Parameter extraction method, device and equipment based on Brillouin scattering spectrum and medium

Abstract

The invention relates to the technical field of signal processing, in particular to a parameter extraction method and device based on a Brillouin scattering spectrum, equipment and a medium. The method comprises the following steps: acquiring to-be-processed Brillouin spectral data, and determining peak data in the Brillouin spectral data; according to the peak value data, a bimodal coefficient corresponding to the Brillouin spectrum is calculated through a preset bimodal feature algorithm; determining a target fitting algorithm for processing the Brillouin spectrum data based on the double-peak coefficient; and reconstructing the Brillouin spectrum data by using the target fitting algorithm, and extracting target parameters from the reconstructed Brillouin spectrum data. By adopting the method, the stability of single-peak and double-peak discrimination processing can be improved, and the accuracy of an analysis result is improved.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a parameter extraction method, device, equipment and medium based on Brillouin scattering spectrum. Background Art

[0002] Stimulated Brillouin Scattering (SBS) is a nonlinear process that occurs in optical fiber transmission. When the pump light power of the input optical fiber reaches the SBS threshold of the optical fiber, the density and dielectric constant of the optical fiber medium will change periodically under the action of the electrostrictive effect, forming an acoustic wave field. When the pump light is incident on the moving acoustic wave field, backscattered light will occur, namely Stokes light. The frequency difference between Stokes light and pump light is the Brillouin frequency shift. The amount of Brillouin frequency shift is not only determined by the material properties of the optical fiber medium itself, but also related to the temperature and stress of the optical fiber.

[0003] Brillouin Optical Time-Domain Analysis (BOTDA) and Brillouin Optical Time-Domain Reflectometry (BOTDR) are sensor analysis technologies based on stimulated Brillouin scattering mechanisms. They can be used for distributed sensing monitoring of temperature and stress of optical fibers, and have broad application prospects in health detection of large infrastructure such as communication cables, buildings, oil pipelines, power lines, and railways that are long-distance and subject to long-term external forces and environmental erosion during service. By analyzing the changes in the energy of the detection light along the optical fiber, the Brillouin gain spectrum curve along the optical fiber is fitted to obtain the center frequency information of the Brillouin gain spectrum, and then based on the linear relationship between the center frequency change of the Brillouin gain spectrum and temperature and strain, the temperature and strain information at each position along the optical fiber is restored.

[0004] In engineering practice, there are sudden changes in the center frequency in the optical fiber fusion zone and the extremely small stress zone, which will cause the measured Brillouin gain spectrum to have a double-peak behavior. In addition, some types of optical fibers themselves have a double-peak Brillouin gain spectrum. If the traditional single-peak Lorentz fitting is still used, it will lead to fitting errors in the center frequency, and ultimately cause false alarms or omissions. Summary of the invention

[0005] Based on this, it is necessary to provide a parameter extraction method, device, equipment and medium based on Brillouin scattering spectrum that can improve the stability of single and double peak discrimination processing and improve the accuracy of analysis results in response to the above technical problems.

[0006] In a first aspect, the present application provides a parameter extraction method based on Brillouin scattering spectrum. The method comprises:

[0007] Acquire the Brillouin spectrum data to be processed, and determine the peak data in the Brillouin spectrum data;

[0008] According to the peak data, a double peak coefficient corresponding to the Brillouin spectrum is calculated by a preset double peak characteristic algorithm;

[0009] Determining a target fitting algorithm for processing the Brillouin spectroscopy data based on the bimodal coefficient;

[0010] The target fitting algorithm is applied to reconstruct the Brillouin spectrum data, and target parameters are extracted from the reconstructed Brillouin spectrum data.

[0011] In one embodiment, determining a target fitting algorithm for processing the Brillouin spectroscopy data based on the bimodal coefficient comprises:

[0012] In response to a first round of processing of the Brillouin spectroscopy data, selecting the target fitting algorithm based on the bimodal coefficient;

[0013] If the bimodal coefficient is lower than a preset initial discrimination threshold, determining the unimodal fitting algorithm as the target fitting algorithm;

[0014] If the bimodal coefficient is not lower than the initial discrimination threshold, the bimodal fitting algorithm is determined as the target fitting algorithm.

[0015] In one embodiment, determining a target fitting algorithm for processing the Brillouin spectroscopy data based on the bimodal coefficient comprises:

[0016] In response to the non-first-round processing of the Brillouin spectrum data, acquiring a historical state of the Brillouin spectrum data;

[0017] If the bimodal coefficient is not higher than the preset unimodal discrimination threshold, determining the unimodal fitting algorithm as the target fitting algorithm;

[0018] If the bimodal coefficient is not lower than the preset bimodal discrimination threshold, determining that the bimodal fitting algorithm is the target fitting algorithm, and the bimodal discrimination threshold is higher than the unimodal discrimination threshold;

[0019] If the bimodal coefficient is in a transition zone between the unimodal discrimination threshold and the bimodal discrimination threshold, the target fitting algorithm is maintained to be the same as the historical state.

