CEEMDAN-based time-frequency signal processing method and device

Abstract

The invention relates to the technical field of data processing, in particular to a CEEMDAN-based time-frequency signal processing method and device, and the method comprises the steps: carrying out the set average empirical mode decomposition of a to-be-processed time-frequency signal through a CEEMDAN, so as to obtain a plurality of initial intrinsic mode functions; performing time-frequency analysis on the plurality of initial intrinsic mode functions to generate a signal analysis parameter of the to-be-processed time-frequency signal; obtaining a plurality of final intrinsic mode functions meeting certain conditions based on the signal analysis parameters and the plurality of initial intrinsic mode functions; and performing information fusion on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function, and processing the to-be-processed time-frequency signal to obtain a processed time-frequency signal. Therefore, the problems that in the prior art, processing of nonlinear and non-stationary signals is not real-time and effective enough, the method cannot be suitable for processing of large-scale data sets, the application range is narrow in actual processing, and the actual requirements of signal processing and analysis cannot be met are solved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly relates to a time-frequency signal processing method and device based on CEEMDAN. Background Art

[0002] A time-frequency signal refers to the characteristics of a signal in two aspects: the time domain (time domain) and the frequency domain (frequency domain). In the field of signal processing, we often use the concepts of the time domain and the frequency domain to describe the properties of signals. In practical applications, time-frequency signal analysis is often used in fields such as audio processing, communication systems, radar systems, timekeeping, time dissemination, satellite navigation, etc. Traditional signal processing methods have certain limitations in dealing with non-linear and non-stationary signals, and traditional methods such as the Fourier transform have deficiencies in dealing with such complex signals.

[0003] In related technologies, adaptive signal decomposition of non-linear and non-stationary signals can be performed through empirical mode decomposition. By decomposing a complex data set into a series of intrinsic mode functions, each intrinsic mode function has symmetry, a local mean of zero, the same number of zero-crossing points and extreme points, and the instantaneous frequency at any point is meaningful. This decomposition method enables each intrinsic mode function to reflect a part of the characteristics in the data, thus facilitating in-depth analysis of complex signals; or the improved CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) can be used to decompose the preprocessed MI-EEG (Motor Imagery Electroencephalogram) data set to obtain a series of intrinsic mode functions, and then the intrinsic mode functions with key information can be obtained through methods such as sample entropy method and K-means clustering method, and then the time-frequency representation of the MI-EEG signal can be obtained.

[0004] However, in related technologies, the processing of non-linear and non-stationary signals is not real-time and effective enough, and it is not applicable to the processing of large-scale data sets. In actual processing, the application range is relatively narrow and cannot meet the actual needs of signal processing and analysis, so it urgently needs to be improved. Summary of the Invention

[0005] This application provides a time-frequency signal processing method and device based on CEEMDAN to solve the problems in related technologies that the processing of non-linear and non-stationary signals is not real-time and effective enough, not applicable to the processing of large-scale data sets, has a relatively narrow application range in actual processing, and cannot meet the actual needs of signal processing and analysis.

[0006] The first aspect embodiment of the present application provides a time-frequency signal processing method based on CEEMDAN, including the following steps: performing ensemble empirical mode decomposition on the time-frequency signal to be processed through CEEMDAN to obtain multiple initial intrinsic mode functions of the time-frequency signal to be processed; performing time-frequency analysis on the multiple initial intrinsic mode functions to generate signal analysis parameters of the time-frequency signal to be processed; based on the signal analysis parameters and the multiple initial intrinsic mode functions, obtaining multiple final intrinsic mode functions that meet preset conditions among the multiple initial intrinsic mode functions; performing information fusion on the multiple final intrinsic mode functions to obtain a fused intrinsic mode function, and processing the time-frequency signal to be processed based on the fused intrinsic mode function to obtain a processed time-frequency signal after processing the time-frequency signal to be processed.

[0007] Optionally, in an embodiment of the present application, the performing ensemble empirical mode decomposition on the time-frequency signal to be processed through CEEMDAN to obtain multiple initial intrinsic mode functions of the time-frequency signal to be processed includes: obtaining Gaussian white noise corresponding to the time-frequency signal to be processed; adding the Gaussian white noise to the time-frequency signal to be processed to obtain a Gaussian time-frequency signal to be processed after adding the Gaussian white noise; performing ensemble empirical mode decomposition on the Gaussian time-frequency signal to be processed through CEEMDAN to obtain the multiple initial intrinsic mode functions.

[0008] Optionally, in an embodiment of the present application, the based on the signal analysis parameters and the multiple initial intrinsic mode functions, obtaining multiple final intrinsic mode functions that meet preset conditions among the multiple initial intrinsic mode functions includes: calculating the spectral characteristics of each initial intrinsic mode function through a target fast Fourier transform to obtain the spectral information of each initial intrinsic mode function; determining frequency components that meet preset amplitude conditions in the spectral information based on the signal analysis parameters and the spectral information; obtaining the multiple final intrinsic mode functions based on the frequency components.

[0009] Optionally, in an embodiment of the present application, the based on the signal analysis parameters and the multiple initial intrinsic mode functions, obtaining multiple final intrinsic mode functions that meet preset conditions among the multiple initial intrinsic mode functions includes: obtaining fitting parameters of the multiple initial intrinsic mode functions based on the multiple initial intrinsic mode functions; calculating the fitting residuals of the multiple initial intrinsic mode functions based on the signal analysis parameters and the fitting parameters; obtaining the multiple final intrinsic mode functions based on the fitting residuals.

