Method, system and equipment for obtaining high-measurement-precision Brillouin frequency shift distribution curve

Abstract

The invention discloses a method, a system and equipment for acquiring a high-measurement-precision Brillouin frequency shift distribution curve, and relates to the technical field of distributed optical fiber sensing. The method comprises the following steps: receiving an original time domain signal, carrying out frame-by-frame fixed step length scanning on the time domain signal by adopting a window function, and carrying out Fourier transform on each frame of signal segment to obtain an original time frequency distribution curve; according to the method, a correlation coefficient optimized variational mode decomposition algorithm is adopted, denoising is carried out on the same frequency point curve in the time frequency data obtained through STFT processing, interference of noise components on the whole time frequency signal analysis result is reduced, the definition and the signal-to-noise ratio of the time frequency signal are remarkably improved, the measurement precision of the system is further enhanced, and the measurement accuracy of the system is improved. Through the denoising means, the influence of noise on the time-frequency signal processing quality can be reduced to the greatest extent while the original characteristics of the signal are maintained, and reliable technical support is provided for accurate signal analysis and system performance optimization.

Description

Technical Field

[0001] The present invention relates to the field of distributed optical fiber sensing technology, and in particular to a method, system and device for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy. Background Art

[0002] In recent years, Brillouin optical time domain reflectometry (BOTDR) has been widely used in many industries, including civil engineering, communications, power transmission, and petrochemicals. This technology uses the linear relationship between the frequency shift of Brillouin scattered light and the strain or temperature change to provide an efficient and accurate monitoring and analysis tool for these fields. In the BOTDR system, the collected time domain data needs to be converted into frequency domain data containing the Brillouin gain spectrum (BGS) through time-to-frequency conversion. At present, there are two main methods for time-to-frequency conversion: frequency scanning method and short-time Fourier transform (STFT). Since STFT can quickly process the broadband signal of the entire Brillouin scattering spectrum and the acquisition time of the broadband signal is short, the system has a fast response speed and is very suitable for engineering applications.

[0003] STFT-BOTDR technology has shown its significant advantages in strain and temperature detection in the field of distributed fiber optic sensing. Its high sensitivity to small changes makes it a powerful tool in related monitoring tasks. However, this technology faces a challenge in practical applications: the optical power generated by the spontaneous Brillouin scattering (SpBS) effect is weak, which directly affects the signal-to-noise ratio of the system and thus affects the measurement accuracy. In long-distance monitoring scenarios, this lack of signal-to-noise ratio may become a key factor limiting the widespread application of STFT-BOTDR technology. Unlike the swept-frequency BOTDR system, the latter can optimize the collected time domain data through signal processing technology, thereby improving the overall performance of the system. However, for the STFT-BOTDR system, due to its unique signal processing mechanism, the application of signal processing technology before STFT may be counterproductive, resulting in a decrease in the signal quality after STFT processing.

[0004] To this end, we propose a method, system and device for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy. Summary of the invention

[0005] The object of the present invention is to provide a method, system and device for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy, so as to solve the problems raised in the above background technology.

[0006] According to a first aspect of the present invention, in order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy, comprising the following steps:

[0007] Receive the original time domain signal, use the window function to scan the time domain signal frame by frame with a fixed step length, and perform Fourier transform on each frame signal fragment to obtain the original time-frequency distribution curve;

[0008] According to the obtained time-frequency distribution curve, the time domain data of each frequency point in the time-frequency distribution curve is extracted to form a new data set;

[0009] Construct a VMD variational model, perform VMD decomposition on the new data set, and obtain a series of intrinsic mode components;

[0010] Calculate the correlation coefficient between each eigenmode component and the original time domain distribution curve;

[0011] Set the threshold of the correlation coefficient, compare the calculated correlation coefficient with the threshold, discard the part with a correlation coefficient smaller than the threshold, and superimpose and reorganize the part larger than the threshold to obtain a denoised signal;

[0012] The frequency point with the largest peak value in the frequency domain curve corresponding to each time point is extracted, and the Brillouin frequency shift distribution curve is obtained by arranging and combining them in time sequence.

