Optical fiber environmental event recognition method based on birefringence FBG

Abstract

The present invention relates to the field of fiber Bragg grating (FBG) technology, and specifically provides a method for identifying fiber environmental events based on birefringent FBGs. The method comprises: constructing a spectral pulse matrix based on the fast-axis spectra and slow-axis spectra of multiple spectral pulses, where the spectral pulses are echo spectral pulses transmitted by the birefringent FBGs; solving a joint optimization problem to separate a first matrix and a second matrix from the spectral pulse matrix; solving a first optimization problem for the first matrix based on a first dictionary learning algorithm to obtain a vibration event identification matrix for characterizing vibration event signals; and solving a second optimization problem based on a second dictionary learning algorithm to obtain a temperature offset eigenvector for characterizing temperature change signals, wherein the second optimization problem is determined based on a combined vector of the second matrix. This method solves the problem of difficult signal decoupling for FBGs in complex environments where both temperature changes and vibration events coexist, as previously described.

Description

Technical Field

[0001] The invention relates to the technical field of fiber Bragg grating (FBG), and in particular to a method for identifying fiber environment events based on birefringence FBG. Background Art

[0002] Fiber Bragg Grating (FBG) has the advantages of high sensitivity, strong anti-interference ability, and suitability for remote monitoring. It 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, fiber Bragg gratings face the problem of difficult signal decoupling. This is because temperature changes can cause thermal expansion or contraction of the optical fiber, resulting in changes in the refractive index and length, which in turn causes the Bragg wavelength to drift. Vibration events can cause dynamic strain in the optical fiber, which will also change the physical properties of the optical fiber, causing the Bragg wavelength to drift, making it difficult to distinguish between the two effects.

[0004] Currently, no effective solution has been proposed to the problem of difficulty in signal decoupling of fiber Bragg gratings in complex environments with simultaneous temperature changes and vibration events. Summary of the Invention

[0005] The present invention provides a method for identifying fiber environment events based on birefringence FBG, which at least solves the problem in related technologies that fiber Bragg gratings are difficult to decouple signals in a complex environment where temperature changes and vibration events coexist.

[0006] The present invention provides an embodiment of a fiber environment event identification method based on birefringent fiber Bragg grating (FBG), comprising: constructing a spectral pulse matrix based on fast-axis spectra and slow-axis spectra of multiple spectral pulses, wherein the spectral pulses are echo spectral pulses 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, 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 vibration event signals; solving a second optimization problem based on a second dictionary learning algorithm to obtain a temperature offset eigenvector, wherein the second optimization problem is determined based on a merging vector, the merging vector is obtained by merging the second matrix, and the temperature offset eigenvector is used to characterize temperature change signals.

[0007] The present invention provides a method for identifying optical fiber environmental events based on birefringent FBGs, which constructs a spectral pulse matrix based on the fast-axis spectra and slow-axis spectra of multiple spectral pulses. The method includes: receiving multiple 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 fused spectral pulse; and constructing a spectral pulse matrix based on the sampling column vectors of the multiple dual-axis fused spectral pulses.

[0008] The optical fiber environment event identification method based on birefringence FBG provided by the embodiment of the present invention separates the first matrix and the second matrix from the spectral pulse matrix by solving a joint optimization problem, including: expressing the spectral pulse matrix as:

[0009] ;

[0010] Where R represents the spectrum pulse matrix, L represents the first matrix, S represents the second matrix, and N represents the noise matrix;

[0011] Based on the spectral pulse matrix, the joint optimization problem is determined as:

[0012] ;

[0013] in, Indicates finding 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;

[0014] 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.

[0015] The optical fiber environment event identification method based on birefringence FBG provided by the embodiment of the present invention, before determining the joint optimization problem based on the spectrum pulse matrix, the above method also includes: determining the truncated nuclear norm by the singular value of the first matrix L :

[0016] ;

[0017] in, represents the i-th singular value of the first matrix L, r represents the truncated rank, It means to find the rank of the matrix.

