively longitudinal sway, transverse sway, and helical displacement. These represent the roll, pitch, and bow angle displacements, respectively, with T being the transpose.

[0031] (3) The platform's six degrees of freedom speed at any moment and acceleration Among them, velocity and acceleration can be directly measured by the experimental system, or obtained by filtering and numerical differentiation from the motion time history.

[0032] (4) Dynamic tension of each mooring cable (i.e., the time history of tension at the top of the mooring cable) .in, , This refers to the number of mooring cables.

[0033] (5) Basic data such as platform mass, moment of inertia, center of gravity position, mooring point position, mooring cable parameters, water depth, current velocity, wave parameters, and test scale ratio.

[0034] To ensure the accuracy of subsequent inversion, the above data needs to be processed to a unified time reference, including time synchronization, unified sampling frequency, outlier removal, filtering, coordinate transformation, and scaling. For platform motion data, it should be transformed to a unified reference coordinate system.

[0035] In step 2) above, a mooring dynamics model consistent with the test setup is established based on the pool test arrangement. The six-degree-of-freedom motion response of the platform is used as the platform end boundary input to solve for the dynamic tension and spatial configuration of each mooring cable. The mooring dynamics model is then verified using the tension time history measured in the test. The mooring dynamics model can be expressed as: ; in, Indicates the first Root mooring cable, This indicates the number of mooring cables. A discrete node; The node's spatial location vector; The equivalent quality matrix of the nodes; and These are the tension vectors exerted on both sides of the node by adjacent line elements; For gravity; For buoyancy; Hydrodynamic resistance; To add mass inertial force; These are the seabed contact forces or other binding forces.

[0036] In this embodiment, the mooring dynamics model includes: the length, diameter, mass per unit length, axial stiffness, equivalent damping, buoyancy, drag coefficient, and added mass coefficient of each mooring cable; the coordinates of the connection point between the upper end of the mooring cable and the platform; the coordinates of the anchor point at the lower end of the mooring cable; the water depth, flow velocity, flow direction, and experimental coordinate system; and the six-degree-of-freedom motion input boundary conditions of the platform.

[0037] The mooring dynamics model can employ the lumped mass method, finite element method, or other numerical methods capable of calculating the dynamic response of the cables. The measured motion of the platform's six degrees of freedom is used as the input motion to the mooring dynamics model, causing the platform connection points to move according to the motion time history measured in the experiment. This allows for the solution of the dynamic tension and spatial shape of each mooring cable. The subsequent calculation of the generalized mooring force, i.e. the mooring force acting on the platform, is then performed based on the dynamic tension and spatial shape.

[0038] In step 2) above, the mooring dynamics model is verified using the tension time history, including: The calculated dynamic tension of the mooring cable is compared with the measured tension time history of the mooring cable. If the comparison result exceeds the preset error range, the parameters of the mooring dynamic model are corrected to make the mooring dynamic model closer to the actual test state. The parameters include, but are not limited to, the axial stiffness of the cable, damping parameters, drag coefficient, anchor position, pretension and connection point coordinates, so that the calculated tension and the measured tension reach the preset error range.

[0039] The comparison indicators include at least one of the following: average tension, root mean square tension, peak tension, tension time history correlation coefficient, tension power spectrum, low-frequency tension component, or wave frequency tension component.

[0040] In this embodiment, the mooring dynamics model can be made closer to the actual experimental conditions, providing a basis for the subsequent accurate separation of mooring loads.

[0041] In step 2) above, the instantaneous generalized force acting on the platform by the mooring system is calculated using the verified mooring dynamics model. The specific implementation process is as follows: After the mooring dynamics model is verified, the instantaneous generalized forces acting on the platform at the connection points of each mooring cable are calculated. The instantaneous generalized forces include instantaneous force and torque.

[0042] The six-degree-of-freedom instantaneous generalized forces acting on the platform by all mooring cables include the effects of nonlinearity of mooring cable tension, changes in cable direction, cable dynamics caused by platform motion, cable inertia, and fluid damping.

[0043] Compared with the method using a linear mooring stiffness matrix, this embodiment can more realistically reflect the instantaneous effect of the mooring system on the platform.

[0044] In step 3) above, the constructed six-degree-of-freedom time-domain motion equations of the platform are: .