[0013] Furthermore, the original time domain signal is received, the time domain signal is scanned frame by frame with a fixed step length using a window function, and Fourier transform is performed on each frame signal fragment to obtain the original time-frequency distribution curve, which is as follows:

[0014] (1) For the time domain signal acquired by the data acquisition card, a window function with an equal interval step size and a fixed time width is used for segmentation. Fourier transform is performed on the signal fragments within each window function to obtain the corresponding spectrum information, and the information is arranged in a time series to construct a time-frequency domain matrix. In this matrix, the horizontal axis represents the time dimension and the vertical axis represents the frequency dimension. It is used to describe the energy distribution of the signal at different time points and frequency points. The specific expression is as follows:

[0015]

[0016] Among them, STFT(t,f) is the result of short-time Fourier transform, which is a function of time t and frequency f, usually called time-frequency spectrum; x(τ) is the original signal, which is a function of time τ; ω(τ-t) is the window function, which is a function of time τ-t and is used to localize the signal. The window function is non-zero near time t, and its shape and size determine the time resolution and frequency resolution of the result of short-time Fourier transform;

[0017] (2) By identifying the energy peak in each window function spectrum, the Brillouin frequency shift distribution curve is constructed to achieve efficient characterization of signal characteristics.

[0018] Furthermore, a VMD variational model is constructed, and VMD decomposition is performed on the new data set to obtain a series of intrinsic mode components, as follows:

[0019] (1) Set constraints, specifically:

[0020]

[0021] Among them, IMF k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, f is the original signal, and k is the number of intrinsic mode functions obtained by VMD decomposition;

[0022] (2) Construct the VMD variational model as follows:

[0023]

[0024] Among them, ωk={ω1,ω2,...,ω k} is the center frequency of each modal component, δ(t) is the unit impulse function, j is the imaginary unit, * represents convolution, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition;

[0025] (3) Using the quadratic penalty factor and the Lagrangian operator, the augmented Lagrangian function is introduced to solve the above VMD variational model, as follows:

[0026]

[0027] Among them, β is the quadratic penalty factor used to constrain noise, λ is the Lagrangian operator, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition, * represents convolution, ω k ={ω1,ω2,...,ω k} is the center frequency of each modal component;

[0028] (4) Use the alternating multiplier direction method to calculate the imf in equation (4)k ,ω k And λ are dynamically updated alternately and iteratively, as follows:

[0029]

[0030] Among them, IMF k (ω), f(ω) and λ(ω) are imf k (t), the Fourier transform of f(t) and λ(t), n is the number of iterations, γ is the noise tolerance, i is the summation variable, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition, * represents convolution, ω k ={ω1,ω2,...,ω k} is the center frequency of each modal component;

[0031] (5) Repeat equation (5) to equation (7) until the iteration constraint is satisfied, as shown below:

[0032]

[0033] Where ε is the convergence parameter, imf(ω) is the Fourier transform of the intrinsic mode function imf(t);

[0034] Furthermore, the correlation coefficient between each intrinsic mode component and the original time domain distribution curve is calculated as follows:

[0035]

[0036] Among them, x i and i are the data points of the original signal and IMF, u x and u y is the mean of the original signal and the IMF, σ x and σ y is the standard deviation of the original signal and the IMF, and n is the number of data points.

[0037] Furthermore, the correlation coefficient has a value range of -1 to 1, and the larger the absolute value of the correlation coefficient is, the higher the similarity between the intrinsic mode component and the original signal is.

[0038] According to a second aspect of the present invention, the present invention provides a system for obtaining a high-precision Brillouin frequency shift distribution curve, comprising:

[0039] The receiving module is used to receive the original time domain signal, scan the time domain signal frame by frame with a fixed step length using a window function, and perform Fourier transform on each frame of the signal fragment to obtain the original time-frequency distribution curve;

[0040] An extraction module is used to extract the time domain data of each frequency point in the time-frequency distribution curve according to the obtained time-frequency distribution curve to form a new data set;

[0041] A construction module is used to construct a VMD variational model, perform VMD decomposition on the composed new data set, and obtain a series of intrinsic mode components;

[0042] A calculation module, used for calculating the correlation coefficient between each intrinsic mode component and the original time domain distribution curve;

[0043] The comparison and screening module is used to set the threshold of the correlation coefficient, compare the calculated correlation coefficient with the threshold, discard the part with a small correlation coefficient, and superimpose and reorganize the part larger than the threshold to obtain a denoised signal;

[0044] The extraction and arrangement module is used to extract the frequency point with the largest peak value in the frequency domain curve corresponding to each time point, and arrange and combine them in time sequence to obtain the Brillouin frequency shift distribution curve.

[0045] According to a third aspect of the present invention, the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned method for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy is adopted.

