A method and system for enhancing time-frequency aggregation of non-stationary signals

Abstract

The present invention discloses a method and system for enhancing the time-frequency aggregation of non-stationary signals, and relates to the technical field of signal time-frequency aggregation enhancement, including collecting non-stationary signals for preprocessing; performing time-frequency analysis on non-stationary signals, and defining weight operators; optimizing the time-frequency analysis and weight operator objective functions, and constructing an amplitude modulation and frequency modulation composite signal model to optimize the time-frequency aggregation of non-stationary signals; evaluating the time-frequency aggregation of the optimized non-stationary signals and outputting them, and storing the data. The present invention proposes a high-order weighted time-frequency sparse representation, defines a weight operator, and uses a fast iterative shrinkage threshold algorithm for optimization to establish an amplitude modulation and frequency modulation composite signal model. The present invention has higher time-frequency aggregation and robustness for fast-changing signals, and the time-frequency results are more accurate than the theoretical instantaneous frequency. Compared with other time-frequency methods, the time-frequency aggregation and decoupling performance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal time-frequency aggregation enhancement, and in particular to a method and system for enhancing the time-frequency aggregation of a non-stationary signal. Background Art

[0002] Non-stationary signal processing is an important branch of the signal processing field, focusing on analyzing and processing signals with time-varying characteristics. Such signals are ubiquitous in nature and human activities, such as seismic waves, biomedical signals, and speech signals. With the rapid development of information technology, the demand for analysis of non-stationary signals is increasing, prompting the continuous advancement of related technologies. In the early days, the processing of non-stationary signals mainly relied on classical time domain analysis and frequency domain analysis methods, such as Fourier transform. However, these methods often have limited effects when processing non-stationary signals with complex time-varying characteristics. Traditional time-frequency analysis methods often ignore the time-frequency aggregation of signals when processing signals, that is, the concentration of signal energy on the time-frequency plane. Time-frequency aggregation is crucial for signal feature extraction and recognition, especially in noisy environments. Therefore, optimizing the time-frequency aggregation of non-stationary signals is the key to improving signal processing performance. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing methods and systems for enhancing the time-frequency aggregation of non-stationary signals, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that traditional time-frequency analysis methods often ignore the time-frequency aggregation of signals when processing signals, and often have limited effects when processing non-stationary signals with complex time-varying characteristics.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a method for enhancing the time-frequency aggregation of non-stationary signals, which includes collecting non-stationary signals for preprocessing; performing time-frequency analysis on the non-stationary signals and defining a weight operator; optimizing the time-frequency analysis and weight operator objective functions, constructing an amplitude modulation and frequency modulation composite signal model to optimize the time-frequency aggregation of non-stationary signals; evaluating the time-frequency aggregation of the optimized non-stationary signals and outputting them, and storing the data.

[0006] As a preferred solution of the method for enhancing the time-frequency aggregation of non-stationary signals described in the present invention, the collection of non-stationary signals for preprocessing refers to cleaning and filtering the collected non-stationary signals, supplementing missing values, and using signal filters to remove noise in the signals.

[0007] As a preferred solution of the method for enhancing the time-frequency aggregation of non-stationary signals of the present invention, wherein: the time-frequency analysis of the non-stationary signal and the definition of the weight operator refer to inputting the pre-processed non-stationary signal into the time-frequency analysis algorithm and defining the weight operator:

[0008]

[0009] Where S(f, t) is the time-frequency representation of the non-stationary signal, x(t) is the non-stationary signal in the time domain, h(t-τ) is the high-order time-frequency window function, t is the time delay parameter, τ is the time, f is the frequency, λ is the adjustment parameter, W(τ, f, λ) is the weight operator, the threshold is [0,1], f0(τ) is the instantaneous frequency of the non-stationary signal at time τ, and δ is a positive constant that controls the width of the threshold.

[0010] As a preferred solution of the method for enhancing the time-frequency aggregation of non-stationary signals of the present invention, the optimization of the time-frequency analysis and weight operator objective function comprises the following steps:

[0011] Initialize the time-frequency representation S 0 (f, t) and the weight operator W 0 (τ, f, λ), set the number of iterations N and the convergence threshold ∈ according to S 0 (f, t) and W 0 (τ, f, λ) starts iteration;

[0012] The sparsity of time-frequency representation is enhanced by updating the weight operator through a fast iterative shrinkage threshold algorithm:

[0013]

[0014] where α n is the shrinkage parameter of n iterations, which decreases gradually with the number of iterations;

[0015] Update the time-frequency representation according to the updated weight operator:

[0016]

[0017] The iterative convergence value is calculated after each iteration. If ||S n+1 (f, t)-S n (f, t)||<∈, then stop the iteration and output the final time-frequency representation S(f, t).