[0018] The optical fiber environment event recognition method based on birefringent FBG provided by the embodiment of the present invention solves the first optimization problem of the first matrix based on the first dictionary learning algorithm to obtain the vibration event recognition matrix, including: constructing a vibration event experience dictionary; based on the vibration event experience dictionary and the first matrix, determining the first optimization problem as:

[0019] ;

[0020] in, Represents the vibration event identification matrix No. p List, P represents the number of spectral pulses, represents the Euclidean norm, L is the first matrix, Represents a dictionary of vibration event experiences;

[0021] The first optimization problem is solved based on the 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.

[0022] The present invention provides an embodiment of a fiber optic environment event recognition method based on birefringent FBG, which constructs an empirical dictionary of vibration events, including: selecting samples from a vibration event database to obtain multiple 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; determining the vibration event dictionary atoms corresponding to each empirical vibration event based on the fast-axis echo spectrum or the slow-axis echo spectrum; and constructing the vibration event empirical dictionary based on the vibration event dictionary atoms.

[0023] The optical fiber environmental event identification method based on birefringent FBG provided by the embodiment of the present invention solves the second optimization problem based on the second dictionary learning algorithm to obtain the temperature offset feature vector, including: constructing a differential temperature identification dictionary; transposing the second matrix and merging it by column to obtain a merged vector; based on the differential temperature identification dictionary and the merged vector, determining the second optimization problem as:

[0024] ;

[0025] in, represents the temperature offset eigenvector, represents the 0 norm, is the merge vector, represents the differential temperature discrimination dictionary;

[0026] The second optimization problem is solved based on the 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 the temperature change signal.

[0027] The present invention provides an optical fiber environment event recognition method based on birefringent FBG, which constructs a differential temperature identification dictionary, including: selecting samples from a temperature database to obtain fast-axis echo spectra and slow-axis echo spectra at different temperatures; determining the fast-axis echo spectra and slow-axis echo spectra under different temperature offsets based on a reference temperature and the fast-axis echo spectra and slow-axis echo spectra at different temperatures; superimposing the fast-axis echo spectra and slow-axis echo spectra under the same temperature offset among the fast-axis echo spectra and slow-axis echo spectra under different temperature offsets to obtain superimposed spectra corresponding to different temperature offsets; determining temperature offset dictionary atoms based on the superimposed spectra; and constructing a differential temperature identification dictionary based on the temperature offset dictionary atoms.

[0028] An embodiment of the present invention provides an electronic device, comprising: a processor, and a memory for storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes any of the above methods.

[0029] The present invention provides a non-transitory machine-readable medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0030] The present invention provides a method for identifying fiber environment events based on birefringent FBG, an electronic device, and a non-transient machine-readable medium storing computer instructions. The method separates a first matrix and a second matrix from a spectral pulse matrix, obtains a vibration event identification matrix representing the vibration event signal based on the first matrix, and obtains a temperature offset characteristic vector representing the temperature change signal based on the second matrix. This method can realize the joint identification of vibration events and temperature changes and effectively reduce the difficulty of signal decoupling. The method directly constructs a first optimization problem based on the first matrix to avoid mutual crosstalk between different vibration events and improve the accuracy of vibration event identification. The method merges the second matrix to obtain a merged vector, and constructs a second optimization problem based on the merged vector to improve the accuracy of temperature change identification. This method solves the problem of difficult signal decoupling of fiber Bragg gratings in a complex environment where temperature changes and vibration events coexist in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without inventive effort.

[0032] Figure 1 This is a flowchart of the steps of a method for identifying optical fiber environment events based on birefringence FBG in an embodiment of the present invention.

[0033] Figure 2 It is a structural diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following describes embodiments of the present invention in more detail with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying 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 described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0035] A fiber Bragg grating (FBG) is a periodic refractive index modulation structure formed in the optical fiber core by ultraviolet exposure. By modulating the grating structure with changes in physical quantities in the optical fiber environment (such as temperature, strain, vibration, etc.), dynamic monitoring and event identification of these physical quantities can be achieved.