[0046] According to a fourth aspect of the present invention, the present invention provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned method for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy.

[0047] The present invention has at least the following beneficial effects:

[0048] The present invention adopts a variational mode decomposition algorithm with optimized correlation coefficient to denoise the same-frequency point curves in the time-frequency data obtained by STFT processing, thereby reducing the interference of noise components on the entire time-frequency signal analysis results, significantly improving the clarity and signal-to-noise ratio of the time-frequency signal, and further enhancing the measurement accuracy of the system. Through this denoising method, it is possible to minimize the impact of noise on the quality of time-frequency signal processing while maintaining the original characteristics of the signal, providing reliable technical support for accurate signal analysis and system performance optimization.

[0049] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the process of the method proposed in the present invention;

[0051] Figure 2 It is a schematic diagram showing a time domain curve of the same frequency point in the time-frequency signal of the present invention;

[0052] Figure 3 This is a schematic diagram of a time domain curve of the same frequency point of the present invention;

[0053] Figure 4 This is a schematic diagram of IMF after VMD decomposition of the time domain curve at the same frequency point of the present invention;

[0054] Figure 5 It is a schematic diagram of the correlation coefficient between each IMF and the original signal in the present invention;

[0055] Figure 6 This is a schematic diagram of the comparison of the time domain curves of the same frequency point before and after denoising of the present invention;

[0056] Figure 7 This is a schematic diagram of the overall BFS distribution curve after denoising in the present invention;

[0057] Figure 8 This is a schematic diagram of the comparison of the overall BFS distribution curve before and after denoising of the present invention;

[0058] Fig. 9 Schematic diagram of the comparison of BFS distribution curves in the heating area before and after denoising in the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0060] See also Figure 1-Figure 9 The present invention provides a technical solution: a method for obtaining a high-precision Brillouin frequency shift distribution curve, comprising the following steps:

[0061] S1. Receive the original time domain signal, use the window function to scan the time domain signal frame by frame with a fixed step length, and perform Fourier transform on each frame signal fragment to obtain the original time-frequency distribution curve, as follows:

[0062] Receive the original time domain signal, use the window function to scan the time domain signal frame by frame with a fixed step length, and perform Fourier transform on each frame signal fragment to obtain the original time-frequency distribution curve, as follows:

[0063] (S1.1) The spectrum data corresponding to the window function of fixed time width are arranged in time series to construct a time-frequency domain matrix. In this matrix, the horizontal axis represents the time dimension and the vertical axis represents the frequency dimension. It is used to describe the energy distribution of the signal at different time points and frequency points. The specific expression is as follows:

[0064]

[0065] Among them, STFT(t,f) is the result of short-time Fourier transform, which is a function of time t and frequency f, usually called time-frequency spectrum; x(τ) is the original signal, which is a function of time τ; ω(τ-t) is the window function, which is a function of time τ-t and is used to localize the signal. The window function is non-zero near time t, and its shape and size determine the time resolution and frequency resolution.

[0066] (S1.2) By identifying the energy peak in each window function spectrum, a Brillouin frequency shift (BFS) distribution curve is constructed to achieve efficient characterization of signal characteristics;

[0067] It should be noted that the STFT-BOTDR technology uses a window function with a fixed time width to scan the acquired time domain signal frame by frame with a fixed step length, and performs Fourier transform on each frame of the signal fragment to extract frequency domain feature information. This spectrum is called BGS.

[0068] S2. According to the obtained time-frequency distribution curve, extract the time domain data of each frequency point in the time-frequency distribution curve to form a new data set;

[0069] S3. Construct a VMD variational model and perform VMD decomposition on the new data set to obtain a series of intrinsic mode components, as follows:

[0070] (S3.1) Set constraints, specifically:

[0071]

[0072] Among them, IMF k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, f is the original signal, and k is the number of intrinsic mode functions obtained by VMD decomposition;

[0073] (S3.2) Construct a VMD variational model as follows:

[0074]

[0075] Among them, ω k={ω1,ω2,...,ω k} is the center frequency of each modal component, δ(t) is the unit impulse function, j is the imaginary unit, * represents convolution, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition;

[0076] (S3.3) In order to solve the above constrained variational model, the constrained variational problem is converted into an unconstrained variational problem. The quadratic penalty factor and the Lagrangian operator are used to introduce the augmented Lagrangian function to solve the above VMD variational model, as follows:

[0077]