[0018] As a preferred solution of the method for enhancing the time-frequency aggregation of non-stationary signals of the present invention, the construction of an AM and FM composite signal model for optimizing the time-frequency aggregation of non-stationary signals includes defining a basic carrier signal c(t) and a dynamic modulation function D(t, S(f, t)):

[0019]

[0020] D(t, S(f, t))=ω*tanh(γ*M(t, S(f, t)))

[0021]

[0022] Among them A c is the carrier amplitude, f c is the carrier frequency, is the initial phase, ω and γ are adjustment coefficients, and M(t, S(f, t)) is the time-frequency characteristic measurement function;

[0023] Construct a composite signal model:

[0024] x AM (t)=[1+D(t, S(f, t))]*c(t)

[0025]

[0026] x AM-FM (t) = x AM (t)*[1+βsin(x FM (t))]

[0027] where x AM (t) is the amplitude modulation part in the composite signal model, x FM (t) is the frequency modulation part in the composite signal model, x AM-FM (t) is the output of the composite signal model, k f is the frequency shift constant, β is the adjustment parameter, and the non-stationary signal is input into the composite signal model for amplitude modulation and frequency modulation adjustment optimization.

[0028] As a preferred solution of the method for enhancing the time-frequency aggregation of non-stationary signals of the present invention, the evaluation of the time-frequency aggregation of the optimized non-stationary signal and output thereof refers to calculating the energy concentration TC1 and TC2 of the original non-stationary signal x(t) and the adjusted and optimized non-stationary signal x(t2) in the time domain to compare and evaluate the optimization effect. The smaller the energy concentration, the stronger the time-frequency aggregation of the representative signal:

[0029]

[0030] If TC1>TC2, it means that the time-frequency aggregation of the optimized non-stationary signal is enhanced, the optimized non-stationary signal is output as the final optimization result, and the optimization process and evaluation results are fed back to the composite signal model for recording;

[0031] If TC1≤TC2, it means that the time-frequency aggregation of the optimized non-stationary signal has not been enhanced. The collected non-stationary signal is re-input into the composite signal model for repeated optimization. If TC1>TC2 is evaluated within 3 optimizations, the optimized non-stationary signal is output. If TC1≤TC2 is still found after more than 3 optimizations, the training parameters of the composite signal model are adjusted for retraining. After the training is completed, the time-frequency aggregation of the non-stationary signal is enhanced and optimized again.

[0032] As a preferred solution of the method for enhancing the time-frequency aggregation of non-stationary signals described in the present invention, the data storage includes storing the collected non-stationary signals, the non-stationary signal optimization process and optimization results, the composite signal model and training parameters, the evaluation process and evaluation results, and the non-stationary signal generation data records formed by the final optimization, and generating data access records for synchronous storage when accessing the data.

[0033] A non-stationary signal time-frequency aggregation enhancement system, characterized by comprising a collection module, an analysis module, an optimization module and an evaluation module;

[0034] The collection module is used to collect non-stationary signals and pre-process the non-stationary signals before sending them to the analysis module for time-frequency analysis;

[0035] The analysis module is used to perform time-frequency analysis on the non-stationary signal and convert the signal into a time-frequency representation;

[0036] The optimization module is used to optimize the time-frequency representation of the non-stationary signal, and at the same time establish a composite signal model to reconvert the optimized time-frequency representation into an optimized non-stationary signal;

[0037] The evaluation module is used to calculate and evaluate the optimization effect of the non-stationary signal before and after optimization, take optimization processing measures and output and store the optimization results.

[0038] A computer device comprises: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the above-mentioned method for enhancing the time-frequency aggregation of non-stationary signals are implemented.

[0039] A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the above-mentioned method for enhancing the time-frequency aggregation of non-stationary signals are implemented.

[0040] The beneficial effects of the present invention are as follows: the present invention proposes a high-order weighted time-frequency sparse representation, defines a weight operator, and uses a fast iterative shrinkage threshold algorithm for optimization to establish an amplitude modulation and frequency modulation composite signal model. It has higher time-frequency aggregation and robustness for fast-changing signals, and the time-frequency results are more accurate than the theoretical instantaneous frequency. Compared with other time-frequency methods, the time-frequency aggregation and decoupling performance are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0042] Figure 1 Schematic diagram of the process of enhancing the time-frequency aggregation of non-stationary signals.

