Method and system for detecting nonlinear modulation of tidal current to sea waves, processing equipment and storage medium
By introducing a nonlinear modulation detection method of tide-to-wave waves into the holographic spectrum method, using the confidence level estimation of the holographic marginal spectrum and the alternative data, the problem of the lack of statistical credibility test of the holographic spectrum method is solved, and the reliability of the detection results is improved.
Patent Information
- Application Number
- CN202510114875.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-24
- Publication Date
- 2025-05-30
AI Technical Summary
The lack of statistical credibility tests in detecting the nonlinear relationship between waves and tides may lead to false conclusions.
A nonlinear modulation detection method for tide waves is proposed. By acquiring and preprocessing observation data, holographic marginal spectrum analysis is performed, and alternative data are generated for confidence level estimation, and the statistical credibility of modulation frequency is determined.
It improves the statistical reliability of the holographic spectrum method, avoids false conclusions, and expands its application prospects in the field of data analysis.
Smart Images
Figure CN120067575A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of signal processing, and particularly to a method, system, processing device and storage medium for detecting the non-linear modulation of ocean current on ocean waves. Background Art
[0002] Ocean waves and ocean currents are the main dynamic factors affecting the dynamic processes in estuaries and coastal areas. They coexist and interact with each other, but the interaction relationship is still not clear. Mining the non-linear relationship between ocean waves and ocean currents from observational data is crucial for understanding their development and changes.
[0003] Spectral methods are powerful tools for presenting the statistical characteristics of data. However, traditional spectral methods (such as Fourier spectra and wavelet spectra) can only give the frequency-domain characteristics of the data itself and cannot capture the cross-scale non-linear interaction relationships hidden in the data. Currently, the holographic spectrum method disclosed in the prior art solves the above problems and can not only describe the characteristics of the data in the frequency domain but also reveal the possible cross-scale non-linear interaction relationships hidden in the data.
[0004] However, the holographic spectrum method lacks statistical credibility tests, which may lead to false conclusions. Therefore, it is very necessary to propose an algorithm to test the statistical reliability of the holographic spectrum method, which can help the holographic spectrum method to have a broader application prospect in the field of data analysis. Summary of the Invention
[0005] In view of the above problems, the object of the present invention is to provide a method, system, processing device and storage medium for detecting the non-linear modulation of ocean current on ocean waves, which can test the reliability of the holographic spectrum method.
[0006] To achieve the above object, the present invention adopts the following technical solutions: In the first aspect, a method for detecting the non-linear modulation of ocean current on ocean waves is provided, including: Obtaining an observation data set of a to-be-detected area and performing preprocessing; Performing holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set; Generating a plurality of groups of surrogate data for the preprocessed observation data set; Performing holographic marginal spectrum analysis on the generated plurality of groups of surrogate data, respectively calculating their corresponding holographic marginal spectra, and taking the percentile of the holographic marginal spectrum values of the plurality of groups of surrogate data as the %-confidence level of the holographic marginal spectrum value of the observation data, where is a preset significance level; For the same modulation frequency, if the holographic marginal spectrum value of the observation data is less than %-confidence level, the modulation of the modulation frequency on the observed data is statistically untrustworthy; conversely, the modulation of the modulation frequency on the observed data is statistically trustworthy.
[0007] Furthermore, the observed data set is a time series of sea wave elements, including the significant wave height, zero-crossing period, and spectral peak period of sea waves.
[0008] Furthermore, the preprocessing includes quality control, interpolation, and detrending of the observed data set.
[0009] Furthermore, performing holographic marginal spectrum analysis on the preprocessed observed data set to determine the holographic marginal spectrum of each observed data in the observed data set, including: Using the Hilbert transform to extract the instantaneous characteristics of each observed data in the preprocessed observed data set; Using ensemble empirical mode decomposition to decompose the extracted instantaneous characteristics into intrinsic modes of different time scales; Using the Hilbert-Huang spectrum method to calculate the time-frequency spectrum matrix of each intrinsic mode, and this time-frequency spectrum matrix is the holographic spectrum matrix of the corresponding observed data; Integrating the time variable in the holographic spectrum matrix to obtain the holographic marginal spectrum of the corresponding observed data.