[0020] In one embodiment, the obtaining of the Brillouin spectrum data to be processed and the determination of the peak data in the Brillouin spectrum data include:

[0021] Preprocessing the Brillouin spectrum data, and performing differential processing on the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum;

[0022] Selecting a target sampling point in the differential scatter spectrum whose adjacent sampling points satisfy the non-negative left side and non-positive right side as a zero point;

[0023] The peak data is obtained based on the Brillouin spectrum data corresponding to the zero point.

[0024] In one embodiment, the preprocessing of the Brillouin spectrum data and the differential processing of the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum includes:

[0025] Acquire a frequency sweep step length and a spectrum width of the Brillouin spectrum data, and determine a differential step length applied to the differential processing based on the frequency sweep step length and the spectrum width;

[0026] Performing K-step differential processing on the Brillouin spectrum data based on the differential step length to obtain the differential scattering spectrum;

[0027] Among them, K in the K-step differential processing is the quotient of the spectrum width and the frequency sweep step length. When the quotient of the spectrum width and the frequency sweep step length is a non-integer, it is rounded off; the differential step length is K times the frequency sweep step length.

[0028] In one embodiment, the calculating, based on the peak data, a double peak coefficient corresponding to the Brillouin spectrum by a preset double peak characteristic algorithm comprises:

[0029] Obtaining the maximum peak amplitude and the second largest peak amplitude in the peak data;

[0030] The bimodality coefficient is determined based on a ratio of the maximum peak amplitude to the second largest peak amplitude.

[0031] In one embodiment, reconstructing the Brillouin spectrum data by applying the target fitting algorithm, and extracting the target parameters from the reconstructed Brillouin spectrum data comprises:

[0032] In response to reconstructing the Brillouin spectrum data based on a double peak fitting algorithm, obtaining a first center frequency corresponding to the reconstructed maximum peak and a second center frequency corresponding to the second largest peak;

[0033] Performing weighted calculation on the first center frequency and the second center frequency to obtain a first target center frequency;

[0034] and / or;

[0035] In response to reconstructing the Brillouin spectrum data based on a single peak fitting algorithm, a second target center frequency is extracted based on the reconstructed Brillouin spectrum data.

[0036] In a second aspect, the present application also provides a parameter extraction device based on Brillouin scattering spectrum. The device comprises:

[0037] A peak data module, used for acquiring the Brillouin spectrum data to be processed and determining the peak data in the Brillouin spectrum data;

[0038] A bimodal coefficient module, used to calculate the bimodal coefficient corresponding to the Brillouin spectrum according to the peak data by a preset bimodal characteristic algorithm;

[0039] A state discrimination module, used for determining a target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient;

[0040] The fitting and extraction module is used to apply the target fitting algorithm to reconstruct the Brillouin spectrum data and extract target parameters from the reconstructed Brillouin spectrum data.

[0041] In one embodiment, the state determination module includes:

[0042] A first-round processing module, configured to select the target fitting algorithm based on the bimodal coefficient in response to the first-round processing of the Brillouin spectrum data;

[0043] A first discrimination module, configured to determine that a unimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is lower than a preset initial discrimination threshold;

[0044] The second discrimination module is used to determine that the bimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is not lower than the initial discrimination threshold.

[0045] In one embodiment, the state determination module includes:

[0046] a non-first-round processing module, configured to obtain a historical state of the Brillouin spectrum data in response to the non-first-round processing of the Brillouin spectrum data;

[0047] A third discrimination module, configured to determine that the unimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is not higher than a preset unimodal discrimination threshold;

[0048] a fourth discrimination module, configured to determine that a bimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is not lower than a preset bimodal discrimination threshold, and the bimodal discrimination threshold is higher than the unimodal discrimination threshold;

[0049] A fifth discrimination module is configured to maintain the target fitting algorithm the same as the historical state if the bimodal coefficient is in a transition zone between the unimodal discrimination threshold and the bimodal discrimination threshold.

[0050] In one embodiment, the peak data module includes:

[0051] A differential processing module is used to preprocess the Brillouin spectrum data and perform differential processing on the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum;

[0052] A zero point selection module, used for selecting a target sampling point in the differential scatter spectrum whose adjacent sampling points satisfy the non-negative left side and non-positive right side as a zero point;

[0053] A peak data construction module is used to obtain the peak data based on the Brillouin spectrum data corresponding to the zero point.

[0054] In one embodiment, the differential processing module includes:

[0055] A step length determination module, used for obtaining a frequency sweep step length and a spectrum width of the Brillouin spectrum data, and determining a differential step length applied to the differential processing based on the frequency sweep step length and the spectrum width;

[0056] A multi-order difference module is used to perform K-step difference processing on the Brillouin spectrum data based on the difference step length to obtain the differential scattering spectrum, wherein K in the K-step difference processing is the quotient of the spectrum width and the frequency sweep step length. When the quotient of the spectrum width and the frequency sweep step length is a non-integer, it is rounded off; and the difference step length is K times the frequency sweep step length.

[0057] In one embodiment, the bimodal coefficient module includes:

[0058] A dual peak amplitude module, used for obtaining the maximum peak amplitude and the second largest peak amplitude in the peak data;

[0059] A bimodal ratio module is used to determine the bimodal coefficient based on the ratio of the maximum peak amplitude to the second largest peak amplitude.

