Brillouin frequency shift extraction method, optical fiber sensing system, device and medium
By enhancing and fitting the Brillouin gain spectrum with the main Lorentz component, the problem of high complexity in multi-Lorentz fitting was solved, and high-precision and rapid measurement of Brillouin frequency shift was achieved.
Patent Information
- Application Number
- CN202411901391.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2026-06-23
AI Technical Summary
Existing techniques for calculating Brillouin frequency shifts rely on Dolorens fitting methods, which are complex, prone to failure, and difficult to measure accurately and quickly.
By enhancing the principal Lorentz component in the Brillouin gain spectrum, and using methods such as moving average and filtering functions, the signal is directly fitted to the center frequency of the principal Lorentz component. A preset iteration termination condition is set, and the fit is a quadratic polynomial relationship.
This method improves the computational accuracy and efficiency of Brillouin frequency shift, reduces computational complexity, and enhances the adaptability and applicability of the method.
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Figure CN122258960A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of fiber optic sensing technology, and more specifically, to a Brillouin frequency shift extraction method, a fiber optic sensing system, equipment, and medium. Background Technology
[0002] Among spectral characteristic parameters affected by temperature and strain, the Brillouin frequency shift exhibits a relatively stable and linear relationship with both temperature and strain. Currently, the core of most fiber-optic distributed sensing measurement technologies based on the Brillouin scattering principle lies in extracting the Brillouin frequency shift. Accurate and rapid measurement of the Brillouin frequency shift is crucial for fiber-optic sensing systems based on Brillouin scattering.
[0003] When calculating the Brillouin frequency shift, the Brillouin gain spectrum may contain non-single Lorentz components. Most related techniques use multi-Lorentz fitting to calculate the Brillouin frequency shift. However, since multi-Lorentz fitting is a non-linear fitting method, its iterative process is relatively complex and computationally expensive. The high computational complexity of the fitting process makes it very easy for the fitting to fail. Summary of the Invention
[0004] The purpose of this application is to provide a Brillouin frequency shift extraction method, an optical fiber sensing system, a device, and a medium to alleviate or improve at least one of the above-mentioned problems.
[0005] In a first aspect, embodiments of this application provide a Brillouin frequency shift extraction method, the method comprising: obtaining an original Brillouin gain spectrum; performing an iterative process until a preset iteration termination condition is met, and obtaining a Brillouin frequency shift based on a first fitted frequency shift obtained in the last iteration process; the iterative process comprising: enhancing the principal Lorentz component in the first Brillouin gain spectrum to obtain a second Brillouin gain spectrum; wherein, in the first iteration process, the first Brillouin gain spectrum is the original Brillouin gain spectrum; fitting the relationship between the Brillouin frequency shift and the Brillouin gain based on a first fitting window of the second Brillouin gain spectrum, and obtaining a fitting result; wherein, the first fitting window includes the maximum value point in the second Brillouin gain spectrum; obtaining the first fitted frequency shift based on the fitting result; and determining the second Brillouin gain spectrum as the new first Brillouin gain spectrum.
[0006] In implementing the above scheme, the principal Lorentz component in the Brillouin gain spectrum is enhanced at least once, allowing direct fitting to the center frequency of the principal Lorentz component during the fitting process. On one hand, this enhancement makes the principal Lorentz component easier to capture, improving the accuracy of the Brillouin frequency shift calculation. On the other hand, the enhanced principal Lorentz component allows direct fitting to its center frequency, reducing the computational complexity of the Brillouin frequency shift extraction scheme.
[0007] In one implementation of the first aspect, the enhancement processing of the principal Lorentz component in the first Brillouin gain spectrum to obtain the second Brillouin gain spectrum includes: performing a first processing and a second processing on the first Brillouin spectrum to obtain a third Brillouin gain spectrum and a fourth Brillouin gain spectrum; wherein the first processing is a moving average processing to characterize a first degree of smoothness; the second processing is a moving average processing to characterize a second degree of smoothness; the first degree of smoothness is less than the second degree of smoothness; and the second Brillouin gain spectrum is obtained based on the third Brillouin gain spectrum and the fourth Brillouin gain spectrum.
[0008] In the implementation of the above scheme, by processing the first Brillouin gain spectrum with two Brillouin gain spectra processed by moving averages with different smoothness, a third Brillouin gain spectrum that retains more information and a smoother fourth Brillouin gain spectrum can be obtained. On the one hand, the above scheme can quickly obtain the enhanced second Brillouin gain spectrum through the third and fourth Brillouin gain spectra; on the other hand, since the principal Lorentz component in the Brillouin gain spectrum is relatively prominent, after the enhancement processing of the above scheme, the principal Lorentz component will be more prominent, which is conducive to improving the enhancement effect of the principal Lorentz component, and thus conducive to improving the Brillouin frequency shift extraction accuracy of the above Brillouin frequency shift extraction method.