[0010] Furthermore, generating several groups of surrogate data for the preprocessed observed data set using the adjusted amplitude Fourier transform algorithm, including: For the preprocessed observed data set of length Arrange it in ascending order to obtain a new observed data set And its rank ; ; For the obtained rank Construct a mapping , generate a Gaussian noise sequence of length , and arrange it in ascending order to obtain a new Gaussian noise sequence , and reorder the new Gaussian noise sequence through the mapping to obtain , , ; For the reordered Gaussian noise sequence Generate FT-surrogate data , and calculate the rank of the FT-surrogate data ; For the obtained rank Construct a mapping , and by mapping For new observational data sets Sort and get the original observation data set AAFT - Alternative Data .
[0011] Furthermore, after preprocessing the observation dataset middle, Only in the The value observed at the first moment does not have to be less than The value observed at any time ; In the new preprocessed observation dataset There must be a relationship .
[0012] Furthermore, the reordered Gaussian noise sequence Generating FT-surrogate data , and calculate the FT-surrogate data Rank ,include: For the reordered Gaussian noise sequence Perform Fourier transform to obtain the complex sequence ; generate Random Phase ; Based on the generated random phase , construct a complex sequence ; For complex sequences Perform inverse Fourier transform to obtain FT-surrogate data ; Calculate FT-surrogate data Rank .
[0013] In a second aspect, a tidal current nonlinear modulation detection system for ocean waves is provided, comprising: The data set acquisition module is used to acquire the observation data set of the area to be measured and perform preprocessing; A first holographic marginal spectrum analysis module is used to perform holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set; A substitute data generation module is used to generate several groups of substitute data for the preprocessed observation data set; The second holographic marginal spectrum analysis module is used to perform holographic marginal spectrum analysis on the generated groups of replacement data, calculate the corresponding holographic marginal spectrum respectively, and convert the first holographic marginal spectrum value of the groups of replacement data into The percentile is used as the %-confidence level of the holographic marginal spectrum value of the observed data, where is a preset significance level; A comparison module is used to compare, for the same modulation frequency, if the holographic marginal spectrum value of the observed data is less than %-confidence level, then the modulation of the observed data by this modulation frequency is statistically untrustworthy, otherwise, the modulation of the observed data by this modulation frequency is statistically trustworthy.
[0014] In a third aspect, a processing device is provided, including computer program instructions, where when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the above-mentioned method for detecting the non-linear modulation of ocean currents on waves.
[0015] In a fourth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored, where when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the above-mentioned method for detecting the non-linear modulation of ocean currents on waves.
[0016] Due to the above technical solutions adopted by the present invention, it has the following advantages: 1. The present invention is not limited to verifying the non-linear interaction relationship between ocean currents and waves, but can also be used to test whether the interaction relationships between other multi-scale dynamic processes in the field of earth science are statistically credible.
[0017] 2. The present invention proposes a feasible solution for the statistical reliability test of the holographic spectrum. Compared with common methods such as the t-test, the solution proposed by the present invention does not require assuming that the data follows a normal distribution, and is especially suitable for signal analysis in the real world such as platform observation data.
[0018] 3. The present invention also provides a feasible solution for the reliability test of other spectral methods.