[0060] In one embodiment, the fitting extraction module includes:

[0061] A bimodal data module, configured to obtain a first center frequency corresponding to a reconstructed maximum peak and a second center frequency corresponding to a second maximum peak in response to reconstructing the Brillouin spectrum data based on a bimodal fitting algorithm;

[0062] A weighted calculation module, used for performing weighted calculation on the first center frequency and the second center frequency to obtain a target center frequency;

[0063] and / or a single peak fitting module, configured to extract a second target center frequency based on the reconstructed Brillouin spectrum data in response to reconstructing the Brillouin spectrum data based on a single peak fitting algorithm.

[0064] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the parameter extraction method based on Brillouin scattering spectrum as described in any embodiment of the first aspect when executing the computer program.

[0065] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the parameter extraction method based on Brillouin scattering spectrum as described in any one of the embodiments in the first aspect.

[0066] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the parameter extraction method based on Brillouin scattering spectrum as described in any one of the embodiments in the first aspect are implemented.

[0067] The above-mentioned parameter extraction method, device, equipment and medium based on Brillouin scattering spectrum can achieve the following beneficial effects corresponding to the technical problems in the background technology by deducing the technical features in the claims:

[0068] The present application provides a parameter extraction method based on Brillouin scattering spectrum, including obtaining Brillouin spectrum data to be processed, determining peak data in the Brillouin spectrum data; according to the peak data, calculating the bimodal coefficient corresponding to the Brillouin spectrum by a preset bimodal characteristic algorithm; determining the target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient; applying the target fitting algorithm to reconstruct the Brillouin spectrum data, and extracting the target parameter from the reconstructed Brillouin spectrum data. In implementation, after obtaining the Brillouin spectrum data, the peak data existing in the spectrum data is determined by processing, and the bimodal coefficient that can characterize whether the Brillouin spectrum data has a bimodal feature is calculated by the peak data. In this way, the bimodal coefficient can be used to determine whether the Brillouin data to be processed is suitable for single-peak fitting or bimodal fitting, as well as the corresponding target fitting algorithm. Finally, the target fitting algorithm can be applied to reconstruct the Brillouin spectrum data, and extract the required target parameters such as peak value, center frequency, etc. Compared with the traditional discrimination method, the stability of processing the Brillouin spectrum data can be improved, thereby ensuring the accuracy of the recognition result under certain noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 FIG. 1 is an application environment diagram of a parameter extraction method based on Brillouin scattering spectrum in one embodiment;

[0070] Figure 2is a schematic diagram of a first process of a parameter extraction method based on Brillouin scattering spectrum in one embodiment;

[0071] Figure 3 is a second flow chart of a parameter extraction method based on Brillouin scattering spectrum in another embodiment;

[0072] Figure 4 is a schematic diagram of a third flow chart of a parameter extraction method based on Brillouin scattering spectrum in another embodiment;

[0073] Figure 5 is a schematic flow chart of a parameter extraction method based on Brillouin scattering spectrum in one embodiment;

[0074] Figure 6 is a schematic diagram of a fourth process of a parameter extraction method based on Brillouin scattering spectrum in another embodiment;

[0075] Figure 7 is a fifth flow chart of a parameter extraction method based on Brillouin scattering spectrum in another embodiment;

[0076] Figure 8 is a sixth flow chart of a parameter extraction method based on Brillouin scattering spectrum in another embodiment;

[0077] Fig. 9 is a seventh flow chart of a parameter extraction method based on Brillouin scattering spectrum in another embodiment;

[0078] Fig.10 A schematic diagram of target parameter extraction in a specific embodiment;

[0079] Fig.11 A schematic diagram of target parameter extraction in another specific embodiment;

[0080] Fig.12 is a structural block diagram of a parameter extraction device based on Brillouin scattering spectrum in one embodiment;

[0081] Fig.13 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0083] In engineering practice, there are sudden changes in the center frequency in the optical fiber fusion zone and the extremely small stress zone, which will cause the measured Brillouin gain spectrum to have a double-peak behavior. In addition, some types of optical fibers themselves have a double-peak Brillouin gain spectrum. If the traditional single-peak Lorentz fitting is still used, it will lead to fitting errors in the center frequency, and ultimately cause false alarms or omissions.

[0084] Based on the above problems, an embodiment of the present application provides a parameter extraction method based on Brillouin scattering spectrum.

[0085] The parameter extraction method based on Brillouin scattering spectrum provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0086] In one embodiment, Figure 2 As shown in the figure, a parameter extraction method based on Brillouin scattering spectrum is provided, and this method is applied to Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0087] Step 202: Acquire Brillouin spectrum data to be processed, and determine peak data in the Brillouin spectrum data.

[0088] Among them, Brillouin scattering spectrum is a spectral technique obtained by detecting the interaction between light and sound waves in materials. Brillouin scattering involves the frequency change of light, which is closely related to the propagation characteristics of sound waves (such as the speed of sound and the frequency of sound waves). In Brillouin spectroscopy, the peak often represents the properties of phonon modes or sound waves in the material. Peak data can include peak position and peak height.

