Manufacturing method of fiber Bragg grating and vibration event detection method

Abstract

The present invention relates to the field of fiber Bragg grating technology, and specifically provides a method for manufacturing a fiber Bragg grating and a method for detecting vibration events. The method comprises: constructing a first transmission matrix and a first grating period distribution function for a first sub-grating with a variable grating period; determining a second transmission matrix for a second sub-grating based on the first transmission matrix, wherein the second sub-grating is complementary and symmetrical to the first sub-grating; obtaining a first optimal transmission matrix, a second optimal transmission matrix, and a first optimal grating period distribution function by solving a joint optimization problem; determining a second grating period distribution function for the second sub-grating based on the first optimal grating period distribution function; and determining the first sub-grating and the second sub-grating, and correspondingly connecting them in series to obtain a target fiber Bragg grating. This method addresses the problem in related arts that fiber Bragg gratings cannot achieve both high sensitivity, signal-to-noise ratio, and stability, while also having a relatively simple design and manufacturing process.

Description

Technical Field

[0001] The present invention relates to the technical field of fiber Bragg gratings, and in particular to a method for manufacturing a fiber Bragg grating and a method for detecting vibration events. Background Art

[0002] Fiber Bragg Grating (FBG) has the advantages of high precision and resistance to electromagnetic interference in sensing technology, and is widely used in the detection of physical quantities such as stress, temperature, and vibration.

[0003] Existing FBG sensing technology primarily relies on uniformly etched fiber Bragg gratings (FBGs). While capable of basic signal detection, their sensitivity and signal-to-noise ratio are often insufficient when detecting weak vibration events, limiting their application in complex environments. To address this issue, non-uniform etching can be used to improve FBG design, modulating the reflection spectrum and enhancing sensing performance. However, simple non-uniform etching designs struggle to simultaneously optimize sensitivity, signal-to-noise ratio, and stability. Furthermore, due to the complexity of the etching process, simplifying the design and manufacturing process while improving performance remains a pressing challenge.

[0004] Currently, no effective solution has been proposed to the problem that fiber Bragg gratings in related technologies cannot achieve high sensitivity, signal-to-noise ratio, stability, and relatively simple design and manufacturing processes. Summary of the Invention

[0005] The present invention provides a method for manufacturing a fiber Bragg grating and a method for detecting vibration events, which at least solve the problem in related technologies that fiber Bragg gratings cannot achieve high sensitivity, signal-to-noise ratio, stability, and a relatively simple design and manufacturing process.

[0006] An embodiment of the present invention provides a method for manufacturing a fiber Bragg grating, comprising: constructing a first transmission matrix and a first grating period distribution function of a first sub-grating, wherein the first sub-grating is a fiber Bragg grating with a variable grating period; determining a second transmission matrix of a second sub-grating based on the first transmission matrix, wherein the second sub-grating is complementary and symmetrical to the first sub-grating; obtaining a first optimal transmission matrix corresponding to the first transmission matrix, a second optimal transmission matrix corresponding to the second transmission matrix, and a first optimal grating period distribution function corresponding to the first grating period distribution function by solving a joint optimization problem; determining a second grating period distribution function of the second sub-grating based on the first optimal grating period distribution function; determining the first sub-grating based on the first optimal transmission matrix and the first optimal grating period distribution function, determining the second sub-grating based on the second optimal transmission matrix and the second grating period distribution function, and connecting the first sub-grating and the second sub-grating in series to obtain a target fiber Bragg grating.

[0007] The present invention provides a method for manufacturing a fiber Bragg grating, which determines a second transmission matrix of a second sub-grating based on a first transmission matrix. The method includes: if the first transmission matrix is ​​T1, then the second transmission matrix T2 is:

[0008] ;

[0009] in, , T1 -1 Represents the inverse matrix of T1.

[0010] The method for manufacturing a fiber Bragg grating provided by an embodiment of the present invention, after determining the second transmission matrix of the second sub-grating based on the first transmission matrix, further comprises: determining a joint optimization problem based on the first transmission matrix, the first grating period distribution function, and the second transmission matrix as follows:

[0011] ;

[0012] in, Indicates solving the minimum value, T1 represents the first transmission matrix, T2 represents the second transmission matrix, represents the transmission matrix of the first fiber Bragg grating, which is a uniform fiber Bragg grating equivalent to the target fiber Bragg grating. represents the first grating period distribution function, z represents the etching position of the first sub-grating, represents the square of the F norm, is the side mode suppression ratio of the cascaded grating, is the first optimization weight coefficient, is the second optimization weight coefficient, is the third optimization weight coefficient.