[0019] In summary, the present invention can be widely applied in the field of signal processing. Description of the Drawings
[0020] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference numerals are used to represent the same components. In the drawings: Figure 1 is a schematic flow chart of the method provided by an embodiment of the present invention; Figure 2 is a schematic diagram of the process curve of the wave zero-crossing period in an embodiment of the present invention; Figure 3 The zero-crossing period of the preprocessed ocean wave provided by an embodiment of the present invention envelope and its intrinsic mode and residue schematic diagram, where Figure 3 (a) is the schematic diagram of the envelope of the zero-crossing period of the preprocessed ocean wave envelope schematic diagram Figure 3 ; (b) is the schematic diagram of the intrinsic mode and residue schematic diagram; Figure 4 is provided by an embodiment of the present invention Figure 3 the Hilbert-Huang spectrum and the Hilbert-Huang marginal spectrum of the intrinsic mode in , where Figure 4 (a) is the schematic diagram of the Hilbert-Huang spectrum Figure 4 (b) is the schematic diagram of the Hilbert-Huang marginal spectrum Figure 4 The vertical axis (carrier frequency) of (a) and Figure 4 the horizontal axis (carrier frequency) of (b) represent the frequency of the intrinsic mode ; Figure 4 HHS in the color scale title of (a) represents the Hilbert-Huang spectrum of the intrinsic mode ; Figure 5 is provided by an embodiment of the present invention the AAFT surrogate data and its holographic marginal spectrum of Figure 5 (a) is the comparison diagram of and one set of AAFT surrogate data Figure 5 (b) is the holographic marginal spectrum of the surrogate data in Figure 2 , the horizontal axis (modulation frequency) represents the frequency of the signal that may have a non-linear modulation effect on the surrogate data Figure 5 (c) is the holographic marginal spectrum of 100 sets of AAFT surrogate data; Figure 6 is provided by an embodiment of the present invention the holographic marginal spectrum (the black dotted line at the bottom) and its 97%-confidence level curve (the red solid line at the top) of , where the small graph in the lower left corner is the enlarged graph of the modulation frequency in the range of 6.5 - 8, indicating that in this range the marginal spectrum value is less than the corresponding 97%-confidence level. Therefore, only when the modulation frequency is about 1.27 (the range within the box), the marginal spectrum value of
[0021] Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.
[0022] It should be understood that the terms used herein are for the purpose of describing particular example embodiments only and are not intended to be limiting. Unless the context clearly dictates otherwise, the singular forms "a", "an", and "the" as used herein may also include the plural forms. The terms "comprising", "including", "containing", and "having" are inclusive and thus specify the presence of the stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring them to be performed in the particular order described or illustrated, unless the order of performance is explicitly stated. It should also be understood that additional or alternative steps may be used.
[0023] Although the terms first, second, third, etc. may be used herein to describe multiple elements, components, regions, layers, and / or sections, these elements, components, regions, layers, and / or sections should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or section from another. Unless the context clearly indicates otherwise, terms such as "first" and "second" and other numerical terms used herein do not imply an order or sequence. Thus, the first element, component, region, layer, or section discussed below may be referred to as the second element, component, region, layer, or section without departing from the teachings of the example embodiments.
[0024] Traditional spectral methods (such as Fourier spectrum and wavelet spectrum) can only give the frequency domain characteristics of the data itself, and cannot capture the cross-scale nonlinear interaction relationship hidden in the data. At present, the holographic spectrum method disclosed in the prior art solves the above-mentioned problem. It can not only describe the characteristics of the data in the frequency domain, but also reveal the cross-scale nonlinear interaction relationship that may exist in the data. However, the holographic spectrum method lacks statistical credibility test, which may lead to false conclusions. Therefore, it is very necessary to propose an algorithm to test the statistical reliability of the holographic spectrum method, which can help the holographic spectrum method to achieve a broader application prospect in the field of data analysis. An embodiment of the present invention provides a method for detecting the nonlinear modulation of waves by tidal currents, including: obtaining an observation data set of a test area and preprocessing it; performing holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set; generating several groups of alternative data for the preprocessed observation data set; performing holographic marginal spectrum analysis on the generated several groups of alternative data, respectively calculating their corresponding holographic marginal spectra, and adding the first of the holographic marginal spectrum values of the several groups of alternative data. Percentiles are used as the holographic marginal spectrum values of the observed data. %-confidence level, where is the pre-set significance level; for the same modulation frequency, if the holographic marginal spectrum value of the observed data is less than %-confidence level, the modulation of the observed data by the modulation frequency is statistically unreliable, otherwise, the modulation of the observed data by the modulation frequency is statistically reliable. The present invention first finds possible nonlinear interactions through the holographic marginal spectrum of the signal, then generates a set of substitute data for the signal based on the Fourier transform of the adjustment amplitude, and estimates the 97%-confidence level of the holographic marginal spectrum of the signal through the holographic marginal spectrum of the substitute data to detect whether the nonlinear interaction in the signal is statistically reliable.