[0045] Therefore, the environmental load on the platform obtained from the inversion is: ; in, The platform mass and moment of inertia matrix; For the additional mass matrix; For still water restoring force; It is either a viscous damping force or an empirical damping force; For instantaneous generalized force; The environmental load to be inverted; For historical time integration variables; This is the impulse response function.

[0046] For small-amplitude, low-frequency motions, the hydrostatic restoring force can be expressed in a linear form: .in, This represents the hydrostatic restoring stiffness matrix. For situations involving significant large-amplitude motion or attitude changes, the hydrostatic restoring force can also be calculated using a nonlinear hydrostatic restoring method, which calculates the restoring force based on the platform's instantaneous attitude and changes in wetted surface area.

[0047] In step 4) above, due to environmental load The environmental load typically includes first-order frequency loads, second-order low-frequency slow-drift loads, and potentially high-frequency noise. Therefore, frequency separation of the environmental load is necessary to extract the equivalent low-frequency slow-drift environmental load component. The specific extraction method is as follows: Low-pass or band-pass filters are used to process the environmental load to filter out first-order frequency load components and high-frequency noise, while retaining components within the difference frequency drift range as equivalent low-frequency drift environmental load components.

[0048] In this embodiment, the low-frequency range can be determined based on the platform's inherent low-frequency frequency, the wave's dominant frequency range, and the test sampling frequency.

[0049] In step 5) above, the second-order difference-frequency transfer function matrix identified using the bispectral analysis method includes: 5.1) The incident wavefront elevation time history is denoted as the input signal. The equivalent low-frequency slow-drift environmental load component of the p-th degree of freedom after inversion and low-frequency separation is denoted as the output signal. .

[0050] 5.2) Perform segmentation, windowing, and Fourier transform on the input and output signals respectively to obtain the complex spectrum of the incident wave's high time history. Complex spectrum of equivalent low-frequency slow-drift environmental load component .

[0051] 5.3) For any two incident wave frequencies and Its difference frequency is The corresponding bispectral for: ; in, It represents the statistical average.

[0052] 5.4) Based on the mutual bispectral The second-order difference frequency transfer function matrix can be estimated. for:

[0053] Where N is the total number of incident waves; It is frequency Wave spectrum at the location; This represents the second-order difference frequency transfer function obtained from bispectral identification.

[0054] Second-order difference frequency transfer function matrix Satisfying the second-order load expression for the difference frequency: , in, Let represent the complex amplitude of the j-th incident wave frequency component; This represents the conjugate of the complex amplitude of the i-th incident wave frequency component. If the irregular wave is discretized into N frequency components, then any two frequencies... and The interaction between them will produce a difference frequency load component.

[0055] if It is not a true second-order process, and QTF estimation may also depend on sea state. Therefore, estimations should be performed for multiple sea states.

[0056] In this embodiment, in actual calculations, the mutual bispectrum and the self-spectrum are subjected to Fourier transform, window function, piecewise averaging, and spectral density normalization conventions to improve stability under limited samples.

[0057] In the above embodiments, in order to verify the reliability of the identified QTF matrix, after identifying the second-order difference frequency transfer function matrix, the present invention sets up a multi-level closed-loop verification step 6). 6.1) Mooring tension verification: The dynamic tension of each mooring cable calculated by the mooring dynamics model is compared with the tension measured by the experiment to verify the accuracy of mooring load separation.

[0058] 6.2) Verification of Second-Order Differential Frequency Wave Force Reconstruction: The second-order differential frequency wave force is reconstructed using the identified second-order differential frequency transfer function matrix and the incident wave height time history, and the reconstructed second-order differential frequency wave force is compared and verified with the extracted equivalent low-frequency slow-drift environmental load components.

[0059] The comparison includes time history, power spectrum, root mean square value, peak value, and phase characteristics.

[0060] 6.3) Platform motion response reconstruction verification: The reconstructed second-order difference frequency wave force is input into the platform's six-degree-of-freedom time-domain motion equation to calculate the reconstructed platform six-degree-of-freedom motion response, and the reconstructed platform six-degree-of-freedom motion response is compared and verified with the platform six-degree-of-freedom motion response obtained from the experiment.