[0089] Exemplarily, the terminal may acquire Brillouin spectrum data to be processed, and determine peak data in the Brillouin spectrum data.

[0090] Step 204: According to the peak data, a double peak coefficient corresponding to the Brillouin spectrum is calculated by a preset double peak characteristic algorithm.

[0091] The double peak feature algorithm may refer to a discrimination parameter used to discriminate whether the Brillouin spectrum is single-peaked or double-peaked.

[0092] Exemplarily, the terminal may calculate, based on the peak data, a double peak coefficient corresponding to the Brillouin spectrum using a preset double peak characteristic algorithm.

[0093] Step 206: Determine a target fitting algorithm for processing the Brillouin spectroscopy data based on the bimodal coefficient.

[0094] Among them, the fitting algorithm of Brillouin spectrum is usually used to extract peak information in the signal, such as position, intensity and width, so as to analyze the acoustic characteristics and other physical properties of the material. The fitting algorithm may include Gaussian fitting, Lorentz fitting, Voigt fitting, etc. In the scenario where there may be multiple peaks in the Brillouin spectrum, since it is necessary to consider the joint fitting of multiple peaks, the fitting algorithm can also be divided into single peak fitting and double peak fitting.

[0095] Exemplarily, the terminal may determine a target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient.

[0096] Step 208: applying the target fitting algorithm to reconstruct the Brillouin spectrum data, and extracting target parameters from the reconstructed Brillouin spectrum data.

[0097] Exemplarily, the terminal may apply the target fitting algorithm to reconstruct the Brillouin spectrum data, and extract the target parameters from the reconstructed Brillouin spectrum data. The specific fitting and reconstruction process may include initial parameter estimation, nonlinear fitting, and spectrum reconstruction using fitting parameters.

[0098] In the above-mentioned parameter extraction method based on Brillouin scattering spectrum, reasonable deduction is performed in combination with the technical features in the embodiment, which can achieve the following beneficial effects of solving the technical problems raised in the background technology:

[0099] The present application provides a parameter extraction method based on Brillouin scattering spectrum, including obtaining Brillouin spectrum data to be processed, determining peak data in the Brillouin spectrum data; according to the peak data, calculating the bimodal coefficient corresponding to the Brillouin spectrum by a preset bimodal characteristic algorithm; determining the target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient; applying the target fitting algorithm to reconstruct the Brillouin spectrum data, and extracting the target parameter from the reconstructed Brillouin spectrum data. In implementation, after obtaining the Brillouin spectrum data, the peak data existing in the spectrum data is determined by processing, and the bimodal coefficient that can characterize whether the Brillouin spectrum data has a bimodal feature is calculated by the peak data. In this way, the bimodal coefficient can be used to determine whether the Brillouin data to be processed is suitable for single-peak fitting or bimodal fitting, as well as the corresponding target fitting algorithm. Finally, the target fitting algorithm can be applied to reconstruct the Brillouin spectrum data, and extract the required target parameters such as peak value, center frequency, etc. Compared with the traditional discrimination method, the stability of processing the Brillouin spectrum data can be improved, thereby ensuring the accuracy of the recognition result under certain noise.

[0100] In one embodiment, if Figure 3 As shown, step 206 includes:

[0101] Step 302: In response to the first round of processing of the Brillouin spectrum data, select the target fitting algorithm based on the bimodal coefficient.

[0102] Exemplarily, the terminal may select the target fitting algorithm based on the bimodal coefficient in response to a first round of processing of the Brillouin spectroscopy data.

[0103] Step 304: If the bimodal coefficient is lower than a preset initial discrimination threshold, the unimodal fitting algorithm is determined as the target fitting algorithm.

[0104] Among them, the initial discrimination threshold can be set in advance by a technician.

[0105] Exemplarily, if the bimodal coefficient is lower than a preset initial discrimination threshold, the terminal may determine that the unimodal fitting algorithm is the target fitting algorithm.

[0106] Step 306: If the bimodal coefficient is not lower than the initial discrimination threshold, the bimodal fitting algorithm is determined as the target fitting algorithm.

[0107] Specifically, the terminal can set the fitting method identification variable , Table LM algorithm single peak fitting, Represents the bimodal fitting of the LM algorithm, and the bimodal coefficient is c.

[0108] Specifically, the first round of Brillouin scattering spectrum fitting method is selected. The value is as follows

[0109]

[0110] Exemplarily, if the bimodal coefficient is not lower than the initial discrimination threshold, the terminal may determine that the bimodal fitting algorithm is the target fitting algorithm.

[0111] In this embodiment, when the device is started for the first time, when fitting the first set of Brillouin spectroscopy data, due to the lack of a basic state, it is processed with an initial discrimination threshold, so that the selected target fitting algorithm can be output normally in the first round of processing, which helps to improve the stability of the algorithm execution and realize the cold start function of the algorithm.