[0025] Example 1 like Figure 1 As shown, this embodiment provides a method for detecting nonlinear modulation of tidal currents on ocean waves, comprising the following steps: 1) Obtain the observation dataset of the area to be measured.
[0026] Specifically, the observation data set is, for example, the time series of ocean wave elements, including the time series of relevant indicators such as the effective wave height, zero-crossing period and spectrum peak period of ocean waves.
[0027] 2) Preprocess the acquired observation data set.
[0028] Specifically, preprocessing includes quality control of the observation data set (removing outliers in the data), interpolation processing (interpolating the data to grid points with equal time intervals), and trend removal operations, in preparation for the subsequent generation of alternative data and calculation of the holographic marginal spectrum.
[0029] 3) Perform holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set.
[0030] Specifically, the non-linear interaction relationships at different scales are often hidden in the instantaneous features of the data (mainly including instantaneous amplitude and instantaneous frequency), which can be obtained by performing holographic marginal spectrum analysis on the observation data. The specific steps are as follows: 3.1) Use Hilbert transform to extract the instantaneous features of each observation data in the preprocessed observation data set.
[0031] 3.2) Use ensemble empirical mode decomposition (EEMD) to decompose the extracted instantaneous features into intrinsic modes at different time scales respectively.
[0032] 3.3) Use Hilbert-Huang spectrum method to calculate the time-frequency spectrum matrix of each intrinsic mode. This time-frequency spectrum matrix is the holographic spectrum matrix of the corresponding observation data. Among them, the frequency in the holographic spectrum matrix is not the frequency of the observation data, but the frequency that may have a non-linear modulation effect on the observation data.
[0033] 3.4) Integrate the time variable in the holographic spectrum matrix to obtain the holographic marginal spectrum of the corresponding observation data. Among them, the frequency corresponding to the spectral peak in the full marginal spectrum may also be just the systematic error of the method, rather than the frequency of the real physical signal. In other words, the non-linear modulation of this frequency on the observation data may be just an illusion caused by calculation error, rather than a real phenomenon. Therefore, further statistical credibility tests are needed.
[0034] It should be noted that Hilbert transform, ensemble empirical mode decomposition (EEMD), and Hilbert-Huang spectrum method are all methods disclosed in the prior art, and the specific processes will not be elaborated here.
[0035] 4) Use the adjusted amplitude Fourier transform (AAFT) algorithm to generate several groups of AAFT surrogate data for the preprocessed observation data set, in preparation for subsequent confidence level estimation. The physical meaning of AAFT surrogate data is: under ideal observation conditions (i.e., the observation process is reversible and there is no time delay effect), the observation data represents a linear stationary Gaussian process. The key point of the AAFT algorithm is: according to a certain sorting principle, randomize the phase of the data after Fourier transform to ensure that the amplitude distribution of the transformed data is the same as that of the original data. Specifically, the steps of the AAFT algorithm include: 4.1) For the preprocessed observation data set with a length of Arrange it in ascending order to obtain a new observation data set and its rank .
[0036] Specifically, in the preprocessed observed data set , only represents the value observed at the th moment, and it does not have to be less than the value observed at the th moment . In the new preprocessed observed data set , there must be a relationship . The rank records the position (in ascending order) of the value observed at the th moment in the original time series . For the rank, since holds, then must hold .