[0010] Optionally, in an embodiment of the present application, obtaining a plurality of final intrinsic mode functions that meet preset conditions from the plurality of initial intrinsic mode functions based on the signal analysis parameters and the plurality of initial intrinsic mode functions includes: selecting target signal analysis parameters that meet preset parameter conditions from the signal analysis parameters; optimizing the target signal analysis parameters to obtain optimized target signal analysis parameters after optimization of the target signal analysis parameters; and obtaining the plurality of final intrinsic mode functions based on the optimized target signal analysis parameters and the plurality of initial intrinsic mode functions.

[0011] Optionally, in an embodiment of the present application, performing information fusion on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function, and processing the to-be-processed time-frequency signal based on the fused intrinsic mode function to obtain a processed time-frequency signal after processing the to-be-processed time-frequency signal includes: obtaining a stability evaluation index of the processed time-frequency signal; determining whether the processed time-frequency signal meets a preset stability evaluation condition based on the stability evaluation index; and if the processed time-frequency signal does not meet the preset stability evaluation condition, re-obtaining the plurality of final intrinsic mode functions until the processed time-frequency signal meets the preset stability condition.

[0012] An embodiment of the second aspect of the present application provides a time-frequency signal processing device based on CEEMDAN, including: a first generation module for performing ensemble empirical mode decomposition on a to-be-processed time-frequency signal through CEEMDAN to obtain a plurality of initial intrinsic mode functions of the to-be-processed time-frequency signal; a second generation module for performing time-frequency analysis on the plurality of initial intrinsic mode functions to generate signal analysis parameters of the to-be-processed time-frequency signal; an acquisition module for obtaining a plurality of final intrinsic mode functions that meet preset conditions from the plurality of initial intrinsic mode functions based on the signal analysis parameters and the plurality of initial intrinsic mode functions; and a processing module for performing information fusion on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function, and processing the to-be-processed time-frequency signal based on the fused intrinsic mode function to obtain a processed time-frequency signal after processing the to-be-processed time-frequency signal.

[0013] Optionally, in an embodiment of the present application, the first generation module includes: a first acquisition unit for acquiring Gaussian white noise corresponding to the to-be-processed time-frequency signal; a first generation unit for adding the Gaussian white noise to the to-be-processed time-frequency signal to obtain a Gaussian to-be-processed time-frequency signal after adding the Gaussian white noise to the to-be-processed time-frequency signal; and a second generation unit for performing ensemble empirical mode decomposition on the Gaussian to-be-processed time-frequency signal through CEEMDAN to obtain the plurality of initial intrinsic mode functions.

[0014] Optionally, in an embodiment of the present application, the obtaining module includes: a third generating unit configured to calculate the spectral features of each initial intrinsic mode function through a target fast Fourier transform to obtain the spectral information of each initial intrinsic mode function; a determining unit configured to determine, based on the signal analysis parameters and the spectral information, the frequency components in the spectral information that satisfy a preset amplitude condition; and a fourth generating unit configured to obtain the plurality of final intrinsic mode functions based on the frequency components.

[0015] Optionally, in an embodiment of the present application, the obtaining module includes: a fifth generating unit configured to obtain the fitting parameters of the plurality of initial intrinsic mode functions based on the plurality of initial intrinsic mode functions; a calculating unit configured to calculate the fitting residuals of the plurality of initial intrinsic mode functions based on the signal analysis parameters and the fitting parameters; and a sixth generating unit configured to obtain the plurality of final intrinsic mode functions based on the fitting residuals.

[0016] Optionally, in an embodiment of the present application, the obtaining module includes: a selecting unit configured to select target signal analysis parameters in the signal analysis parameters that satisfy a preset parameter condition; an optimizing unit configured to optimize the target signal analysis parameters to obtain optimized target signal analysis parameters after optimization of the target signal analysis parameters; and a seventh generating unit configured to obtain the plurality of final intrinsic mode functions based on the optimized target signal analysis parameters and the plurality of initial intrinsic mode functions.

[0017] Optionally, in an embodiment of the present application, the processing module includes: a second obtaining unit configured to obtain a stability evaluation index of the processed time-frequency signal; a determining unit configured to determine whether the processed time-frequency signal satisfies a preset stability evaluation condition based on the stability evaluation index; and a third obtaining unit configured to, when the processed time-frequency signal does not satisfy the preset stability evaluation condition, re-obtain the plurality of final intrinsic mode functions until the processed time-frequency signal satisfies the preset stability condition.

[0018] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the time-frequency signal processing method based on CEEMDAN as described in the above embodiment.

[0019] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the time-frequency signal processing method based on CEEMDAN as described above is implemented.

[0020] The fifth aspect of the present application provides a computer program product, including a computer program, which when executed implements the above-mentioned CEEMDAN-based time-frequency signal processing method.

[0021] In the embodiments of the present application, the CEEMDAN can be used to perform ensemble empirical mode decomposition on the time-frequency signal to be processed, obtaining a plurality of initial intrinsic mode functions. Then, time-frequency analysis is performed on the plurality of initial intrinsic mode functions to generate signal analysis parameters, and a plurality of final intrinsic mode functions that meet certain conditions are obtained from the plurality of initial intrinsic mode functions. Furthermore, information fusion is performed on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function, so as to process the time-frequency signal to be processed and obtain a processed time-frequency signal, providing more comprehensive and accurate data for further signal processing and analysis, being more effective in processing non-linear and non-stationary signals, having real-time performance, providing an efficient and flexible solution for the time-frequency characteristics of complex signals, being applicable to the processing of large-scale data sets, and providing advanced and practical technical means for signal analysis in multiple fields. Thus, the problems in the related art are solved, such as the processing of non-linear and non-stationary signals being insufficiently real-time and effective, being unable to be applied to the processing of large-scale data sets, having a narrow application range in actual processing, and being unable to meet the actual needs of signal processing and analysis.

