Brillouin scattering spectrum center frequency extraction method and optical fiber strain sensing system

Abstract

The invention provides a Brillouin scattering spectrum center frequency extraction method and an optical fiber strain sensing system, and the method comprises the steps: obtaining discrete Brillouin scattering spectrum data; performing interpolation on the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data; performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data by using the weighted weight to obtain a frequency weighted sum; and acquiring the center frequency of the Brillouin scattering spectrum data based on the weight sum of the weighted weights and the frequency weighted sum. According to the scheme, through combination of interpolation and amplitude weighting, the method has high adaptability to shape change of the Brillouin spectrum, and improvement of the center frequency extraction stability of the Brillouin scattering spectrum center frequency extraction method is facilitated.

Description

Technical Field

[0001] The present application relates to the field of optical fiber sensing technology, and in particular to a method for extracting the center frequency of a Brillouin scattering spectrum and an optical fiber strain sensing system. Background Art

[0002] Stimulated Brillouin Scattering (SBS) is a nonlinear process that occurs in optical fiber transmission. When the pump light power input into an optical fiber reaches the SBS threshold, the electrostrictive effect causes the density and dielectric constant of the optical fiber medium to undergo periodic variations, generating an acoustic wave field. When the pump light is incident on this moving acoustic wave field, backscattered light, known as Stokes light, is generated. The frequency difference between the Stokes light and the pump light is the Brillouin frequency shift. The amount of the Brillouin frequency shift is determined not only by the material properties of the optical fiber itself but also by the temperature and stress to which the fiber is exposed.

[0003] Currently, most related technologies use fitting algorithms (such as single-peak fitting algorithms) to obtain the center frequency of the Brillouin spectrum and thus calculate the Brillouin frequency shift. However, in practical applications, noise is superimposed on the Brillouin gain spectrum data, and the center frequency extraction schemes used in related technologies have poor noise robustness. Even slightly large fluctuations in the data values ​​near the peak of the acquired Brillouin spectrum can lead to drastic changes in the center frequency fitting results, ultimately degrading the fitting stability. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method for extracting the center frequency of a Brillouin scattering spectrum and an optical fiber strain sensing system to solve the above-mentioned technical problems.

[0005] In a first aspect, an embodiment of the present application provides a method for extracting the center frequency of a Brillouin scattering spectrum, comprising: obtaining discrete Brillouin scattering spectrum data; interpolating the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data; performing a weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights to obtain a frequency-weighted sum; wherein the weighted weight of each data point in the incremental Brillouin scattering spectrum data is proportional to the amplitude of the data point; and obtaining the center frequency of the Brillouin scattering spectrum data based on the weighted sum of the weighted weights and the frequency-weighted sum.

[0006] In the implementation process of the above scheme, the center frequency is extracted by combining interpolation and amplitude weighting, so that the extraction method has high adaptability to the shape changes of the Brillouin spectrum, which is beneficial to improving the center frequency extraction stability of the above Brillouin scattering spectrum center frequency extraction method; on the other hand, interpolation can increase the resolution of the data, making the details of the Brillouin scattering spectrum richer, helping to more accurately locate the spectrum peak, reduce the frequency estimation error caused by insufficient data points, and improve the accuracy of center frequency extraction; on the other hand, the weighted weight is proportional to the amplitude. By assigning higher weights to points with larger amplitudes, the calculation of the center frequency focuses more on the effective signal part, which is beneficial to improving the anti-noise ability of the above Brillouin scattering spectrum center frequency extraction method; on the other hand, the interpolation and amplitude weighting calculation process is relatively simple, with small computational complexity, and is suitable for real-time processing and large-scale data processing.

[0007] In an implementation of the first aspect, performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights to obtain a frequency-weighted sum includes: Obtaining the spectral density of each data point in the incremental Brillouin scattering spectrum data; The spectral density is used as a weighted weight to perform weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data to obtain a frequency weighted sum.

[0008] In the implementation process of the above scheme, interpolation and spectral density weighting are used to extract the center frequency of the Brillouin scattering spectrum. Spectral density weighting can give higher weights to points with larger amplitudes, so that the calculation of the center frequency focuses more on the main part of the spectrum line. In the case of double peaks or multiple peaks, even if there are multiple peaks, spectral density weighting can ensure that the calculation of the center frequency is more inclined to the position of the main spectral peak, thereby improving the stability and accuracy of the center frequency extraction. On the other hand, compared with the method of fitting to obtain the center frequency in related technologies, the above scheme can quickly obtain the center frequency through simple linear interpolation and spectral density weighted calculation, which is suitable for real-time processing and large-scale data processing.

[0009] In an implementation of the first aspect, before performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights, the method further includes: performing denoising processing on the incremental Brillouin scattering spectrum data; The step of performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights includes performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data after denoising using weighted weights.

[0010] In the implementation process of the above scheme, denoising can effectively remove noise points in low signal-to-noise ratio areas, reduce the influence of outliers and interference signals, and make the data purer, thereby providing a more reliable basis for subsequent weighted calculations, which is conducive to improving the data quality of incremental Brillouin scattering spectrum data, thereby improving the accuracy of center frequency extraction; on the other hand, in the denoised data, the characteristics of the effective signal are more prominent, and the amplitude distribution is more realistic. When frequency-weighted summation is performed using weighted weights, the contribution of the main spectral peaks can be more accurately reflected, avoiding the interference of noise on the results, thereby improving the accuracy of center frequency calculation; on the other hand, the denoised data reduces the influence of random fluctuations and outliers, making the center frequency obtained by weighted calculation more stable, more repeatable, and the results more credible.