[0037] For example: for a time series of length 4, its ascending-ordered time series can be calculated, and its rank .
[0038] 4.2) Construct a mapping for the obtained rank , generate a Gaussian noise sequence of length , and arrange it in ascending order to obtain a new Gaussian noise sequence . Reorder the new Gaussian noise sequence through the mapping to obtain . , at this time, the rank of the reordered Gaussian noise sequence is the same as that of the original observed data set .
[0039] For example: for the example in step 4.1), at this time the mapping generates a Gaussian noise sequence of length 4, its ascending-ordered new Gaussian noise sequence is , and the reordered Gaussian noise sequence is . Obviously, the rank of is also
[0040] 4.3) Based on the Fourier (FT) transform algorithm, generate FT surrogate data for the reordered Gaussian noise sequence , and calculate the FT - surrogate data rank : 4.3.1) Perform Fourier transform on the re - ordered Gaussian noise sequence to obtain a complex - number sequence .
[0041] 4.3.2) Considering the symmetry of the Fourier transform, only generate random phases .
[0042] 4.3.3) Based on the generated random phases , construct a complex - number sequence .
[0043] Specifically, when , the value of the element is the product of and , that is , where represents the imaginary unit, represents the exponential function; when , the value of the element is , where represents the conjugate complex number of the element .
[0044] 4.3.4) Perform inverse Fourier transform on the complex - number sequence to obtain the FT - surrogate data .
[0045] 4.3.5) Calculate the rank of the FT - surrogate data .
[0046] 4.4) Construct a mapping for the obtained rank , and sort the new observed data set through the mapping to obtain the AAFT - surrogate data of the original observed data set . It is not difficult to understand that the AAFT - surrogate data has the same rank as the FT - surrogate data .
[0047] It should be noted that the present invention adopts a one - sided test. Therefore, the minimum value of the number of generated surrogate data groups should be no less than groups, is the significance level. For example: The present invention selects The number of groups of the generated alternative data should be no less than 33 groups. Usually, it is recommended that the value be 100 groups.
[0048] 5) Perform holographic marginal spectrum analysis on several groups of AAFT-alternative data generated, calculate their corresponding holographic marginal spectra respectively, and take the 97th percentile of the holographic marginal spectrum values of several groups of AAFT-alternative data as the 97%-confidence level of the holographic marginal spectrum value of the observed data.
[0049] Specifically, the alternative data method is a statistical test method, and the significance level needs to be set before analyzing the data , that is, the probability that the null hypothesis is true but is rejected. The concept associated with the significance level is the confidence level, and its probability is . The smaller the significance level, the higher the confidence level, indicating that the original hypothesis is more reliable. As mentioned above, the original hypothesis of AAFT-alternative data is that the data represents a stationary linear Gaussian process. Therefore, wrongly rejecting this hypothesis (that is, misjudging that the data contains a certain non-linear modulation process when the original hypothesis is true) may more easily lead to deviation from the true law. Therefore, in the present invention, the value should be as small as possible, but not too small so that the test result loses its meaning. The test theory does not give a guiding principle for choosing the value. In practice, the value of is usually between 0.05 and 0.01, and the scientific background, convention and convenience of the problem need to be considered comprehensively. The present invention selects , that is, corresponding to the 97%-confidence level.
[0050] 6) For the same modulation frequency, if the holographic marginal spectrum value of the observed data is less than the 97%-confidence level, then the modulation of the observed data by this modulation frequency is not statistically credible; otherwise, the modulation of the observed data by this modulation frequency is statistically credible.
[0051] The following details the method for detecting the non-linear modulation of ocean waves by the tide of the present invention through specific embodiments: 1) Obtain the observed data set of the area to be measured: The time series of ocean wave elements in the eastern coastal area of Guangdong, China, including the zero-crossing period ( ), and the significant wave height ( ), etc., are collected by marine observations. In this embodiment, the holographic marginal spectrum of the zero-crossing period is analyzed, and the original data process curve of the zero-crossing period is as shown by the solid line in Figure 2 .