[0022] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above-mentioned and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0024] Figure 1 FIG. 1 is a flowchart of a CEEMDAN-based time-frequency signal processing method according to an embodiment of the present application;

[0025] FIG. 2(a) is a partial flowchart of signal decomposition using CEEMDAN according to an embodiment of the present application;

[0026] FIG. 2(b) is another partial flowchart of signal decomposition using CEEMDAN according to an embodiment of the present application;

[0027] Figure 3 FIG. 3 is a block diagram of a CEEMDAN-based time-frequency signal processing device according to an embodiment of the present application;

[0028] Figure 4 FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0030] The time-frequency signal processing method and device based on CEEMDAN according to the embodiments of the present application will be described below with reference to the accompanying drawings. Aiming at the problems mentioned in the above background technology that the processing of non-linear and non-stationary signals is not real-time and effective enough, cannot be applied to the processing of large-scale data sets, has a narrow application range in actual processing, and cannot meet the actual needs of signal processing and analysis, the present application provides a time-frequency signal processing method based on CEEMDAN. In this method, the ensemble empirical mode decomposition of the time-frequency signal to be processed can be performed through CEEMDAN to obtain a plurality of initial intrinsic mode functions, and then time-frequency analysis is performed on the plurality of initial intrinsic mode functions to generate signal analysis parameters, and a plurality of final intrinsic mode functions that meet certain conditions are obtained from the plurality of initial intrinsic mode functions. Furthermore, information fusion is performed on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function to process the time-frequency signal to be processed, and the processed time-frequency signal is obtained, providing more comprehensive and accurate data for further signal processing and analysis, being more effective in processing non-linear and non-stationary signals, having real-time performance, providing an efficient and flexible solution for the time-frequency characteristics of complex signals, being applicable to the processing of large-scale data sets, and providing advanced and practical technical means for signal analysis in multiple fields. Thus, the problems in the related technology that the processing of non-linear and non-stationary signals is not real-time and effective enough, cannot be applied to the processing of large-scale data sets, has a narrow application range in actual processing, and cannot meet the actual needs of signal processing and analysis are solved.

[0031] Specifically, Figure 1 FIG. is a flowchart of a time-frequency signal processing method based on CEEMDAN according to an embodiment of the present application.

[0032] As Figure 1 shown, the time-frequency signal processing method based on CEEMDAN includes the following steps:

[0033] In step S101, the ensemble empirical mode decomposition of the time-frequency signal to be processed is performed through CEEMDAN to obtain a plurality of initial intrinsic mode functions of the time-frequency signal to be processed.

[0034] It can be understood that in practical applications of the embodiments of the present application, time-frequency signal analysis is often used in fields such as audio processing, communication systems, radar systems, timekeeping, time transfer, and satellite navigation. Considering that the time-frequency signal not only contains the time or frequency information output by the clock, but also includes other signals formed due to factors such as environmental influence, signal propagation, clock aging, and frequency drift. Therefore, signal processing methods need to be used to decompose the time-frequency signal to be processed, and remove interference signals such as periodic terms and quadratic terms found therein from the time-frequency signal to be processed, in order to obtain a purer time-frequency signal.

[0035] Further, in the embodiments of the present application, the time-frequency signal to be processed can be a second pulse signal or a frequency signal (such as a 5 MHz frequency signal, a 10 MHz frequency signal, a 100 MHz frequency signal, etc., which are not specifically limited in the present application) output by an atomic clock (such as a rubidium clock, a cesium clock, a hydrogen clock, etc., which are not specifically limited in the present application); it can also be time data or frequency data obtained by a time interval comparator or a frequency comparator comparing two or more atomic clocks; it can also be a time series reflecting the operation of the atomic clock obtained by using GNSS (Global Navigation Satellite System) time transfer, satellite two-way time-frequency transfer, etc., which can be specifically set by those skilled in the art according to the actual situation and are not specifically limited in the present application.

[0036] As a possible implementation manner, the embodiments of the present application can perform ensemble empirical mode decomposition on the time-frequency signal to be processed by CEEMDAN, and then obtain a plurality of initial intrinsic mode functions.

[0037] Optionally, in an embodiment of the present application, performing ensemble empirical mode decomposition on the time-frequency signal to be processed by CEEMDAN to obtain a plurality of initial intrinsic mode functions of the time-frequency signal to be processed includes: obtaining Gaussian white noise corresponding to the time-frequency signal to be processed; adding the Gaussian white noise to the time-frequency signal to be processed to obtain a Gaussian time-frequency signal to be processed after adding the Gaussian white noise; performing ensemble empirical mode decomposition on the Gaussian time-frequency signal to be processed by CEEMDAN to obtain a plurality of initial intrinsic mode functions.

[0038] In some embodiments, the embodiments of the present application can add Gaussian white noise to the time-frequency signal to be processed to obtain a Gaussian time-frequency signal to be processed after adding the Gaussian white noise, perform empirical mode decomposition on the Gaussian time-frequency signal to be processed, and perform geometric averaging, and then obtain a plurality of initial intrinsic mode functions to reduce the influence of noise and obtain the result of time-frequency analysis.

[0039] In step S102, perform time-frequency analysis on a plurality of initial intrinsic mode functions to generate signal analysis parameters of the time-frequency signal to be processed.

[0040] As a possible implementation manner, the embodiments of the present application can perform time-frequency analysis on a plurality of initial intrinsic mode functions to obtain multiple signal analysis parameters such as the period information, frequency information, and noise signal-to-noise ratio of the time-frequency signal to be processed. Among them, the signal analysis parameters can be set by those skilled in the art according to the actual situation, and the present application does not make specific limitations.