[0011] In an implementation of the first aspect, the denoising process on the incremental Brillouin scattering spectrum data includes: obtaining a maximum amplitude of the Brillouin scattering spectrum data; obtaining a selected point threshold based on the maximum amplitude and a preset proportional coefficient; and denoising the incremental Brillouin scattering spectrum data based on the selected point threshold.

[0012] In the implementation of the above scheme, the maximum amplitude reflects the strongest signal in the data set. When combined with the preset scaling coefficient, the point selection threshold can effectively distinguish between signal and noise, remove noise points in low signal-to-noise ratio areas, improve data quality, and thus improve the accuracy of center frequency extraction. On the other hand, linear operations based on the maximum amplitude and preset scaling coefficient are easy to implement, have low requirements on computing resources, do not rely on complex mathematical models or assumptions, and are suitable for engineering and real-time applications.

[0013] In an implementation of the first aspect, the denoising process on the incremental Brillouin scattering spectrum data includes: obtaining a maximum amplitude of the Brillouin scattering spectrum data, a first amplitude corresponding to a minimum frequency point, and a second amplitude corresponding to a maximum frequency point; obtaining a point selection threshold based on the first amplitude, the second amplitude, the maximum amplitude, and a preset proportional coefficient; wherein the point selection threshold is the maximum value of the first amplitude, the second amplitude, and the product of the maximum amplitude and the preset proportional coefficient; and denoising the incremental Brillouin scattering spectrum data based on the point selection threshold.

[0014] In the implementation process of the above scheme, the maximum amplitude, minimum frequency amplitude and maximum frequency amplitude are combined, which can more comprehensively reflect the characteristics of the Brillouin scattering spectrum data, so that the point selection threshold can more accurately reflect the actual situation of the data, thereby more effectively removing noise points. In a complex noise environment, the above scheme can better adapt to different noise distributions and interference conditions, and improve the robustness of denoising; on the other hand, by considering the amplitudes of the minimum frequency and the maximum frequency, it can avoid misjudgment caused by abnormal amplitude of a single frequency point, ensure that valid data points at the starting and end ends are not mistakenly filtered out, and can more carefully retain key details in the signal, especially for those spectral peaks with important characteristics at the starting or end ends, thereby improving data integrity.

[0015] In an implementation of the first aspect, interpolating the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data includes: Linear interpolation is performed on the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data.

[0016] In the implementation process of the above scheme, the data resolution is improved by uniformly or non-uniformly inserting new data points during the linear interpolation process. The higher data resolution helps to more accurately depict the shape of the Brillouin scattering spectrum line, especially the more accurate characterization of characteristics such as the position, width and intensity of the spectrum peak, which is beneficial to improving the accuracy of center frequency extraction. On the other hand, linear interpolation is easier to implement, which is beneficial to improving the center frequency extraction efficiency of the above Brillouin scattering spectrum center frequency extraction method.

[0017] In an implementation of the first aspect, after acquiring the center frequency of the Brillouin scattering spectrum data, the method further includes: calculating a Brillouin frequency shift of the Brillouin scattering spectrum data based on the center frequency of the Brillouin scattering spectrum data.

[0018] During the implementation of the above scheme, by accurately extracting the center frequency, the Brillouin frequency shift can be calculated more accurately, which is beneficial to improving the calculation accuracy of the Brillouin frequency. On the other hand, the calculated Brillouin frequency shift can be used in scenarios such as temperature measurement, strain measurement, and optical fiber characteristic analysis, which is beneficial to improving the adaptability of the above-mentioned Brillouin scattering spectrum center frequency extraction method.

[0019] In a second aspect, an embodiment of the present application provides a fiber optic strain sensing method, comprising: using a sensing fiber to collect discrete Brillouin scattering spectrum data; wherein the sensing fiber is installed in a structure to be monitored; Extracting a center frequency from the Brillouin scattering spectrum data; wherein the center frequency is obtained using the method provided by the first aspect or any possible implementation of the first aspect; Based on the center frequency, strain monitoring is performed on the structure to be monitored.

[0020] In a third aspect, an embodiment of the present application provides an optical fiber strain sensing system, comprising: a host computer, a light detector, and a sensing optical fiber, wherein the light detector is electrically connected to the host computer, and the sensing optical fiber is connected to the light detector, wherein: The host computer is used to obtain discrete Brillouin scattering spectrum data; interpolate the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data; use weighted weights to weighted sum the frequencies of all data points in the incremental Brillouin scattering spectrum data to obtain a frequency-weighted sum; wherein the weighted weight of each data point in the incremental Brillouin scattering spectrum data is proportional to the amplitude of the data point; and obtain the center frequency of the Brillouin scattering spectrum data based on the weighted sum of the weighted weights and the frequency-weighted sum.

[0021] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a communication bus, wherein the processor and the memory communicate with each other through the communication bus; the memory stores computer program instructions that can be executed by the processor, and when the computer program instructions are read and run by the processor, the method provided by the first aspect or any possible implementation of the first aspect or the second aspect or any possible implementation of the second aspect is executed.

[0022] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are read and run by a processor, the method provided by the first aspect or any possible implementation of the first aspect or the second aspect or any possible implementation of the second aspect is executed.