[0013] The method for manufacturing a fiber Bragg grating provided by an embodiment of the present invention, after determining a joint optimization problem based on a first transmission matrix, a first grating period distribution function, and a second transmission matrix, further includes: determining an objective function J based on the joint optimization problem as:

[0014] ;

[0015] , , ;

[0016] Determine the first gradient of the first transfer matrix based on the objective function J J. The second gradient of the second transmission matrix J. The third gradient of the first grating period distribution function J are:

[0017] , ;

[0018] , ;

[0019] ;

[0020] Among them, the objective function J, the first gradient J. Second Gradient J. Third gradient J is used to solve the joint optimization problem.

[0021] The present invention provides a manufacturing method for a fiber Bragg grating, which solves a joint optimization problem through a gradient algorithm to obtain a first optimal transmission matrix, a second optimal transmission matrix, and a first optimal grating period distribution function, including: setting a first initial value of the first transmission matrix, a second initial value of the second transmission matrix, and a third initial value of the first grating period distribution function, and setting the first momentum term of the first transmission matrix, the second momentum term of the second transmission matrix, and the third momentum term of the first grating period distribution function corresponding to the initial iteration count value to a zero vector, wherein the initial iteration count value is zero; based on the first transmission matrix, the second transmission matrix, and the first grating period distribution function corresponding to the current iteration count value, calculating the first gradient, the second gradient, and the third gradient corresponding to the current iteration count value; based on a preset momentum factor and the current iteration count value The corresponding momentum terms and gradients are used to calculate the momentum terms corresponding to the next iteration count value; based on the preset learning rate, the first transfer matrix, the second transfer matrix, the first grating period distribution function corresponding to the current iteration count value, and the momentum terms corresponding to the next iteration count value, the first transfer matrix, the second transfer matrix, and the first grating period distribution function corresponding to the next iteration count value are calculated; based on the objective function corresponding to the current iteration count value and the objective function corresponding to the next iteration count value, the iteration residual corresponding to the next iteration count value is calculated, and when the iteration residual corresponding to the next iteration count value is less than or equal to the preset iteration residual threshold, the first transfer matrix, the second transfer matrix, and the first grating period distribution function corresponding to the next iteration count value are used as the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function.

[0022] An embodiment of the present invention provides a vibration event detection method based on a fiber Bragg grating, wherein the fiber Bragg grating is a target fiber Bragg grating manufactured according to any of the above methods, and the above detection method includes: obtaining a first sampled observation signal of a first sub-grating and a second sampled observation signal of a second sub-grating; based on a wavelength offset dictionary, expressing the first sampled observation signal and the second sampled observation signal as a linear combination of a sparse feature vector and a noise vector, respectively, to obtain a group sparse model, and determining a group sparse optimization problem, wherein each atom in the wavelength offset dictionary represents a wavelength offset pattern caused by a corresponding vibration event; introducing a symmetric offset constraint into the group sparse optimization problem to obtain a sparse optimization problem, and solving the problem to obtain a first optimal sparse feature vector of the first sampled observation signal and a second optimal sparse feature vector of the second sampled observation signal; performing feature domain integration on the first optimal sparse feature vector and the second optimal sparse feature vector to obtain an integrated feature vector; and detecting vibration events based on the detection algorithm and the integrated feature vector.

[0023] The present invention provides a method for detecting vibration events based on fiber Bragg gratings. The group sparse optimization problem is:

[0024] ;

[0025] in, Represents the first sample observation signal The sparse feature vector of Represents the second sampling observation signal The sparse feature vector of , D represents the wavelength offset dictionary, represents the square of the Euclidean norm, represents the first sparsity regularization parameter, express norm.

[0026] The vibration event detection method based on fiber Bragg grating provided by the embodiment of the present invention introduces a symmetric offset constraint into the group sparse optimization problem, and the sparse optimization problem is obtained as follows:

[0027] ;

[0028] in, represents the second sparsity regularization parameter.

[0029] The present invention provides a vibration event detection method based on fiber Bragg grating, which performs feature domain integration on a first optimal sparse feature vector and a second optimal sparse feature vector to obtain an integrated feature vector, including: arranging the elements in the second optimal sparse feature vector in reverse order to obtain a vector to be integrated; and adding the vector to be integrated to the first optimal sparse feature vector to obtain an integrated feature vector.