[0043] Figure 2 This is a structural diagram of the time-frequency aggregation enhancement system for non-stationary signals. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0047] Example 1

[0048] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for enhancing the time-frequency aggregation of non-stationary signals. The method for enhancing the time-frequency aggregation of non-stationary signals includes the following steps:

[0049] S1, collect non-stationary signals for preprocessing;

[0050] Specifically, collecting non-stationary signals for preprocessing means cleaning and filtering the collected non-stationary signals, supplementing missing values, and using signal filters to remove noise in the signals.

[0051] After collecting non-stationary signals, preprocessing is the crucial first step. This process includes cleaning, filtering and supplementing missing values ​​of the signal. During the signal cleaning process, we remove outliers and interference introduced by the acquisition equipment or environmental factors. These outliers may be caused by equipment failure, external interference or sensor errors, which will distort the true characteristics of the signal. Then, for the missing values ​​in the signal, we use a suitable interpolation method to fill them, such as linear interpolation or interpolation based on nearest neighbor data to ensure the integrity and continuity of the signal. The main function of the signal filter is to remove noise from the signal and improve the quality of the signal. In non-stationary signal processing, the use of filters is crucial for accurately extracting the characteristics of the signal. The filter can be low-pass, high-pass, band-pass or band-stop, depending on the type of noise that needs to be eliminated. The preprocessing stage is the basis of the present invention, which provides clear and accurate signal input for subsequent time-frequency analysis and signal optimization. Through effective signal cleaning and filtering processing, the errors and uncertainties in subsequent analysis can be greatly reduced, thereby improving the accuracy and reliability of the entire method.

[0052] S2, perform time-frequency analysis on non-stationary signals and define weight operators;

[0053] Specifically, performing time-frequency analysis on a non-stationary signal and defining a weight operator means inputting the pre-processed non-stationary signal into the time-frequency analysis algorithm and defining a weight operator:

[0054]

[0055] Where S(f, t) is the time-frequency representation of the non-stationary signal, which is used to convert the time domain signal into the time-frequency domain representation, x(t) is the non-stationary signal in the time domain, h(t-τ) is the high-order time-frequency window function, t is the time delay parameter, τ is the time, f is the frequency, λ is the adjustment parameter, W(τ, f, λ) is the weight operator, the threshold is [0,1], which is used to adjust the importance or priority of the time-frequency representation, f0(τ) is the instantaneous frequency of the non-stationary signal at time τ, and δ is a positive constant that controls the threshold width.

[0056] Time-frequency representation can reveal the frequency characteristics of signals at different time points, which is crucial for analyzing and processing non-stationary signals. High-order time-frequency window functions are a commonly used mathematical tool in the field of signal processing and spectrum analysis. They are usually used to analyze the characteristics of signals in the time and frequency domains, especially in the processing of non-stationary signals, such as audio signals, speech signals, images, etc. Common high-order time-frequency window functions include Gabor window functions, Morlet window functions, linear frequency modulation window functions, and Hilbert-Huang transform window functions. The choice of window function depends on the application requirements and the nature of the signal. In the present invention, linear frequency modulation window functions are preferably used. By defining appropriate weight operators, we can emphasize or reduce the importance of specific components in the time-frequency representation, adjust the weight operators and time-frequency representation, and make the results of time-frequency analysis more accurate and reliable. This optimization process is crucial to improving the accuracy and efficiency of signal processing, especially in complex or rapidly changing signals.

[0057] S3, optimize the time-frequency analysis and weight operator objective function, build an amplitude modulation and frequency modulation composite signal model to optimize the time-frequency aggregation of non-stationary signals;

[0058] Specifically, optimizing the time-frequency analysis and weight operator objective function includes the following steps:

[0059] Initialize the time-frequency representation S 0 (f, t) and the weight operator W 0 (τ, f, λ), set the number of iterations N and the convergence threshold ∈ according to S 0 (f, t) and W 0 (τ, f, λ) starts iteration;

[0060] The sparsity of time-frequency representation is enhanced by updating the weight operator through a fast iterative shrinkage threshold algorithm:

[0061]

[0062] where α n is the shrinkage parameter of n iterations, which gradually decreases with the number of iterations, and the weight operator is continuously updated as the number of iterations increases;

[0063] Update the time-frequency representation according to the updated weight operator:

[0064]

[0065] The iterative convergence value is calculated after each iteration. If ||S n+1 (f, t)-S n (f, t)||<∈, then stop the iteration and output the final time-frequency representation S(f, t).

