Decomposition method and device for strong nonlinear signal of maritime work structure

Abstract

The invention relates to the technical field of ocean engineering, in particular to a method and a device for decomposing a strong nonlinear signal of a maritime work structure. Obtaining a strong nonlinear signal of the maritime work structure in a preset time length; based on the strong nonlinear signal, obtaining a signal for determining a boundary; segmenting the signal with the determined boundary according to a preset decomposition control factor to obtain time domain signal components of the signal features of the plurality of regions; and determining representation form data of the time domain signal component based on the time domain signal component of the signal feature of the regional signal, so that the method can improve the precision of strong nonlinear signal decomposition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ocean engineering, and in particular to a method and device for decomposing strong nonlinear signals of marine structures. BACKGROUND

[0002] Ocean engineering structures are subjected to the coupling of multiple sources such as waves, ocean currents, wind loads, etc. for a long time, and their dynamic response signals have significant strong nonlinearity, non-stationarity and time-varying characteristics. These dynamic signals contain key information related to the safety operation of the structure and are the core basis for health monitoring and safety evaluation. However, due to the interference of ocean environmental noise, sensor measurement errors and nonlinear dynamics under extreme loads, the original signals often exhibit complex patterns of multi-scale aliasing, and traditional signal decomposition analysis methods cannot effectively extract modal components with physical meaning, which restricts the identification accuracy of structural dynamic characteristics and real-time warning capability.

[0003] With the development of ocean engineering towards the deep sea and intelligence, higher requirements are put forward for signal decomposition technology: on the one hand, it needs to adapt to the non-stationary signal characteristics in complex marine environments to achieve accurate separation of high-frequency vibration signals and low-frequency slowly varying characteristics; on the other hand, it needs to consider the robustness and computational efficiency of the algorithm to meet the real-time processing needs of long-term monitoring systems. Although a variety of signal decomposition methods have been introduced into the engineering field, the most similar implementation scheme to the present application mainly includes signal processing technologies based on empirical mode decomposition (EMD) and its improved methods, and variational mode decomposition (VMD). EMD adaptively extracts intrinsic mode functions (IMF) through an iterative selection process, but it is prone to modal aliasing and end effects in a strong noise environment, and the lack of mathematical constraints leads to insufficient stability of decomposition. VMD constrains the modal bandwidth by constructing a variational model, which to some extent suppresses modal aliasing, but its performance is highly dependent on the preset number of modes and penalty factors, and its adaptability to time-varying signals in ocean engineering is poor. In recent years, some studies have attempted to combine wavelet threshold denoising or deep learning to optimize parameter selection, but there are still defects such as high computational complexity and insufficient real-time performance. In addition, existing improved methods are mostly aimed at a single problem (such as noise suppression or parameter optimization), lack of systematic design for multiple constraint scenarios in ocean engineering, and are difficult to meet the needs of decomposition accuracy, adaptability and engineering practicability.

[0004] Therefore, the present application provides a method and device for decomposing strong nonlinear signals of marine structures to solve the problem of how to improve the accuracy of strong nonlinear signal decomposition. SUMMARY

[0005] In order to solve the problem of how to improve the accuracy of strong nonlinear signal decomposition, the present application provides a method and device for decomposing strong nonlinear signals of marine structures.

[0006] In a first aspect, an embodiment of the present application provides a method for decomposing a strong nonlinear signal of a marine structure, the method comprising: obtaining a strong nonlinear signal of the marine structure for a preset time length; obtaining a signal with determined boundaries based on the strong nonlinear signal; segmenting the signal with determined boundaries according to a preset decomposition control factor to obtain time-domain signal components of signal features of signals of multiple regions; determining representation form data of the time-domain signal components based on the time-domain signal components of signal features of the regional signals.