[0061] The verification method is as follows: if the reconstructed six-degree-of-freedom motion response of the platform is within the error range in terms of time history, power spectrum, RMS, peak value, phase characteristics and low-frequency dominant frequency, then it indicates that the identified QTF matrix has high reliability.

[0062] In summary, this invention solves the problems of high quantity, high cost, and low efficiency in extracting QTF conditions using traditional two-color wave tests. This invention utilizes the incident wave high time history and platform response time history from irregular wave pool tests, and uses signal processing and system identification methods to reverse-identify the difference frequency QTF matrix, thereby reducing reliance on numerous two-color wave frequency combination tests and improving QTF acquisition efficiency.

[0063] Furthermore, this invention addresses the problem that QTF calculated based on potential flow theory is insufficient to reflect real experimental slow-drift loads. This invention utilizes measured data from water tank tests to invert environmental loads and identify empirical QTFs, enabling the identification results to reflect the combined effects of the platform's actual structural characteristics, mooring status, viscous influence, and wave-current coupling within the scope of the experimental data, thereby improving the engineering applicability of slow-drift load prediction.

[0064] Furthermore, this invention addresses the problem of accurately considering the effects of nonlinear mooring coupling when inverting QTF based on existing linear mooring models. Some existing QTF identification methods, when inverting the slow-drift wave excitation force from the platform's low-frequency motion response, typically simplify the mooring system to a linear stiffness matrix or an equivalent linear spring model. This approach is applicable to small-amplitude motions and weak mooring nonlinearity, but for actual moored floating platforms, especially in low-frequency, large-amplitude slow-drift motions, medium-to-high sea states, or current-carrying environments, the mooring cable may exhibit significant geometric nonlinearity, tension nonlinearity, cable direction changes, cable inertial effects, and fluid damping effects. If a linear mooring model is still used for load separation, the inverted slow-drift wave excitation force may contain nonlinear load components from the mooring system, or omit the influence of actual mooring coupling on platform motion, leading to deviations in the identified empirical QTF matrix. Therefore, this invention inputs the six-degree-of-freedom motion of the measured platform into the mooring dynamics model, back-calculates the instantaneous loads of each mooring cable acting on the platform, and verifies them by combining the experimentally measured tension at the top of the mooring cable. This allows for more accurate separation of mooring loads before environmental load inversion, reducing the impact of nonlinear mooring coupling on the QTF matrix identification results.

[0065] Furthermore, this invention addresses the lack of closed-loop verification in existing QTF identification processes. This invention ensures the reliability of identification results through multi-level verification: comparing the cable tension calculated by the mooring dynamics model with the experimentally measured cable tension; comparing the second-order difference-frequency wave force reconstructed from the identified QTF with the equivalent low-frequency slow-drift environmental load components derived from the inversion of the motion equations; and reconstructing the platform motion response after inputting the identified QTF into a low-frequency motion model and comparing it with the low-frequency motion in the pool test. Through the above closed-loop verification, the credibility of the QTF identification results is improved.

[0066] In one embodiment of the present invention, a system for retrieving QTF based on multi-source response data is provided, comprising: The data acquisition module collects multi-source response data of the mooring platform during the pool test and performs preprocessing. The multi-source response data includes the incident wave height time history, the platform's six-degree-of-freedom motion response, the platform's acceleration response, and the tension time history of multiple mooring cables. The mooring force verification and correction module establishes a mooring dynamics model consistent with the pool test layout. It takes the platform's six-degree-of-freedom motion response as input, solves the mooring dynamics model to obtain the dynamic tension and spatial shape of each mooring cable, verifies the mooring dynamics model using the tension time history, and then uses the verified mooring dynamics model to calculate the instantaneous generalized force of the mooring system acting on the platform. The environmental inversion module, based on the constructed six-degree-of-freedom time-domain motion equations of the platform, uses the platform's acceleration response, instantaneous generalized force, and preset hydrodynamic parameters to invert the environmental loads on the platform. The equivalent low-frequency slow-drift environmental load component extraction module performs frequency separation on the environmental load and extracts the equivalent low-frequency slow-drift environmental load components. The identification module uses bispectral analysis to identify the second-order difference frequency transfer function matrix based on the incident wave's high time history and the equivalent low-frequency slow-drift environmental load components.