[0052] 2) Preprocess the obtained observed data set: Remove outliers and missing values from the observed data, and interpolate the data (usually using linear interpolation method) onto equally spaced time grid points. After interpolation, the curve is as shown by the Figure 2 dashed line in . It can be seen that the time series (dashed line) after interpolation basically coincides with its original data (solid line). Then, remove the quadratic polynomial trend from the interpolated Figure 2 curve (such as the dash-dotted line in ) to obtain the preprocessed time series. Unless otherwise specified, in the following discussions and figures,
[0053] 3) Perform holographic marginal spectrum analysis on the preprocessed observed data set to determine the holographic marginal spectrum of each observed data in the observed data set: Use the Hilbert transform to extract the instantaneous amplitude time series (such as the Figure 3 curve in ), and use ensemble empirical mode decomposition (EEMD) to extract the eigenmodes (such as the Figure 3 in ) and residuals (such as the Figure 3 in ) of the instantaneous amplitude. Each eigenmode represents a different time scale. Perform Hilbert-Huang transform on the above eigenmodes respectively to obtain their Hilbert-Huang spectra of time-frequency distribution, and integrate the time variable of the Hilbert-Huang spectra to obtain the Hilbert-Huang marginal spectra. In fact, these Hilbert-Huang spectra are the holographic spectra, and the Hilbert-Huang marginal spectra are the holographic marginal spectra, where the frequencies corresponding to the spectral peaks may have a non-linear modulation effect on the ocean waves. Taking the 3rd mode ( Figure 3 ) in as an example, its Hilbert-Huang spectrum is as shown in Figure 4 (a), and the corresponding Hilbert-Huang marginal spectrum is as shown in Figure 4 (b). It can be seen from Figure 4 (b) that the central frequency is around about 2 (days -1 ), that is, in the semidiurnal tide frequency band range, indicating that the semidiurnal tide may have a non-linear modulation effect on the zero-crossing period of the ocean waves . Similarly, for other modes (excluding the residual Figure 3 ) in , Hilbert-Huang marginal spectra similar to Figure 4 (b) can be calculated. Summing up the Hilbert-Huang marginal spectra of all eigenmodes, the holographic marginal spectrum curve can be obtained (such as inFigure 6 the black dotted line located below in
[0054] 4) Adopt the adjusted amplitude Fourier transform algorithm for Generate 100 groups of AAFT surrogate data: As an example, as Figure 5 shown in (a), compares with one group of AAFT surrogate data. As Figure 5 shown in (b), is Figure 5 the holographic marginal spectrum of the AAFT surrogate data in (a).
[0055] 5) Conduct holographic marginal spectrum analysis on the generated 100 groups of AAFT surrogate data, calculate their corresponding holographic marginal spectra respectively, and take the 97th percentile of the holographic marginal spectrum values of the 100 groups of AAFT surrogate data as the 97%-confidence level of the holographic marginal spectrum value of the observed data: Calculate the holographic marginal spectra shown in Figure 5 (b) for the 100 groups of AAFT surrogate data respectively, and obtain Figure 5 (c). In Figure 5 (c), each point on the horizontal axis corresponds to 100 spectral values respectively. Take the 97th percentile of these 100 spectral values, and the 97%-confidence level curve of the holographic marginal spectrum curve can be obtained (such as the red solid line located above in Figure 6 ).
[0056] 6) For the same modulation frequency, if the holographic marginal spectrum value of the observed data is less than the 97%-confidence level, then the modulation of this modulation frequency on the observed data is not statistically credible. On the contrary, if the holographic marginal spectrum value of the observed data is greater than the 97%-confidence level, then the modulation of this modulation frequency on the observed data is statistically credible: According to Figure 6 , only when the horizontal axis value is approximately 1.27 (about 0.79 days, see the position circled by the box in the figure), the holographic marginal spectrum value is greater than the corresponding 97%-confidence level, then the modulation of this modulation frequency on the observed data is statistically credible, indicating that the signal with a period of about 0.79 days has a non-linear modulation effect on the zero-crossing period of the ocean waves, which is consistent with the mainly irregular semi-diurnal tidal current in the eastern Guangdong sea area.