[0066] Initialize the time-frequency representation S 0 (f, t) and the weight operator W0 (τ, f, λ) and set the number of iterations N and the convergence threshold ∈, determine the number of iterations and the stopping condition, ensure the efficiency of the algorithm and the accuracy of the results, and lay the foundation for the optimization process. Reasonable initialization and parameter setting are the key to successful optimization, which can ensure the effectiveness and efficiency of the algorithm. By dynamically adjusting the weight operator to continuously optimize the sparsity of the time-frequency representation, the accuracy of signal processing is improved, especially when processing signals in complex or noisy environments. It can significantly improve the identifiability and analysis quality of the signal, enhance the adaptability and flexibility of the algorithm, and enable it to effectively cope with various complex signal environments. This method is of great significance for improving the quality of signals and extracting key features of signals, and is especially suitable for fields that require high-precision signal processing.

[0067] Furthermore, the AM and FM composite signal model is constructed to optimize the time-frequency aggregation of non-stationary signals, including defining the basic carrier signal c(t) and the dynamic modulation function D(t, S(f, t)):

[0068] The carrier signal is usually a sine wave:

[0069]

[0070] The dynamic modulation function is defined based on the time-frequency characteristic measurement function:

[0071] D(t, S(f, t))=ω*tanh(γ*M(t, S(f, t)))

[0072]

[0073] Among them A c is the carrier amplitude, f c is the carrier frequency, is the initial phase, ω and γ are adjustment coefficients, M(t, S(f, t)) is the time-frequency characteristic measurement function, which reflects the frequency distribution energy density of the signal;

[0074] Construct a composite signal model:

[0075] The amplitude modulated signal carries information by changing the amplitude of the carrier, which is expressed as:

[0076] x AM (t)=[1+D(t, S(f, t))]*c(t)

[0077] FM signals carry information by changing the frequency of the carrier wave, expressed as:

[0078]

[0079] x AM-FM (t) = x AM(t)*[1+βsin(x FM (t))]

[0080] where x AM (t) is the amplitude modulation part in the composite signal model, x FM (t) is the frequency modulation part in the composite signal model, x AM-FM (t) is the output of the composite signal model, k f is the frequency shift constant, β is the adjustment parameter, and the non-stationary signal is input into the composite signal model for amplitude modulation and frequency modulation adjustment optimization.

[0081] Through the D(t, S(f, t)) function, the modulation parameters can be dynamically adjusted according to the time-frequency characteristics of the signal, so that the composite signal model is more adapted to the actual characteristics of the signal. The tanh function is used to ensure the smooth change of the modulation parameters, avoid mutations, and improve the stability and quality of the signal. Through M(t, S(f, t)), the time-frequency characteristics of the signal are fully utilized. Especially when processing complex or rapidly changing signals, the amplitude modulation and frequency modulation parameters are dynamically adjusted according to the results of the time-frequency sparse representation, so that the modulation of the signal is more adapted to its time-frequency characteristics. By combining the amplitude modulation and frequency modulation signals in a nonlinear way, a new composite signal construction method is provided, which not only increases the complexity of the model, but also improves the flexibility and effect of signal processing. The time-frequency sparse representation of the signal is fully utilized, so that the composite signal model is more in line with the characteristics of the actual signal, especially when processing rapidly changing signals.

[0082] S4, evaluating the time-frequency aggregation of the optimized non-stationary signal and outputting it, and storing the data;

[0083] Specifically, evaluating the time-frequency aggregation of the optimized non-stationary signal and outputting it means calculating the energy concentration TC1 and TC2 of the original non-stationary signal x(t) and the adjusted and optimized non-stationary signal x(t2) in the time domain to compare and evaluate the optimization effect. The smaller the energy concentration, the stronger the time-frequency aggregation of the signal:

[0084]

[0085] If TC1>TC2, it means that the time-frequency aggregation of the optimized non-stationary signal is enhanced, the optimized non-stationary signal is output as the final optimization result, and the optimization process and evaluation results are fed back to the composite signal model for recording;

[0086] If TC1≤TC2, it means that the time-frequency aggregation of the optimized non-stationary signal has not been enhanced. The collected non-stationary signal is re-input into the composite signal model for repeated optimization. If TC1>TC2 is evaluated within 3 optimizations, the optimized non-stationary signal is output. If TC1≤TC2 is still found after more than 3 optimizations, the training parameters of the composite signal model are adjusted for retraining. After the training is completed, the time-frequency aggregation of the non-stationary signal is enhanced and optimized again.