[0041] In step S103, based on the signal analysis parameters and the plurality of initial intrinsic mode functions, a plurality of final intrinsic mode functions that meet the preset conditions are obtained from the plurality of initial intrinsic mode functions.

[0042] Those skilled in the art can understand that the embodiments of the present application can analyze the signal analysis parameters through an objective function, and then obtain a plurality of final intrinsic mode functions that meet certain conditions from the plurality of initial intrinsic mode functions. Among them, the certain conditions can be set by those skilled in the art according to the actual situation, and the present application does not make specific limitations.

[0043] Exemplarily, the embodiments of the present application can determine whether the signal analysis parameters meet certain conditions through an objective function, and when the signal analysis parameters meet the certain conditions, retain the corresponding initial intrinsic mode function; otherwise, delete the corresponding initial intrinsic mode function. Thus, the embodiments of the present application can obtain a plurality of final intrinsic mode functions that meet the certain conditions.

[0044] Optionally, in an embodiment of the present application, obtaining a plurality of final intrinsic mode functions that meet the preset conditions based on the signal analysis parameters and the plurality of initial intrinsic mode functions includes: calculating the spectral characteristics of each initial intrinsic mode function through an objective fast Fourier transform to obtain the spectral information of each initial intrinsic mode function; determining the frequency components that meet the preset amplitude condition based on the signal analysis parameters and the spectral information; and obtaining a plurality of final intrinsic mode functions based on the frequency components.

[0045] In some embodiments, the embodiments of the present application can calculate the spectral characteristics of each initial intrinsic mode function, that is, perform an objective fast Fourier transform on the initial intrinsic mode function, and then obtain the corresponding spectral information, and find the frequency components that meet a certain amplitude condition (such as the maximum amplitude, etc., where the certain amplitude condition can be set by those skilled in the art according to the actual situation, and the present application does not make specific limitations) in the spectral information, and then determine whether the amplitude meets a certain condition (such as being less than or equal to the target significance threshold, where the target significance threshold can be set by those skilled in the art according to the actual situation, and the present application does not make specific limitations).

[0046] In some embodiments, when the amplitude of the embodiments of the present application meets certain conditions, that is, the amplitude is less than or equal to the target significance threshold, it is determined that the initial intrinsic mode function does not have significant periodicity, and the corresponding initial intrinsic mode function is retained, thereby obtaining a plurality of final intrinsic mode functions.

[0047] In some embodiments, when the amplitude of the embodiments of the present application does not meet certain conditions, that is, the amplitude is greater than the target significance threshold, it is determined that the initial intrinsic mode function has significant periodicity, and the corresponding initial intrinsic mode function is deleted, thereby obtaining a plurality of final intrinsic mode functions.

[0048] Optionally, in an embodiment of the present application, based on signal analysis parameters and a plurality of initial intrinsic mode functions, a plurality of final intrinsic mode functions that meet preset conditions among the plurality of initial intrinsic mode functions are obtained, including: obtaining fitting parameters of the plurality of initial intrinsic mode functions based on the plurality of initial intrinsic mode functions; calculating fitting residuals of the plurality of initial intrinsic mode functions based on the signal analysis parameters and the fitting parameters; and obtaining a plurality of final intrinsic mode functions based on the fitting residuals.

[0049] In some embodiments, the embodiments of the present application can perform quadratic polynomial fitting on each initial intrinsic mode function, record the fitting parameters of the plurality of initial intrinsic mode functions, then calculate the fitting residuals, and determine whether the fitting residuals meet certain conditions (such as being greater than or equal to the target threshold, where the target threshold can be set by those skilled in the art according to the actual situation, and the present application does not make specific limitations).

[0050] In some embodiments, when the fitting residuals meet certain conditions, that is, the fitting residuals are greater than or equal to the target threshold, it is determined that the initial intrinsic mode function does not have an obvious quadratic characteristic, and the corresponding initial intrinsic mode function is retained, thereby obtaining a plurality of final intrinsic mode functions.

[0051] In some embodiments, when the fitting residuals do not meet certain conditions, that is, the fitting residuals are less than the target threshold, it is determined that the initial intrinsic mode function has an obvious quadratic characteristic, and the corresponding initial intrinsic mode function is deleted, thereby obtaining a plurality of final intrinsic mode functions.

[0052] Optionally, in an embodiment of the present application, based on signal analysis parameters and a plurality of initial intrinsic mode functions, a plurality of final intrinsic mode functions that meet preset conditions among the plurality of initial intrinsic mode functions are obtained, including: selecting target signal analysis parameters that meet preset parameter conditions from the signal analysis parameters; optimizing the target signal analysis parameters to obtain optimized target signal analysis parameters after optimization of the target signal analysis parameters; and obtaining a plurality of final intrinsic mode functions based on the optimized target signal analysis parameters and the plurality of initial intrinsic mode functions.

[0053] In some embodiments, embodiments of the present application may, according to actual application requirements, add other screening conditions or constraints, such as selecting target signal analysis parameters that meet certain parameter conditions, such as specific signal characteristics, frequency ranges, noise levels, etc. The present application does not make specific limitations. Further, embodiments of the present application optimize the selected target signal analysis parameters to obtain optimized target signal analysis parameters, so as to ensure that the multiple finally obtained intrinsic mode functions can effectively represent the actual physical modes of the signal.

[0054] In step S104, information fusion is performed on multiple finally obtained intrinsic mode functions to obtain a fused intrinsic mode function, and the to-be-processed time-frequency signal is processed based on the fused intrinsic mode function to obtain a processed time-frequency signal after processing the to-be-processed time-frequency signal.

[0055] As a possible implementation manner, embodiments of the present application perform information fusion on multiple finally obtained intrinsic mode functions to obtain a fused intrinsic mode function, and then process the to-be-processed time-frequency signal to obtain a processed time-frequency signal after processing.