[0030] 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-mentioned detection methods.

[0031] The present invention provides a method for manufacturing a fiber Bragg grating (FBG), wherein the grating period of the first sub-grating is variable, and the second sub-grating is complementary and symmetrical to the first sub-grating. This method can significantly enhance the sensitivity and signal-to-noise ratio of the target fiber Bragg grating, while providing more degrees of freedom for the target fiber Bragg grating. This method can optimize wavelength selectivity and bandwidth while maintaining the grating reflection characteristics, further suppress environmental noise, and improve system robustness. A first optimal transmission matrix, a second optimal transmission matrix, and a first optimal grating period distribution function are obtained by solving a joint optimization problem. The second grating period distribution function is determined based on the first optimal grating period distribution function. This method improves the symmetry of the target fiber Bragg grating, reduces the amount of fiber Bragg grating etching, simplifies the design and manufacturing process, and improves the structural strength of the optical fiber in the case of large-scale grating distribution. This method solves the problem in related arts that fiber Bragg gratings cannot achieve high sensitivity, signal-to-noise ratio, stability, and a relatively simple design and manufacturing process.

[0032] In addition, vibration event detection based on the above-mentioned target fiber Bragg grating can amplify tiny wavelength deviations that are originally difficult to measure, equivalently enhancing the characteristic domain energy of the observation signal, which helps to improve the detection accuracy of the optical fiber system for weak vibration events. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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.

[0034] Figure 1 The present invention is a flowchart of a method for manufacturing a fiber Bragg grating in an embodiment of the present invention.

[0035] Figure 2 Schematic diagram of the structure of the target fiber Bragg grating in the embodiment of the present invention.

[0036] Figure 3 This is a flowchart of the steps of a vibration event detection method based on fiber Bragg grating in an embodiment of the present invention.

[0037] Figure 4It is a structural diagram of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] FBG sensing technology primarily relies on uniformly etched fiber Bragg gratings (FBGs). While capable of basic signal detection, their sensitivity and signal-to-noise ratio are often insufficient when detecting weak vibration events, limiting their application in complex environments. To address this issue, non-uniform etching can be used to improve FBG design, modulating the reflection spectrum and enhancing sensing performance. However, simple non-uniform etching designs struggle to simultaneously optimize sensitivity, signal-to-noise ratio, and stability. Furthermore, due to the complexity of the etching process, simplifying the design and manufacturing process while improving performance remains a pressing challenge.

[0040] To do this, please refer to Figure 1 As shown, the present invention provides a method for manufacturing a fiber Bragg grating, which includes steps S101 to S105.

[0041] Step S101 : constructing a first transmission matrix and a first grating period distribution function of a first sub-grating, wherein the first sub-grating is a fiber Bragg grating with a variable grating period.

[0042] Step S102 : determining a second transfer matrix of a second sub-grating based on the first transfer matrix, wherein the second sub-grating is complementary to the first sub-grating.

[0043] Step S103 , by solving a joint optimization problem, obtain a first optimal transmission matrix corresponding to the first transmission matrix, a second optimal transmission matrix corresponding to the second transmission matrix, and a first optimal grating period distribution function corresponding to the first grating period distribution function.

[0044] Step S104 : determining a second grating period distribution function of the second sub-grating based on the first optimal grating period distribution function.

[0045] Step S105: determining a first sub-grating based on the first optimal transmission matrix and the first optimal grating period distribution function, determining a second sub-grating based on the second optimal transmission matrix and the second grating period distribution function, and connecting the first sub-grating and the second sub-grating in series to obtain a target fiber Bragg grating.

[0046] It is understood that a fiber Bragg grating (FBG) utilizes the photosensitivity of the optical fiber material to form a structure with a periodic refractive index variation inside the optical fiber through ultraviolet light irradiation or other methods. The etching in this embodiment can be understood as the process of forming the structure with a periodic refractive index variation.

[0047] The transfer matrix describes the reflection and transmission characteristics of an FBG for optical signals. The grating period distribution function (GDF) defines the spatial distribution of the refractive index modulation in the fiber core, directly affecting the FBG's filtering performance. Therefore, the corresponding FBG can be determined based on the transfer matrix and the GDF.