[0009] In one implementation of the first aspect, obtaining the second Brillouin gain spectrum based on the third and fourth Brillouin gain spectra includes: obtaining the second Brillouin gain spectrum based on the third, fourth, and scaling factors; wherein the scaling factors are used to characterize the degree of influence of the fourth Brillouin gain spectrum on the third Brillouin gain spectrum.
[0010] In the implementation of the above scheme, on the one hand, by setting the scaling factor, the enhancement effect of the main Lorentz component can be controlled, making it easier to capture, which in turn helps to improve the Brillouin frequency shift extraction effect of the above Brillouin frequency shift extraction method; on the other hand, by controlling the scaling factor, it helps to reduce the influence of noise on the Brillouin frequency shift extraction process, which is conducive to further improving the Brillouin frequency shift extraction effect of the above Brillouin frequency shift extraction method.
[0011] In one implementation of the first aspect, the step of enhancing the principal Lorentz component in the first Brillouin gain spectrum to obtain the second Brillouin gain spectrum includes: enhancing the principal Lorentz component in the first Brillouin gain spectrum using a filtering function to obtain the second Brillouin gain spectrum.
[0012] In the implementation of the above scheme, a filtering function can also be used to enhance the first Brillouin gain spectrum, so that the above Brillouin frequency shift extraction method can be applied to more application scenarios, which is beneficial to improving the adaptability of the above Brillouin extraction method.
[0013] In one implementation of the first aspect, the preset iteration termination condition includes:
[0014] The fitting stability index of the fitting result meets the first preset requirement;
[0015] And / or, the fitting symmetry index of the fitting result meets the second preset requirement;
[0016] And / or, the number of iterations in the iterative process satisfies a third preset requirement.
[0017] In the implementation of the above scheme, the preset iteration termination condition can be at least one of the following: fitting stability index, fitting symmetry index, and number of iterations. On the one hand, the preset iteration termination condition can balance the Brillouin frequency shift extraction accuracy and extraction efficiency, which is beneficial to improving the extraction accuracy and extraction efficiency of the above Brillouin frequency shift extraction method. On the other hand, it makes the above Brillouin frequency shift extraction method applicable to more application scenarios, which is beneficial to improving the adaptability of the above Brillouin frequency shift extraction method.
[0018] In one implementation of the first aspect, fitting the relationship between the Brillouin frequency shift and the Brillouin gain includes: fitting the relationship between the Brillouin frequency shift and the Brillouin gain as a quadratic polynomial.
[0019] In the implementation of the above scheme, by fitting the relationship between Brillouin frequency shift and Brillouin gain as a quadratic polynomial, the complex nonlinear problem between Brillouin frequency shift and Brillouin gain is transformed into a more easily handled linear problem, which is beneficial to improving the computational complexity, computational efficiency and Brillouin frequency shift extraction accuracy of the above Brillouin frequency shift extraction method.
[0020] In one implementation of the first aspect, the method further includes, before performing the iterative process, denoising the original Brillouin gain spectrum.
[0021] In the implementation of the above scheme, denoising the original Brillouin gain spectrum helps to improve the data quality of the Brillouin data, thereby improving the Brillouin frequency shift extraction accuracy of the above Brillouin frequency shift extraction method.
[0022] Secondly, embodiments of this application provide an optical fiber sensing system, the system including an optical fiber sensor and a data processing module; wherein, the data processing module is used to acquire the original Brillouin gain spectrum; execute an iterative process until a preset iteration termination condition is met, and acquire the Brillouin frequency shift based on the first fitted frequency shift acquired in the last iteration process; the iterative process includes: enhancing the principal Lorentz component in the first Brillouin gain spectrum to acquire a second Brillouin gain spectrum; wherein, in the first iteration process, the first Brillouin gain spectrum is the original Brillouin gain spectrum; fitting the relationship between the Brillouin frequency shift and the Brillouin gain based on a first fitting window of the second Brillouin gain spectrum to acquire a fitting result; wherein, the first fitting window includes the maximum value point in the second Brillouin gain spectrum; acquiring the first fitted frequency shift based on the fitting result; and determining the second Brillouin gain spectrum as the new first Brillouin gain spectrum.
[0023] Thirdly, embodiments of this application provide an electronic device, including: 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 the computer program instructions are read and executed by the processor to perform the method provided in the first aspect or any possible implementation of the first aspect.
[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the method provided in the first aspect or any possible implementation thereof.