[0067] In the above embodiments, the mooring dynamics model is verified using tension time history, including: The calculated dynamic tension of the mooring cable is compared with the measured tension time history of the mooring cable. If the comparison result exceeds the preset error range, the parameters of the mooring dynamic model are corrected to make the mooring dynamic model closer to the actual test state. The parameters include the axial stiffness of the cable, damping parameters, drag coefficient, anchor position, pretension and connection point coordinates. The comparison indicators include at least one of the following: average tension, root mean square tension, peak tension, tension time history correlation coefficient, tension power spectrum, low-frequency tension component, or wave frequency tension component.

[0068] In the above embodiments, the environmental loads on the platform obtained by inversion are: ; in, The platform mass and moment of inertia matrix; For the additional mass matrix; For still water restoring force; It is either a viscous damping force or an empirical damping force; For instantaneous generalized force; The environmental load to be inverted; For historical time integration variables; This is the impulse response function.

[0069] In the above embodiments, the environmental load is frequency separated to extract the equivalent low-frequency slow-drift environmental load component. Specifically, the environmental load is processed by using a low-pass filter or a band-pass filter to filter out the first-order wave frequency load component and high-frequency noise, and retain the component within the difference frequency slow drift frequency range as the equivalent low-frequency slow-drift environmental load component.

[0070] In the above embodiments, the second-order difference frequency transfer function matrix is ​​identified using the bispectral analysis method as follows:

[0071] in, For two incident wave frequencies and mutual bispectral; It is frequency Wave spectrum at that location.

[0072] In the above embodiments, after identifying the second-order difference frequency transfer function matrix, the method further includes a second-order difference frequency wave force reconstruction verification step: The second-order difference frequency wave force was reconstructed using the identified second-order difference frequency transfer function matrix and the incident wave high time history, and the reconstructed second-order difference frequency wave force was compared and verified with the extracted equivalent low-frequency slow drift environmental load components.

[0073] In the above embodiments, after the second-order difference frequency wave force reconstruction verification step, a platform motion response reconstruction verification step is also included: The reconstructed second-order difference-frequency wave force is input into the platform's six-degree-of-freedom time-domain motion equations to calculate the reconstructed six-degree-of-freedom motion response of the platform. The reconstructed six-degree-of-freedom motion response of the platform is then compared and verified with the acquired six-degree-of-freedom motion response of the platform.

[0074] The verification method is as follows: if the reconstructed six-degree-of-freedom motion response of the platform is within the error range in terms of time history, power spectrum, RMS, peak value, phase characteristics and low-frequency dominant frequency, then the QTF matrix obtained by the experiment has high reliability.

[0075] The system provided in this embodiment is used to execute the above-described method embodiments. For specific processes and details, please refer to the above embodiments, which will not be repeated here.

[0076] In one embodiment of the present invention, a computing device is provided, which may be a terminal and may include: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, and the one or more programs include instructions for performing any of the methods in the above embodiments.

[0077] Furthermore, when the aforementioned instructions can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] In one embodiment of the present invention, a computer program product is provided, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0079] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, which stores server instructions that cause a computer to perform the methods provided in the above embodiments.

[0080] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for inverting QTF based on multi-source response data, characterized in that, include: Multi-source response data of the mooring platform were collected during the pool test and preprocessed. The multi-source response data included incident wave height time history, platform six-degree-of-freedom motion response, platform acceleration response, and tension time history of multiple mooring cables. Based on the pool test setup, a mooring dynamics model consistent with the pool test was established. The six-degree-of-freedom motion response of the platform was used as input to solve the mooring dynamics model to obtain the dynamic tension and spatial shape of each mooring cable. The mooring dynamics model was then checked using the tension time history. Finally, the instantaneous generalized force of the mooring system acting on the platform was calculated using the checked mooring dynamics model. Based on the constructed six-degree-of-freedom time-domain motion equations of the platform, the environmental loads on the platform are obtained by inversion using the platform's acceleration response, instantaneous generalized force, and preset hydrodynamic parameters. Frequency separation of the environmental load is performed to extract the equivalent low-frequency slow-drift environmental load component; Based on the incident wave high time history and the equivalent low-frequency slow-drift environmental load components, the second-order difference frequency transfer function matrix is ​​identified using the bispectral analysis method.