[0036] Single-axis FBGs lack sufficient recognition capabilities in complex environments where temperature changes and vibration events coexist. This limits their application in multi-physics monitoring, particularly in scenarios requiring high-precision and real-time analysis, such as bridge monitoring and oil pipeline inspection. Clearly, single-axis FBG-based methods for identifying fiber-optic environmental events suffer from low accuracy and reliability.

[0037] By incorporating the birefringence effect, birefringent fiber optic bridges (FBGs) possess two orthogonal polarization axes: a fast axis and a slow axis. This allows for simultaneous measurement of wavelength changes in both the fast and slow axes. However, existing methods for identifying fiber-optic environmental events based on birefringent FBGs fail to effectively reduce the coupling between temperature change signals and vibration event signals. In complex environments where both temperature changes and vibration events coexist, reliability is relatively low, and signal decoupling is relatively difficult.

[0038] To do this, please refer to Figure 1 As shown, this embodiment provides a method for identifying optical fiber environment events based on birefringence FBG, comprising: step S101, constructing a spectral pulse matrix according to fast-axis spectra and slow-axis spectra of multiple spectral pulses, wherein the spectral pulses are echo spectral pulses transmitted by a birefringence fiber Bragg grating (FBG).

[0039] Step S102 : separating a first matrix and a second matrix from the spectrum pulse matrix by solving a joint optimization problem, wherein the joint optimization problem is determined based on the spectrum pulse matrix.

[0040] Step S103 : solving a first optimization problem of the first matrix based on a first dictionary learning algorithm to obtain a vibration event recognition matrix, wherein the vibration event recognition matrix is used to characterize the vibration event signal.

[0041] Step S104 , solving the second optimization problem based on the second dictionary learning algorithm to obtain a temperature offset feature vector, wherein the second optimization problem is determined based on the merged vector, the merged vector is obtained by merging the second matrix, and the temperature offset feature vector is used to characterize the temperature change signal.

[0042] Methods for obtaining the above-mentioned spectrum pulses include but are not limited to: acquisition through a spectrum analyzer, or measurement based on a Mach-Zehnder interferometer or a Michelson interferometer, or detection using an optical time domain reflectometer (OTDR).

[0043] The spectral pulse matrix can include only the first matrix and the second matrix, or it can include at least the first matrix, the second matrix, and a noise matrix. A preferred embodiment of this embodiment is one in which the spectral pulse matrix includes the first matrix, the second matrix, and the noise matrix. The noise matrix is introduced to characterize signal fluctuations and uncertainties caused by various non-ideal, random, or interfering factors. This reduces the difficulty of signal decoupling and improves recognition accuracy while maintaining high computational efficiency. This preferred embodiment is described in detail later.

[0044] Methods for determining the joint optimization problem based on the spectral pulse matrix include, but are not limited to, equality-constrained optimization, inequality-constrained optimization, boundary-constrained optimization, or logical-constrained optimization. This embodiment preferably employs equality-constrained optimization, representing the spectral pulse matrix as the sum of multiple matrices to determine the corresponding joint optimization problem. This method offers advantages of precision and clarity. This preferred method will be described in detail later.

[0045] The optimal estimate for a joint optimization problem can be a minimum value, a maximum value, or a target value under preset constraints. This embodiment preferably uses the minimum value under the preset constraints as the optimal estimate, which has the advantages of stability and relatively convenient mathematical solution. This preferred approach will be described in detail later.

[0046] Considering that the fast axis and slow axis of the birefringent FBG respond differently to temperature changes and vibration events, this embodiment uses this difference in the two axes to distinguish wavelength drift caused by temperature changes and vibration events, and conducts the following principle analysis.

[0047] Fast axis central wavelength of birefringent FBG and slow axis central wavelength Can be expressed as:

[0048] ;

[0049] ;

[0050] Where Λ is the grating period, n F is the effective refractive index of the fast axis, n s is the effective refractive index of the slow axis.