[0023] Other features and advantages of the present application will be described in the following description and, in part, will become apparent from the description or be understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0025] Figure 1A schematic diagram of a process for extracting the center frequency of a Brillouin scattering spectrum provided in an embodiment of the present application; Figure 2 A comparison chart of the fitting accuracy of the single-peak Lorentz spectrum using the single-peak Lorentz fitting scheme and the linear interpolation-spectral density weighted extraction scheme at different noise levels in the comparison scenario 1 provided in the embodiments of the present application; Figure 3 A comparison chart of the fitting accuracy of the single-peak Lorentz spectrum using the single-peak Lorentz fitting scheme and the linear interpolation-spectral density weighted extraction scheme at different noise levels in the comparison scenario 2 provided in the embodiment of the present application; Figure 4 A schematic diagram of the fitting results of the single-peak Lorentz fitting scheme for fitting single-peak data in the control scenario 1 provided in the embodiment of the present application; Figure 5 A schematic diagram of the fitting results of multimodal data using a single-peak Lorentz fitting scheme in the control scenario 2 provided in the embodiment of the present application; Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application and are therefore only examples and cannot be used to limit the scope of protection of the present application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0028] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0029] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0031] Currently, the noise in Brillouin spectrum data mainly comes from the photoelectric conversion noise of the photodetector and the random noise of the environment. These noises are random and will be superimposed on the real Brillouin spectrum signal, causing the measured signal to deviate from the true value. The effects of noise on the shape of the Brillouin spectrum include: (1) Amplitude fluctuation: The presence of noise will cause the amplitude of the Brillouin spectrum to fluctuate randomly at different frequencies. (2) Spectral line broadening or distortion: Noise may cause the spectral line of the Brillouin spectrum to broaden or distort its shape, thereby obscuring the true spectral line shape.

[0032] In the presence of noise, the fitting results obtained using the fitting algorithm may fluctuate as the noise changes, resulting in unstable fitting results.

[0033] Based on this, an embodiment of the present application provides a method for extracting the center frequency of a Brillouin scattering spectrum. The method extracts the center frequency by combining interpolation and amplitude weighting, so that the extraction method has high adaptability to changes in the shape of the Brillouin spectrum, which is beneficial to improving the center frequency extraction stability of the above-mentioned Brillouin scattering spectrum center frequency extraction method; on the other hand, interpolation can increase the resolution of the data, making the details of the Brillouin scattering spectrum richer, helping to more accurately locate the spectrum peak, reduce the frequency estimation error caused by insufficient data points, and improve the accuracy of center frequency extraction; on the other hand, the weighted weight is proportional to the amplitude. By assigning higher weights to points with larger amplitudes, the calculation of the center frequency focuses more on the effective signal part, which is beneficial to improving the anti-noise ability of the above-mentioned Brillouin scattering spectrum center frequency extraction method; on the other hand, the interpolation and amplitude weighting calculation process is relatively simple, the computational complexity is small, and it is suitable for real-time processing and large-scale data processing.

[0034] See Figure 1The flowchart of the method for extracting the center frequency of a Brillouin scattering spectrum provided by an embodiment of the present application is shown. The method for extracting the center frequency of a Brillouin scattering spectrum provided by an embodiment of the present application can be applied to electronic devices, which may include physical devices such as servers, PCs, tablets, or smartphones, or virtual devices such as virtual machines or containers. The electronic device may be a single device, a combination of multiple devices, or a cluster of a large number of devices. The above-mentioned method for extracting the center frequency of a Brillouin scattering spectrum may include: Step S110: Obtaining discrete Brillouin scattering spectrum data.

[0035] Exemplarily, the above step S110 can be obtained by Brillouin optical time domain analysis (BOTDA) or Brillouin optical time domain reflectometry (BOTDR), specifically: The main steps of using Brillouin optical time-domain analysis (BOTDA) to obtain Brillouin scattering spectrum data can include: (1) Injection of pump light and probe light: First, the pump light and probe light are injected into the optical fiber at the same time. The pump light is usually generated by a high-power laser and is used to generate an acoustic wave field in the optical fiber; the probe light has a frequency slightly lower than the pump light and is used to detect the Brillouin gain spectrum information. (2) Generation and interaction of acoustic wave fields: When the pump light propagates in the optical fiber, the density and dielectric constant of the optical fiber medium will undergo periodic changes under the action of the electrostrictive effect, forming an acoustic wave field. The probe light interacts with this acoustic wave field. When the frequency of the probe light matches the Brillouin frequency shift generated by the acoustic wave field, the probe light will obtain Brillouin gain. (3) Detection of the signal by the optical detector: After propagating through a section of optical fiber, the returned probe light signal is detected by the optical detector. At this time, the intensity and frequency characteristics of the probe light have been affected by the Brillouin scattering effect and carry the Brillouin scattering spectrum information along the optical fiber. (4) Data acquisition and processing: The electrical signals detected by the light detector are collected and converted into digital signals through analog-to-digital conversion (ADC). Then, the collected data are analyzed and processed using signal processing algorithms to extract discrete Brillouin scattering spectrum data.

[0036] The main steps of using a Brillouin optical time-domain reflectometer (BOTDR) to obtain Brillouin scattering spectrum data can include: (1) Pulse light injection: a pulse light signal is emitted into the optical fiber. The pulse light is usually generated by a short pulse laser. (2) Brillouin scattered light generation: when the pulse light propagates in the optical fiber, Brillouin scattered light is generated. The frequency of these scattered lights has a Brillouin frequency shift with the frequency of the incident light, and the Brillouin frequency shift is related to the temperature and strain of the optical fiber. (3) Detection of scattered light by optical detector: the returned Brillouin scattered light signal is detected by the optical detector. Since the intensity of the scattered light is relatively weak, a highly sensitive optical detector is usually required to detect these signals. (4) Data acquisition and processing: the electrical signal detected by the optical detector is also collected and converted into analog-to-digital, and the discrete Brillouin scattering spectrum data is extracted through the signal processing algorithm.