[0048] Variable grating period refers to the ability to change the length of the periodic variation in refractive index. The variable grating period of the first sub-grating indicates that the first sub-grating is non-uniform. The second sub-grating is complementary to the first sub-grating, so the second sub-grating is non-uniform. The target fiber Bragg grating derived from the first and second sub-gratings is also non-uniform, providing more degrees of freedom during grating etching.

[0049] For example, see Figure 2 As shown, the first sub-grating FBG1 and the second sub-grating FBG2 are connected in series to obtain the target fiber Bragg grating. Figure 2 The entire stripe shadow shown in the black solid line frame represents the target fiber Bragg grating, and the blank area shown in the black solid line frame represents the area in the fiber core where the refractive index modulation is not performed.

[0050] It can be understood that the target fiber Bragg grating is an inversely symmetric cascade structure of two FBG sub-arrays.

[0051] The complementarity between the first sub-grating and the second sub-grating means that the two complement each other in function or characteristics. When detecting weak vibration events, the wavelength shifts of the two show an opposite change relationship. Combined, they can reflect the vibration information more comprehensively and accurately, thereby making up for the limitations of single detection.

[0052] Among them, weak vibration events can be judged by those skilled in the art based on prior values ​​and actual conditions.

[0053] For example, when using an accelerometer to measure the amplitude of a vibration signal, a vibration event with an acceleration less than 0.1 m / s² can be considered a weak vibration event. For rotating machinery in industrial equipment, a vibration event with an RMS vibration velocity less than 2.8 mm / s can be considered a weak vibration event. For buildings and bridges, a vibration event with a displacement less than 0.1 mm or an acceleration less than 0.01 times the acceleration of gravity can be considered a weak vibration event.

[0054] The symmetry between the first sub-grating and the second sub-grating means that their structures are symmetrical and they have similar physical properties. Under the same external conditions, their responses are consistent and comparable, which can simplify the signal processing process and improve detection accuracy and reliability.

[0055] The joint optimization problem can be determined by the first transfer matrix and the first grating period distribution function, or by the first transfer matrix, the first grating period distribution function and the second transfer matrix, or by the first transfer matrix, the first grating period distribution function, the second transfer matrix and the second grating period distribution function, or by the first transfer matrix, the first grating period distribution function, the second transfer matrix, the second grating period distribution function and other influencing variables.

[0056] This embodiment preferably determines a joint optimization problem based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix, offering the advantages of balancing computational efficiency and accuracy. In this case, the second grating period distribution function used to determine the second sub-grating is determined based on the first optimal grating period distribution function obtained from solving the optimization problem.

[0057] Algorithms for solving joint optimization problems include, but are not limited to, gradient algorithms, adaptive moment estimation algorithms, and adaptive incremental algorithms. This embodiment preferably uses a gradient algorithm to solve joint optimization problems, which has the advantages of high computational efficiency, few local optimal pitfalls, and smooth parameter updates. Details will be described later.

[0058] In summary, the manufacturing method of the above-mentioned fiber Bragg grating provided in this embodiment has a variable grating period of the first sub-grating, and the second sub-grating is complementary and symmetrical to the first sub-grating. On the one hand, it can significantly enhance the sensitivity and signal-to-noise ratio of the target fiber Bragg grating. On the other hand, it provides more degrees of freedom for the target fiber Bragg grating, and can optimize the wavelength selectivity and bandwidth while maintaining the grating reflection characteristics, further suppressing environmental noise and improving the robustness of the system.

[0059] By solving a joint optimization problem to obtain the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function, and then determining the second grating period distribution function based on the first optimal grating period distribution function, the symmetry of the target fiber Bragg grating can be improved. While obtaining an equivalent transmission matrix, the amount of fiber Bragg grating etching can be reduced, simplifying the design and manufacturing process. In the case of large-scale grating distribution, the structural strength of the optical fiber can be improved. This solves the problem that fiber Bragg gratings in related technologies cannot achieve high sensitivity, signal-to-noise ratio, stability, and relatively simple design and manufacturing processes.

[0060] It can be understood that two gratings having equivalent transmission matrices means that their phase and amplitude modulation effects on optical signals are exactly the same, even though their physical structures may be different, such as period distribution and length.

[0061] Specifically, step S101 constructs a first transfer matrix and a first grating period distribution function of the first sub-grating, including: setting the first transfer matrix to T1, and the first grating period distribution function to , z represents the etching position of the first sub-grating.