[0087] The time domain concentration calculates the energy concentration of the signal in the time domain. The smaller the calculated value, the more concentrated the energy and the stronger the signal aggregation. The optimization effect can be quantified by calculating and comparing the energy concentration of the original non-stationary signal and the optimized signal. If the optimized non-stationary signal shows stronger time-frequency aggregation (i.e., reduced energy concentration), it is output as a valid optimization result, and the evaluation result is fed back to the composite signal model. If the time-frequency aggregation of the optimized signal does not show improvement, the efficiency and effect of the signal processing method can be continuously improved through repeated optimization and model parameter adjustment. Through effective evaluation and optimization mechanisms, the present invention can improve the signal recognition and analysis capabilities, and bring innovative solutions to the field of non-stationary signal processing.

[0088] Furthermore, storing the data includes storing the collected non-stationary signals, non-stationary signal optimization process and optimization results, composite signal models and training parameters, evaluation process and evaluation results, and the non-stationary signal generation data records formed by the final optimization, and generating data access records for synchronous storage when accessing the data.

[0089] Recording the entire optimization process and results provides a basis for subsequent analysis and verification. Such data storage is essential for understanding the effects of each step, adjusting method parameters, and optimizing the entire processing flow. Storing this information helps to recall or further optimize the model in future applications, especially when gradually improving the model or adapting to new types of non-stationary signals. These records provide necessary references. By recording the evaluation process and results, we can better understand the effectiveness of the optimization strategy and provide a basis for future improvements. This not only helps to track data usage, but also provides support for data security and management. By creatively combining data storage with non-stationary signal processing, the transparency, repeatability, and innovation of non-stationary signal processing technology are significantly improved.

[0090] Example 2

[0091] Reference Figure 2 , which is the second embodiment of the present invention, and which is different from the previous embodiment, provides a system for enhancing the time-frequency aggregation of non-stationary signals, characterized in that it includes a collection module, an analysis module, an optimization module and an evaluation module;

[0092] The collection module is used to collect non-stationary signals and pre-process the non-stationary signals before sending them to the analysis module for time-frequency analysis;

[0093] The analysis module is used to perform time-frequency analysis on the non-stationary signal and convert the signal into a time-frequency representation;

[0094] The optimization module is used to optimize the time-frequency representation of the non-stationary signal, and at the same time establish a composite signal model to reconvert the optimized time-frequency representation into an optimized non-stationary signal;

[0095] The evaluation module is used to calculate and evaluate the optimization effect of the non-stationary signal before and after optimization, take optimization processing measures and output and store the optimization results.

[0096] If the functions are 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 this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0098] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0099] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0100] Example 3

[0101] This is the third embodiment of the present invention. This embodiment is different from the previous two embodiments. In order to verify the beneficial effects of the method of the present invention, the method of the present invention is compared with the prior art to demonstrate and prove the comparison results. The comparison results are shown in Table 1 and Table 2.

[0102] Table 1: Comparison and demonstration table of the method of the present invention and the prior art

[0103]

[0104] Table 2: Comparison table of the method of the present invention and the prior art experiments

[0105]

[0106]