[0025] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description
[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0027] Figure 1 A schematic flowchart of the Brillouin frequency shift extraction method provided in the embodiments of this application;
[0028] Figure 2 This is a schematic diagram of an iterative process in a certain scenario provided in an embodiment of this application;
[0029] in, Figure 2 (a) Figure 2 (b) Figure 2 (c) and Figure 2 (d) represents the original Brillouin gain spectrum, the second Brillouin gain spectrum obtained after the first iteration, the second Brillouin gain spectrum obtained after the second iteration, and the second Brillouin gain spectrum obtained after the third iteration, respectively.
[0030] Figure 3 This is a schematic diagram of the structure of the fiber optic sensing system provided in the embodiments of this application;
[0031] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0032] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of this application, and are therefore merely examples and should not be used to limit the scope of protection of this application.
[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.
[0034] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0035] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0036] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0037] The presence of non-single Lorentz components in a Brillouin gain spectrum usually refers to the occurrence of multiple Lorentz peaks in the Brillouin gain spectrum. This is generally caused by the following reasons: material correlation, non-uniform strain distribution, material property changes caused by temperature gradients, external field influences, sample size effects, defects and impurities, non-ejection scattering, and non-uniformity during sample preparation.
[0038] For Brillouin gain spectra with non-single Lorentz components, most related techniques use multi-Lorentz fitting to obtain the Brillouin frequency shift. However, multi-Lorentz fitting is a nonlinear fitting method with high computational complexity. Specifically, the iterative process of multi-Lorentz fitting is very complex, computationally expensive, and sensitive to initial values, making it prone to fitting failure.
[0039] Based on this, embodiments of this application provide a Brillouin frequency shift extraction method. This method enhances the principal Lorentz component in the Brillouin gain spectrum at least once, and directly fits to the center frequency of the principal Lorentz component during fitting. On the one hand, the above scheme enhances the principal Lorentz component, making it easier to capture and improving the calculation accuracy of the Brillouin frequency shift. On the other hand, since the principal Lorentz component is enhanced, it can be directly fitted to the center frequency of the principal Lorentz component during fitting, which helps to improve the computational complexity of the Brillouin frequency shift extraction scheme.
[0040] The Brillouin frequency shift extraction method described above is explained in detail below. Please refer to [link / reference]. Figure 1 This application provides a Brillouin frequency shift extraction method, which includes:
[0041] Step S110: Obtain the original Brillouin gain spectrum;
[0042] The iterative process is executed until the preset iteration termination condition is met, and the Brillouin frequency shift is obtained based on the first fitted frequency shift obtained in the last iteration process.
[0043] The above iterative process includes:
[0044] Step S120: Enhance the principal Lorentz component in the first Brillouin gain spectrum to obtain the second Brillouin gain spectrum; wherein, in the first iteration, the first Brillouin gain spectrum is the original Brillouin gain spectrum;
[0045] Step S130: Based on the first fitting window of the second Brillouin gain spectrum, fit the relationship between Brillouin frequency shift and Brillouin gain to obtain the fitting result; wherein, the first fitting window includes the maximum value point in the second Brillouin gain spectrum;
[0046] Step S140: Based on the fitting results, obtain the first fitting frequency shift;
[0047] Step S150: Determine the second Brillouin gain spectrum as the new first Brillouin gain spectrum.
[0048] It is understandable that the original Brillouin gain spectrum obtained in step S110 refers to the spectrum relating the Brillouin gain to the Brillouin frequency offset during frequency sweep measurement. Specifically, when the reference electrical signal generated by the fiber optic sensor's signal generator is output via frequency sweep (i.e., the signal frequency changes gradually from its initial value at certain frequency and time intervals until it reaches a termination value), the associated modulation sidebands are also swept at the same frequency and time intervals. During this process, the change in Brillouin gain with the Brillouin frequency offset is observed, and the resulting spectrum is the Brillouin gain spectrum.
[0049] It is understandable that the principal Lorentz component in the first Brillouin gain spectrum refers to the corresponding Brillouin gain in the first Brillouin gain spectrum. The first fitted frequency shift refers to the frequency shift corresponding to the peak point of the curve determined after fitting the relationship between the Brillouin frequency shift and the Brillouin gain of the second Brillouin gain spectrum.
