Optical fiber environment event identification method based on birefringence FBG

Abstract

The invention relates to the technical field of fiber bragg gratings, and particularly provides a birefringent FBG-based fiber environment event identification method, which comprises the following steps: constructing a spectral pulse matrix according to fast axis spectrums and slow axis spectrums of a plurality of spectral pulses, the spectral pulses being echo spectral pulses transmitted by a birefringent FBG; solving a joint optimization problem, and separating a first matrix and a second matrix from the spectral pulse matrix; solving a first optimization problem of the first matrix based on a first dictionary learning algorithm to obtain a vibration event recognition matrix used for representing a vibration event signal; and solving a second optimization problem based on a second dictionary learning algorithm to obtain a temperature offset feature vector used for representing the temperature change signal, the second optimization problem being determined according to the merged vector of the second matrix. The problem of difficulty in signal decoupling of the FBG in a complex environment in which temperature change and vibration events exist at the same time in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of fiber Bragg gratings, and particularly to a method for identifying fiber optic environmental events based on birefringent FBG. Background Art

[0002] Fiber Bragg Grating (FBG for short) has the advantages of high sensitivity, strong anti-interference ability, and being suitable for remote monitoring, and is widely used in the detection of physical quantities such as strain, temperature, and vibration.

[0003] In a complex environment where temperature changes and vibration events coexist, there is a problem of difficult signal decoupling for fiber Bragg gratings. Because temperature changes will cause the fiber to thermally expand or contract, resulting in changes in refractive index and length, and thus causing the Bragg wavelength to drift; vibration events will cause dynamic strain in the fiber, which will also change the physical properties of the fiber and cause the Bragg wavelength to drift, so it is difficult to distinguish the effects of the two.

[0004] In view of the problem of difficult signal decoupling for fiber Bragg gratings in a complex environment where temperature changes and vibration events coexist in the related art, no effective solution has been proposed yet. Summary of the Invention

[0005] The method for identifying fiber optic environmental events based on birefringent FBG provided by the embodiments of the present invention can at least solve the problem of difficult signal decoupling for fiber Bragg gratings in a complex environment where temperature changes and vibration events coexist in the related art.

[0006] The method for identifying fiber optic environmental events based on birefringent FBG provided by the embodiments of the present invention includes: constructing a spectral pulse matrix according to the fast-axis spectrum and slow-axis spectrum of multiple spectral pulses, where the spectral pulse is the echo spectral pulse transmitted by a birefringent fiber Bragg grating FBG; separating a first matrix and a second matrix from the spectral pulse matrix by solving a joint optimization problem, where the joint optimization problem is determined according to the spectral pulse matrix; solving a first optimization problem of the first matrix based on a first dictionary learning algorithm to obtain a vibration event recognition matrix, where the vibration event recognition matrix is used to characterize the vibration event signal; solving a second optimization problem based on a second dictionary learning algorithm to obtain a temperature offset feature vector, where the second optimization problem is determined according to a combined vector, the combined vector is obtained by performing a combination process on the second matrix, and the temperature offset feature vector is used to characterize the temperature change signal.

[0007] The fiber optic environmental event recognition method based on birefringent FBG provided by the embodiment of the present invention creates a spectral pulse matrix according to the fast-axis spectrum and slow-axis spectrum of multiple spectral pulses, including: receiving multiple spectral pulses; adding the fast-axis spectrum and slow-axis spectrum of the i-th spectral pulse to obtain the i-th biaxial fusion spectral pulse; constructing a spectral pulse matrix based on the sampling column vectors of multiple biaxial fusion spectral pulses.

[0008] The fiber optic environmental event recognition method based on birefringent FBG provided by the embodiment of the present invention creates separates the first matrix and the second matrix from the spectral pulse matrix by solving a joint optimization problem, including: representing the spectral pulse matrix as: ; where R represents the spectral pulse matrix, L represents the first matrix, S represents the second matrix, and N represents the noise matrix; Based on the spectral pulse matrix, the joint optimization problem is determined as: ; where represents solving for the minimum value, is the regularization parameter corresponding to the first matrix L, is the truncated nuclear norm of the first matrix L, is the regularization parameter corresponding to the second matrix S, is the sparse representation of the second matrix S, is the noise suppression term, and s.t. represents the constraint condition; By solving the joint optimization problem through the first optimization algorithm, the optimal estimate of the first matrix and the optimal estimate of the second matrix are obtained. Among them, the optimal estimate of the first matrix is used to determine the first optimization problem, and the optimal estimate of the second matrix is used to determine the second optimization problem.