[0056] Optionally, in an embodiment of the present application, performing information fusion on multiple finally obtained intrinsic mode functions to obtain a fused intrinsic mode function, and processing the to-be-processed time-frequency signal based on the fused intrinsic mode function to obtain a processed time-frequency signal after processing the to-be-processed time-frequency signal includes: obtaining a stability evaluation index of the processed time-frequency signal; determining whether the processed time-frequency signal meets a preset stability evaluation condition based on the stability evaluation index; if the processed time-frequency signal does not meet the preset stability evaluation condition, re-obtain multiple finally obtained intrinsic mode functions until the processed time-frequency signal meets the preset stability condition.

[0057] In addition, it should be noted that after obtaining the processed time-frequency signal, embodiments of the present application can also evaluate the stability of the processed time-frequency signal. The main content may be: First, embodiments of the present application can first obtain a stability evaluation index of the processed time-frequency signal, and then determine whether the processed time-frequency signal meets certain stability evaluation conditions. When the processed time-frequency signal does not meet certain stability evaluation conditions, re-obtain multiple finally obtained intrinsic mode functions until the processed time-frequency signal meets the preset stability condition. Among them, certain stability evaluation conditions can be set by those skilled in the art according to actual situations, and the present application does not make specific limitations.

[0058] In addition, in the embodiments of the present application, the stability evaluation indicators may include, but are not limited to, Allan variance, modified Allan variance, Hadamard variance, etc. The present application does not make specific limitations. These variance indicators can quantify the frequency changes of oscillators at different time scales, and then analyze the stability of oscillators. By analyzing the random fluctuations of oscillators, it can distinguish different types of noise (such as white noise, random walk noise, etc., which are not specifically limited in the present application), and reveal the stability performance of oscillators under the action of these noises. Therefore, these three evaluation methods are widely used in fields such as atomic clocks and frequency synthesizers, providing a scientific basis for the time-frequency signal processing effect.

[0059] Furthermore, in the embodiments of the present application, Allan variance is a statistical method for evaluating the short-term stability of time-frequency signals. By analyzing the frequency changes within a time interval, it can effectively distinguish different types of random noise, and is particularly suitable for describing the signal stability within a short time. Its calculation formula can be expressed as, but is not limited to:

[0060]

[0061] where y i is the i-th relative frequency measurement value within the measurement (sampling) interval, τ is the sampling interval, and M is the number of consecutive measurements.

[0062] The modified Allan variance introduces the averaging of frequency information on this basis, improving the resolution ability for high-frequency noise and being suitable for signal analysis with complex noise. Its calculation formula can be expressed as, but is not limited to:

[0063]

[0064] where M is the data length, m is the overlapping point number or sub-interval size, which is a parameter controlling the segmented processing of the time series. Specifically, it defines the number of samples used when calculating each sub-interval, mainly used for "segmenting" the time series, so as to evaluate the fluctuations of the signal at different time scales.

[0065] Hadamard variance, on the other hand, focuses more on eliminating the influence of linear drift and is particularly effective for medium- and long-term stability evaluation. Its calculation formula can be expressed as, but is not limited to:

[0066]

[0067] Next, the working principle of the time-frequency signal processing method based on CEEMDAN proposed in the embodiments of the present application will be introduced in combination with multiple specific embodiments.

[0068] Embodiment 1:

[0069] Among them, Fig. 2(a) is a partial flowchart of signal decomposition using CEEMDAN according to an embodiment of the present application.

[0070] Fig. 2(b) is another partial flowchart of signal decomposition using CEEMDAN according to an embodiment of the present application.

[0071] Among them, as shown in Fig. 2(a) and Fig. 2(b), the main content of signal decomposition using CEEMDAN in the embodiment of the present application is as follows: First, select the time-frequency signal to be processed, such as signal x, add different Gaussian white noises to x, and then perform empirical mode decomposition to obtain multiple initial intrinsic mode functions imf 1 (j), where i = 1, 2,..., k, j = 1, 2,..., n, n is the number of Gaussian white noises, and k is the number of redundant signals in the signal to be processed. Average these initial intrinsic mode functions imf 1 to obtain a final intrinsic mode function IMF 1 , which is the final result output by CEEMDAN. Then, remove this final intrinsic mode function IMF 1 from signal x, and perform the above loop until the residual signal can no longer be decomposed by empirical mode decomposition, that is, all final intrinsic mode functions are obtained.

[0072] Example 2: Time signal analysis

[0073] Experimental purpose:

[0074] Among them, the experimental purpose of the embodiment of the present application is to verify the application effect of the embodiment of the present application in time signal analysis, and focus on the processing performance for non-linear and non-stationary signals.

[0075] Experimental device:

[0076] Among them, the experimental device of the embodiment of the present application is: atomic clock, data acquisition device, CEEMDAN time-frequency data processing software and computer.

[0077] Experimental steps:

[0078] Among them, the experimental steps of the embodiments of the present application are as follows: (1) Use an atomic clock to output a time signal to be processed that contains non-linear and non-stationary characteristics; (2) Input the output time signal to be processed into a data acquisition device to collect signal data; (3) Use the time-frequency signal processing method based on CEEMDAN to process the collected time signal to be processed, including steps such as ensemble empirical mode decomposition, selection of intrinsic mode functions, and time-frequency analysis; (4) Observe the processed time-frequency data and analyze whether the instantaneous characteristics and spectral changes in the time signal to be processed are better captured; (5) Evaluate the superiority of the embodiments of the present application in extracting the dynamic characteristics of the signal by comparing with traditional methods (such as time-domain analysis and frequency-domain analysis).