[0051] Based on the above expression, the fast axis wavelength drift of the birefringent FBG is and slow axis wavelength shift Can be expressed as:

[0052] ;

[0053] ;

[0054] in, Indicates the change in ambient temperature. represents the vibration displacement, Indicates that the temperature sensitivity of the fast axis is sparse, represents the temperature sensitivity coefficient of the slow axis, Indicates the vibration displacement sensitivity coefficient shared by the fast axis and the slow axis.

[0055] As can be seen, for a birefringent FBG, the fast and slow axes have different temperature sensitivity coefficients, so external temperature changes can be viewed as independent disturbances to the fast and slow axes. Conversely, since the fast and slow axes have the same vibration displacement sensitivity coefficients, external vibration events can be viewed as a combined disturbance to both axes.

[0056] Further analysis shows that: between the spectral pulses received under the influence of the same external vibration event, since the spectral peak displacement is the same, the echo spectral pulse matrix under the influence of the same vibration event has a low-rank characteristic. This embodiment preferably determines the first matrix used to characterize the influence of the vibration event based on this low-rank characteristic.

[0057] At the same time, between the spectral pulses received under the same external temperature change, since the spectral peak displacements are different and the displacement differential values are the same, the echo spectral pulse matrix will exhibit a group sparse characteristic. This embodiment preferably determines the second matrix for characterizing the influence of temperature change based on this group sparse characteristic.

[0058] The fiber environment event identification method based on birefringent FBGs provided in this embodiment separates a first matrix and a second matrix from a spectral pulse matrix. Based on the first matrix, a vibration event identification matrix representing the vibration event signal is obtained. Based on the second matrix, a temperature offset eigenvector representing the temperature change signal is obtained. This method can achieve joint identification 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 mutual crosstalk between different vibration events and improve the accuracy of vibration event identification. Combining the second matrix to obtain a combined vector, and constructing a second optimization problem based on the combined vector can improve the accuracy of temperature change identification. This method solves the problem of difficult signal decoupling of fiber Bragg gratings in complex environments where temperature changes and vibration events coexist in related technologies.

[0059] Preferably, step S101, constructing a spectral pulse matrix according to the fast-axis spectra and slow-axis spectra of a plurality of spectral pulses, includes steps S1011-S1013.

[0060] Step S1011: Receive multiple spectrum pulses. For example, use a spectrometer or a fiber Bragg grating demodulator to receive multiple spectrum pulses at a signal processing end.

[0061] Step S1012: Add the fast-axis spectrum and the slow-axis spectrum of the i-th spectrum pulse to obtain the i-th dual-axis fusion spectrum pulse. The specific formula is:

[0062] ;

[0063] in, represents the i-th dual-axis fusion spectrum pulse, represents the fast axis spectrum of the i-th spectral pulse, represents the slow axis spectrum of the i-th spectral pulse.

[0064] Step S1013: construct a spectrum pulse matrix based on the sampling column vectors of multiple dual-axis fused spectrum pulses. The specific formula is:

[0065] ;

[0066] Where R represents the spectral pulse matrix, represents the sampling column vector of the i-th dual-axis fusion spectrum pulse, , P represents the number of dual-axis fusion spectrum pulses, and T represents transposition.

[0067] It can be understood that the sampling column vector represents the discretization of the dual-axis fusion spectrum pulses, converting continuous spectral information into structured digital information. For example, if each dual-axis fusion spectrum pulse is sampled a times within a certain range, the values obtained from each sampling can be sequentially arranged into an a×1 column vector. The number of dual-axis fusion spectrum pulses is equal to the number of spectral pulses.

[0068] Preferably, step S102, separating the first matrix and the second matrix from the spectral pulse matrix by solving a joint optimization problem, includes steps S1021-S1022.

[0069] Step S1021, the spectrum pulse matrix is expressed as:

[0070] ;

[0071] Where R represents the spectrum pulse matrix, L represents the first matrix, S represents the second matrix, and N represents the noise matrix.