[0009] Before determining the joint optimization problem based on the spectral pulse matrix in the fiber optic environmental event recognition method based on birefringent FBG provided by the embodiment of the present invention creates, the above method further includes: determining the truncated nuclear norm through the singular values of the first matrix L : ; where represents the i-th singular value of the first matrix L, r represents the truncated rank, represents finding the rank of the matrix.

[0010] The fiber optic environmental event recognition method based on birefringent FBG provided by the embodiment of the present invention creates solves the first optimization problem of the first matrix through the first dictionary learning algorithm to obtain a vibration event recognition matrix, including: constructing a vibration event empirical dictionary; based on the vibration event empirical dictionary and the first matrix, determining the first optimization problem as: ; Among them, represents the th p column of the vibration event recognition matrix P represents the number of spectral pulses, represents the Euclidean norm, L is the first matrix, represents the vibration event empirical dictionary; Based on the second optimization algorithm, solve the first optimization problem to obtain the optimal solution of the vibration event recognition matrix. Among them, the optimal solution of the vibration event recognition matrix is used to characterize the vibration event signal.

[0011] The fiber optic environment event recognition method based on birefringent FBG provided by the embodiment of the present invention creates a vibration event empirical dictionary, including: selecting samples from the vibration event database to obtain a plurality of empirical vibration events; calculating the fast-axis echo spectrum corresponding to each empirical vibration event, or calculating the slow-axis echo spectrum corresponding to each empirical vibration event; based on the fast-axis echo spectrum or the slow-axis echo spectrum, determining the vibration event dictionary atoms corresponding to each empirical vibration event; constructing a vibration event empirical dictionary based on the vibration event dictionary atoms.

[0012] The fiber optic environment event recognition method based on birefringent FBG provided by the embodiment of the present invention creates a temperature offset feature vector by solving the second optimization problem based on the second dictionary learning algorithm, including: constructing a differential temperature discrimination dictionary; transposing and combining the second matrix column by column to obtain a combined vector; based on the differential temperature discrimination dictionary and the combined vector, determining the second optimization problem as: ; Among them, represents the temperature offset feature vector, represents the 0 norm, is the combined vector, represents the differential temperature discrimination dictionary; Based on the third optimization algorithm, solve the second optimization problem to obtain the optimal solution of the temperature offset feature vector. Among them, the optimal solution of the temperature offset feature vector is used to characterize the temperature change signal.

[0013] The method for identifying fiber optic environmental events based on birefringent FBG provided by the embodiment of the present invention creates a differential temperature discrimination dictionary, including: selecting samples from a temperature database to obtain the fast-axis echo spectrum and slow-axis echo spectrum at different temperatures; determining the fast-axis echo spectrum and slow-axis echo spectrum at different temperature offsets based on a reference temperature and the fast-axis echo spectrum and slow-axis echo spectrum at different temperatures; superimposing the fast-axis echo spectrum and slow-axis echo spectrum at the same temperature offset among the fast-axis echo spectra and slow-axis echo spectra at different temperature offsets to obtain the superimposed spectra corresponding to different temperature offsets respectively; determining temperature offset dictionary atoms based on the superimposed spectra; and constructing a differential temperature discrimination dictionary based on the temperature offset dictionary atoms.

[0014] An electronic device provided by the embodiment of the present invention includes: a processor, and a memory storing a program, where the program includes instructions that, when executed by the processor, cause the processor to execute any of the above methods.

[0015] A non-transitory machine-readable medium storing computer instructions provided by the embodiment of the present invention, where the computer instructions are used to cause a computer to execute any of the above methods.

[0016] The method for identifying fiber optic environmental events based on birefringent FBG, electronic device, and non-transitory machine-readable medium storing computer instructions provided by the embodiment of the present invention separate a first matrix and a second matrix from a spectral pulse matrix, obtain a vibration event recognition matrix representing a vibration event signal based on the first matrix, and obtain a temperature offset eigenvector representing a temperature change signal based on the second matrix, which can realize the joint recognition of vibration events and temperature changes and effectively reduce the difficulty of signal decoupling; directly constructing a first optimization problem based on the first matrix can avoid the mutual crosstalk between different vibration events and improve the accuracy of vibration event recognition; performing a merging process on the second matrix to obtain a merged vector and constructing a second optimization problem based on the merged vector can improve the accuracy of temperature change recognition. This solves the problem of difficult signal decoupling in the related art for fiber Bragg gratings in a complex environment with both temperature changes and vibration events. Description of the Drawings

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other embodiments based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of the steps of a method for identifying fiber optic environmental events based on birefringent FBG in the embodiment of the present invention.