[0007] In a second aspect, an embodiment of the present application provides a device for decomposing a strong nonlinear signal of a marine structure, comprising: an obtaining module configured to obtain a strong nonlinear signal of the marine structure for a preset time length; a first data processing module configured to obtain a signal with determined boundaries based on the strong nonlinear signal; a second data processing module configured to segment the signal with determined boundaries according to a preset decomposition control factor to obtain time-domain signal components of signal features of signals of multiple regions; a third data processing module configured to determine representation form data of the time-domain signal components based on the time-domain signal components of signal features of the regional signals.

[0008] In a third aspect, an embodiment of the present application further provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any of the embodiments of the present application.

[0009] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium storing a computer program, and the computer program, when executed in a computer, causes the computer to perform the method according to any of the embodiments of the present application.

[0010] The embodiment of the present application provides a kind of decomposition method and device of strong nonlinear signal of marine structure, first for offshore platform, wind power pile foundation and other marine structures, collect the strong nonlinear dynamic response signal (such as structural vibration acceleration, strain signal etc.) of preset length.This "preset length" needs to be set in combination with marine environmental load characteristics, both to cover the complete wave action period (usually take 5-20 seconds, adapt the wave frequency range of most marine environment), ensure to capture the periodic characteristics of signal.Based on the strong nonlinear original signal collected, the signal of determined boundary is obtained.This step solves the "multi-scale feature aliasing" problem caused by marine environment noise interference, multi-source load coupling of original signal, by separating the feature interval (such as wave impact vibration of high frequency band, structural vibration of medium-low frequency band, slow response of low frequency band of sea current) of different physical meaning in signal, provide support for segmentation, avoid the precision loss caused by "feature cross interference" in traditional decomposition method.Subsequently, according to the preset decomposition control factor, the signal of determined boundary is regionally segmented, and the time domain signal component of the signal characteristics of multiple regions is obtained.Finally, based on the time domain signal component of the signal characteristics of regional signal, the representation form data (such as amplitude, phase, damping, frequency) of time domain signal component is determined.These representation data can map the dynamic response state of marine structure under different load actions, effectively avoid the defects such as "modal confusion" and "boundary effect" in traditional decomposition method, and improve the precision of strong nonlinear signal decomposition. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0012] Figure 1 A flow chart of the decomposition method of strong nonlinear signal of marine structure according to one embodiment is shown. Figure 2 It is a hardware architecture diagram of an electronic device provided by the embodiment of the present application. Figure 3 A structural diagram of the decomposition device of strong nonlinear signal of marine structure according to one embodiment is shown. Figure 4 A time history curve diagram of mooring system tension data tested according to one embodiment is shown. Figure 5 A normalized frequency spectrum diagram and boundary adaptive segmentation diagram according to one embodiment are shown. Figure 6The frequency domain and time domain results of each component after adaptive boundary segmentation according to one embodiment are shown. Figure 7 A diagram showing the reconstruction results of each segmented component according to one embodiment is provided. Detailed Implementation

[0013] 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 some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Please refer to Figure 1 This invention provides a method for decomposing strongly nonlinear signals in marine engineering structures, the method comprising: Step 100: Obtain the strong nonlinear signal of the marine structure for a preset duration; Step 102: Based on the strong nonlinear signal, obtain the signal that determines the boundary; Step 104: Segment the signal at the defined boundary according to the preset decomposition control factor to obtain the time-domain signal components of the signal characteristics of multiple regions; Step 106: Based on the signal characteristics of the regional signal, determine the representation data of the time-domain signal components.

[0015] In this embodiment, strong nonlinear dynamic response signals (such as structural vibration acceleration and strain signals) for a preset duration are first collected for marine structures such as offshore platforms and wind turbine foundations. The "preset duration" here needs to be set in combination with the characteristics of marine environmental loads, which should cover the complete wave action cycle (usually 5-20 seconds, which is suitable for the wave frequency range of most marine environments) and ensure the periodicity of the captured signals.