[0072] It can be understood that the first matrix L represents the matrix of spectral components affected only by vibration events, and the second matrix S represents the matrix of spectral components affected only by temperature changes.

[0073] Based on the spectral pulse matrix, the joint optimization problem is determined as:

[0074] ;

[0075] in, Indicates finding the minimum value. is the regularization parameter corresponding to the first matrix L, which is used to balance the influence of low-rank characteristics. is the truncated nuclear norm of the first matrix L, is the regularization parameter corresponding to the second matrix S, which is used to balance the influence of the group sparsity characteristics. is the sparse representation of the second matrix S, is the noise suppression term, and st represents the constraint condition.

[0076] Step S1022, solving the joint optimization problem by using the first optimization algorithm to obtain the optimal estimate of the first matrix and the 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.

[0077] The first optimization algorithm is the alternating direction method of multipliers (ADMM), the proximal gradient descent method, or the target cascade method, which are prior art and will not be described in detail in this embodiment. Those skilled in the art may also use other optimization algorithms to solve the above joint optimization problem based on their own experience and actual conditions.

[0078] Specifically, for the signal changes caused by vibration events, since the fast axis and slow axis of the birefringent FBG have a strong correlation in response to the vibration event, it can be considered that the vibration event has a low-rank characteristic in the signal matrix. This low-rank characteristic can be characterized by singular value decomposition (SVD) or nuclear norm minimization.

[0079] For example, the first matrix L can be expressed as follows:

[0080] ;

[0081] Where U and V are both orthogonal matrices, Σ is a diagonal matrix with a low rank. In this case, the first matrix L can reflect the combined impact of the vibration event on both the fast and slow axes. A low rank of Σ means that most of the eigenvalues of the diagonal matrix Σ are zero. Those skilled in the art can determine the specific numerical range for most of these values based on a priori values or actual conditions.

[0082] 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 :

[0083] ;

[0084] in, represents the i-th singular value of the first matrix L, r represents the truncated rank, It means to find the rank of the matrix.

[0085] 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.

[0086] Preferably, for signal changes caused by temperature variations, since the fast and slow axes of a birefringent FBG respond relatively independently to temperature changes, the representation of temperature changes in the signal matrix can be considered to exhibit group sparsity. Group sparsity indicates that only a small number of wavelengths will experience significant drift under temperature variations, which manifests as a sparse pattern on the fast and slow axes. This sparse representation can be represented using group sparsity regularization.

[0087] For example, the sparse representation of the second matrix S is:

[0088] ;

[0089] in, represents the jth parameter vector of the second matrix S, express The L2 norm of .

[0090] Preferably, step S103, solving the first optimization problem of the first matrix based on the first dictionary learning algorithm to obtain the vibration event recognition matrix, includes steps S1031-S1033.

[0091] Step S1031: construct a vibration event experience dictionary.

[0092] Step S1032: Based on the vibration event experience dictionary and the first matrix, determine the first optimization problem as:

[0093] ;

[0094] in, Represents the vibration event identification matrix No. p List, P represents the number of spectral pulses, represents the Euclidean norm, L is the first matrix, Represents a dictionary of vibration event experiences.

[0095] Step S1033 : solving the first optimization problem based on the 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.

[0096] The second optimization algorithm is any one of the alternating direction multiplier method, the proximal gradient descent method, and the target cascade method, and the second optimization algorithm can be the same as the first optimization algorithm.

[0097] By analyzing the positions of non-zero elements in the optimal solution of the vibration event identification matrix, it is possible to determine the vibration event corresponding to each echo light pulse moment and accurately identify the vibration event signal. Therefore, the above method provided in this embodiment has significant advantages for monitoring and identifying short-term vibration events.

[0098] Preferably, in step S1031 , a vibration event experience dictionary is constructed, including steps S10311 - S10314 .

[0099] Step S10311: Select samples from the vibration event database to obtain multiple empirical vibration events.