[0019] Figure 2 It is a schematic structural diagram of an electronic device in an embodiment of the present invention. Detailed implementation manners

[0020] The embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0021] The fiber Bragg grating (FBG) is a periodic refractive index modulation structure formed in the fiber core by ultraviolet exposure. Through the modulation of the grating structure by changes in physical quantities of the fiber environment (such as temperature, strain, vibration, etc.), these physical quantities can be dynamically monitored and event identification can be achieved.

[0022] The uniaxial FBG lacks sufficient discrimination ability in a complex environment where temperature changes and vibration events coexist, which limits its application in multi-physical quantity monitoring, especially in scenarios that require high-precision and real-time analysis, such as bridge monitoring and oil pipeline detection. Obviously, the accuracy and reliability of the fiber environment event identification method based on the uniaxial FBG are relatively low.

[0023] The birefringent FBG has two orthogonal polarization axes, the fast axis and the slow axis, by introducing the birefringence effect, and can simultaneously measure the wavelength changes of the fast axis and the slow axis. However, the existing fiber environment event identification methods based on the birefringent FBG fail to effectively reduce the coupling between the temperature change signal and the vibration event signal, and have relatively low reliability and relatively great difficulty in signal decoupling in a complex environment where temperature changes and vibration events coexist.

[0024] Therefore, please refer to Figure 1 As shown, this embodiment provides a fiber environment event identification method based on a birefringent FBG, including: Step S101, constructing a spectral pulse matrix according to the fast-axis spectrum and the slow-axis spectrum of a plurality of spectral pulses, where the spectral pulse is an echo spectral pulse transmitted by the birefringent fiber Bragg grating (FBG).

[0025] Step S102, separating a first matrix and a second matrix from the spectral pulse matrix by solving a joint optimization problem, where the joint optimization problem is determined according to the spectral pulse matrix.

[0026] Step S103: Solve the first optimization problem of the first matrix based on the first dictionary learning algorithm to obtain a vibration event recognition matrix, where the vibration event recognition matrix is used to characterize the vibration event signal.

[0027] Step S104: Solve the second optimization problem based on the second dictionary learning algorithm to obtain a temperature offset feature vector, where the second optimization problem is determined according to a combined vector, and the combined vector is obtained by performing a combination process on the second matrix. The temperature offset feature vector is used to characterize the temperature change signal.

[0028] The methods for obtaining the above spectral pulses include, but are not limited to: collecting through a spectral analyzer, or measuring based on a Mach-Zehnder interferometer or a Michelson interferometer, or detecting using an optical time domain reflectometer (OTDR).

[0029] The spectral pulse matrix may only include the first matrix and the second matrix, or may at least include the first matrix, the second matrix, and a noise matrix. The preferred solution in this embodiment is that the spectral pulse matrix includes the first matrix, the second matrix, and the noise matrix. Introducing the noise matrix to characterize the signal fluctuations and uncertainties caused by various non-ideal, random, or interfering factors can reduce the difficulty of signal decoupling and improve the recognition accuracy while ensuring relatively high computational efficiency. This preferred solution will be specifically introduced later.

[0030] The methods for determining the joint optimization problem based on the spectral pulse matrix include, but are not limited to: equality constraint optimization, or inequality constraint optimization, or boundary constraint optimization, or logical constraint optimization. In this embodiment, the preferred method is to use equality constraint optimization. Represent the above spectral pulse matrix in the form of the sum of multiple matrices, and then determine the corresponding joint optimization problem, which has the advantages of accuracy and clarity. This preferred method will be specifically introduced later.

[0031] The optimal estimate of the joint optimization problem can be the minimum value, the maximum value, or the target value under preset constraint conditions. In this embodiment, the preferred method is to use the minimum value under preset constraint conditions as the optimal estimate, which has the advantages of stability orientation and relatively convenient mathematical solution. This preferred method will be specifically introduced later.