[0062] Preferably, step S102, determining a second transfer matrix of the second sub-grating based on the first transfer matrix, includes:

[0063] The first transmission matrix is ​​T1, and the second transmission matrix T2 is:

[0064] ;

[0065] in, , T1 -1 represents the inverse matrix of the first transmission matrix T1.

[0066] It can be understood that determining T2 based on T1 provides a basis for the subsequent manufacture of the target fiber Bragg grating.

[0067] Furthermore, after determining the second transfer matrix of the second sub-grating based on the first transfer matrix, the method further includes determining a joint optimization problem based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix:

[0068] ;

[0069] in, Indicates finding the minimum value. represents the transmission matrix of the first fiber Bragg grating, which is a uniform fiber Bragg grating equivalent to the target fiber Bragg grating. represents the square of the F norm, is the side mode suppression ratio of the cascaded grating, is the first optimization weight coefficient, is the second optimization weight coefficient, is the third optimization weight coefficient.

[0070] It is understood that a uniform fiber Bragg grating is a fixed-period grating. The essence of the equivalence between a variable-period grating and a fixed-period grating is that by designing the period distribution function, the variable-period grating can achieve the same transmission characteristics as the fixed-period grating under preset conditions.

[0071] The specific definition of side mode suppression ratio is:

[0072] ;

[0073] in, Indicates the corresponding Bragg wavelength The reflection intensity at It represents the reflection intensity at the non-Bragg wavelength, which is the secondary wavelength.

[0074] Furthermore, after determining the joint optimization problem based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix, the method further includes:

[0075] Based on the joint optimization problem, the objective function J is determined as:

[0076] ;

[0077] , , ;

[0078] Determine the first gradient of the first transfer matrix based on the objective function J J. The second gradient of the second transmission matrix J. The third gradient of the first grating period distribution function J are:

[0079] , ;

[0080] , ;

[0081] ;

[0082] Among them, the objective function J, the first gradient J. Second Gradient J. Third gradient J is used to solve the joint optimization problem.

[0083] It is understandable that right The gradient of , right The gradient, right The gradients of are all zero, so the first transfer matrix The total gradient is the first gradient .

[0084] right The gradient of , right The gradient, right The gradients of are all zero, so the second transfer matrix The total gradient is the second gradient .

[0085] right The gradient of is zero, right The gradient of , right The gradient of , therefore, the first grating period distribution function The total gradient is .

[0086] Furthermore, the joint optimization problem is solved by a gradient algorithm to obtain a first optimal transmission matrix, a second optimal transmission matrix, and a first optimal grating period distribution function, including steps S1031 to S1035.

[0087] Step S1031: Set the first transmission matrix The first initial value , the second transmission matrix The second initial value of , the first grating period distribution function The third initial value , and the first momentum term of the first transfer matrix corresponding to the initial iteration count value , the second momentum term of the second transfer matrix , the third momentum term of the first grating period distribution function are all set to zero vectors for accumulating gradients, where the initial iteration count value is zero.

[0088] At the same time, set the learning rate and momentum factor .

[0089] Specifically, the learning rate The value range of is 0.001 to 0.1, for example, 0.01 or 0.05.

[0090] Momentum Factor The value range of is 0.85 to 0.95, for example, 0.85 or 0.9.

[0091] Step S1032: Based on the first transmission matrix corresponding to the current iteration count value k , the second transmission matrix , the first grating period distribution function , calculate the first gradient corresponding to the current iteration count value k , second gradient , the third level .

[0092] Step S1033, based on the preset momentum factor , as well as the momentum terms and gradients corresponding to the current iteration count value k, calculate the momentum terms corresponding to the next iteration count value k+1.

[0093] Specifically, the first transmission matrix corresponding to the next iteration count value k+1 is The first momentum term for:

[0094] ;

[0095] in, Indicates the first transmission matrix corresponding to the current iteration count value k The first momentum term of .

[0096] Similarly, the first transmission matrix corresponding to the next iteration count value k+1 is The second momentum term , and the first grating period distribution function The third momentum term They are:

[0097] ;

[0098] ;

[0099] in, Represents the second transmission matrix corresponding to the current iteration count value k The second momentum term of Represents the first grating period distribution function corresponding to the current iteration count value k The third momentum term of .

[0100] Step S1034, based on the preset learning rate , the first transmission matrix corresponding to the current iteration count value k , the second transmission matrix , the first grating period distribution function , and the momentum terms corresponding to the next iteration count value k+1, calculate the first transmission matrix corresponding to the next iteration count value k+1 , the second transmission matrix , the first grating period distribution function , specifically:

[0101] ;

[0102] ;

[0103] .