[0078] Among them, β is the quadratic penalty factor used to constrain noise, λ is the Lagrangian operator, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition, * represents convolution, ω k ={ω1,ω2,...,ω k} is the center frequency of each modal component;

[0079] (S3.4) Use the alternating multiplier direction method to calculate the imf in equation (4) k ,ω k And λ are dynamically updated alternately and iteratively, as follows:

[0080]

[0081] Among them, IMF k (ω), f(ω) and λ(ω) are imf k (t), the Fourier transform of f(t) and λ(t), n is the number of iterations, γ is the noise tolerance, i is the summation variable, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k} is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition, * represents convolution, ω k ={ω1,ω2,...,ω k} is the center frequency of each modal component;

[0082] (S3.5) Repeat equation (5) to equation (7) until the iteration constraint is satisfied, as shown below:

[0083]

[0084] Where ε is the convergence parameter, imf(ω) is the Fourier transform of the intrinsic mode function imf(t);

[0085] It should be noted that VMD is a signal decomposition algorithm that adaptively decomposes a signal into a series of intrinsic mode components (IMFs) with limited bandwidth. Under the constraint that the sum of all modes is equal to the original signal, the constraint is shown in formula (2). The optimal solution of the variational model is solved through an iterative optimization process to determine the center frequency of each intrinsic mode component and its corresponding bandwidth. Figure 3 and Figure 4 The curve of a single frequency point in the time-frequency signal in the time domain after STFT processing and the result after VMD decomposition are shown;

[0086] S4. Calculate the correlation coefficient between each eigenmode component and the original time domain distribution curve, as follows:

[0087]

[0088] Among them, x i and i are the data points of the original signal and IMF, u x and u y is the mean of the original signal and the IMF, σ x and σ y is the standard deviation of the original signal and the IMF, and n is the number of data points;

[0089] S5. Set a threshold value of the correlation coefficient, compare the calculated correlation coefficient with the threshold value, discard the part with a correlation coefficient smaller than the threshold value, and superimpose and reorganize the part with a correlation coefficient larger than the threshold value to obtain a denoised signal;

[0090] Specifically, in order to achieve the purpose of denoising, it is necessary to screen the multiple intrinsic mode components (IMFs) obtained by VMD decomposition. This process is completed by calculating the correlation coefficient between each IMF and the original signal. The correlation coefficient is an indicator to measure the similarity between two signals. It is expressed by formula (9) and its value range is between -1 and 1. The larger the absolute value of the correlation coefficient, the higher the similarity between the IMF and the original signal. Therefore, it can be considered that these IMFs carry more useful information. The correlation coefficient between each IMF and the original signal is as follows: Figure 4As shown in the figure, by setting a threshold of correlation coefficient, those IMFs whose absolute values ​​of correlation coefficient are lower than the threshold are deleted, and those IMFs whose absolute values ​​of correlation coefficient are higher than the threshold are retained. Finally, these retained IMFs are superimposed and recombined to obtain the denoised signal. The signal comparison before and after denoising is as follows: Figure 6 As shown;

[0091] S6. extracting the frequency point with the largest peak value in the frequency domain curve corresponding to each time point, and arranging and combining them in time sequence to obtain a Brillouin frequency shift distribution curve;

[0092] After denoising each time domain curve with the same frequency point, the denoised time-frequency signal can be obtained, and then the frequency corresponding to each BGS peak power is extracted to construct the BFS curve, such as Figure 7 shown.

[0093] The comparison effect before and after the signal processing method of this embodiment is as follows: Figure 8 and Fig. 9 As shown in the figure, the data processing results show that the standard deviation of the BFS normal temperature data is reduced from 3.6885MHz to 2.1510MHz. Correspondingly, the measurement accuracy is improved from the original ±1.4187℃ to ±0.82731℃, achieving a temperature measurement accuracy improvement of about 41.69%. Fig. 9 It can be seen that this method does not affect the spatial resolution of the heating section;

[0094] In summary, the present embodiment aims to implement denoising processing on the time domain signal corresponding to the fixed frequency point in the time-frequency signal analysis. The core of the method described in the present embodiment is to effectively suppress the noise of the time domain signal of the specific frequency component to reduce the interference of the noise component on the entire time-frequency signal analysis result. This significantly improves the clarity and signal-to-noise ratio of the time-frequency signal, thereby enhancing the measurement accuracy of the system. Through this denoising method, the present invention can minimize the impact of noise on the quality of time-frequency signal processing while maintaining the original characteristics of the signal, and provide reliable technical support for accurate signal analysis and system performance optimization.