[0112] Furthermore, the applicant has also found that various single and double peak discrimination parameters are usually constructed in the double peak fitting technology, and the single peak or double peak of the Brillouin gain spectrum is distinguished by a single threshold. However, the current bistable single and double peak identification and center frequency extraction algorithm based on the Brillouin scattering spectrum, when using a monotonic threshold for single and double peak discrimination, will cause the single and double peak judgment structure in the threshold critical state to jump frequently, resulting in an increased probability of underreporting and false alarms in the analysis results.

[0113] Based on the above problems, in one embodiment, it can be as follows Figure 4 As shown, step 206 includes:

[0114] Step 402: In response to the non-first-round processing of the Brillouin spectrum data, a historical state of the Brillouin spectrum data is obtained.

[0115] Exemplarily, the terminal may acquire the historical state of the Brillouin spectrum data in response to the non-first-round processing of the Brillouin spectrum data.

[0116] Step 404: If the bimodal coefficient is not higher than the preset unimodal discrimination threshold, the unimodal fitting algorithm is determined as the target fitting algorithm.

[0117] Step 406: If the bimodal coefficient is not lower than a preset bimodal discrimination threshold, the bimodal fitting algorithm is determined to be the target fitting algorithm, and the bimodal discrimination threshold is higher than the unimodal discrimination threshold.

[0118] Step 408: If the bimodal coefficient is in a transition zone between the unimodal discrimination threshold and the bimodal discrimination threshold, the target fitting algorithm is maintained to be the same as the historical state.

[0119] Selection of fitting method for Brillouin scattering spectrum collected in non-first round, The value is as follows

[0120]

[0121] Specifically, the discrimination process of the target fitting algorithm can be as follows Figure 5 shown.

[0122] In this embodiment, in the non-first round processing state, that is, in most implementation scenarios of this algorithm, when selecting the target fitting algorithm, the two algorithms are discriminated by the single-peak discrimination threshold and the double-peak discrimination threshold. Since two thresholds are selected to discriminate the two target fitting algorithms, and the double-peak discrimination threshold is greater than the single-peak discrimination threshold, the double-peak coefficient will not fluctuate around a single threshold, resulting in continuous jumping of the discrimination result, which helps to improve the stability of Brillouin spectroscopy data processing.

[0123] In one embodiment, the Figure 6 As shown, step 202 includes:

[0124] Step 602: preprocessing the Brillouin spectrum data, and performing differential processing on the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum.

[0125] Among them, differential processing is to calculate the change of the signal to highlight the local features and peaks. In Brillouin spectroscopy, differential calculation is often used to extract higher frequency features. Preprocessing can include denoising, data smoothing, normalization, etc.

[0126] Exemplarily, the terminal may preprocess the Brillouin spectrum data, and perform differential processing on the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum.

[0127] Step 604: select a target sampling point in the differential scatter spectrum whose adjacent sampling points satisfy the non-negative left side and non-positive right side as the zero point.

[0128] Exemplarily, the terminal may select a target sampling point in the differential scatter spectrum whose adjacent sampling points satisfy the condition that the left side is non-negative and the right side is non-positive as the zero point.

[0129] Step 606: Obtain the peak data based on the Brillouin spectrum data corresponding to the zero point.

[0130] Specifically, the terminal can obtain the measured discrete Brillouin scattering spectrum data ,in , is the number of frequency sweep points, is the initial frequency, and the sweep step is Then, the scattering spectrum data Normalize the amplitude (0,1) to get the normalized Brillouin scattering spectrum The terminal can perform normalized Brillouin scattering spectrum Step difference to get Step Difference Scattering Spectroscopy , It must be an integer multiple of the sweep step length and can be set according to the characteristic half-width of the actual fiber Brillouin gain spectrum. The differential scattering spectrum can be shown as follows:

[0131]

[0132] At this point, the terminal can calculate Step Difference Scattering Spectroscopy The zero point in the equation satisfies the condition that the left side is non-negative and the right side is non-positive , is the number of zero points, and the judgment conditions are as follows:

[0133]

[0134] The terminal can calculate the normalized Brillouin scattering spectrum as follows exist All Brillouin scatter peaks within the range , :

[0135]

[0136] In this embodiment, the differential scattering spectrum is obtained by performing differential processing on the Brillouin spectrum data. At this time, the value of the differential scattering spectrum can characterize the rise and fall of the signal amplitude corresponding to each sampling point. In this way, only the sampling points that are non-negative on the left and non-positive on the right need to be selected. These target sampling points can represent all the peak points in the Brillouin spectrum data, that is, the zero point. Finally, the peak data can be obtained by relying on the Brillouin spectrum data corresponding to the zero point, which helps to improve the accuracy of the peak data.

[0137] In one embodiment, if Figure 7 As shown, step 602 includes:

[0138] Step 702: Acquire the frequency sweep step length and spectrum width of the Brillouin spectrum data, and determine the differential step length applied to the differential processing based on the frequency sweep step length and the spectrum width.

[0139] The frequency sweep step size can refer to the increase from one frequency point to the next frequency point during the frequency sweep process. The differential step size can refer to the differential interval (step size) selected when calculating the differential in sequence analysis.

[0140] Step 704: performing K-step differential processing on the Brillouin spectrum data based on the differential step length to obtain the differential scattering spectrum.