[0057] Embodiment 2 This embodiment provides a system for detecting the non-linear modulation of tidal current on ocean waves, including: A data set acquisition module, used to acquire the observed data set of the area to be measured and perform preprocessing; A first holographic marginal spectrum analysis module, used to perform holographic marginal spectrum analysis on the preprocessed observed data set to determine the holographic marginal spectrum of each observed data in the observed data set; An alternative data generation module for generating several groups of alternative data for the preprocessed observation data set; A second holographic marginal spectrum analysis module for performing holographic marginal spectrum analysis on the generated several groups of alternative data, respectively calculating their corresponding holographic marginal spectra, and taking the percentile of the holographic marginal spectrum values of the several groups of alternative data as the %-confidence level of the holographic marginal spectrum value of the observation data, where is a preset significance level; A comparison module for, for the same modulation frequency, if the holographic marginal spectrum value of the observation data is less than %-confidence level, then the modulation of the modulation frequency on the observation data is not statistically credible, otherwise, the modulation of the modulation frequency on the observation data is statistically credible.
[0058] The system provided in this embodiment is used to execute the above method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.
[0059] Embodiment 3 This embodiment provides a processing device corresponding to the method for detecting the non-linear modulation of ocean waves by tidal currents provided in Embodiment 1. The processing device can be a processing device suitable for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of Embodiment 1.
[0060] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, the memory, and the communication interface are connected through the bus to complete communication with each other. The memory stores a computer program that can run on the processing device. When the processing device runs the computer program, it executes the method for detecting the non-linear modulation of ocean waves by tidal currents provided in Embodiment 1 of this embodiment.
[0061] In some implementations, the memory can be a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory.
[0062] In other implementations, the processor can be a central processing unit (CPU), a digital signal processor (DSP), or various other types of general-purpose processors, which are not limited here.
[0063] In addition, when the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.
[0064] Those skilled in the art can understand that the structure of the above-mentioned computing device is only a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computing device to which the solution of the present invention is applied. The specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.
[0065] Embodiment 4 This embodiment provides a computer program product corresponding to the tidal current's non-linear modulation detection method provided in Embodiment 1. The computer program product may include a computer-readable storage medium, on which computer-readable program instructions for executing the tidal current's non-linear modulation detection method described in Embodiment 1 are loaded.
[0066] A computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above.
[0067] The computer-readable storage medium provided in the above-mentioned embodiment has the same implementation principle and technical effect as the above method embodiment, and will not be elaborated here.
[0068] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.
[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.
[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.
[0071] The above embodiments are only used to illustrate the present invention, and the structures, connection manners, manufacturing processes, etc. of the components can all be changed. Any equivalent transformation and improvement based on the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims
1. A method for detecting nonlinear modulation of tidal currents on ocean waves, characterized in that: include: Obtain the observation data set of the area to be measured and perform preprocessing; Performing holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set; Generate several groups of alternative data for the preprocessed observation data set; Perform holographic marginal spectrum analysis on the generated groups of substitution data, calculate their corresponding holographic marginal spectra respectively, and calculate the first holographic marginal spectrum values of the groups of substitution data. Percentiles are used as the holographic marginal spectrum values of the observed data. %-confidence level, where is the pre-set significance level; For the same modulation frequency, if the holographic marginal spectrum value of the observed data is less than %-confidence level, the modulation of the observed data by the modulation frequency is statistically unreliable; otherwise, the modulation of the observed data by the modulation frequency is statistically reliable.
2. A method for detecting nonlinear modulation of tidal currents on ocean waves as claimed in claim 1, characterized in that: The observation data set is a time series of ocean wave elements, including the significant wave height, zero-crossing period and spectrum peak period of ocean waves.