[0037] Step S120: interpolating the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data.

[0038] Optionally, the above step S120 may include: performing linear interpolation on the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data.

[0039] It is understandable that in actual measurement, due to the limitation of sampling interval, some subtle spectral line changes may be missed. Therefore, the Brillouin scattering spectrum data can be interpolated to improve the data resolution and thus improve the accuracy of center frequency extraction. The above step S120 can use uniform linear interpolation or non-uniform linear interpolation to interpolate the Brillouin scattering spectrum data, where: (1) Uniform interpolation means that the intervals between interpolation points within the interpolation interval are equal, and the data points after interpolation are evenly distributed, which facilitates frequency domain analysis and the use of certain algorithms that require data uniformity, and is relatively simple to implement. For example, three new data points are uniformly inserted between two adjacent data points in the above discrete Brillouin scattering spectrum data.

[0040] (2) Non-uniform interpolation allows the intervals between interpolation points inserted within the interpolation interval to be unequal, and the distribution of interpolation points can be adjusted according to the changing characteristics of the data. Non-uniform interpolation can better adapt to the changing characteristics of the data, inserting more interpolation points in areas where the data changes dramatically to capture details; inserting fewer interpolation points in areas where the data changes slowly to save computing resources. For example: In general, the above-mentioned discrete Brillouin scattering spectrum data changes slowly in the low-frequency band with larger intervals, and changes dramatically in the high-frequency band with smaller intervals. Therefore, when interpolating, fewer data points can be inserted in the low-frequency band, and more data points can be inserted in the high-frequency band.

[0041] In practical applications, the choice between uniform and non-uniform interpolation can be based on data characteristics and specific requirements. For example, if the data sampling interval is relatively uniform, or if regular data is required for subsequent analysis, uniform interpolation is usually chosen. If the data variation characteristics are uneven, or if it is necessary to capture details in certain areas, non-uniform interpolation can be considered.

[0042] The above scheme improves the data resolution by uniformly or non-uniformly inserting new data points during the linear interpolation process. The higher data resolution helps to more accurately depict the shape of the Brillouin scattering spectrum line, especially the more accurate characterization of characteristics such as the position, width and intensity of the spectrum peak, which is beneficial to improving the accuracy of center frequency extraction. On the other hand, linear interpolation is easier to implement, which is beneficial to improving the center frequency extraction efficiency of the above Brillouin scattering spectrum center frequency extraction method.

[0043] Step S130: performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights to obtain a frequency weighted sum; wherein the weighted weight of each data point in the incremental Brillouin scattering spectrum data is proportional to the amplitude of the data point.

[0044] For example, the weighted value is proportional to the amplitude of the point, so that points with larger amplitudes can be given higher weights, so that the calculation of the center frequency focuses more on the main part of the spectrum. The weighted value can be implemented in at least several ways: The first implementation method is to directly use the amplitude of the data point as its weighted weight. The weighted weight at this time can directly reflect the signal strength. The calculation method is relatively simple and the weighted summation operation can be quickly implemented.

[0045] The second implementation method is to use the normalized amplitude of the data point as its weighted weight. The weighted weight at this time can compress the amplitude to between 0 and 1, which can adapt to a larger dynamic range.

[0046] For the three implementation methods: the signal-to-noise ratio of the data point is used as its weighted weight, and the weighted weight at this time can take into account both signal strength and noise level.

[0047] The fourth implementation method is to use the nth power of the data point amplitude as its weight. In this case, the value of n can be used to adjust the influence of the main peak on the calculation result. Generally speaking, the larger the value of n, the greater the influence of the main peak amplitude on the center frequency calculation result. When n=2, the above weight is the spectral density. The specific scheme of using spectral density as the weight is: Optionally, the step S130 may include: obtaining a spectral density of each data point in the incremental Brillouin scattering spectrum data; and performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using the spectral density as a weighted weight to obtain a frequency weighted sum.

[0048] The above spectral density refers to the amplitude spectral density of the Brillouin scattering spectrum, that is, the distribution of the square of the signal amplitude as the frequency changes. The spectral density reflects the energy distribution of the Brillouin scattering spectrum at different frequency points. Points with larger amplitudes have higher spectral densities and usually correspond to more significant spectral line features, such as spectral peaks. The calculation method of spectral density is: For discrete Brillouin scattering spectrum data ,in is the frequency point, is the corresponding amplitude. The spectral density can be defined as the square of the amplitude, that is .

[0049] The above scheme uses a combination of interpolation and spectral density weighting to extract the center frequency of the Brillouin scattering spectrum. Spectral density weighting can give points with larger amplitudes higher weights, so that the calculation of the center frequency focuses more on the main part of the spectrum line. In the case of double or multi-peaks, even if there are multiple peaks, spectral density weighting can ensure that the calculation of the center frequency is more inclined to the position of the main spectral peak, thereby improving the stability and accuracy of the center frequency extraction; on the other hand, compared with the method of fitting to obtain the center frequency in related technologies, the above scheme can quickly obtain the center frequency through simple interpolation and spectral density weighted calculation, which is suitable for real-time processing and large-scale data processing.

[0050] It is understandable that the noise points in the Brillouin scattering spectrum are usually random and irregular, and their amplitudes may be close to or even higher than the amplitudes of the valid signal points. During the weighted calculation, these noise points will be assigned certain weights, thereby affecting the weighted results and, in turn, the accuracy of center frequency extraction. Based on this, the embodiments of the present application provide the following solutions: Optionally, before step S130, the method for extracting the center frequency of the Brillouin scattering spectrum may further include: before performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights, the method further includes: denoising the incremental Brillouin scattering spectrum data; and performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights, including: performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data after denoising using the weighted weights.