[0141] Among them, K in the K-step differential processing is the quotient of the spectrum width and the frequency sweep step length. When the quotient of the spectrum width and the frequency sweep step length is a non-integer, it is rounded off; the differential step length is K times the frequency sweep step length.

[0142] Explanatory, spectral width refers to the half-maximum full width of the stimulated Brillouin scattering spectrum in the optical fiber. The specific method for determining the spectral width is the frequency range corresponding to when the amplitude of the Brillouin gain spectrum drops to half. More specifically, after obtaining the Brillouin spectrum data, the Brillouin spectrum data can be discrete Brillouin scattering spectrum data, and then the half-height of the spectrum composed of the discrete Brillouin scattering spectrum data is determined, that is, half of the maximum Brillouin gain value corresponds to the half-height position, and the scatter points corresponding to the half-height position are obtained, and then the spectral width can be estimated.

[0143] In the embodiment, the stability of the sequence can be enhanced by K-step differential processing, which is more stable than the traditional first-order differential, reduces the error caused by noise, and further enhances the accuracy of the peak data. On the other hand, selecting the quotient of the spectrum width and the sweep step length as K in the K-step differential processing of the differential step length helps to improve the coverage of the differential scattering spectrum, and can improve the accuracy of the processing result of the Brillouin spectrum compared to other values ​​in the traditional processing.

[0144] In one embodiment, if Figure 8 As shown, step 204 includes:

[0145] Step 802: Obtain the maximum peak amplitude and the second largest peak amplitude in the peak data.

[0146] Step 804: Determine the bimodal coefficient based on the ratio of the maximum peak amplitude to the second largest peak amplitude.

[0147] Specifically, the terminal can calculate the Brillouin scatter spectrum peaks Maximum value and the next largest value , , and calculate the bimodal coefficient

[0148]

[0149] In this embodiment, the double peak coefficient is determined by the ratio of the maximum peak amplitude and the second largest peak amplitude in the peak data, so as to realize the single peak or double peak state discrimination of the Brillouin spectrum to be processed, which helps to improve the accuracy of state discrimination.

[0150] In one embodiment, if Fig. 9 As shown, step 208 includes:

[0151] Step 902: In response to reconstructing the Brillouin spectrum data based on a double-peak fitting algorithm, a first center frequency corresponding to the reconstructed maximum peak and a second center frequency corresponding to the second largest peak are obtained.

[0152] Specifically, the terminal can identify the variables according to the fitting method Perform LM single-peak or double-peak fitting on the normalized Brillouin scattering spectrum and reconstruct the scattering spectrum data according to the fitting parameters , the expression is as follows; for LM single peak fitting, the fitting parameters include the peak , center frequency , half-height half-width ; For LM bimodal fitting, the fitting parameters include the first peak , the first center frequency , first half-height half-width , the second peak , the second center frequency , the second half-height half-width

[0153]

[0154] Step 904: Perform weighted calculation on the first center frequency and the second center frequency to obtain a target center frequency.

[0155] Specifically, in the extraction process of the center frequency F, for LM single peak fitting, the center frequency The center frequency parameter of the fit ; while LM bimodal fitting, the center frequency is the weighted average of the two peaks, as shown below:

[0156]

[0157] And / or step 906: in response to reconstructing the Brillouin spectrum data based on the single peak fitting algorithm, extracting a second target center frequency based on the reconstructed Brillouin spectrum data.

[0158] In this embodiment, in the scenario of bimodal fitting, the target center frequency finally calculated is the weighted calculation result of the first center frequency and the second center frequency, which helps to retain the characteristics of the main peak and the secondary peak at the same time and improve the accuracy of the center frequency calculation result. On the other hand, by first judging the bimodal coefficient and then performing single and double peak fitting, compared with first performing single peak fitting and then judging whether the fitting is accurate before adjusting the bimodal fitting, the amount of calculation can be effectively saved and repeated fitting can be avoided.

[0159] In a specific example, Fig.10As shown in (a), the scattering spectrum is a bimodal distribution, the ratio of the secondary peak to the main peak is about 0.15, and there is a certain intensity of Gaussian noise (including multiplicative and additive). Using the above steps provided in the embodiment of the present application for processing, the output center frequency is as follows Fig.10 As shown in (b), the standard deviation (1σ) of the center frequency fluctuation of the proposed scheme is 0.097MHz, while the standard deviation (1σ) of the center frequency fluctuation of the traditional single-threshold single-peak and double-peak recognition scheme is 0.179MHz. The center frequency fluctuation of the proposed scheme is significantly improved.

[0160] In another specific example, Fig.11 As shown in (a), the scattering spectrum is a bimodal distribution, the peak ratio of the main peak to the secondary peak is about 1, and there is a certain intensity of Gaussian noise (including multiplicative and additive). The above steps provided in the embodiment of the present application are used for processing. Fig.11 (b) shows the comparison of the fluctuation of the final output center frequency of different center frequency calculation methods. The center frequency calculated by the bimodal weighted algorithm has a smaller fluctuation, while the center frequency calculated by the bimodal highest peak algorithm exhibits a binary jump phenomenon under the influence of tiny noise.