3. A method for detecting nonlinear modulation of tidal current on ocean waves as claimed in claim 1, characterized in that: The preprocessing includes quality control, interpolation processing and trend removal operations on the observation data set.
4. A method for detecting nonlinear modulation of tidal currents on ocean waves as claimed in claim 1, characterized in that: The performing of holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set includes: The Hilbert transform is used to extract the instantaneous features of each observation data in the preprocessed observation data set; The ensemble empirical mode decomposition is used to decompose the extracted instantaneous features into eigenmodes of different time scales. The Hilbert-Huang spectrum method is used to calculate the time-frequency spectrum matrix of each eigenmode, which is the holographic spectrum matrix corresponding to the observed data; By integrating the time variables in the holographic spectrum matrix, the holographic marginal spectrum corresponding to the observed data is obtained.
5. A method for detecting nonlinear modulation of tidal current on ocean waves as claimed in claim 1, characterized in that: The method of generating a plurality of groups of substitute data from the preprocessed observation data set using an adjusted amplitude Fourier transform algorithm includes: For length The preprocessed observation dataset Arrange in ascending order to obtain a new observation data set and its rank ; The obtained rank Constructing a Map , the generated length is The Gaussian noise sequence , and arrange them in ascending order to obtain a new Gaussian noise sequence , by mapping For the new Gaussian noise sequence Rearrange to get , ; For the reordered Gaussian noise sequence Generating FT-surrogate data , and calculate the FT-surrogate data Rank ; The obtained rank Constructing a Map , and by mapping For new observational data sets Sort and get the original observation data set AAFT - Alternative Data .
6. A method for detecting nonlinear modulation of tidal current on ocean waves as claimed in claim 5, characterized in that: After preprocessing, the observation dataset middle, Only in the The value observed at the first moment does not have to be less than The value observed at any time ; In the new preprocessed observation dataset There must be a relationship .
7. A method for detecting nonlinear modulation of tidal current on ocean waves as claimed in claim 5, characterized in that: The reordered Gaussian noise sequence Generating FT-surrogate data , and calculate the FT-surrogate data Rank ,include: For the reordered Gaussian noise sequence Perform Fourier transform to obtain the complex sequence ; generate Random Phase ; Based on the generated random phase , construct a complex sequence ; For complex sequences Perform inverse Fourier transform to obtain FT-surrogate data ; Calculate FT-surrogate data Rank .
8. A tidal current nonlinear modulation detection system for ocean waves, characterized in that: include: The data set acquisition module is used to acquire the observation data set of the area to be measured and perform preprocessing; A first holographic marginal spectrum analysis module is used to perform holographic marginal spectrum analysis on the preprocessed observation data set to determine the holographic marginal spectrum of each observation data in the observation data set; A substitute data generation module is used to generate several groups of substitute data for the preprocessed observation data set; The second holographic marginal spectrum analysis module is used to perform holographic marginal spectrum analysis on the generated groups of replacement data, calculate the corresponding holographic marginal spectrum respectively, and convert the first holographic marginal spectrum value of the groups of replacement data into Percentiles are used as the holographic marginal spectrum values of the observed data. %-confidence level, where is the pre-set significance level; The comparison module is used to compare the holographic marginal spectrum value of the observed data with the same modulation frequency. %-confidence level, the modulation of the observed data by the modulation frequency is statistically unreliable; otherwise, the modulation of the observed data by the modulation frequency is statistically reliable.
9. A processing device, characterized in that: It comprises computer program instructions, wherein when the computer program instructions are executed by a processing device, they are used to implement the steps corresponding to the method for detecting nonlinear modulation of tidal currents on ocean waves according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, wherein the computer program instructions, when executed by a processor, are used to implement steps corresponding to the method for detecting nonlinear modulation of tidal currents on ocean waves according to any one of claims 1-7.