[0104] Step S1035: Based on the objective function corresponding to the current iteration count value k , and the objective function corresponding to the next iteration count value k+1 , calculate the iterative residual corresponding to the next iteration count value k+1 , the iteration residual corresponding to the next iteration count value k+1 Less than or equal to the preset iterative residual threshold In the case of the next iteration count value k+1 corresponding to the first transmission matrix , the second transmission matrix , the first grating period distribution function , as the first optimal transmission matrix , the second optimal transmission matrix , the first optimal grating period distribution function .

[0105] Specifically, the iteration residual corresponding to the next iteration count value k+1 for:

[0106] .

[0107] exist In the case of , the iteration is completed and the first optimal transmission matrix is ​​output , the second optimal transmission matrix , the first optimal grating period distribution function .

[0108] exist In the case of , continue iteration, set k = k + 1, repeat steps S1032 to S1035 until the new iteration residual is less than or equal to the iteration residual threshold , the first transfer matrix, the second transfer matrix, and the first grating period distribution function of the next iteration count value corresponding to the new iteration residual are used as the first optimal transfer matrix, the second optimal transfer matrix, and the first optimal grating period distribution function.

[0109] It can be understood that the above method of solving the joint optimization problem to obtain the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function has more significant advantages than the gradient algorithm in the related technology, such as faster convergence speed, fewer local optimal traps, and smoother parameter updates.

[0110] In addition, the gradient algorithm for solving the above joint optimization problem may also be an accelerated gradient algorithm or an adaptive gradient algorithm.

[0111] The accelerated gradient algorithm converges quickly and has good convergence guarantees on convex optimization problems, but its implementation is relatively complex and its effect on non-convex problems is unstable.

[0112] The adaptive gradient algorithm can reduce hyperparameter adjustment, but the learning rate decreases monotonically, the convergence speed is slow, and it may cause the gradient to disappear.

[0113] Those skilled in the art can select any one of the above-mentioned gradient algorithm, accelerated gradient algorithm, and adaptive gradient algorithm provided in this embodiment to solve the joint optimization problem according to actual conditions.

[0114] Further, according to the first optimal grating period distribution function The grating etching position of the first sub-grating FBG1 can be determined.

[0115] make is the inversion of z, then the second grating period distribution function of the second sub-grating FBG2 is , the grating etching position of the second sub-grating FBG2 can be determined.

[0116] Furthermore, based on the first optimal transmission matrix and the first optimal grating period distribution function The first sub-grating FBG1 is determined.

[0117] Based on the second optimal transmission matrix and the second grating period distribution function The second sub-grating FBG2 is determined.

[0118] The target fiber Bragg grating is obtained by correspondingly connecting the first sub-grating FBG1 and the second sub-grating FBG2 in series.

[0119] In addition, the specific manufacture of the target fiber Bragg grating requires multiple steps, including but not limited to fiber pretreatment, phase mask preparation, exposure system setting, exposure writing, annealing treatment, performance testing and packaging, which belongs to the existing technology and will not be repeated in this embodiment.

[0120] Please refer to Figure 3 As shown, the present invention also provides a vibration event detection method based on a fiber Bragg grating, where the fiber Bragg grating is a target fiber Bragg grating manufactured according to any of the above manufacturing methods, and the above detection method includes steps S301 to S305.

[0121] Step S301 : Acquire a first sampled observation signal of a first sub-grating and a second sampled observation signal of a second sub-grating.

[0122] Step S302: Based on the wavelength offset dictionary, the first sampled observation signal and the second sampled observation signal are respectively represented as linear combinations of sparse feature vectors and noise vectors to obtain a group sparse model and determine a group sparse optimization problem, wherein each atom in the wavelength offset dictionary represents a wavelength offset pattern caused by a corresponding vibration event.

[0123] Step S303 : introducing a symmetric offset constraint into the group sparse optimization problem to obtain a sparse optimization problem, and solving the problem to obtain a first optimal sparse feature vector of the first sampled observation signal and a second optimal sparse feature vector of the second sampled observation signal.

[0124] Step S304 : performing feature domain integration on the first optimal sparse feature vector and the second optimal sparse feature vector to obtain an integrated feature vector.

[0125] Step S305 : performing vibration event detection based on the detection algorithm and the integrated feature vector.