[0051] The denoising process in the above scheme can effectively remove noise points in low signal-to-noise ratio areas, reduce the influence of outliers and interference signals, and make the data purer, thereby providing a more reliable basis for subsequent weighted calculations, which is conducive to improving the data quality of incremental Brillouin scattering spectrum data, thereby improving the accuracy of center frequency extraction; on the other hand, in the denoised data, the characteristics of the effective signal are more prominent, and the amplitude distribution is more realistic. When the weighted weights are used for frequency-weighted summation, the contribution of the main spectral peaks can be more accurately reflected, and the interference of noise on the results can be avoided, thereby improving the accuracy of the center frequency calculation; on the other hand, the denoised data reduces the influence of random fluctuations and outliers, making the center frequency obtained by weighted calculation more stable, more repeatable, and the results more credible.

[0052] The above scheme can use at least the following denoising methods to denoise the incremental Brillouin scattering spectrum data: The first denoising method: denoising based on filtering technology; The incremental Brillouin scattering spectrum data can be denoised using linear filtering or nonlinear filtering, where: Linear filtering: Such as moving average filtering and Gaussian filtering. Moving average filtering smoothes data by calculating the average of adjacent data points, making it suitable for smoothing and reducing random noise. Gaussian filtering performs a weighted average of data based on the shape of a Gaussian function, effectively removing Gaussian noise while preserving the signal's key features.

[0053] Nonlinear filtering: such as median filtering and bilateral filtering. Median filtering removes noise by replacing a data point with the median of its adjacent data points, effectively removing outliers such as impulse noise. Bilateral filtering considers both the neighborhood information of data points and the similarity of their grayscale values, effectively removing noise while preserving important signal features such as edges.

[0054] The second denoising method: denoising based on the model; The incremental Brillouin scattering spectrum data can be denoised using an autoregressive model or a wavelet transform model, where: Autoregressive (AR) models assume that a signal can be represented as a linear combination of signals from several past moments plus a white noise term. By fitting the model parameters, the original signal can be estimated from a noisy signal. This model is suitable for denoising signals with autocorrelation.

[0055] Wavelet transform: The wavelet transform has excellent time-frequency localization properties, allowing it to decompose signals into different scales and locations for analysis. Denoising is achieved by selecting an appropriate threshold function to process the wavelet coefficients. This method effectively preserves important signal features such as sudden changes and spikes while removing noise, making it particularly suitable for denoising non-stationary signals.

[0056] The third denoising method: denoising based on machine learning and deep learning; The incremental Brillouin scattering spectrum data can be denoised using a denoising autoencoder (DAE) or a generative adversarial network (GAN), where: Denoising Autoencoder (DAE): This is a neural network-based denoising method. It builds an autoencoder network to encode and decode input data, while adding noise during the encoding process. This allows the network to learn how to reconstruct the original data from the noisy data, thereby achieving denoising. This method has good denoising effects when processing complex datasets, but requires a large amount of training data and computing resources.

[0057] Generative Adversarial Networks (GANs): Through adversarial training between a generator and a discriminator, the generator learns to generate realistic signals, while the discriminator learns to distinguish between real and generated signals. During training, the generator gradually learns to generate clean signals from noisy data, thus achieving denoising. While GANs have achieved remarkable results in fields such as image denoising, their application to Brillouin scattering spectrum denoising is still under exploratory.

[0058] The fourth method: using point selection threshold for denoising; Optionally, the denoising process on the incremental Brillouin scattering spectrum data includes: obtaining the maximum amplitude of the Brillouin scattering spectrum data; obtaining a point selection threshold based on the maximum amplitude and a preset proportional coefficient; and denoising the incremental Brillouin scattering spectrum data based on the point selection threshold.

[0059] For example, the preset scaling factor can be empirically selected or determined based on the statistical characteristics of noise in fiber optic sensing. For example, a value of 0.2 can effectively filter out most noise points while retaining the main spectral peak data without being overly strict. Furthermore, a method for denoising the incremental Brillouin scattering spectrum data based on a selected point threshold can include removing data points from the incremental Brillouin scattering spectrum data whose amplitudes are not greater than the selected point threshold, and retaining data points whose amplitudes are greater than the selected point threshold.

[0060] The maximum amplitude in the above scheme reflects the strongest signal in the data set. When combined with the preset proportional coefficient, the point selection threshold can effectively distinguish between signal and noise, remove noise points in low signal-to-noise ratio areas, improve data quality, and thus improve the accuracy of center frequency extraction. On the other hand, linear operations based on the maximum amplitude and preset proportional coefficient are easy to implement, have low requirements on computing resources, do not rely on complex mathematical models or assumptions, and are suitable for engineering and real-time applications.

[0061] Optionally, the above-mentioned denoising process for the incremental Brillouin scattering spectrum data includes: obtaining the maximum amplitude of the Brillouin scattering spectrum data, the first amplitude corresponding to the minimum frequency point, and the second amplitude corresponding to the maximum frequency point; obtaining a point selection threshold based on the first amplitude, the second amplitude, the maximum amplitude, and a preset proportional coefficient; wherein the point selection threshold is the maximum value of the product of the first amplitude, the second amplitude, and the maximum amplitude and the preset proportional coefficient; and denoising the incremental Brillouin scattering spectrum data based on the point selection threshold. This embodiment, for example, calculates the measured discrete Brillouin scattering spectrum data. The maximum value of , calculate the discrete Brillouin scattering spectrum data corresponding to the minimum and maximum frequency points The amplitudes of and ;calculate The maximum value in the selection point is obtained, which is recorded as .