[0095] Embodiment 2:

[0096] The present invention provides a system for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy, which specifically includes:

[0097] The receiving module is used to receive the original time domain signal, scan the time domain signal frame by frame with a fixed step length using a window function, and perform Fourier transform on each frame of the signal fragment to obtain the original time-frequency distribution curve;

[0098] An extraction module is used to extract the time domain data of each frequency point in the time-frequency distribution curve according to the obtained time-frequency distribution curve to form a new data set;

[0099] A construction module is used to construct a VMD variational model, perform VMD decomposition on the composed new data set, and obtain a series of intrinsic mode components;

[0100] A calculation module, used for calculating the correlation coefficient between each intrinsic mode component and the original time domain distribution curve;

[0101] A comparison and screening module is used to set a threshold value of the correlation coefficient, compare the calculated correlation coefficient with the threshold value, discard the part with a correlation coefficient smaller than the threshold value, and superimpose and reorganize the part with a correlation coefficient larger than the threshold value to obtain a denoised signal;

[0102] The extraction and arrangement module is used to extract the frequency point with the largest peak value in the frequency domain curve corresponding to each time point, and arrange and combine them in time sequence to obtain the Brillouin frequency shift distribution curve.

[0103] Specifically, the above-mentioned receiving module, extraction module, construction module, calculation module, comparison and screening module and extraction and arrangement module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of obtaining the high measurement accuracy Brillouin frequency shift distribution curve according to the above-mentioned method for obtaining the high measurement accuracy Brillouin frequency shift distribution curve; the above-mentioned receiving module, extraction module, construction module, calculation module, comparison and screening module and extraction and arrangement module can perform operations according to the specific steps given in the method for obtaining the high measurement accuracy Brillouin frequency shift distribution curve.

[0104] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the receiving module can be a separately established processing element, or it can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and a certain processing element of the above device calls and executes the functions of the above signal processing module. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0105] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0106] Embodiment three:

[0107] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned method for obtaining a Brillouin frequency shift distribution curve with high measurement accuracy is adopted.

[0108] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses.

[0109] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and the present application does not impose any restrictions on this.

[0110] Embodiment 4:

[0111] The present invention provides a storage medium containing computer executable instructions, characterized in that the computer executable instructions are used to execute the above-mentioned method for obtaining a high measurement accuracy Brillouin frequency shift distribution curve when executed by a computer processor.

[0112] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0113] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0114] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0116] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does 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 more embodiments or examples in a suitable manner.

Claims

1. A method for obtaining a high-precision Brillouin frequency shift distribution curve, characterized in that: The following steps are involved: Receive the original time domain signal, use the window function to scan the time domain signal frame by frame with a fixed step length, and perform Fourier transform on each frame signal fragment to obtain the original time-frequency distribution curve; According to the obtained time-frequency distribution curve, the time domain data of each frequency point in the time-frequency distribution curve is extracted to form a new data set; Construct a VMD variational model, perform VMD decomposition on the new data set, and obtain a series of intrinsic mode components; Calculate the correlation coefficient between each eigenmode component and the original time domain distribution curve; Set the threshold of the correlation coefficient, compare the calculated correlation coefficient with the threshold, discard the part with a correlation coefficient smaller than the threshold, and superimpose and reorganize the part larger than the threshold to obtain a denoised signal; The frequency point with the largest peak value in the frequency domain curve corresponding to each time point is extracted, and the Brillouin frequency shift distribution curve is obtained by arranging and combining them in time sequence.

2. The method for obtaining a high-precision Brillouin frequency shift distribution curve according to claim 1, characterized in that: Receive the original time domain signal, use the window function to scan the time domain signal frame by frame with a fixed step length, and perform Fourier transform on each frame signal fragment to obtain the original time-frequency distribution curve, as follows: (1) For the time domain signal acquired by the data acquisition card, a window function with an equal interval step size and a fixed time width is used for segmentation. Fourier transform is performed on the signal fragments within each window function to obtain the corresponding spectrum information, and the information is arranged in a time series to construct a time-frequency domain matrix. In this matrix, the horizontal axis represents the time dimension and the vertical axis represents the frequency dimension. It is used to describe the energy distribution of the signal at different time points and frequency points. The specific expression is as follows: Among them, STFT(t,f) is the result of short-time Fourier transform, which is a function of time t and frequency f, usually called time-frequency spectrum; x(τ) is the original signal, which is a function of time τ; ω(τ-t) is the window function, which is a function of time τ-t and is used to localize the signal. The window function is non-zero near time t, and its shape and size determine the time resolution and frequency resolution of the result of short-time Fourier transform; (2) By identifying the energy peak in each window function spectrum, the Brillouin frequency shift distribution curve is constructed to achieve efficient characterization of signal characteristics.