[0050] The following describes the scheme for enhancing the principal Lorentz component in step S120 of the above iteration process:
[0051] As one embodiment of the above-described Brillouin frequency shift extraction method, step S120 includes: performing a first processing and a second processing on the first Brillouin spectrum to obtain a third Brillouin gain spectrum and a fourth Brillouin gain spectrum; wherein, the first processing is a moving average processing to characterize a first degree of smoothness; the second processing is a moving average processing to characterize a second degree of smoothness; the first degree of smoothness is less than the second degree of smoothness; and the second Brillouin gain spectrum is obtained based on the third and fourth Brillouin gain spectra. An example of this embodiment is:
[0052] The Brillouin gain spectrum can be represented as: [X,Y]=[(x1,y1),(x2,y2),…,(x n ,y n ]], where X is the Brillouin frequency shift, Y is the Brillouin gain, and n is the number of sampling points in the Brillouin gain spectrum;
[0053] The Brillouin gain Y1 of the third Brillouin gain spectrum can be expressed as:
[0054] Y1=mavg1(Y,win_len1,weight1)
[0055] Where mavg1(·) represents the first processing step; win_len1 represents the length of the first window; and weight1 represents the weight of the first window.
[0056] The Brillouin gain Y2 of the fourth Brillouin gain spectrum can be expressed as:
[0057] Y2=mavg2(Y,win_len2,weight2)
[0058] Where mavg2(·) represents the second processing step; win_len2 represents the length of the second window; and weight2 represents the weight of the second window.
[0059] Brillouin gain Y of the second Brillouin gain spectrum e The calculation method is as follows:
[0060] Y e =Y1-Y2
[0061] Understandably, the smoothness of the moving average process can be controlled by adjusting the window length (win_len) and the window weight (weight).
[0062] Understandably, in some scenarios, the third Brillouin gain spectrum can directly use the first Brillouin gain spectrum, meaning the first smoothing degree of the first processing is zero, and therefore no moving average processing is applied to the first Brillouin. In this case, the Brillouin gain Y of the second Brillouin gain spectrum... e The calculation method is as follows:
[0063] Y e =Y1-Y2=Y-mavg2(Y,win_len2,weight2)
[0064] The above scheme processes the first Brillouin gain spectrum using two Brillouin gain spectra processed with moving averages of different smoothness levels. This allows for the acquisition of a third Brillouin gain spectrum that retains more information, as well as a smoother fourth Brillouin gain spectrum. On one hand, the above scheme can quickly obtain the enhanced second Brillouin gain spectrum using the third and fourth Brillouin gain spectra. On the other hand, since the principal Lorentz component in the Brillouin gain spectrum is relatively prominent, it becomes even more prominent after the above scheme is applied, which helps to improve the enhancement effect of the principal Lorentz component and thus improves the Brillouin frequency shift extraction accuracy of the above Brillouin frequency shift extraction method.
[0065] Understandably, in some scenarios, the first window length win_len1 and the second window length win_len2 can be set to the same value, that is: win_len1 = win_len2. In this case, the Brillouin gain Y of the second Brillouin gain spectrum will be... e The calculation method can be expressed as:
[0066] Y e =Y1-Y2=mave(Y,win_len1,weight1-weight2)
[0067] In this case, the first and second processes can be combined into a single moving average process with a window weight of weight1-weight2. Therefore, the enhanced second Brillouin gain spectrum can be obtained directly by performing a single moving average process on the first Brillouin gain spectrum.
[0068] As one implementation of the aforementioned Brillouin frequency shift extraction method, obtaining the second Brillouin gain spectrum based on the third and fourth Brillouin gain spectra includes: obtaining the second Brillouin gain spectrum based on the third and fourth Brillouin gain spectra and a scaling factor; wherein the scaling factor is used to characterize the degree of influence of the fourth Brillouin gain spectrum on the third Brillouin gain spectrum. An example of this implementation is:
[0069] Brillouin gain Y of the second Brillouin gain spectrum e The calculation method is as follows:
[0070] Y e =Y1-r*Y2
[0071] Where r is the scaling factor, which is used to adjust the difference between the third and fourth Brillouin gain spectra. By changing the size of the scaling factor, the enhancement of the principal Lorentz component can be controlled.
[0072] Understandably, the following constraints are usually imposed on the above scaling factors:
[0073] r <max(Y1) / max(Y2)
[0074] Where Y1 is the Brillouin gain of the third Brillouin gain spectrum in the current iteration; Y2 is the Brillouin gain of the fourth Brillouin gain spectrum in the current iteration. It is understandable that in scenarios where the window length and window weight of the first and second processes are not equal, the value of the scaling factor r is usually close to 1, can be less than 1, equal to 1, or greater than 1, but should not exceed max(Y1) / max(Y2).