2. The method for inverting QTF based on multi-source response data as described in claim 1, characterized in that, The mooring dynamics model was verified using tension time history, including: The calculated dynamic tension of the mooring cable is compared with the measured tension time history of the mooring cable. If the comparison result exceeds the preset error range, the parameters of the mooring dynamic model are corrected to make the mooring dynamic model closer to the actual test state. The parameters include the axial stiffness of the cable, damping parameters, drag coefficient, anchor position, pretension and connection point coordinates. The comparison indicators include at least one of the following: average tension, root mean square tension, peak tension, tension time history correlation coefficient, tension power spectrum, low-frequency tension component, or wave frequency tension component.

3. The method for inverting QTF based on multi-source response data as described in claim 1, characterized in that, The environmental loads experienced by the platform, obtained through inversion, are as follows: ； in, The platform mass and moment of inertia matrix; For the additional mass matrix; For still water restoring force; It is either a viscous damping force or an empirical damping force; For instantaneous generalized force; The environmental load to be inverted; For a specific moment; For historical time integration variables; Acceleration for the platform's six degrees of freedom; For the platform's six degrees of freedom speed; This is the impulse response function.

4. The method for inverting QTF based on multi-source response data as described in claim 1, characterized in that, Frequency separation of the environmental load yields the equivalent low-frequency slow-drift environmental load component, specifically: Low-pass or band-pass filters are used to process the environmental load to filter out first-order frequency load components and high-frequency noise, while retaining components within the difference frequency drift range as equivalent low-frequency drift environmental load components.

5. The method for inverting QTF based on multi-source response data as described in claim 1, characterized in that, The second-order difference frequency transfer function matrix was identified using the cross-bispectral analysis method as follows: ； in, For two incident wave frequencies and mutual bispectral; It is frequency Wave spectrum at that location.

6. The method for inverting QTF based on multi-source response data as described in claim 1, characterized in that, After identifying the second-order difference frequency transfer function matrix, the process also includes second-order difference frequency wave force reconstruction verification and platform motion response reconstruction verification steps: Second-order difference frequency wave force reconstruction verification: The second-order difference frequency wave force is reconstructed using the identified second-order difference frequency transfer function matrix and incident wave height time history, and the reconstructed second-order difference frequency wave force is compared and verified with the extracted equivalent low-frequency slow drift environmental load components. Platform motion response reconstruction verification: The reconstructed second-order difference frequency wave force is input into the platform's six-degree-of-freedom time-domain motion equation to calculate the reconstructed platform six-degree-of-freedom motion response, and the reconstructed platform six-degree-of-freedom motion response is compared and verified with the acquired platform six-degree-of-freedom motion response.

7. The method for inverting QTF based on multi-source response data as described in claim 6, characterized in that, The verification method is as follows: If the reconstructed six-degree-of-freedom motion response of the platform matches the experimentally acquired six-degree-of-freedom motion response within the error range in terms of time history, power spectrum, RMS, peak value, phase characteristics, and low-frequency dominant frequency, then the identified QTF matrix has high reliability.

8. A system for retrieving QTF based on multi-source response data, characterized in that, include: The data acquisition module collects multi-source response data of the mooring platform during the pool test and performs preprocessing. The multi-source response data includes the incident wave height time history, the platform's six-degree-of-freedom motion response, the platform's acceleration response, and the tension time history of multiple mooring cables. The mooring force verification and correction module establishes a mooring dynamics model consistent with the pool test layout. It takes the platform's six-degree-of-freedom motion response as input, solves the mooring dynamics model to obtain the dynamic tension and spatial shape of each mooring cable, verifies the mooring dynamics model using the tension time history, and then uses the verified mooring dynamics model to calculate the instantaneous generalized force of the mooring system acting on the platform. The environmental inversion module, based on the constructed six-degree-of-freedom time-domain motion equations of the platform, uses the platform's acceleration response, instantaneous generalized force, and preset hydrodynamic parameters to invert the environmental loads on the platform. The equivalent low-frequency slow-drift environmental load component extraction module performs frequency separation on the environmental load and extracts the equivalent low-frequency slow-drift environmental load components. The identification module uses bispectral analysis to identify the second-order difference frequency transfer function matrix based on the incident wave's high time history and the equivalent low-frequency slow-drift environmental load components.

9. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 7.

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