[0032] Considering that the fast axis and slow axis of the birefringent FBG have different responses to temperature changes and vibration events, this embodiment utilizes this difference of the biaxiality to distinguish the wavelength drifts caused by temperature changes and vibration events, and the following principle analysis is carried out.

[0033] The central wavelength of the fast axis of the birefringent FBG and the central wavelength of the slow axis can be respectively expressed as: ; ; where Λ is the grating period, and n F is the effective refractive index of the fast axis, and n s is the effective refractive index of the slow axis.

[0034] Based on the above representation, the wavelength drift of the fast axis of the birefringent FBG and the wavelength drift of the slow axis can be respectively expressed as: ; ; where represents the change in ambient temperature, represents the vibration displacement, represents the temperature sensitivity coefficient of the fast axis, represents the temperature sensitivity coefficient of the slow axis, represents the vibration displacement sensitivity coefficient common to the fast axis and the slow axis.

[0035] It can be seen that for the birefringent FBG, the temperature sensitivity coefficients of the fast axis and the slow axis are different. Therefore, the external temperature change can be regarded as an independent interference to the fast axis and the slow axis respectively. Relatively, since the vibration displacement sensitivity coefficients of the fast axis and the slow axis are the same, the external vibration event can be regarded as a common interference to the fast axis and the slow axis.

[0036] Further analysis shows that: among the spectral pulses received under the influence of the same external vibration event, due to the same spectral peak displacement, the echo spectral pulse matrix under the influence of the same vibration event has a low-rank characteristic. In this embodiment, it is preferably to determine the first matrix for characterizing the influence of the vibration event based on this low-rank characteristic.

[0037] At the same time, among the spectral pulses received under the influence of the same external temperature change, due to different spectral peak displacements and the same displacement difference value, the echo spectral pulse matrix will exhibit a group-sparse characteristic. In this embodiment, it is preferably to determine the second matrix for characterizing the influence of the temperature change based on this group-sparse characteristic.

[0038] The fiber optic environmental event recognition method based on birefringent FBG provided in this embodiment separates the first matrix and the second matrix from the spectral pulse matrix, obtains a vibration event recognition matrix characterizing the vibration event signal based on the first matrix, and obtains a temperature offset eigenvector characterizing the temperature change signal based on the second matrix, which can realize the joint recognition of vibration events and temperature changes and effectively reduce the difficulty of signal decoupling; directly constructing a first optimization problem based on the first matrix can avoid the mutual crosstalk between different vibration events and improve the accuracy of vibration event recognition; performing a merging process on the second matrix to obtain a merged vector and constructing a second optimization problem based on the merged vector can improve the accuracy of temperature change recognition. This is to solve the problem of difficult signal decoupling in the related art for fiber Bragg gratings in a complex environment with simultaneous temperature changes and vibration events.

[0039] Preferably, in step S101, according to the fast-axis spectrum and slow-axis spectrum of multiple spectral pulses, a spectral pulse matrix is constructed, including steps S1011 - S1013.

[0040] In step S1011, multiple spectral pulses are received. For example, a spectrometer or a fiber grating demodulator is used to receive multiple spectral pulses at the signal processing end.

[0041] In step S1012, the fast-axis spectrum and slow-axis spectrum of the i-th spectral pulse are added to obtain the i-th biaxial fusion spectral pulse. The specific formula is: ; where represents the i-th biaxial fusion spectral pulse, represents the fast-axis spectrum of the i-th spectral pulse, represents the slow-axis spectrum of the i-th spectral pulse.

[0042] In step S1013, based on the sampling column vectors of multiple biaxial fusion spectral pulses, a spectral pulse matrix is constructed. The specific formula is: ; where R represents the spectral pulse matrix, represents the sampling column vector of the i-th biaxial fusion spectral pulse, , P represents the number of biaxial fusion spectral pulses, and T represents the transpose.

[0043] It can be understood that the sampling column vector refers to the discretized representation of the biaxial fusion spectral pulse, converting continuous spectral information into structured digital information. For example, if each biaxial fusion spectral pulse is sampled a times within a certain range, the values obtained from each sampling can be arranged in sequence to form an a×1 column vector. The number of biaxial fusion spectral pulses is equal to the number of spectral pulses.

[0044] Preferably, in step S102, the first matrix and the second matrix are separated from the spectral pulse matrix by solving a joint optimization problem, including steps S1021 - S1022.