[0075] In some scenarios, when the window length of the first processing is equal to the window length of the second processing, the Brillouin gain Y of the second Brillouin gain spectrum... e The calculation method is as follows:
[0076] Y e =Y1-r*Y2=mavg(Y,win) len1 (weight1-r*weight2)
[0077] In some scenarios, when the third Brillouin gain spectrum directly adopts the first Brillouin gain spectrum, the Brillouin gain Y of the second Brillouin gain spectrum... e The calculation method is as follows:
[0078] Y e =Y1-r*Y2=Yr*mavg2(Y,win_len2,weight2)
[0079] Additionally, it is understandable that, based on experience, when the third Brillouin gain spectrum directly adopts the first Brillouin gain spectrum, if win_len2 = 1, then the scaling factor r will range from [0.95, 1].
[0080] On the one hand, by setting the scaling factor, the above scheme can control the enhancement effect of the main Lorentz component, making it easier to capture, which in turn helps to improve the Brillouin frequency shift extraction effect of the above Brillouin frequency shift extraction method. On the other hand, by controlling the scaling factor, it helps to reduce the influence of noise on the Brillouin frequency shift extraction process, which is conducive to further improving the Brillouin frequency shift extraction effect of the above Brillouin frequency shift extraction method.
[0081] In addition to the enhancement processing scheme described above, step S120 can also use the following scheme to enhance the principal Lorentz component in the first Brillouin gain spectrum:
[0082] As one implementation of the above-described Brillouin frequency shift extraction method, step S120 includes: enhancing the principal Lorentz component in the first Brillouin gain spectrum using a filtering function to obtain the second Brillouin gain spectrum. For example, this implementation may use a Gaussian filtering function or a Savitzky-Golay filtering (SG filtering) function to enhance the principal Lorentz component in the first Brillouin gain spectrum to obtain the second Brillouin gain spectrum.
[0083] The above scheme can also use other filtering functions to enhance the first Brillouin gain spectrum, so that the above Brillouin frequency shift extraction method can be applied to more application scenarios, which is beneficial to improving the adaptability of the above Brillouin extraction method.
[0084] In addition, it is understood that the above step S120 can also employ a one-dimensional convolution operation to enhance the principal Lorentz component.
[0085] For example, the same enhancement method can be used in different iterations, or different enhancement methods can be used. For instance, in the first iteration, an enhancement method based on moving smoothing can be used, while in the second iteration, an enhancement method based on filtering functions can be used.
[0086] The following describes the scheme for fitting the relationship between Brillouin frequency shift and Brillouin gain in step S130 above:
[0087] For example, the first fitting window in step S130 above can be a window centered on the maximum point in the second Brillouin gain spectrum, for example:
[0088] Let (X) max ,Y max Y is the second Brillouin gain spectrum. e The maximum value point in x c Let be the index of the maximum value point. Then, the first fitting window described above can be represented as:
[0089] x = X[x] c -w:x c +w]
[0090] y = Y e [x c -w:x c +w]
[0091] Where w is the distance from point x cThe width of the fitting window centered on the first fitting window is expanded, and the length of the first fitting window is 2w.
[0092] It is understandable that, before fitting, the data points contained in the second Brillouin gain spectrum are discrete data points, and the maximum value point in the second Brillouin gain spectrum refers to the maximum value point among the discrete data points.
[0093] The following describes the scheme for fitting the relationship between Brillouin frequency shift and Brillouin gain using the first fitting window mentioned above:
[0094] As one implementation of the above-mentioned Brillouin extraction method, step S130 of fitting the relationship between Brillouin frequency shift and Brillouin gain includes: fitting the relationship between Brillouin frequency shift and Brillouin gain into a quadratic polynomial.
[0095] Since the Brillouin gain spectrum approximately satisfies a quadratic polynomial model within a certain range, and the quadratic polynomial model is relatively simple and has low computational complexity, the relationship between the Brillouin frequency shift and the Brillouin gain can be fitted as a quadratic polynomial:
[0096] g B (v) = av 2 +bv+c
[0097] Among them, g B (v) represents the fitted Brillouin gain; v represents the Brillouin frequency shift; a, b, and c are the coefficients of the quadratic polynomial, respectively.
[0098] Understandably, in addition to fitting the relationship between Brillouin frequency shift and Brillouin gain as a quadratic polynomial, the relationship between Brillouin frequency shift and Brillouin gain can also be fitted as a Lorentz model, a Gaussian model, a pseudo-Voigt model, and a Voigt model, etc.
[0099] The above scheme transforms the complex nonlinear problem between Brillouin frequency shift and Brillouin gain into a more manageable linear problem by fitting the relationship between Brillouin frequency shift and Brillouin gain to a quadratic polynomial. This improves the computational complexity, computational efficiency, and Brillouin frequency shift extraction accuracy of the above-mentioned Brillouin frequency shift extraction method.