[0062] The following are the following 、 and The role of determining the point selection threshold: (1) : The maximum amplitude in the entire Brillouin gain spectrum represents the strongest part of the signal. By multiplying it by 0.2, we can obtain a threshold related to the overall signal strength, ensuring that the selected data points have a certain degree of significance overall.

[0063] (2) Amplitude corresponding to the minimum frequency point : The amplitude at the minimum frequency in the Brillouin gain spectrum Reflects the signal strength at the beginning of the spectrum. In some cases, there may be effective spectrum peaks or important signal features at the beginning. As one of the references for point selection threshold, it can avoid misjudgment of data points with abnormally low or high amplitudes due to noise or other abnormal conditions at the starting end, thereby ensuring that valid data points at the starting end can be correctly retained.

[0064] (3) Amplitude corresponding to the maximum frequency : Similarly, the amplitude at the maximum frequency Reflects the signal strength at the end of the spectrum. The end of the spectrum may also contain valid peaks or important signal features. As one of the references for point selection threshold, it can avoid misjudgment of abnormally low or high amplitude data points at the end of the spectrum due to noise or other abnormal conditions, and ensure that valid data points at the end can be correctly retained.

[0065] In addition, the above point selection threshold At least have the following functions: (1) Filter out noise points in low signal-to-noise ratio areas (such as baseline fluctuations): The noise points in the low signal-to-noise ratio area usually have smaller amplitudes, and the amplitudes of these points are often lower than the point selection threshold. By filtering out the values ​​with amplitude greater than The data points can effectively remove the interference of noise points, improve the purity of the data, and make the subsequent center frequency extraction more accurate.

[0066] (2) Retain all peak data that are significantly higher than the noise (regardless of the number of peaks): Significant peak data usually have higher amplitudes, which are often greater than the point selection threshold. Regardless of whether the spectral peaks are single, double, or multiple, as long as their amplitudes are significantly higher than the noise, they are retained. This ensures that all important spectral peak information is included in the subsequent processed data set, providing a guarantee for accurate extraction of the center frequency.

[0067] The above scheme combines the maximum amplitude, the minimum frequency amplitude and the maximum frequency amplitude, which can more comprehensively reflect the characteristics of the Brillouin scattering spectrum data, so that the point selection threshold more accurately reflects the actual situation of the data, thereby more effectively removing noise points. In a complex noise environment, the above scheme can better adapt to different noise distributions and interference conditions, and improve the robustness of denoising; on the other hand, by considering the amplitudes of the minimum frequency and the maximum frequency, it can avoid misjudgment caused by abnormal amplitude of a single frequency point, ensure that valid data points at the starting and end ends are not mistakenly filtered out, and can more carefully retain key details in the signal, especially for those spectral peaks with important characteristics at the starting or end ends, thereby improving data integrity.

[0068] Step S140: obtaining the center frequency of the Brillouin scattering spectrum data based on the weighted sum of the weighted weights and the frequency weighted sum.

[0069] Optionally, after step S140, the method for extracting the center frequency of the Brillouin scattering spectrum may further include: calculating the Brillouin frequency shift of the Brillouin scattering spectrum data based on the center frequency of the Brillouin scattering spectrum data.

[0070] The Brillouin frequency shift is the frequency difference between the scattered light (Stokes light) and the incident pump light during stimulated Brillouin scattering. It is a key parameter in fiber-optic sensing technologies such as Brillouin optical time-domain analysis (BOTDA) and Brillouin optical time-domain reflectometry (BOTDR). These technologies exploit the linear relationship between the Brillouin frequency shift and temperature and strain. By analyzing the Brillouin gain spectrum along an optical fiber, they can determine temperature and strain information at various locations along the fiber. This allows for health monitoring of large-scale infrastructure such as communication cables, buildings, oil pipelines, power lines, and railways.

[0071] By precisely extracting the center frequency, the above scheme can more accurately calculate the Brillouin frequency shift, which is beneficial to improving the calculation accuracy of the Brillouin frequency. On the other hand, the calculated Brillouin frequency shift can be used in scenarios such as temperature measurement, strain measurement, and optical fiber characteristic analysis, which is beneficial to improving the adaptability of the above-mentioned Brillouin scattering spectrum center frequency extraction method.

[0072] The following provides the specific application steps of the above-mentioned Brillouin scattering spectrum center frequency extraction method in a certain application scenario: Step 1: Obtain measured discrete Brillouin scattering spectrum data ,in , is the number of frequency sweep points, is the initial frequency, and the sweep step is ; Step 2: Calculate the measured discrete Brillouin scattering spectrum data The maximum value of , calculate the discrete Brillouin scattering spectrum data corresponding to the minimum and maximum frequency points The amplitudes of and ; Step 3: Calculation The maximum value in the selection point is obtained, which is recorded as ; The point selection threshold can effectively remove the influence of noise and non-main spectral peaks. In the case of double peaks or multiple peaks, it can highlight the characteristics of the main spectral peak, so that the subsequent center frequency calculation will focus more on the main part of the real Brillouin gain spectrum. For example, in a double peak spectrum, if the amplitude of one peak is low and may be affected by noise, by setting the appropriate , it can be excluded or its influence can be weakened, thus focusing on the dominant spectral peak; Step 4: Discrete Brillouin scattering spectrum data According to the principle of linear interpolation, three new data points are evenly inserted into adjacent sweep frequency points to obtain the Brillouin scattering spectrum data after linear interpolation. , the new step length is , the total number of sweep points is ; Assume that the frequency and amplitude of adjacent data points are and , then the frequency and amplitude of the three new data points added by linear interpolation are , , ; By linearly interpolating three new data points evenly between adjacent frequency sweep points, the originally discrete Brillouin scattering spectrum data becomes more detailed, increasing the number of data points and thus improving the data resolution. When faced with double-peak or multi-peak spectra, the shape of the spectral line can be more carefully depicted, avoiding the omission of key spectral features due to too few data points, providing richer information for subsequent center frequency extraction. Step 5: Brillouin scattering spectrum data after linear interpolation The median value is greater than The data points are selected and a new data set is constructed , and calculate the number of data points in the data set, recorded as ; Step 6: Select the data set Perform spectral density weighted calculation to extract the center frequency:

[0073]

[0074] in, is the center frequency; is the number of data points; Frequency spectral density; Frequency The amplitude of .

[0075] The weighted average frequency is calculated using spectral density (amplitude squared) as weight, assigning higher weights to points with larger amplitudes. In bimodal or multimodal situations, even with multiple peaks, the center frequency calculation is biased toward the location of the primary spectral peak. Because the primary spectral peak typically has a larger amplitude and higher spectral density, it dominates the weighted calculation, improving the stability and accuracy of center frequency extraction.

[0076] In addition, the present embodiment also uses two sets of control scenarios to demonstrate the effectiveness of the above-mentioned Brillouin scattering spectrum center frequency extraction method, specifically: Comparison scenario 1: Construct a Brillouin gain spectrum with an amplitude of 100, a full width at half maximum of 40 MHz, a center frequency of 100 MHz, and superimpose multiplicative noise. Figure 2 , Figure 2The comparison of the fitting accuracy (stability) of the single-peak Lorentz fitting scheme and the above-mentioned linear interpolation-spectral density weighted extraction scheme under different noise levels is shown. Figure 2 It can be seen that under the condition of the same noise level, compared with the single-peak Lorentz fitting scheme, the above linear interpolation-spectral density weighted extraction scheme has higher measurement accuracy and stronger noise resistance.

[0077] Comparison scenario 2: Constructing a Brillouin spectrum with the center frequencies corresponding to 10, 20, and 25, forming a three-peak distribution. Figure 3 , Figure 3 The comparison of the fitting accuracy (stability) of the single-peak Lorentz spectrum under different noise levels between the single-peak Lorentz fitting scheme and the above-mentioned linear interpolation-spectral density weighted extraction scheme is shown. Figure 3 It can be seen that under the condition of the same noise level, compared with the single-peak Lorentz fitting scheme, the above linear interpolation-spectral density weighted extraction scheme has higher measurement accuracy and stronger noise resistance.

[0078] Also, see Figure 4 and Figure 5 , Figure 4 shows the fitting results of the single-peak Lorentzian fitting scheme for the single-peak data. Figure 5 Figure 2 shows the fitting results of the multi-peak data using a single-peak Lorentz fitting scheme. Figure 4 and Figure 5 It can be seen that the fitting scheme in the related technology is highly dependent on the mathematical model, and when the model does not match, Figure 5 The abnormal situation shown. The Brillouin scattering spectrum center frequency extraction method provided in the present embodiment directly processes the measured data through interpolation, weighting, and other methods, and does not rely on a specific mathematical model. This method focuses more on the characteristics and processing of the data itself and can better adapt to Brillouin spectra of different shapes.

[0079] Based on the same inventive concept, an embodiment of the present application further provides a method for optical fiber strain sensing, comprising: Using a sensing fiber to collect discrete Brillouin scattering spectrum data; wherein the sensing fiber is installed in the structure to be monitored; Extracting the center frequency from the Brillouin scattering spectrum data; wherein the center frequency is obtained by the above-mentioned Brillouin scattering spectrum center frequency extraction method; Based on the center frequency, the strain of the monitored structure is monitored.

[0080] It is understandable that the solution of using the center frequency for strain monitoring is a relatively mature technology in this field. Please refer to the relevant technology for its specific implementation method, and the embodiments of this application will not be repeated.

[0081] Based on the same inventive concept, an embodiment of the present application further provides an optical fiber strain sensing system, comprising a host computer, a light detector, and a sensing optical fiber, wherein the light detector is electrically connected to the host computer, and the sensing optical fiber is connected to the light detector, wherein: The host computer is used to obtain discrete Brillouin scattering spectrum data; interpolate the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data; use weighted weights to weightedly sum the frequencies of all data points in the incremental Brillouin scattering spectrum data to obtain a frequency-weighted sum; wherein the weighted weight of each data point in the incremental Brillouin scattering spectrum data is proportional to the amplitude of the data point; and obtain the center frequency of the Brillouin scattering spectrum data based on the weighted sum of the weighted weights and the frequency-weighted sum.

[0082] Figure 6 This is a schematic diagram of an electronic device provided in an embodiment of the present application. Figure 6 The electronic device 200 includes a processor 210, a memory 220, and a communication interface 230. These components are interconnected and communicate with each other via a communication bus 240 and / or other forms of connection mechanisms (not shown).

[0083] The memory 220 includes one or more (only one is shown in the figure), which may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM). The processor 210 and other possible components can access the memory 220 and read and / or write data therein.