[0045] Step S1021, represent the spectral pulse matrix as: ; where, R represents the spectral pulse matrix, L represents the first matrix, S represents the second matrix, and N represents the noise matrix.

[0046] It can be understood that the first matrix L represents the spectral component matrix affected only by vibration events, and the second matrix S represents the spectral component matrix affected only by temperature changes.

[0047] Based on the spectral pulse matrix, the joint optimization problem is determined as: ; where, represents solving for the minimum value, is the regularization parameter corresponding to the first matrix L, used to balance the influence of the low-rank property, is the truncated nuclear norm of the first matrix L, is the regularization parameter corresponding to the second matrix S, used to balance the influence of the group sparsity property, is the sparse representation of the second matrix S, is the noise suppression term, s.t. represents the constraint condition.

[0048] Step S1022, solve the joint optimization problem through the first optimization algorithm to obtain the optimal estimate of the first matrix and the optimal estimate of the second matrix, where the optimal estimate of the first matrix is used to determine the first optimization problem, and the optimal estimate of the second matrix is used to determine the second optimization problem.

[0049] The first optimization algorithm is the Alternating Direction Method of Multipliers (ADMM) or the proximal gradient descent method or the objective cascading method, which belongs to the prior art and will not be elaborated in this embodiment. Those skilled in the art can also use other optimization algorithms to solve the above joint optimization problem according to their own experience and actual situation.

[0050] Specifically, for the signal change caused by vibration events, since the fast axis and slow axis of the birefringent FBG have strong correlation in the response to vibration events, it can be considered that the performance of vibration events in the signal matrix has the low-rank property, and this low-rank property can be characterized by singular value decomposition (SVD) or nuclear norm minimization.

[0051] For example, the first matrix L can be represented in the following form: ; Among them, both U and V are orthogonal matrices, Σ is a diagonal matrix, and Σ has a low rank. In this case, the first matrix L can reflect the common influence of vibration events on the fast axis and the slow axis. The fact that Σ has a low rank means that most of the eigenvalues of the diagonal matrix Σ are zero, and those skilled in the art can determine the specific numerical range of most according to prior values or actual situations.

[0052] Before determining the joint optimization problem based on the spectral pulse matrix, the truncated nuclear norm is determined by the singular values of the first matrix L : ; Among them, represents the i-th singular value of the first matrix L, r represents the truncated rank, represents finding the rank of the matrix.

[0053] It can be understood that using the truncated nuclear norm as a constraint condition can make the first matrix L have a lower rank in the process of solving the joint optimization problem.

[0054] Preferably, for the signal change caused by temperature change, since the fast axis and the slow axis of the birefringent FBG respond to temperature change relatively independently, it can be considered that the performance of temperature change in the signal matrix has the group sparse characteristic. The group sparse characteristic means that under temperature change, only a small number of wavelengths will have significant drift, and this situation shows a sparse pattern on the fast axis and the slow axis, and its sparse representation can be represented by group sparse regularization.

[0055] For example, the sparse representation of the second matrix S is: ; Among them, represents the j-th group of parameter vectors of the second matrix S, represents the L2 norm of.

[0056] Preferably, in step S103, based on the first dictionary learning algorithm, the first optimization problem of the first matrix is solved to obtain the vibration event recognition matrix, including steps S1031 - S1033.

[0057] Step S1031, construct an empirical dictionary of vibration events.

[0058] Step S1032, based on the empirical dictionary of vibration events and the first matrix, determine the first optimization problem as: ; Among them, represents the -th p column of the vibration event recognition matrix P represents the number of spectral pulses, denotes the Euclidean norm, and L is the first matrix. denotes the empirical dictionary of vibration events.

[0059] Step S1033: Solve the first optimization problem based on the second optimization algorithm to obtain the optimal solution of the vibration event recognition matrix, where the optimal solution of the vibration event recognition matrix is used to characterize the vibration event signal.

[0060] The second optimization algorithm is any one of the alternating direction multiplier method, the proximal gradient descent method, or the objective cascading method, and the second optimization algorithm can be the same as the above first optimization algorithm.

[0061] By analyzing the positions of the non-zero elements in the optimal solution of the vibration event recognition matrix, the vibration event corresponding to each echo optical pulse moment can be determined, and the vibration event signal can be accurately identified. Therefore, the above method provided in this embodiment has significant advantages for the monitoring and recognition of short-time vibration events.

[0062] Preferably, in step S1031, constructing the empirical dictionary of vibration events includes steps S10311 - S10314.