[0100] The following describes the preset iteration termination conditions for the above iterative process:
[0101] As one implementation of the above Brillouin frequency shift extraction method, the preset iteration termination condition includes:
[0102] The fitting stability index of the fitting results meets the first preset requirement;
[0103] And / or, the fitting symmetry index of the fitting result meets the second preset requirement;
[0104] And / or, the number of iterations in the iterative process satisfies the third preset requirement.
[0105] The above-mentioned fitting stability index is calculated as follows:
[0106] Based on the second fitting window of the first Brillouin gain spectrum, the relationship between the Brillouin frequency shift and the Brillouin gain is fitted to obtain the fitting result; the second fitted frequency shift is obtained based on the fitting result. The second fitting window includes the maximum point in the first Brillouin gain spectrum. It is understood that the implementation of the second fitting window is similar to that of the first fitting window described above; please refer to the description of the first fitting window for details.
[0107] Determine whether the fitting stability index err1 meets the first preset requirement;
[0108] err1 = |f1 - f2| <tol1
[0109] Where f1 and f2 are the first and second fitted frequency shifts, respectively; tol1 is the first preset threshold.
[0110] Understandably, the above-mentioned fitting stability index is used to characterize the stability of the fitting result, that is, the difference between the current fitting result and the previous fitting result. When the fitting result is relatively stable, it means that the fitting result is closer to the true result. The accuracy of the extracted Brillouin frequency offset can be controlled by the fitting stability index.
[0111] The above-mentioned fitting symmetry index is calculated as follows:
[0112] Using the first fitting frequency obtained from the fitting results as the axis of symmetry, calculate the symmetry error of several points on both sides of the axis of symmetry, and determine whether the symmetry error err2 meets the second preset requirement:
[0113] err2=∑|(g B (v+i)-g B (vi))| <tol2
[0114] It is understandable that the symmetry of the above fitting results can also characterize the stability of the fitting results from another perspective. Generally speaking, a high degree of symmetry in the fitting results means that the fitting results have good stability.
[0115] In addition, it is understandable that after the number of iterations meets the third preset requirement, that is, after the preset number of iterations is reached, the fitting result gradually tends to stabilize. Therefore, the number of iterations can also be used to judge whether the fitting result is stable.
[0116] The preset iteration termination condition in the above scheme can be at least one of the following: fitting stability index, fitting symmetry index, and number of iterations. On the one hand, the preset iteration termination condition can balance the Brillouin frequency shift extraction accuracy and extraction efficiency, which is beneficial to improving the extraction accuracy and extraction efficiency of the above Brillouin frequency shift extraction method. On the other hand, it makes the above Brillouin frequency shift extraction method applicable to more application scenarios, which is beneficial to improving the adaptability of the above Brillouin frequency shift extraction method.
[0117] The following describes the scheme for obtaining the first fitted frequency shift based on the fitting results in step S140 above:
[0118] Understandably, the goal of the aforementioned Brillouin frequency shift extraction method is to extract the center frequency of the principal Lorentz component in the Brillouin gain spectrum. Therefore, after obtaining the fitting result, the center frequency of the fitting result, i.e., the Brillouin frequency shift when the Brillouin gain is at its peak, can be calculated. The calculation method is as follows:
[0119]
[0120] It is understandable that the impact of noise in the original Brillouin gain spectrum on the real data is related to the number of sampling points. For example, compared to 10,000 sampling rounds, the Brillouin data formed by 10,000 sampling rounds better reflects the trends and characteristics of the real data, and the noise has a smaller impact on the real data; while the Brillouin data formed by 100 sampling rounds contains more noise and has a greater impact on the real data, and the aforementioned Brillouin extraction method is more sensitive to noise. Based on this, the embodiments of this application provide the following solution:
[0121] As one implementation of the above-mentioned Brillouin frequency shift extraction method, the above-mentioned Brillouin frequency shift extraction method further includes: noise reduction processing of the original Brillouin gain spectrum before performing the iteration process.
[0122] For example, the original Brillouin gain spectrum can be denoised using denoising methods that do not cause uncontrollable frequency shifts or cause only small uncontrollable frequency shifts. Examples of denoising methods include Gaussian filtering, moving average processing, SVD denoising, and spline smoothing filtering.
[0123] Additionally, it is understandable that the noise reduction processing of the original Brillouin gain spectrum can be adaptively triggered based on the estimated noise level of the original Brillouin gain spectrum, and the estimated noise level of the Brillouin gain spectrum can be obtained based on the number of sampling rounds.
[0124] The above scheme improves the data quality of Brillouin data by denoising the original Brillouin gain spectrum, thereby improving the Brillouin frequency shift extraction accuracy of the above-mentioned Brillouin frequency shift extraction method.