[0016] Based on the acquired strong nonlinear raw signal, signals with defined boundaries are obtained. This step addresses the "multi-scale feature aliasing" problem caused by marine environmental noise interference and multi-source load coupling in the raw signal. By separating feature regions with different physical meanings in the signal (such as high-frequency wave impact vibration, mid-to-low-frequency structural vibration, and low-frequency slow current response), it supports segmentation and avoids the accuracy loss caused by "feature cross-interference" in traditional decomposition methods. Subsequently, the signals with defined boundaries are regionally segmented according to preset decomposition control factors, obtaining time-domain signal components of signal characteristics in multiple regions. Finally, based on the time-domain signal components of the regional signals, the representational data of the time-domain signal components (such as amplitude, phase, damping, and frequency) are determined. These representational data can map the dynamic response state of marine structures under different loads, effectively avoiding the defects of "modal confusion" and "boundary effects" in traditional decomposition methods, and improving the accuracy of strong nonlinear signal decomposition.

[0017] In one embodiment of the present invention, obtaining a signal with defined boundaries based on a strongly nonlinear signal includes: The strongly nonlinear signal is preprocessed to obtain spectral data; Based on the spectral data, a signal with defined boundaries is obtained; The preprocessing includes Fourier transform processing, normalization processing, and mirror processing.

[0018] In this embodiment, "obtaining a signal with defined boundaries based on a strong nonlinear signal" requires two steps: "preprocessing to transform spectral data and determining signal boundaries based on the spectrum." The preprocessing step needs to specifically address the inherent defects of the original signal, as follows: First, preprocessing is performed on the collected strong nonlinear signals of the marine structure (such as vibration acceleration, strain signals, etc.), and the final output is spectral data that can be used for boundary identification. Then, based on the preprocessed spectral data, the effective boundaries of the signal are determined through feature analysis, forming a signal to be decomposed with clear boundaries. The preprocessing includes Fourier transform processing, normalization processing, and mirror processing, each designed to address different issues with marine engineering signals. The core function of Fourier transform processing is to convert highly nonlinear signals (such as signals intertwined with high-frequency wave impact vibrations and low-frequency slow-varying ocean current responses) that are difficult to distinguish in the time domain into frequency domain spectral data. This allows for a more intuitive presentation of signal characteristics (such as frequency-amplitude distribution) across different frequency ranges, providing a frequency-dimensional analytical basis for subsequent boundary identification. Normalization processing maps the amplitude of the Fourier-transformed spectrum to a unified range (usually [0,1] or [-1,1]), eliminating interference from load intensity fluctuations (such as signal amplitude differences caused by wave height variations) and ensuring that subsequent feature determination relies solely on frequency distribution patterns, rather than the overall signal strength. Mirror processing addresses the "spectral leakage" problem that easily arises with Fourier transform by supplementing symmetrical mirror spectra at the spectral edges (high-frequency or low-frequency ends), filling the spectral energy diffusion gap caused by signal truncation, ensuring the integrity of the spectral data, and improving the accuracy of highly nonlinear signal decomposition.

[0019] In this embodiment, for the measured strong nonlinear signal of the marine structure The frequency domain expression of its Fourier transform It can be represented as: in Then, the spectrum was normalized to... Based on symmetry, the frequency range can be derived as follows: within the spectrum In order to achieve a normalized spectrum Adaptive segmentation is achieved by introducing a local extremum method. To avoid bias in recognition results caused by endpoint effects, the spectrum is... Mirroring is performed, denoted as the spectrum. In the process of identifying the maximum value, the endpoint effect may lead to deviations in the identification results. To avoid the influence of the endpoint effect, the spectrum is first analyzed. Mirror the image and record it as spectrum data. .

[0020] In one embodiment of the present invention, determining the boundary signal based on spectrum data includes: Based on the spectrum data, identify multiple local maxima in the spectrum data; Based on multiple local maxima and spectral data, the minimum value of the spectral data between every two adjacent local maxima is determined and recorded as the boundary, in order to obtain the signal with defined boundaries.