3. The method for obtaining a high-precision Brillouin frequency shift distribution curve according to claim 1, characterized in that: Construct a VMD variational model, perform VMD decomposition on the new data set, and obtain a series of intrinsic mode components, as follows: (1) Set constraints, specifically: Among them, IMF k ={imf1,imf2,...,imf k } is the k intrinsic mode functions obtained by VMD decomposition, f is the original signal, and k is the number of intrinsic mode functions obtained by VMD decomposition; (2) Construct the VMD variational model as follows: Among them, ωk={ω1,ω2,...,ω k } is the center frequency of each modal component, δ(t) is the unit impulse function, j is the imaginary unit, * represents convolution, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k } is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition; (3) Using the quadratic penalty factor and the Lagrangian operator, the augmented Lagrangian function is introduced to solve the above VMD variational model, as follows: Among them, β is the quadratic penalty factor used to constrain noise, and λ is the Lagrangian operator; Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k } is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition, * represents convolution, ω k ={ω1,ω2,...,ω k } is the center frequency of each modal component; (4) Use the alternating multiplier direction method to calculate the imf in equation (4) k ,ω k And λ are dynamically updated alternately and iteratively, as follows: Among them, IMF k (ω), f(ω) and λ(ω) are imf k (t), the Fourier transform of f(t) and λ(t), n is the number of iterations, γ is the noise tolerance; i is the summation variable, Indicates the first-order partial derivative of variable t, imf k ={imf1,imf2,...,imf k } is the k intrinsic mode functions obtained by VMD decomposition, k is the number of intrinsic mode functions obtained by VMD decomposition, * represents convolution, ω k ={ω1,ω2,...,ω k } is the center frequency of each modal component; (5) Repeat equation (5) to equation (7) until the iteration constraint is satisfied, as shown below: Where ε is the convergence parameter and imf(ω) is the Fourier transform of the intrinsic mode function imf(t).

4. The method for obtaining a high-precision Brillouin frequency shift distribution curve according to claim 3, characterized in that: Calculate the correlation coefficient between each eigenmode component and the original time domain distribution curve as follows: Among them, x i and i are the data points of the original signal and IMF, u x and u y is the mean of the original signal and the IMF, σ x and σ y is the standard deviation of the original signal and the IMF, and n is the number of data points.

5. The method for obtaining a high-precision Brillouin frequency shift distribution curve according to claim 4, characterized in that: The correlation coefficient has a value range of -1 to 1. The larger the absolute value of the correlation coefficient is, the higher the similarity between the intrinsic mode component and the original signal is.

6. A system for obtaining a high-precision Brillouin frequency shift distribution curve, characterized in that: include: The receiving module is used to receive the original time domain signal, scan the time domain signal frame by frame with a fixed step length using a window function, and perform Fourier transform on each frame of the signal fragment to obtain the original time-frequency distribution curve; An extraction module is used to extract the time domain data of each frequency point in the time-frequency distribution curve according to the obtained time-frequency distribution curve to form a new data set; A construction module is used to construct a VMD variational model, perform VMD decomposition on the composed new data set, and obtain a series of intrinsic mode components; A calculation module, used for calculating the correlation coefficient between each intrinsic mode component and the original time domain distribution curve; A comparison and screening module is used to set a threshold value of the correlation coefficient, compare the calculated correlation coefficient with the threshold value, discard the part with a correlation coefficient smaller than the threshold value, and superimpose and reorganize the part with a correlation coefficient larger than the threshold value to obtain a denoised signal; The extraction and arrangement module is used to extract the frequency point with the largest peak value in the frequency domain curve corresponding to each time point, and arrange and combine them in time sequence to obtain the Brillouin frequency shift distribution curve.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the method for obtaining a high-measurement-precision Brillouin frequency shift distribution curve according to any one of claims 1 to 5 is adopted.

8. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, the computer executable instructions are used to perform the method for obtaining a high measurement accuracy Brillouin frequency shift distribution curve according to any one of claims 1 to 5.

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