[0100] It is understandable that the vibration signal data corresponding to different empirical vibration events stored in the vibration event database may be vibration signals that have been actually monitored or vibration signals that have been simulated and generated.

[0101] 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.

[0102] For example, preprocessing the vibration signal data of the empirical vibration event, performing light intensity difference calculation, and fast Fourier transform (FFT) to calculate the corresponding fast axis echo spectrum belongs to the existing technology and will not be repeated in this embodiment.

[0103] It can be understood that when the fast-axis echo spectrum data or slow-axis echo spectrum data of the birefringent FBG corresponding to each empirical vibration event is already stored in the vibration event database, there is no need to perform the above calculation, that is, the above steps S10311 and S10312 can be combined as follows: select samples from the vibration event database to obtain fast-axis echo spectra corresponding to multiple empirical vibration events, or obtain slow-axis echo spectra corresponding to multiple empirical vibration events.

[0104] Step S10313 : determining the vibration event dictionary atoms corresponding to each empirical vibration event based on the fast axis echo spectrum or the slow axis echo spectrum.

[0105] Taking the fast axis echo spectrum as an example, the dimension or number of the empirical vibration events is Q, then the vibration event dictionary atom corresponding to the qth empirical vibration event is:

[0106] ;

[0107] in, represents the fast axis echo spectrum calculated under the influence of the qth empirical vibration event.

[0108] Step S10314: construct a vibration event experience dictionary based on the vibration event dictionary atoms.

[0109] For example, based on the vibration event dictionary atoms corresponding to Q empirical vibration events, the vibration event experience dictionary is constructed It can be expressed as:

[0110] .

[0111] Preferably, step S104, solving the second optimization problem based on a second dictionary learning algorithm to obtain a temperature offset feature vector, includes steps S1041-S1044.

[0112] Step S1041: construct a differential temperature identification dictionary.

[0113] Step S1042: transpose the second matrix and merge columns to obtain a merged vector.

[0114] The formulas for transposing and merging by columns are as follows:

[0115] ;

[0116] in, Represents the merge vector The nth element of , sum() means sum, Representation matrix The nth row of is the transposed matrix of the second matrix S.

[0117] Step S1043: Based on the differential temperature identification dictionary and the merged vector, the second optimization problem is determined as:

[0118] ;

[0119] in, represents the temperature offset eigenvector, represents the 0 norm, is the merge vector, Represents the differential temperature discrimination dictionary.

[0120] Step S1044 : Solve the second optimization problem based on the 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 the temperature change signal.

[0121] The third optimization algorithm is any one of the alternating direction multiplier method, the proximal gradient descent method, and the target cascade method, and the third optimization algorithm can be the same as the above-mentioned first optimization algorithm and the above-mentioned second optimization algorithm.

[0122] It can be understood that the position index of the non-zero element in the optimal solution of the temperature offset feature vector corresponds to the temperature offset value at the target moment, thereby accurately identifying the temperature change signal.

[0123] Preferably, in step S1041 , a differential temperature identification dictionary is constructed, including steps S10411 - S10415 .

[0124] Step S10411: Select samples from the temperature database to obtain fast-axis echo spectra and slow-axis echo spectra at different temperatures.

[0125] It is understandable that the temperature change signal data stored in the temperature database may be a temperature change signal that has been actually monitored, or a temperature change signal that has been simulated and generated.

[0126] By preprocessing the temperature change signal data, calculating the light intensity difference, and performing fast Fourier transform (FFT), the corresponding fast axis echo spectrum and slow axis echo spectrum can be calculated.

[0127] In the case where the fast-axis echo spectrum data and slow-axis echo spectrum data corresponding to the birefringent FBG at different temperatures are already stored in the temperature database, there is no need to perform the above calculations and samples can be directly selected.

[0128] Step S10412 : determining the fast-axis echo spectra and the slow-axis echo spectra at different temperature offsets based on the reference temperature and the fast-axis echo spectra and the slow-axis echo spectra at different temperatures.