[0079] Experimental results:

[0080] Among them, the experimental results of the embodiments of the present application are as follows: In the analysis of time signals, the embodiments of the present application can better retain the non-linear and non-stationary properties of the signals, enabling the instantaneous changes and spectral characteristics of the signals to be observed more clearly in the time-frequency domain. Compared with traditional methods, the embodiments of the present application can more accurately reflect the instantaneous events and frequency changes in the signals, confirming its application advantages in time signal analysis.

[0081] Embodiment 3: Frequency signal analysis

[0082] Experimental purpose:

[0083] Among them, the experimental purpose of the embodiments of the present application is to verify the application effect of the embodiments of the present application in frequency signal analysis, and particularly focus on its processing performance for signals with fast-changing and non-stationary frequencies.

[0084] Experimental device:

[0085] Among them, the experimental device of the embodiments of the present application is: an atomic clock, a frequency comparator, CEEMDAN time-frequency data processing software, and a computer.

[0086] Experimental steps:

[0087] Among them, the experimental steps of the embodiments of the present application are as follows: (1) Use an atomic clock to output a to-be-processed frequency signal containing rapidly changing and non-stationary frequency characteristics; (2) Use a frequency comparator to perform high-frequency sampling on the generated to-be-processed frequency signal to obtain high-frequency signal data; (3) Use a time-frequency signal processing method based on CEEMDAN to process the collected to-be-processed time signal, including steps such as ensemble empirical mode decomposition, selection of intrinsic mode functions, and time-frequency analysis; (4) Observe the processed time-frequency data and analyze whether the instantaneous frequency changes and spectral characteristics in the to-be-processed frequency signal are better captured; (5) By comparing with traditional methods (such as short-time Fourier transform), evaluate the superiority of the embodiments of the present application in extracting the dynamic characteristics of high-frequency signals.

[0088] Experimental results:

[0089] Among them, the experimental results of the embodiments of the present application are as follows: The embodiments of the present application can better process signals with rapidly changing and non-stationary frequencies in frequency signal analysis, enabling the instantaneous frequency changes and spectral characteristics of the signal to be more clearly observed in the time-frequency domain. Compared with traditional methods, the embodiments of the present application can more accurately reflect the instantaneous frequency changes in the signal, confirming its application advantages in frequency signal analysis.

[0090] Embodiment 4: Processing data of the method for measuring gravity potential by time-frequency comparison

[0091] Experimental purpose:

[0092] Among them, the experimental purpose of the embodiments of the present application is to verify whether the embodiments of the present application can improve the accuracy of the data of the method for measuring gravity potential by time-frequency comparison.

[0093] Experimental device:

[0094] Among them, the experimental device of the embodiments of the present application is: a time-frequency comparison measurement system, a high-precision atomic clock, a coaxial cable, a data acquisition device, CEEMDAN time-frequency data processing software, and a computer.

[0095] Experimental steps:

[0096] Among them, the experimental steps of the embodiments of the present application are as follows: (1) Use a time-frequency comparison measurement system to perform time-frequency comparison between two measurement stations, and use the relativistic method to calculate the gravity potential difference data, including the time-frequency comparison results; (2) Connect the measurement system and the data acquisition device through a coaxial cable to collect time-frequency comparison data; (3) Use the time-frequency signal processing method based on CEEMDAN to process the to-be-processed time signal collected, including steps such as ensemble empirical mode decomposition, selection of intrinsic mode functions, time-frequency analysis, etc.; (4) Observe the processed time-frequency data to analyze whether the gravity potential change characteristics in the time-frequency comparison data are better captured; by comparing with traditional methods, evaluate the superiority of the embodiments of the present application in extracting the dynamic characteristics of gravity potential changes.

[0097] Experimental results:

[0098] Among them, the experimental results of the embodiments of the present application are as follows: The embodiments of the present application can better process the data of the method for measuring the gravity potential by time-frequency comparison, and improve the accuracy of gravity potential measurement. Compared with traditional methods, the embodiments of the present application can more accurately reflect the characteristics of various signals in the measurement, confirming its application advantages in processing the data of measuring the gravity potential by time-frequency comparison.

[0099] According to the time-frequency signal processing method based on CEEMDAN proposed by the embodiments of the present application, the ensemble empirical mode decomposition of the to-be-processed time-frequency signal can be performed by CEEMDAN to obtain a plurality of initial intrinsic mode functions, and then time-frequency analysis is performed on the plurality of initial intrinsic mode functions to generate signal analysis parameters, and a plurality of final intrinsic mode functions that meet certain conditions are obtained from the plurality of initial intrinsic mode functions. Furthermore, information fusion is performed on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function to process the to-be-processed time-frequency signal and obtain the processed time-frequency signal, providing more comprehensive and accurate data for further signal processing and analysis, being more effective in processing non-linear and non-stationary signals, having real-time performance, providing an efficient and flexible solution for the time-frequency characteristics of complex signals, being applicable to the processing of large-scale data sets, and providing advanced and practical technical means for signal analysis in multiple fields. Thus, the problems in the related technologies that the processing of non-linear and non-stationary signals is not real-time and effective enough, cannot be applied to the processing of large-scale data sets, has a narrow application range in actual processing, and cannot meet the actual needs of signal processing and analysis are solved.

[0100] Secondly, describe the time-frequency signal processing device based on CEEMDAN proposed according to the embodiments of the present application with reference to the accompanying drawings.

[0101] Figure 3 It is a block diagram of the time-frequency signal processing device based on CEEMDAN provided according to the embodiments of the present application.

[0102] As Figure 3 shown, the time-frequency signal processing device 30 based on CEEMDAN includes: a first generation module 100, a second generation module 200, an acquisition module 300, and a processing module 400.