[0063] Step S10311: Select samples from the vibration event database to obtain multiple empirical vibration events.

[0064] It can be understood that the vibration signal data corresponding to different empirical vibration events stored in the vibration event database can be the actually monitored vibration signals or the simulated vibration signals.

[0065] Step S10312: Calculate the fast-axis echo spectrum corresponding to each empirical vibration event, or calculate the slow-axis echo spectrum corresponding to each empirical vibration event.

[0066] For example, preprocessing the vibration signal data of the empirical vibration event, calculating the optical intensity difference, and performing fast Fourier transform (FFT) to calculate the corresponding fast-axis echo spectrum, which belongs to the prior art and will not be elaborated in this embodiment.

[0067] It can be understood that when the fast-axis echo spectrum data or the slow-axis echo spectrum data corresponding to each empirical vibration event of the birefringent FBG has been stored in the vibration event database, the above calculations are not required, that is, the above steps S10311 and S10312 can be combined as: Select samples from the vibration event database to obtain the fast-axis echo spectrum corresponding to multiple empirical vibration events, or obtain the slow-axis echo spectrum corresponding to multiple empirical vibration events.

[0068] Step S10313: Determine the vibration event dictionary atoms corresponding to each empirical vibration event based on the fast-axis echo spectrum or the slow-axis echo spectrum.

[0069] Taking the fast-axis echo spectrum as an example, if the dimension or the number of empirical vibration events is Q, then the vibration event dictionary atom corresponding to the q-th empirical vibration event is: ; where represents the fast-axis echo spectrum calculated under the influence of the q-th empirical vibration event.

[0070] Step S10314, construct an empirical dictionary of vibration events based on the vibration event dictionary atoms.

[0071] For example, the empirical dictionary of vibration events constructed based on the vibration event dictionary atoms corresponding to Q empirical vibration events can be expressed as: .

[0072] Preferably, in step S104, solve the second optimization problem based on the second dictionary learning algorithm to obtain the temperature offset feature vector, including steps S1041 - S1044.

[0073] Step S1041, construct a differential temperature discrimination dictionary.

[0074] Step S1042, transpose the second matrix and merge it column by column to obtain a merged vector.

[0075] The specific formula for transposing and merging column by column is: ; where represents the n-th element of the merged vector , sum() represents summation, represents the n-th row of the matrix , is the transposed matrix of the second matrix S.

[0076] Step S1043, based on the differential temperature discrimination dictionary and the merged vector, determine the second optimization problem as: ; where represents the temperature offset feature vector, represents the 0-norm, is the merged vector, represents the differential temperature discrimination dictionary.

[0077] Step S1044, solve the second optimization problem based on the third optimization algorithm to obtain the optimal solution of the temperature offset feature vector, where the optimal solution of the temperature offset feature vector is used to characterize the temperature change signal.

[0078] The third optimization algorithm is any one of the alternating direction method of multipliers, the proximal gradient descent method, or the objective cascading method, and the third optimization algorithm can be the same as the above-mentioned first optimization algorithm and the above-mentioned second optimization algorithm.

[0079] It can be understood that the position index of the non-zero elements in the optimal solution of the temperature offset eigenvector corresponds to the temperature offset value at the target moment, and thus the temperature change signal can be accurately identified.

[0080] Preferably, in step S1041, a differential temperature discrimination dictionary is constructed, including steps S10411 - S10415.

[0081] In step S10411, samples are selected from the temperature database to obtain the fast-axis echo spectrum and the slow-axis echo spectrum at different temperatures.

[0082] It can be understood that the temperature change signal data stored in the temperature database can be the actually monitored temperature change signal or the simulated generated temperature change signal.

[0083] By preprocessing the temperature change signal data, calculating the optical intensity difference, and performing a fast Fourier transform (FFT), the corresponding fast-axis echo spectrum and slow-axis echo spectrum can be calculated.

[0084] When the fast-axis echo spectrum data and the slow-axis echo spectrum data corresponding to different temperatures of the birefringent FBG are already stored in the temperature database, there is no need to perform the above calculations, and samples can be directly selected.

[0085] In step S10412, based on the reference temperature and the fast-axis echo spectrum and the slow-axis echo spectrum at different temperatures, the fast-axis echo spectrum and the slow-axis echo spectrum under different temperature offsets are determined.