[0125] Please refer to Figure 2 , the following introduces the specific implementation steps of the above Brillouin frequency shift extraction method in a certain scenario. This scenario uses the fitting stability index and the number of iterations as the preset iteration stop conditions. The specific steps are as follows:
[0126] Step 1: Obtain the original Brillouin gain spectrum containing multiple Lorentz components and perform noise reduction processing on it. The obtained Brillouin gain spectrum is as shown in Figure 2 (a). The yellow long dashed line in the figure represents the main Lorentz component, the green dashed line represents the secondary Lorentz component, and the blue solid line is the Brillouin gain spectrum synthesized based on the main Lorentz component and the secondary Lorentz component;
[0127] At this time, the gap between the center frequency (orange solid line) of the synthesized Brillouin gain spectrum and the center frequency of the main Lorentz component, that is, the Brillouin frequency shift (blue dashed line), is relatively large.
[0128] Step 2: Perform fitting based on the original Brillouin gain spectrum to obtain the second fitting frequency f2; [[ID=l4]]
[0129] Step 3: Enhance the main Lorentz component in the first Brillouin gain spectrum (in the first round of iteration, the first Brillouin gain spectrum is the original Brillouin gain spectrum) to obtain the second Brillouin gain spectrum Y e ;
[0130] Step 4: Perform fitting based on the second Brillouin gain spectrum to obtain the first fitting frequency f1;
[0131] Step 5: The number of iterations c = c + 1;
[0132] Step 6: Determine whether the preset iteration termination condition is satisfied, that is, whether |f1 - f2| < tol1 or c ≥ c max ?, if satisfied, terminate the iteration and output f1 as the final result, otherwise let f2 = f1, Y = Y e . Among them, c max is the preset iteration number threshold.
[0133] The second Brillouin gain spectra after the first iteration, the second iteration, and the third iteration are respectively as shown in Figure 2 (b), Figure 2 (c) and Figure 2 (d). It can be found that: after the second iteration, the main peak in the second Brillouin gain spectrum is already relatively prominent. After the third iteration, the fitting result is also relatively stable, and the calculated Brillouin frequency shift has a small gap with the true result, and the calculation accuracy is relatively high.
[0134] Please refer to Figure 3Based on the same inventive concept, this application also provides an optical fiber sensing system 200, which includes an optical fiber sensor 210 and a data processing module 220. The data processing module 220 is used to: acquire the original Brillouin gain spectrum; execute an iterative process until a preset iteration termination condition is met, and acquire a Brillouin frequency shift based on the first fitted frequency shift acquired in the last iteration; the iterative process includes: enhancing the principal Lorentz component in the first Brillouin gain spectrum to acquire a second Brillouin gain spectrum; wherein, in the first iteration, the first Brillouin gain spectrum is the original Brillouin gain spectrum; fitting the relationship between the Brillouin frequency shift and the Brillouin gain based on a first fitting window of the second Brillouin gain spectrum to acquire a fitting result; wherein, the first fitting window includes the maximum value point in the second Brillouin gain spectrum; acquiring a first fitted frequency shift based on the fitting result; and determining the second Brillouin gain spectrum as the new first Brillouin gain spectrum.
[0135] It is understood that the data processing module 220 can execute any of the above-mentioned Brillouin frequency shift extraction methods. For specific solutions, please refer to the introduction of Brillouin frequency shift extraction methods in the foregoing content. The embodiments of this application will not be repeated here.
[0136] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. (Refer to...) Figure 4 The electronic device 300 includes a processor 310, a memory 320, and a communication interface 330. These components are interconnected and communicate with each other via a communication bus 340 and / or other forms of connection mechanism (not shown).
[0137] The memory 320 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), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The processor 310 and other possible components may access the memory 320 to read and / or write data therein.
[0138] Processor 310 includes one or more (only one is shown in the figure), which can be an integrated circuit chip with signal processing capabilities. The processor 310 described above 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.
[0139] Communication interface 330 includes one or more (only one is shown in the figure) and can be used to communicate directly or indirectly with other devices to exchange data. For example, communication interface 330 can be an Ethernet interface; it can be a mobile communication network interface, such as an interface for 3G, 4G, or 5G networks; or it can be other types of interfaces with data transmission and reception functions.
[0140] One or more computer program instructions may be stored in the memory 320, and the processor 310 may read and run these computer program instructions to implement the Brillouin frequency shift extraction method provided in the embodiments of this application and other desired functions.
[0141] Understandable. Figure 4 The structure shown is for illustrative purposes only; the electronic device 300 may also include components that are more advanced than those shown. Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown. Figure 4 The components shown can be implemented using hardware, software, or a combination thereof. For example, electronic device 300 can be a single server (or other device with computing power), a combination of multiple servers, a cluster of a large number of servers, etc., and can be either a physical device or a virtual device.