[0021] In this embodiment, firstly, based on the preprocessed spectral data (including frequency-amplitude distribution information), a feature recognition algorithm is used to locate multiple local maxima. These local maxima correspond to the main frequency components with different physical meanings in the signal (such as the high-frequency component of wave impact and the mid-frequency component of structural natural vibration), serving as a reference for dividing signal intervals. Subsequently, using these local maxima as anchor points, combined with the complete spectral data, the minimum spectral amplitude between every two adjacent local maxima is selected, and the location of this minimum value is recorded as the signal boundary. This operation can accurately divide signal intervals with different frequency characteristics, avoiding cross-interference between different components during subsequent decomposition, and ultimately improving the accuracy of strongly nonlinear signal decomposition.

[0022] In one embodiment of the present invention, the preset decomposition control factor is determined by the following formula: In the formula, The preset decomposition control factor, For the i-th boundary, For frequency.

[0023] In this embodiment, traditional filtering techniques are susceptible to inaccurate results due to transition segments in order to extract signal components in the frequency domain, especially in spectra with significant noise interference and dense bands. To address the unavoidable transition segment problem in traditional filtering techniques, a preset decomposition control factor is introduced. Using this decomposition control factor, the signal can be independently decomposed at each frequency component, overcoming the decomposition error caused by transition segments in traditional filtering methods. This improves the accuracy of strongly nonlinear signal decomposition.

[0024] In one embodiment of the present invention, the time-domain signal component of the signal characteristics is determined by the following formula: In the formula, The time-domain signal components that are characteristic of the signal. for and Spectral data between This is spectrum data.

[0025] In one embodiment of the present invention, the representational data includes a first complex exponential parameter and a second complex exponential parameter.

[0026] In one embodiment of the present invention, the representational data is determined by the following formula: In the formula, The time-domain signal components that are characteristic of the signal. For the first complex exponential parameter, For the second complex exponential parameter, Let k be the characteristic matrix, and k be the k-th sampling point of the time-domain signal component of the signal characteristic. For amplitude, For phase, For damping, For frequency, j It is an imaginary number.

[0027] In this embodiment, the use of complex exponential parameters avoids the periodicity assumptions of traditional methods and typical problems in Fourier transform processes such as spectral leakage or aliasing, while also fully considering the nonlinear characteristics of signals from actual marine engineering structures. To solve for the complex exponential parameters, a Hankel matrix (characteristic matrix) is constructed instead of directly finding the roots of the characteristic polynomial, avoiding the problem of poor numerical conditionality and significantly improving the solution accuracy of the complex exponential method under complex noise backgrounds.

[0028] like Figure 4 As shown, in this embodiment, the present invention uses the mooring system tension data from a 1:100 scale semi-submersible platform physical model for decomposition of strongly nonlinear signals. The experiment was conducted in a wave-driven water tank with lengths of 60m, widths of 36m, and heights of 1.5m. Tension sensors were used to test the tension of the mooring cables, and the data obtained was decomposed using the technology of this invention. The wave conditions of the experimental environment were simulated using the JONSWAP spectrum. The wave height was set to 0.06m and the period to 2s. The obtained mooring tension data is shown below. Figure 4 As shown. From Figure 4 As can be seen, under the action of irregular waves, the tension data of the semi-submersible platform mooring system exhibits obvious strong nonlinear characteristics.

[0029] like Figure 5 and Figure 6As shown, in this embodiment, when decomposing the nonlinear signal using the method of the present invention, it is first transformed to the frequency domain through Fourier transform, and the result is as follows. Figure 5 As shown by the blue curve. Based on this, the local quadratic extremum method of this invention is first used for boundary adaptive partitioning. After identifying the maxima in the spectrum, as shown... Figure 5 As shown by the red 0 in the diagram. Then, a second identification is performed between every two maxima to obtain the minimum value of the spectrum in the segment of two adjacent local maxima, as shown in the diagram. Figure 5 As shown by the green dot in the image. Figure 6 (a) and (b) show the frequency and time domain results of the first four components after adaptive boundary segmentation, respectively. It can be seen that the frequency domain results of the decomposed components obtained by using the decomposition control factor are consistent with the spectrum of the measured signal, and the transition error problem that traditional filtering methods cannot overcome is avoided.