[0129] The reference temperature is determined by those skilled in the art based on experience and requirements of actual application scenarios.

[0130] Based on different temperatures T and reference temperature The difference between the two determines the different temperature offsets .

[0131] Step S10413 , superimposing the fast-axis echo spectra and the slow-axis echo spectra under different temperature offsets and the fast-axis echo spectra and the slow-axis echo spectra under the same temperature offset to obtain superimposed spectra corresponding to different temperature offsets.

[0132] For example, temperature excursion The corresponding superposition spectrum is ,in, and Represents temperature offset The fast axis echo spectrum and slow axis echo spectrum under .

[0133] Step S10414: determining the temperature offset dictionary atoms based on the superimposed spectra corresponding to the different temperature offsets.

[0134] For example, temperature excursion Corresponding temperature offset dictionary atoms .

[0135] 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 expressed as .

[0136] Step S10415: construct a differential temperature discrimination dictionary based on the temperature offset dictionary atoms.

[0137] Differential Temperature Identification Dictionary It can be expressed as:

[0138] .

[0139] In summary, the present invention provides an embodiment of an optical fiber environment event recognition method based on birefringent FBG, determines the joint optimization problem reflecting different signals, and combines the optimization algorithm of low rank and sparse joint representation. It can effectively separate and identify temperature change signals and vibration event signals in a complex environment where temperature change and vibration events coexist.

[0140] The present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method of the present invention.

[0141] 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 present invention.

[0142] 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 memory stores a computer program executable by the at least one processor, wherein the computer program, when executed by the at least one processor, causes the electronic device to perform the method of the present invention.

[0143] refer to Figure 2 , a structural block diagram of an electronic device that can be used as a server or client of an embodiment of the present invention will now be described, which 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 processing, 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 required herein.

[0144] like Figure 2 As 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. RAM 203 may also store various programs and data required for the operation of the electronic device. The computing unit 201, ROM 202, and RAM 203 are interconnected via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0145] Multiple components within 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 capable of inputting information into the electronic device. The input unit 206 can receive input numeric or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 207 can be any type of device capable of presenting 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, a magnetic disk or an optical disk. The communication unit 209 allows the electronic device to exchange information / data with other devices via 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.

[0146] The computing unit 201 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing units, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 201 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention may 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 may 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 may be configured to perform the above-described methods by any other suitable means (e.g., via firmware).

[0147] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may 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 machine-readable storage media may 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), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0149] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, 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 other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative and not restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more". The descriptions of the terms "first", "second", etc. are for descriptive purposes only and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features.

[0150] The various steps described in the method implementation methods provided by the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0151] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referenced to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0152] The above-described embodiments merely represent several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of protection. It should be noted that a person of ordinary skill in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on 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 slow-axis spectra of a plurality of spectrum pulses, wherein the spectrum pulses are echo spectrum pulses 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, 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; Solving a second optimization problem 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 obtained by merging the second matrices, and the temperature offset feature vector is used to represent a temperature change signal; 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, Indicates finding 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.

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 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 the matrix.

4. The method according to claim 1, wherein 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 a vibration event signal.

5. The method according to claim 4, characterized in that Construct a vibration event experience dictionary, including: Select samples from the vibration event database to obtain multiple empirical vibration events; Calculating a fast-axis echo spectrum corresponding to each of the empirical vibration events, or calculating a 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.

6. 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 discrimination dictionary and the merged vector, the second optimization problem is determined as: ； in, represents the temperature offset eigenvector, represents the 0 norm, is the merge vector, represents the differential temperature discrimination 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.

7. The method according to claim 6, 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 fast-axis echo spectra and slow-axis echo spectra at different temperature offsets based on a reference temperature and the fast-axis echo spectra and slow-axis echo spectra at different temperatures; superimposing the fast-axis echo spectrum and the slow-axis echo spectrum under the same temperature offset in the fast-axis echo spectrum and the slow-axis echo spectrum under the different temperature offsets 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.

8. 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 7.

9. 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 7.

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