[0161] Obviously, the bimodal fitting algorithm proposed in the embodiment of the present application utilizes the numerical weighting of the two fitted Lorentz spectra in the frequency domain as the center frequency of the scattering spectrum, avoiding the discrimination of the primary and secondary peaks of the bimodal scattering spectrum, and effectively suppressing the center frequency jump problem caused by the misjudgment of the main peak. In the traditional bimodal fitting, only the center frequency of the main peak with a larger amplitude is extracted, and the information of the secondary peak is discarded. When the amplitudes of the main and secondary peaks are similar, slight noise changes will also cause the main and secondary peaks to switch with each other, and finally cause the fluctuation of the extracted center frequency, and the system has weak anti-noise ability. Compared with the traditional single-threshold single-double peak discrimination method, the bistable single-double peak recognition algorithm has strong noise robustness, which ensures the accuracy of the recognition results under certain noise, and can significantly improve the stability of the extraction of the center frequency of the scattering spectrum in the Brillouin sensing system.

[0162] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0163] Based on the same inventive concept, the embodiment of the present application also provides a parameter extraction device based on Brillouin scattering spectrum for implementing the parameter extraction method based on Brillouin scattering spectrum involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the parameter extraction device based on Brillouin scattering spectrum provided below can refer to the limitations of the parameter extraction method based on Brillouin scattering spectrum above, and will not be repeated here.

[0164] In one embodiment, Fig.12 As shown, a parameter extraction device based on Brillouin scattering spectrum is provided, including: a peak data module, a double peak coefficient module, a state discrimination module and a fitting extraction module, wherein:

[0165] A peak data module, used for acquiring the Brillouin spectrum data to be processed and determining the peak data in the Brillouin spectrum data;

[0166] A bimodal coefficient module, used to calculate the bimodal coefficient corresponding to the Brillouin spectrum according to the peak data by a preset bimodal characteristic algorithm;

[0167] A state discrimination module, used for determining a target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient;

[0168] The fitting and extraction module is used to apply the target fitting algorithm to reconstruct the Brillouin spectrum data and extract target parameters from the reconstructed Brillouin spectrum data.

[0169] In one embodiment, the state determination module includes:

[0170] A first-round processing module, configured to select the target fitting algorithm based on the bimodal coefficient in response to the first-round processing of the Brillouin spectrum data;

[0171] A first discrimination module, configured to determine that a unimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is lower than a preset initial discrimination threshold;

[0172] The second discrimination module is used to determine that the bimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is not lower than the initial discrimination threshold.

[0173] In one embodiment, the state determination module includes:

[0174] a non-first-round processing module, configured to obtain a historical state of the Brillouin spectrum data in response to the non-first-round processing of the Brillouin spectrum data;

[0175] A third discrimination module, configured to determine that the unimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is not higher than a preset unimodal discrimination threshold;

[0176] a fourth discrimination module, configured to determine that a bimodal fitting algorithm is the target fitting algorithm if the bimodal coefficient is not lower than a preset bimodal discrimination threshold, and the bimodal discrimination threshold is higher than the unimodal discrimination threshold;

[0177] A fifth discrimination module is configured to maintain the target fitting algorithm the same as the historical state if the bimodal coefficient is in a transition zone between the unimodal discrimination threshold and the bimodal discrimination threshold.

[0178] In one embodiment, the peak data module includes:

[0179] A differential processing module is used to preprocess the Brillouin spectrum data and perform differential processing on the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum;

[0180] A zero point selection module, used for selecting a target sampling point in the differential scatter spectrum whose adjacent sampling points satisfy the non-negative left side and non-positive right side as a zero point;

[0181] A peak data construction module is used to obtain the peak data based on the Brillouin spectrum data corresponding to the zero point.

[0182] In one embodiment, the differential processing module includes:

[0183] A step length determination module, used for obtaining a frequency sweep step length and a spectrum width of the Brillouin spectrum data, and determining a differential step length applied to the differential processing based on the frequency sweep step length and the spectrum width;

[0184] A multi-order difference module, used for performing K-step difference processing on the Brillouin spectrum data based on the difference step length to obtain the differential scattering spectrum;

[0185] Among them, K in the K-step differential processing is the quotient of the spectrum width and the frequency sweep step length. When the quotient of the spectrum width and the frequency sweep step length is a non-integer, it is rounded off; the differential step length is K times the frequency sweep step length.

[0186] In one embodiment, the bimodal coefficient module includes:

[0187] A dual peak amplitude module, used for obtaining the maximum peak amplitude and the second largest peak amplitude in the peak data;

[0188] A bimodal ratio module is used to determine the bimodal coefficient based on the ratio of the maximum peak amplitude to the second largest peak amplitude.

[0189] In one embodiment, the fitting extraction module includes:

[0190] A bimodal data module, configured to obtain a first center frequency corresponding to a reconstructed maximum peak and a second center frequency corresponding to a second maximum peak in response to reconstructing the Brillouin spectrum data based on a bimodal fitting algorithm;

[0191] A weighted calculation module, used for performing weighted calculation on the first center frequency and the second center frequency to obtain a target center frequency;

[0192] And / or, a single peak fitting module, configured to extract a second target center frequency based on the reconstructed Brillouin spectrum data in response to reconstructing the Brillouin spectrum data based on a single peak fitting algorithm.