[0030] like Figure 7 As shown, in this embodiment, after obtaining the segmentation signal, in order to prepare and characterize its component feature information, the third step of the present invention is introduced to characterize each segmentation component, and the result is as follows. Figure 7 As shown, the decomposition and reconstruction results of each component are consistent with the segmented components, realizing the solution of the nonlinear signal component characteristics. It can also provide a new technical means for the nonlinear dynamic response analysis of related structures and has certain engineering application prospects.

[0031] In summary, this invention has the following advantages: 1. It overcomes the bottleneck of mode aliasing and improves the accuracy of nonlinear signal decomposition. Compared with traditional empirical mode decomposition methods that rely on fixed screening criteria and suffer from mode aliasing, this invention uses a signal decomposition technology driven by decomposition control factors. Through transition-free filtering, it avoids the transition error problem of traditional methods, significantly improving the mode aliasing problem of traditional methods. 2. It eliminates dependence on artificial parameters and enhances the adaptive capability of the algorithm. Compared with variational mode decomposition and other methods that require preset mode numbers and penalty factors, the adaptive segmentation boundary technology proposed in this invention dynamically detects and adaptively segments the local extreme points of the signal. It adaptively adjusts the segmentation interval length according to the energy distribution characteristics of the low-frequency dominant components. Combined with mirror preprocessing technology, it effectively suppresses high-frequency distortion caused by endpoint effects, significantly improving the interpretability of nonlinear signal characteristics under complex sea conditions. 3. By integrating matrix analysis and state-space modeling, this invention overcomes the challenge of noise sensitivity. Addressing the instability caused by the ill-conditioned nature of existing complex exponential parameter estimation methods (such as the Prony algorithm), the high-precision local feature extraction technique of this invention achieves parameter estimation under high background noise interference through Hankel matrix construction and state-state model solving, significantly improving the engineering applicability of the algorithm in harsh marine environments.

[0032] like Figure 2, Figure 3 As shown, this embodiment of the invention provides a device for decomposing strong nonlinear signals in marine engineering structures. The device can be implemented in software, hardware, or a combination of both. From a hardware perspective, as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for decomposing strong nonlinear signals in a marine engineering structure, as provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0033] like Figure 3 As shown in the figure, this embodiment provides a device for decomposing strong nonlinear signals of marine engineering structures, comprising: The acquisition module 300 is used to acquire strong nonlinear signals of marine structures for a preset duration. The first data processing module 302 is used to obtain a signal that determines the boundary based on the strong nonlinear signal; The second data processing module 304 is used to segment the signal of the determined boundary according to the preset decomposition control factor to obtain the time-domain signal components of the signal characteristics of multiple regions. The third data processing module 306 is used to determine the representation form data of the time-domain signal components based on the time-domain signal components of the signal characteristics of the regional signal.

[0034] In one embodiment of the present invention, the first data processing module 302 is configured to perform the following operations: The strongly nonlinear signal is preprocessed to obtain spectral data; Based on the spectral data, the signal at the defined boundary is obtained; The preprocessing includes Fourier transform processing, normalization processing, and mirror processing.

[0035] In one embodiment of the present invention, when the first data processing module 302 obtains the signal with the defined boundary based on the spectrum data, it performs the following operations: Based on the spectrum data, determine multiple local maxima in the spectrum data; Based on the multiple local maxima and the spectral data, the minimum value of the spectral data between every two adjacent local maxima is determined and recorded as the boundary, so as to obtain the signal of the determined boundary.