[0086] The reference temperature is determined by those skilled in the art according to the empirical value and the requirements of the actual application scenario.

[0087] Based on the difference between different temperatures T and the reference temperature different temperature offsets are determined .

[0088] In step S10413, the fast-axis echo spectrum and the slow-axis echo spectrum under the same temperature offset among the fast-axis echo spectrum and the slow-axis echo spectrum under different temperature offsets are superimposed to obtain the superimposed spectrum corresponding to each different temperature offset.

[0089] For example, the superimposed spectrum corresponding to the temperature offset is , where and respectively represent the temperature offset Fast-axis echo spectra and slow-axis echo spectra under

[0090] Step S10414: Determine temperature-offset dictionary atoms based on the superimposed spectra corresponding to different temperature offsets.

[0091] For example, the temperature offset The corresponding temperature-offset dictionary atom .

[0092] Define the temperature-offset scanning interval as , where M is the number of temperature-offset scans, 1 ≤ m ≤ M, and the corresponding temperature-offset dictionary atoms can be respectively expressed as .

[0093] Step S10415: Construct a differential temperature discrimination dictionary based on the temperature-offset dictionary atoms.

[0094] The differential temperature discrimination dictionary can be expressed as: .

[0095] In summary, the embodiment of the present invention provides a method for identifying fiber optic environmental events based on birefringent FBG, determines the joint optimization problem reflecting different signals, and combines the optimization algorithm of low-rank and sparse joint representation, which can effectively separate and identify temperature change signals and vibration event signals in a complex environment with simultaneous temperature changes and vibration events.

[0096] The embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the above computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present invention.

[0097] The embodiment of the present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present invention.

[0098] The embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The above memory stores a computer program that can be executed by the at least one processor, and the above computer program, when executed by the at least one processor, is used to cause the electronic device to execute the method of the embodiment of the present invention.

[0099] Reference Figure 2, the structural block diagram of an electronic device that can be a server or a client in an embodiment of the present invention will now be described. It is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0100] As Figure 2 shown, the electronic device includes a computing unit 201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 202 or a computer program loaded from a storage unit 208 into a random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the electronic device can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0101] Multiple components in the electronic device are connected to the I / O interface 205, including: an input unit 206, an output unit 207, a storage unit 208, and a communication unit 209. The input unit 206 can be any type of device that can input information into the electronic device. The input unit 206 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function controls of the electronic device. The output unit 207 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 208 can include, but is not limited to, magnetic disks and optical discs. The communication unit 209 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0102] The computing unit 201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 201 include, but are not limited to, a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 201 executes the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as a computer program tangibly embodied in a machine-readable medium, such as the storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 202 and / or the communication unit 209. In some embodiments, the computing unit 201 can be configured to execute the above-described methods in any other suitable manner (e.g., by means of firmware).

[0103] The computer program for implementing the method of the embodiments of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of the embodiments of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] It should be noted that the term "including" and its variants used in the embodiments of the present invention are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more". The descriptions of terms such as "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features.

[0106] In the method embodiments provided by the embodiments of the present invention, the various steps recorded can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The protection scope of the present invention is not limited in this regard.

[0107] The term "embodiment" in this specification means that the specific features, structures or characteristics described in connection with the embodiment may be included in at least one embodiment of the present invention. The phrase appears in various positions in the specification and does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referred to each other. In particular, for device, equipment, and system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiments.

[0108] The above-described embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the protection scope. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A method for identifying fiber environment events based on birefringence FBG, characterized in that: include: Constructing a spectrum pulse matrix according to the fast axis spectra and the slow axis spectra of the plurality of spectrum pulses, wherein the spectrum pulses are echo spectrum pulses transmitted by the birefringent fiber Bragg grating (FBG); Separating a first matrix and a second matrix from the spectral pulse matrix by solving a joint optimization problem, wherein the joint optimization problem is determined based on the spectral pulse matrix; Solving a first optimization problem of the first matrix based on a first dictionary learning algorithm to obtain a vibration event identification matrix, wherein the vibration event identification matrix is ​​used to characterize a vibration event signal; A second optimization problem is solved based on a second dictionary learning algorithm to obtain a temperature offset feature vector, wherein the second optimization problem is determined based on a merged vector, the merged vector is obtained by merging the second matrices, and the temperature offset feature vector is used to characterize a temperature change signal.