[0103] Among them, the first generation module 100 is configured to perform ensemble empirical mode decomposition on the time-frequency signal to be processed through CEEMDAN to obtain a plurality of initial intrinsic mode functions of the time-frequency signal to be processed.

[0104] The second generation module 200 is configured to perform time-frequency analysis on the plurality of initial intrinsic mode functions to generate signal analysis parameters of the time-frequency signal to be processed.

[0105] The acquisition module 300 is configured to obtain a plurality of final intrinsic mode functions that meet preset conditions among the plurality of initial intrinsic mode functions based on the signal analysis parameters and the plurality of initial intrinsic mode functions.

[0106] The processing module 400 is configured to perform information fusion on the plurality of final intrinsic mode functions to obtain a fused intrinsic mode function, and process the time-frequency signal to be processed based on the fused intrinsic mode function to obtain a processed time-frequency signal after processing the time-frequency signal to be processed.

[0107] Optionally, in an embodiment of the present application, the first generation module 100 includes: a first acquisition unit, a first generation unit, and a second generation unit.

[0108] Among them, the first acquisition unit is configured to acquire Gaussian white noise corresponding to the time-frequency signal to be processed.

[0109] The first generation unit is configured to add the Gaussian white noise to the time-frequency signal to be processed to obtain a Gaussian time-frequency signal to be processed after adding the Gaussian white noise to the time-frequency signal to be processed.

[0110] The second generation unit is configured to perform ensemble empirical mode decomposition on the Gaussian time-frequency signal to be processed through CEEMDAN to obtain a plurality of initial intrinsic mode functions.

[0111] Optionally, in an embodiment of the present application, the acquisition module 300 includes: a third generation unit, a determination unit, and a fourth generation unit.

[0112] Among them, the third generation unit is configured to calculate the spectral characteristics of each initial intrinsic mode function through target fast Fourier transform to obtain the spectral information of each initial intrinsic mode function.

[0113] The determination unit is configured to determine frequency components that meet preset amplitude conditions in the spectral information based on the signal analysis parameters and the spectral information.

[0114] The fourth generation unit is configured to obtain a plurality of final intrinsic mode functions based on frequency components.

[0115] Optionally, in an embodiment of the present application, the acquisition module 300 includes: a fifth generation unit, a calculation unit, and a sixth generation unit.

[0116] Among them, the fifth generation unit is configured to obtain fitting parameters of a plurality of initial intrinsic mode functions based on the plurality of initial intrinsic mode functions.

[0117] The calculation unit is configured to calculate fitting residuals of the plurality of initial intrinsic mode functions based on signal analysis parameters and fitting parameters.

[0118] The sixth generation unit is configured to obtain a plurality of final intrinsic mode functions based on the fitting residuals.

[0119] Optionally, in an embodiment of the present application, the acquisition module 300 includes: a selection unit, an optimization unit, and a seventh generation unit.

[0120] Among them, the selection unit is configured to select target signal analysis parameters that meet preset parameter conditions in the signal analysis parameters.

[0121] The optimization unit is configured to optimize the target signal analysis parameters to obtain optimized target signal analysis parameters after optimization of the target signal analysis parameters.

[0122] The seventh generation unit is configured to obtain a plurality of final intrinsic mode functions based on the optimized target signal analysis parameters and the plurality of initial intrinsic mode functions.

[0123] Optionally, in an embodiment of the present application, the processing module 400 includes: a second acquisition unit, a judgment unit, and a third acquisition unit.

[0124] Among them, the second acquisition unit is configured to obtain a stability evaluation index for processing the time-frequency signal.

[0125] The judgment unit is configured to judge whether the processed time-frequency signal meets preset stability evaluation conditions based on the stability evaluation index.

[0126] The third acquisition unit is configured to, when the processed time-frequency signal does not meet the preset stability evaluation conditions, re-obtain a plurality of final intrinsic mode functions until the processed time-frequency signal meets the preset stability conditions.

[0127] It should be noted that the foregoing explanation of the embodiments of the time-frequency signal processing method based on CEEMDAN also applies to the time-frequency signal processing device based on CEEMDAN in this embodiment, and will not be elaborated here.

[0128] The time-frequency signal processing device based on CEEMDAN proposed according to the embodiments of the present application can perform ensemble empirical mode decomposition of the time-frequency signal to be processed through CEEMDAN to obtain multiple initial intrinsic mode functions, and then perform time-frequency analysis on the multiple initial intrinsic mode functions to generate signal analysis parameters, and obtain multiple final intrinsic mode functions that meet certain conditions among the multiple initial intrinsic mode functions. Furthermore, information fusion is performed on the multiple final intrinsic mode functions to obtain a fused intrinsic mode function to process the time-frequency signal to be processed and obtain a processed time-frequency signal, providing more comprehensive and accurate data for further signal processing and analysis, being more effective in processing non-linear and non-stationary signals, having real-time performance, providing an efficient and flexible solution for the time-frequency characteristics of complex signals, being applicable to the processing of large-scale data sets, and providing advanced and practical technical means for signal analysis in multiple fields. Thus, it solves the problems in the related art that the processing of non-linear and non-stationary signals is not real-time and effective enough, cannot be applied to the processing of large-scale data sets, has a narrow application range in actual processing, and cannot meet the actual needs of signal processing and analysis, etc.

[0129] Figure 4 FIG. is a schematic structural diagram of an electronic device provided according to an embodiment of the present application. The electronic device may include:

[0130] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0131] When the processor 402 executes the program, it implements the time-frequency signal processing method based on CEEMDAN provided in the above embodiment.

[0132] Furthermore, the electronic device further includes:

[0133] A communication interface 403 for communication between the memory 401 and the processor 402.

[0134] The memory 401 is used to store a computer program executable on the processor 402.