[0036] In one embodiment of the present invention, the preset decomposition control factor is determined by the following formula: In the formula, The preset decomposition control factor, For the i-th boundary, For frequency.

[0037] In one embodiment of the present invention, the time-domain signal component of the signal feature is determined by the following formula: In the formula, The time-domain signal components that define the signal characteristics. for and Spectral data between The spectrum data is referred to here.

[0038] In one embodiment of the present invention, the representational data includes a first complex exponential parameter and a second complex exponential parameter.

[0039] In one embodiment of the present invention, the representational data is determined by the following formula: In the formula, The time-domain signal components that define the signal characteristics. Let be the first complex exponential parameter. This is the second complex exponential parameter. Let k be the k-th sampling point of the time-domain signal component of the signal feature, which is the feature matrix. For amplitude, For phase, For damping, For frequency, j It is an imaginary number.

[0040] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a device for decomposing strong nonlinear signals of marine structures. In other embodiments of the present invention, a device for decomposing strong nonlinear signals of marine structures may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0041] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0042] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for decomposing strongly nonlinear signals of marine structures according to any embodiment of this invention.

[0043] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a method for decomposing a strongly nonlinear signal of a marine structure according to any embodiment of this invention.

[0044] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0045] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0046] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0047] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0048] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the functions of any of the embodiments described above.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0050] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0051] 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 decomposing strongly nonlinear signals in marine engineering structures, characterized in that, include: Acquire strong nonlinear signals of marine structures for a preset duration; Based on the strong nonlinear signal, a signal with defined boundaries is obtained; The signal at the defined boundary is segmented according to a preset decomposition control factor to obtain the time-domain signal components of the signal characteristics of multiple regions. Based on the signal characteristics of the regional signal, the time-domain signal components are used to determine the representational data of the time-domain signal components.

2. The method according to claim 1, characterized in that, The process of obtaining a signal with defined boundaries based on the strong nonlinear signal includes: The strongly nonlinear signal is preprocessed to obtain spectral data; Based on the spectral data, the signal at the defined boundary is obtained; The preprocessing includes Fourier transform processing, normalization processing, and mirror processing.

3. The method according to claim 2, characterized in that, The signal for determining the boundary based on the spectrum data includes: Based on the spectrum data, determine multiple local maxima in the spectrum data; Based on the multiple local maxima and the spectral data, the minimum value of the spectral data between every two adjacent local maxima is determined and recorded as the boundary, so as to obtain the signal of the determined boundary.

4. The method according to claim 3, characterized in that, The preset decomposition control factor is determined by the following formula: In the formula, The preset decomposition control factor, For the i-th boundary, For frequency.

5. The method according to claim 4, characterized in that, The time-domain signal components of the signal characteristics are determined by the following formula: In the formula, The time-domain signal components that define the signal characteristics. for and Spectral data between The spectrum data is referred to here.

6. The method according to claim 1, characterized in that, The representational data includes a first complex exponential parameter and a second complex exponential parameter.

7. The method according to claim 6, characterized in that, The representational data is determined by the following formula: In the formula, The time-domain signal components that define the signal characteristics. Let be the first complex exponential parameter. This is the second complex exponential parameter. Let k be the k-th sampling point of the time-domain signal component of the signal feature, which is the feature matrix. For amplitude, For phase, For damping, For frequency, j It is an imaginary number.

8. A device for decomposing strongly nonlinear signals in marine engineering structures, characterized in that, include: The acquisition module is used to acquire strong nonlinear signals of marine structures for a preset duration. The first data processing module is used to obtain a signal that determines the boundary based on the strong nonlinear signal; The second data processing module is used to segment the signal at the defined boundary according to a preset decomposition control factor to obtain the time-domain signal components of the signal characteristics of multiple regions. The third data processing module is used to determine the representation form data of the time-domain signal components based on the time-domain signal components of the signal characteristics of the regional signal.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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