[0084] The processor 210 includes one or more (only one is shown in the figure), which can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 210 can be a general-purpose processor, including a central processing unit (CPU), a microcontroller unit (MCU), a network processor (NP), or other conventional processors; it can also be a special-purpose processor, including a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0085] Communication interface 230 includes one or more (only one is shown in the figure) interfaces that can be used to communicate directly or indirectly with other devices to exchange data. For example, communication interface 230 can be an Ethernet interface; a mobile communication network interface, such as a 3G, 4G, or 5G network interface; or other types of interfaces capable of transmitting and receiving data.

[0086] One or more computer program instructions may be stored in the memory 220 , and the processor 210 may read and execute these computer program instructions to implement the Brillouin scattering spectrum center frequency extraction method and other desired functions provided in the embodiments of the present application.

[0087] I understand. Figure 6 The structure shown is for illustration only. The electronic device 200 may also include Figure 6 More or fewer components than shown, or with Figure 6 Different configurations shown. Figure 6 Each component shown in the figure can be implemented using hardware, software, or a combination thereof. For example, the electronic device 200 can be a single server (or other device with computing processing capabilities), a combination of multiple servers, a cluster of a large number of servers, etc., and can be both a physical device and a virtual device.

[0088] The present application also provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are read and executed by a computer processor, the method for extracting the center frequency of the Brillouin scattering spectrum provided in the present application is executed. For example, the computer-readable storage medium can be implemented as Figure 6The memory 220 in the electronic device 200.

[0089] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0090] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0092] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for extracting the center frequency of a Brillouin scattering spectrum, characterized in that: The method comprises: Obtain discrete Brillouin scattering spectrum data; interpolating the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data; Performing a weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using a weighted weight to obtain a frequency-weighted sum; wherein the weighted weight of each data point in the incremental Brillouin scattering spectrum data is proportional to the amplitude of the data point; The center frequency of the Brillouin scattering spectrum data is obtained based on the weight sum of the weighted weights and the frequency weighted sum.

2. The method for extracting the center frequency of the Brillouin scattering spectrum according to claim 1, wherein: The step of performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights to obtain a frequency weighted sum includes: Obtaining the spectral density of each data point in the incremental Brillouin scattering spectrum data; The spectral density is used as a weighted weight to perform weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data to obtain a frequency weighted sum.

3. The method for extracting the center frequency of the Brillouin scattering spectrum according to claim 1, wherein: Before performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights, the method further comprises: performing denoising processing on the incremental Brillouin scattering spectrum data; The step of performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data using weighted weights includes performing weighted summation on the frequencies of all data points in the incremental Brillouin scattering spectrum data after denoising using weighted weights.

4. The method for extracting the center frequency of the Brillouin scattering spectrum according to claim 3, wherein: The denoising process of the incremental Brillouin scattering spectrum data includes: Obtaining the maximum amplitude of the Brillouin scattering spectrum data; Based on the maximum amplitude and a preset proportional coefficient, obtaining a point selection threshold; Based on the selected point threshold, denoising is performed on the incremental Brillouin scattering spectrum data.

5. The method for extracting the center frequency of the Brillouin scattering spectrum according to claim 3, wherein: The denoising process of the incremental Brillouin scattering spectrum data includes: Obtaining the maximum amplitude of the Brillouin scattering spectrum data, the first amplitude corresponding to the minimum frequency point, and the second amplitude corresponding to the maximum frequency point; Obtaining a point selection threshold based on the first amplitude, the second amplitude, the maximum amplitude, and a preset proportional coefficient; wherein the point selection threshold is the maximum value of the product of the first amplitude, the second amplitude, and the maximum amplitude and the preset proportional coefficient; Based on the selected point threshold, denoising is performed on the incremental Brillouin scattering spectrum data.

6. The method for extracting the center frequency of a Brillouin scattering spectrum according to any one of claims 1 to 5, characterized in that: The interpolating the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data includes: Linear interpolation is performed on the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data.

7. The method for extracting the center frequency of a Brillouin scattering spectrum according to any one of claims 1 to 5, characterized in that: After obtaining the center frequency of the Brillouin scattering spectrum data, the method further includes: The Brillouin frequency shift of the Brillouin scattering spectrum data is calculated based on the center frequency of the Brillouin scattering spectrum data.

8. A fiber optic strain sensing method, characterized in that: The method comprises: Using a sensing optical fiber to collect discrete Brillouin scattering spectrum data; wherein the sensing optical fiber is installed in the structure to be monitored; Extracting a center frequency from the Brillouin scattering spectrum data; wherein the center frequency is obtained using the Brillouin scattering spectrum center frequency extraction method according to any one of claims 1 to 7; Based on the center frequency, strain monitoring is performed on the structure to be monitored.

9. An optical fiber strain sensing system, characterized in that: include: A host computer, a light detector and a sensing fiber, wherein the light detector is electrically connected to the host computer, and the sensing fiber is connected to the light detector, wherein: The host computer is used to obtain discrete Brillouin scattering spectrum data; interpolate the Brillouin scattering spectrum data to obtain incremental Brillouin scattering spectrum data; use weighted weights to weighted sum the frequencies of all data points in the incremental Brillouin scattering spectrum data to obtain a frequency-weighted sum; wherein the weighted weight of each data point in the incremental Brillouin scattering spectrum data is proportional to the amplitude of the data point; and obtain the center frequency of the Brillouin scattering spectrum data based on the weighted sum of the weighted weights and the frequency-weighted sum.

10. An electronic device, characterized in that: include: A processor, a memory and a communication bus, wherein the processor and the memory communicate with each other via the communication bus; The memory stores program instructions that can be executed by the processor, and the processor can execute the method according to any one of claims 1 to 8 by calling the program instructions.

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