[0135] The memory 401 may include a high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0136] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 only a thick line is used in Figure 4 , but it does not mean that there is only one bus or one type of bus.

[0137] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a single chip, the memory 401, the processor 402, and the communication interface 403 can communicate with each other through an internal interface.

[0138] The processor 402 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0139] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned CEEMDAN-based time-frequency signal processing method is implemented.

[0140] The embodiments of the present application also provide a computer program product, including a computer program, and when the program is executed, the above-mentioned CEEMDAN-based time-frequency signal processing method is implemented.

[0141] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0142] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0143] Any process or method description shown in a flowchart or described in other ways herein can be understood as representing a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0145] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented by a combination of any one or more of the following techniques known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0146] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0147] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist separately as individual physical units, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0148] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A time-frequency signal processing method based on CEEMDAN, characterized in that: The following steps are involved: Performing ensemble average empirical mode decomposition of the time-frequency signal to be processed by adaptive noise complete ensemble empirical mode decomposition CEEMDAN to obtain multiple initial intrinsic mode functions of the time-frequency signal to be processed; Performing time-frequency analysis on the multiple initial intrinsic mode functions to generate signal analysis parameters of the time-frequency signal to be processed; Based on the signal analysis parameter and the multiple initial intrinsic mode functions, obtaining multiple final intrinsic mode functions that meet preset conditions from the multiple initial intrinsic mode functions; Information fusion is performed on the multiple final intrinsic mode functions to obtain a fused intrinsic mode function, and the time-frequency signal to be processed is processed based on the fused intrinsic mode function to obtain a processed time-frequency signal after the time-frequency signal to be processed is processed.

2. The method according to claim 1, characterized in that The method of performing ensemble averaging of the time-frequency signal to be processed by CEEMDAN to obtain a plurality of initial intrinsic mode functions of the time-frequency signal to be processed includes: Obtaining Gaussian white noise corresponding to the time-frequency signal to be processed; Adding the Gaussian white noise to the time-frequency signal to be processed to obtain a Gaussian time-frequency signal to be processed after the Gaussian white noise is added to the time-frequency signal to be processed; The Gaussian time-frequency signal to be processed is subjected to ensemble average empirical mode decomposition by CEEMDAN to obtain the multiple initial intrinsic mode functions.

3. The method according to claim 1, characterized in that The step of acquiring, based on the signal analysis parameter and the multiple initial intrinsic mode functions, multiple final intrinsic mode functions that meet preset conditions from the multiple initial intrinsic mode functions comprises: Calculating the frequency spectrum characteristics of each initial intrinsic mode function by target fast Fourier transform to obtain the frequency spectrum information of each initial intrinsic mode function; Determine, based on the signal analysis parameter and the spectrum information, a frequency component in the spectrum information that meets a preset amplitude condition; The plurality of final eigenmode functions are obtained based on the frequency components.

4. The method according to claim 1, characterized in that: The step of acquiring, based on the signal analysis parameter and the multiple initial intrinsic mode functions, multiple final intrinsic mode functions that meet preset conditions from the multiple initial intrinsic mode functions comprises: Obtaining fitting parameters of the multiple initial eigenmode functions based on the multiple initial eigenmode functions; Calculating fitting residuals of the plurality of initial intrinsic mode functions based on the signal analysis parameters and the fitting parameters; The multiple final intrinsic mode functions are obtained based on the fitting residuals.

5. The method according to claim 1, characterized in that The step of acquiring, based on the signal analysis parameter and the multiple initial intrinsic mode functions, multiple final intrinsic mode functions that meet preset conditions from the multiple initial intrinsic mode functions comprises: Selecting target signal analysis parameters that meet preset parameter conditions from the signal analysis parameters; Optimizing the target signal analysis parameters to obtain optimized target signal analysis parameters after the target signal analysis parameters are optimized; The multiple final intrinsic mode functions are obtained based on the optimized target signal analysis parameters and the multiple initial intrinsic mode functions.

6. The method according to claim 1, characterized in that The step of fusing information of the multiple final intrinsic mode functions to obtain a fused intrinsic mode function, and processing the time-frequency signal to be processed based on the fused intrinsic mode function to obtain a processed time-frequency signal after the time-frequency signal to be processed is as follows: Obtaining a stability evaluation index of the processed time-frequency signal; Determining whether the processed time-frequency signal meets a preset stability evaluation condition based on the stability evaluation index; If the processed time-frequency signal does not satisfy the preset stability evaluation condition, the multiple final intrinsic mode functions are reacquired until the processed time-frequency signal satisfies the preset stability condition.

7. A time-frequency signal processing device based on CEEMDAN, characterized in that: include: A first generating module is used for performing ensemble average empirical mode decomposition on the time-frequency signal to be processed by CEEMDAN to obtain a plurality of initial intrinsic mode functions of the time-frequency signal to be processed; A second generating module, used for performing time-frequency analysis on the multiple initial intrinsic mode functions to generate signal analysis parameters of the time-frequency signal to be processed; An acquisition module, configured to acquire, based on the signal analysis parameter and the multiple initial intrinsic mode functions, multiple final intrinsic mode functions that meet preset conditions among the multiple initial intrinsic mode functions; A processing module is used to perform information fusion on the multiple final intrinsic mode functions to obtain a fused intrinsic mode function, and process the time-frequency signal to be processed based on the fused intrinsic mode function to obtain a processed time-frequency signal after the time-frequency signal to be processed is processed.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the CEEMDAN-based time-frequency signal processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the time-frequency signal processing method based on CEEMDAN as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises a computer program, which, when executed, is used to implement the time-frequency signal processing method based on CEEMDAN as claimed in any one of claims 1 to 6.

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