2. The method according to claim 1, characterized in that According to the fast-axis spectra and slow-axis spectra of multiple spectral pulses, a spectral pulse matrix is ​​constructed, including: receiving a plurality of said spectral pulses; Adding the fast-axis spectrum and the slow-axis spectrum of the i-th spectral pulse to obtain the i-th dual-axis fusion spectral pulse; The spectrum pulse matrix is ​​constructed based on a plurality of sampling column vectors of the dual-axis fused spectrum pulses.

3. The method according to claim 1, characterized in that: Separating a first matrix and a second matrix from the spectral pulse matrix by solving a joint optimization problem includes: The spectral pulse matrix is ​​expressed as: ； Wherein, R represents the spectrum pulse matrix, L represents the first matrix, S represents the second matrix, and N represents the noise matrix; The joint optimization problem is determined based on the spectral pulse matrix as follows: ； in, It means to find the minimum value. is the regularization parameter corresponding to the first matrix L, is the truncated nuclear norm of the first matrix L, is the regularization parameter corresponding to the second matrix S, is the sparse representation of the second matrix S, is the noise suppression term, st represents the constraint condition; The joint optimization problem is solved by a first optimization algorithm to obtain an optimal estimate of the first matrix and an optimal estimate of the second matrix, wherein the optimal estimate of the first matrix is ​​used to determine the first optimization problem, and the optimal estimate of the second matrix is ​​used to determine the second optimization problem.

4. The method according to claim 3, characterized in that Before determining the joint optimization problem based on the spectral pulse matrix, the method further includes: The truncated nuclear norm is determined by the singular values ​​of the first matrix L : ； in, represents the i-th singular value of the first matrix L, r represents the truncated rank, It means to find the rank of a matrix.

5. The method according to claim 1, characterized in that Solving the first optimization problem of the first matrix based on the first dictionary learning algorithm to obtain a vibration event recognition matrix includes: Constructing a dictionary of vibration event experiences; Based on the vibration event experience dictionary and the first matrix, the first optimization problem is determined as: ； in, Represents the vibration event identification matrix No. p List, P represents the number of the spectrum pulses, represents the Euclidean norm, L is the first matrix, Representing the vibration event experience dictionary; The first optimization problem is solved based on a second optimization algorithm to obtain an optimal solution of the vibration event identification matrix, wherein the optimal solution of the vibration event identification matrix is ​​used to characterize the vibration event signal.

6. The method according to claim 5, characterized in that Construct a vibration event experience dictionary, including: Select samples from the vibration event database to obtain multiple empirical vibration events; Calculating the fast-axis echo spectrum corresponding to each of the empirical vibration events, or calculating the slow-axis echo spectrum corresponding to each of the empirical vibration events; Determining vibration event dictionary atoms corresponding to each of the empirical vibration events based on the fast-axis echo spectrum or the slow-axis echo spectrum; The vibration event experience dictionary is constructed based on the vibration event dictionary atoms.

7. The method according to claim 1, characterized in that The second optimization problem is solved based on the second dictionary learning algorithm to obtain the temperature offset feature vector, including: Construct differential temperature identification dictionary; Transposing the second matrix and merging columns to obtain the merged vector; Based on the differential temperature identification dictionary and the merged vector, the second optimization problem is determined as: ； in, represents the temperature offset eigenvector, represents the zero norm, is the merge vector, represents the differential temperature identification dictionary; The second optimization problem is solved based on a third optimization algorithm to obtain an optimal solution of the temperature offset characteristic vector, wherein the optimal solution of the temperature offset characteristic vector is used to characterize a temperature change signal.

8. The method according to claim 7, characterized in that Construct a differential temperature identification dictionary, including: Select samples from the temperature database to obtain fast axis echo spectra and slow axis echo spectra at different temperatures; Determining the fast-axis echo spectrum and the slow-axis echo spectrum under different temperature offsets based on the reference temperature and the fast-axis echo spectrum and the slow-axis echo spectrum under different temperatures; Among the fast-axis echo spectra and the slow-axis echo spectra under different temperature offsets, the fast-axis echo spectra and the slow-axis echo spectra under the same temperature offset are superimposed to obtain superimposed spectra corresponding to the different temperature offsets; determining a temperature-shifted dictionary atom based on the superimposed spectra; The differential temperature discrimination dictionary is constructed based on the temperature offset dictionary atoms.

9. An electronic device, comprising: A processor and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 8.

10. A non-transitory machine-readable medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 8.

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