[0107] In summary, the method of the present invention exhibits significant advantages over the prior art, has strong adaptability, and can process various types of non-stationary signals, which makes it possible to apply it in multiple fields. By optimizing the algorithm, the data processing steps are simplified, the complexity of the operation is reduced, and the overall processing efficiency is improved. It is suitable for a wider range of practical application scenarios, showing stronger application potential and practical value than the prior art.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for enhancing the time-frequency aggregation of non-stationary signals, characterized by: include, Collect non-stationary signals for preprocessing; Perform time-frequency analysis on non-stationary signals and define weight operators; Optimize the time-frequency analysis and weight operator objective functions, and construct an AM and FM composite signal model to optimize the time-frequency aggregation of non-stationary signals; Evaluate the time-frequency aggregation of the optimized non-stationary signal and output it, and store the data; The performing time-frequency analysis on the non-stationary signal and defining the weight operator refers to inputting the pre-processed non-stationary signal into the time-frequency analysis algorithm and defining the weight operator: Where S(f, t) is the time-frequency representation of the non-stationary signal, x(t) is the non-stationary signal in the time domain, h(t-τ) is the high-order time-frequency window function, t is the time delay parameter, τ is the time, f is the frequency, λ is the adjustment parameter, W(τ, f, λ) is the weight operator, the threshold is [0,1], f0(τ) is the instantaneous frequency of the non-stationary signal at time τ, and δ is a positive constant that controls the width of the threshold; The optimization of the time-frequency analysis and weight operator objective function comprises the following steps: Initialize the time-frequency representation S 0 (f, t) and the weight operator W 0 (τ, f, λ), set the number of iterations N and the convergence threshold ∈ according to S 0 (f, t) and W 0 (τ, f, λ) starts iteration; The sparsity of time-frequency representation is enhanced by updating the weight operator through a fast iterative shrinkage threshold algorithm: where α n is the shrinkage parameter of n iterations, which decreases gradually with the number of iterations; Update the time-frequency representation according to the updated weight operator: The iterative convergence value is calculated after each iteration. If ||S n+1 (f, t)-S n (f, t)||<∈, then stop the iteration and output the final time-frequency representation S(f, t); The construction of the AM and FM composite signal model for optimizing the time-frequency aggregation of non-stationary signals includes defining a basic carrier signal c(t) and a dynamic modulation function D(t, S(f, t)): D(t, S(f, t))=ω*tanh(γ*M(t, S(f, t))) Among them A c is the carrier amplitude, f c is the carrier frequency, is the initial phase, ω and γ are adjustment coefficients, and M(t, S(f, t)) is the time-frequency characteristic measurement function; Construct a composite signal model: x AM (t)=[1+D(t,S(f,t))]*c(t) x AM-FM (t)=x AM (t)*[1+βsin(x FM (t))] where x AM (t) is the amplitude modulation part in the composite signal model, x FM (t) is the frequency modulation part in the composite signal model, x AM-FM (t) is the output of the composite signal model, k f is the frequency shift constant, β is the adjustment parameter, and the non-stationary signal is input into the composite signal model to optimize the amplitude modulation and frequency modulation; The evaluation of the time-frequency aggregation of the optimized non-stationary signal and outputting it refers to calculating the energy concentrations TC1 and TC2 of the original non-stationary signal x(t) and the adjusted and optimized non-stationary signal x(t2) in the time domain to compare and evaluate the optimization effect. The smaller the energy concentration, the stronger the time-frequency aggregation of the signal: If TC1>TC2, it means that the time-frequency aggregation of the optimized non-stationary signal is enhanced, the optimized non-stationary signal is output as the final optimization result, and the optimization process and evaluation results are fed back to the composite signal model for recording; If TC1≤TC2, it means that the time-frequency aggregation of the optimized non-stationary signal has not been enhanced. The collected non-stationary signal is re-input into the composite signal model for repeated optimization. If TC1>TC2 is evaluated within 3 optimizations, the optimized non-stationary signal is output. If TC1≤TC2 is still found after more than 3 optimizations, the training parameters of the composite signal model are adjusted for retraining. After the training is completed, the time-frequency aggregation of the non-stationary signal is enhanced and optimized again.

2. The method for enhancing the time-frequency aggregation of non-stationary signals according to claim 1, characterized in that: The collecting of non-stationary signals for preprocessing refers to cleaning and filtering the collected non-stationary signals, supplementing missing values, and using signal filters to remove noise in the signals.

3. The method for enhancing the time-frequency aggregation of non-stationary signals according to claim 2, characterized in that: The data storage includes storing the collected non-stationary signals, non-stationary signal optimization process and optimization results, composite signal models and training parameters, evaluation process and evaluation results, and the non-stationary signal generation data records formed by the final optimization, and generating data access records for synchronous storage when accessing data.

4. A system based on the method for enhancing the time-frequency aggregation of non-stationary signals according to any one of claims 1 to 3, characterized in that: Including collection module, analysis module, optimization module and evaluation module; The collection module is used to collect non-stationary signals and pre-process the non-stationary signals before sending them to the analysis module for time-frequency analysis; The analysis module is used to perform time-frequency analysis on the non-stationary signal and convert the signal into a time-frequency representation; The optimization module is used to optimize the time-frequency representation of the non-stationary signal, and at the same time establish a composite signal model to reconvert the optimized time-frequency representation into an optimized non-stationary signal; The evaluation module is used to calculate and evaluate the optimization effect of the non-stationary signal before and after optimization, take optimization processing measures and output and store the optimization results.

5. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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