[0193] Each module in the above-mentioned parameter extraction device based on Brillouin scattering spectrum can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0194] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.13 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a parameter extraction method based on the Brillouin scattering spectrum is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0195] Those skilled in the art will understand that Fig.13 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0196] In one embodiment, a computer device is also provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0197] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0198] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0199] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0200] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0201] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0202] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A parameter extraction method based on Brillouin scattering spectrum, characterized in that: The method comprises: Acquire the Brillouin spectrum data to be processed, and determine the peak data in the Brillouin spectrum data; According to the peak data, a double peak coefficient corresponding to the Brillouin spectrum is calculated by a preset double peak characteristic algorithm; Determining a target fitting algorithm for processing the Brillouin spectroscopy data based on the bimodal coefficient; The target fitting algorithm is applied to reconstruct the Brillouin spectrum data, and target parameters are extracted from the reconstructed Brillouin spectrum data.

2. The method according to claim 1, characterized in that The target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient comprises: In response to a first round of processing of the Brillouin spectroscopy data, selecting the target fitting algorithm based on the bimodal coefficient; If the bimodal coefficient is lower than a preset initial discrimination threshold, determining the unimodal fitting algorithm as the target fitting algorithm; If the bimodal coefficient is not lower than the initial discrimination threshold, the bimodal fitting algorithm is determined as the target fitting algorithm.

3. The method according to claim 1, characterized in that The target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient comprises: In response to the non-first-round processing of the Brillouin spectrum data, acquiring a historical state of the Brillouin spectrum data; If the bimodal coefficient is not higher than the preset unimodal discrimination threshold, determining the unimodal fitting algorithm as the target fitting algorithm; If the bimodal coefficient is not lower than the preset bimodal discrimination threshold, determining that the bimodal fitting algorithm is the target fitting algorithm, and the bimodal discrimination threshold is higher than the unimodal discrimination threshold; If the bimodal coefficient is in a transition zone between the unimodal discrimination threshold and the bimodal discrimination threshold, the target fitting algorithm is maintained to be the same as the historical state.

4. The method according to claim 1, characterized in that: The obtaining of the Brillouin spectrum data to be processed and determining the peak data in the Brillouin spectrum data comprises: Preprocessing the Brillouin spectrum data, and performing differential processing on the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum; Selecting a target sampling point in the differential scatter spectrum whose adjacent sampling points satisfy the non-negative left side and non-positive right side as a zero point; The peak data is obtained based on the Brillouin spectrum data corresponding to the zero point.

5. The method according to claim 4, characterized in that The preprocessing of the Brillouin spectrum data and differential processing of the preprocessed Brillouin spectrum data to obtain a differential scattering spectrum includes: Acquire a frequency sweep step length and a spectrum width of the Brillouin spectrum data, and determine a differential step length applied to the differential processing based on the frequency sweep step length and the spectrum width; Performing K-step differential processing on the Brillouin spectrum data based on the differential step length to obtain the differential scattering spectrum; Among them, K in the K-step differential processing is the quotient of the spectrum width and the frequency sweep step length. When the quotient of the spectrum width and the frequency sweep step length is a non-integer, it is rounded off; the differential step length is K times the frequency sweep step length.

6. The method according to claim 1, characterized in that The step of calculating the double peak coefficient corresponding to the Brillouin spectrum by a preset double peak characteristic algorithm according to the peak data includes: Obtaining the maximum peak amplitude and the second largest peak amplitude in the peak data; The bimodality coefficient is determined based on a ratio of the maximum peak amplitude to the second largest peak amplitude.

7. The method according to any one of claims 1 to 6, characterized in that: Reconstructing the Brillouin spectrum data by applying the target fitting algorithm, and extracting target parameters from the reconstructed Brillouin spectrum data comprises: In response to reconstructing the Brillouin spectrum data based on a double peak fitting algorithm, obtaining a first center frequency corresponding to the reconstructed maximum peak and a second center frequency corresponding to the second largest peak; Performing weighted calculation on the first center frequency and the second center frequency to obtain a first target center frequency; and / or; In response to reconstructing the Brillouin spectrum data based on a single peak fitting algorithm, a second target center frequency is extracted based on the reconstructed Brillouin spectrum data.

8. A parameter extraction device based on Brillouin scattering spectrum, characterized in that: The device comprises: A peak data module, used for acquiring the Brillouin spectrum data to be processed and determining the peak data in the Brillouin spectrum data; A bimodal coefficient module, used to calculate the bimodal coefficient corresponding to the Brillouin spectrum according to the peak data by a preset bimodal characteristic algorithm; A state discrimination module, used for determining a target fitting algorithm for processing the Brillouin spectrum data based on the bimodal coefficient; The fitting and extraction module is used to apply the target fitting algorithm to reconstruct the Brillouin spectrum data and extract target parameters from the reconstructed Brillouin spectrum data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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