[0142] This application also provides a computer-readable storage medium storing computer program instructions. These computer program instructions are read and executed by a computer processor to perform the Brillouin frequency shift extraction method provided in this application. For example, the computer-readable storage medium can be implemented as follows: Figure 4The memory 320 in the electronic device 300.
[0143] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0144] Furthermore, the units described as separate components may or may not be physically separate. 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 can be selected to achieve the purpose of this embodiment according to actual needs.
[0145] Furthermore, the functional modules in the various embodiments of this 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.
[0146] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.
Claims
1. A Brillouin frequency shift extraction method, characterized by, The method includes: Obtain the original Brillouin gain spectrum; The iterative process is executed until the preset iteration termination condition is met, and the Brillouin frequency shift is obtained based on the first fitted frequency shift obtained in the last iteration process. The iterative process includes: The principal Lorentz component in the first Brillouin gain spectrum is enhanced to obtain the second Brillouin gain spectrum; wherein, in the first iteration, the first Brillouin gain spectrum is the original Brillouin gain spectrum; Based on the first fitting window of the second Brillouin gain spectrum, the relationship between the Brillouin frequency shift and the Brillouin gain is fitted to obtain the fitting result; wherein, the first fitting window includes the maximum value point in the second Brillouin gain spectrum; Based on the fitting results, the first fitted frequency shift is obtained; The second Brillouin gain spectrum is determined as the new first Brillouin gain spectrum.
2. The Brillouin frequency shift extraction method according to claim 1, characterized in that, The enhancement process for the principal Lorentz component in the first Brillouin gain spectrum to obtain the second Brillouin gain spectrum includes: The first Brillouin spectrum is subjected to a first processing and a second processing to obtain a third Brillouin gain spectrum and a fourth Brillouin gain spectrum; wherein, the first processing is a moving average processing to characterize a first degree of smoothness; the second processing is a moving average processing to characterize a second degree of smoothness; the first degree of smoothness is less than the second degree of smoothness; The second Brillouin gain spectrum is obtained based on the third and fourth Brillouin gain spectra.
3. The Brillouin frequency shift extraction method according to claim 2, characterized in that, The step of obtaining the second Brillouin gain spectrum based on the third and fourth Brillouin gain spectra includes: A second Brillouin gain spectrum is obtained based on the third Brillouin gain spectrum, the fourth Brillouin gain spectrum, and the scaling factor; wherein the scaling factor is used to characterize the degree of influence of the fourth Brillouin gain spectrum on the third Brillouin gain spectrum.
4. The Brillouin frequency shift extraction method according to claim 1, characterized in that, The enhancement process for the principal Lorentz component in the first Brillouin gain spectrum to obtain the second Brillouin gain spectrum includes: The main Lorentz component in the first Brillouin gain spectrum is enhanced using a filtering function to obtain the second Brillouin gain spectrum.
5. The Brillouin frequency shift extraction method according to any one of claims 1 to 4, characterized in that, The preset iteration termination conditions include: The fitting stability index of the fitting result meets the first preset requirement; And / or, the fitting symmetry index of the fitting result meets the second preset requirement; And / or, the number of iterations in the iterative process satisfies a third preset requirement.
6. The Brillouin frequency shift extraction method according to any one of claims 1 to 4, characterized in that, The fitting of the relationship between the Brillouin frequency shift and the Brillouin gain includes: The relationship between the Brillouin frequency shift and the Brillouin gain is fitted as a quadratic polynomial.
7. The Brillouin frequency shift extraction method according to any one of claims 1 to 4, characterized in that, Before performing the iterative process, the method further includes: The original Brillouin gain spectrum is subjected to noise reduction processing.
8. A fiber optic sensing system, characterized in that, The system includes fiber optic sensors and a data processing module; The data processing module is used to acquire the original Brillouin gain spectrum; execute an iterative process until a preset iteration termination condition is met; and acquire the Brillouin frequency shift based on the first fitted frequency shift acquired in the last iteration. The iterative process includes: enhancing the principal Lorentz component in the first Brillouin gain spectrum to acquire a second Brillouin gain spectrum; wherein, in the first iteration, the first Brillouin gain spectrum is the original Brillouin gain spectrum; fitting the relationship between the Brillouin frequency shift and the Brillouin gain based on a first fitting window of the second Brillouin gain spectrum to acquire a fitting result; wherein, the first fitting window includes the maximum value point in the second Brillouin gain spectrum; acquiring the first fitted frequency shift based on the fitting result; and determining the second Brillouin gain spectrum as the new first Brillouin gain spectrum.
9. 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 as described in any one of claims 1 to 7 by calling the program instructions.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform the method as described in any one of claims 1 to 7.