[0126] The above detection algorithms include but are not limited to modal decomposition algorithms and event dictionary learning algorithms, which belong to the existing technology and will not be described in detail in this embodiment.

[0127] The vibration event detection method provided in this embodiment is implemented based on the target fiber Bragg grating manufactured by the above manufacturing method, and has the same beneficial effects as the above manufacturing method, and can take into account high sensitivity, signal-to-noise ratio, stability, and relatively simple design and manufacturing process.

[0128] In addition, the vibration event detection method provided in this embodiment, through the reverse symmetric cascade structure of the two FBG subarrays, can achieve reverse shifts in the central wavelengths corresponding to the two FBGs under the influence of the same vibration event. This can amplify tiny wavelength shifts that are originally difficult to measure, equivalently enhance the characteristic domain energy of the observation signal, and help improve the detection accuracy of the optical fiber system for weak vibration events.

[0129] Specifically, step S301, obtain the first sampling observation signal of the first sub-grating FBG1 and the second sampling observation signal of the second sub-grating FBG2 , specifically:

[0130] ;

[0131] ;

[0132] in, The wavelength shift is The sampling vector of the wavelength shift signal, The wavelength shift is The sampling vector of the wavelength shift signal is as follows:

[0133] ;

[0134] ;

[0135] in, represents the reflection wavelength of the first sub-grating FBG1, represents the first reference wavelength, represents the reflection wavelength of the second sub-grating FBG2, Indicates the second reference wavelength.

[0136] It is understandable that the first reference wavelength and the second reference wavelength can be determined by those skilled in the art based on a priori values ​​and actual conditions.

[0137] The sampling device for scanning the reflected waves of the first sub-grating FBG1 and the second sub-grating FBG2 may be a spectrometer or a fiber Bragg grating sensor demodulation system, or other signal acquisition devices.

[0138] The first sub-grating FBG1 and the second sub-grating FBG2 provided in this embodiment are complementary and symmetrical, so the wavelength shift and wavelength offset There is a symmetrical relationship, namely: .

[0139] In other words, the wavelength shifts of the first sub-grating FBG1 and the second sub-grating FBG2 are opposite and the shift amounts are the same, which facilitates processing the wavelength shift signals of the first sub-grating FBG1 and the second sub-grating FBG2 as a set of joint events, wherein the first sampling observation signal Represents the wavelength shift signal of the first sub-grating FBG1, the second sampling observation signal Represents the wavelength shift signal of the second sub-grating FBG2.

[0140] Preferably, in step S302, based on the wavelength offset dictionary, the first sampled observation signal and the second sampled observation signal are respectively represented as a linear combination of a sparse feature vector and a noise vector to obtain a group sparse model, including:

[0141] The set of all wavelength offsets obtained by scanning is ,in, is the total number of wavelength offsets obtained by scanning, and the corresponding wavelength offset dictionary D can be expressed as:

[0142] ;

[0143] It can be understood that each atom in the wavelength shift dictionary D represents a wavelength shift pattern caused by a certain vibration event.

[0144] The group sparse model can be obtained by first sampling the observation signal and the second sampling observation signal Expressed as:

[0145] ;

[0146] in, Represents the first sample observation signal The sparse feature vector of represents the first noise vector, Represents the second sampling observation signal The sparse feature vector of represents the second noise vector.

[0147] It can be understood that the introduction of noise vectors can fully consider the influence of noise and improve detection accuracy.

[0148] Furthermore, the group sparse optimization problem is:

[0149] ;

[0150] in, represents the Euclidean norm, represents the first sparsity regularization parameter, express norm.

[0151] It can be understood that the above-mentioned group sparse optimization problem is constructed based on the above-mentioned group sparse model, and the goal of constructing the above-mentioned group sparse optimization problem is to find a sparse solution that enables the first sampled observation signal and the second sampled observation signal to be composed of a small number of dictionary atoms.

[0152] The specific value of the small amount can be determined by those skilled in the art based on a priori values, or a ratio of the total number of dictionary atoms, or a sparsity level of the signal. For example, the specific value of the small amount is 10%-20% of the total number of dictionary atoms.

[0153] Furthermore, by introducing the symmetric offset constraint into the group sparse optimization problem, the sparse optimization problem is obtained as follows:

[0154] ;

[0155] in, represents the second sparsity regularization parameter.

[0156] It can be understood that by adding the symmetric shift constraint during the construction of the wavelength shift dictionary D, the symmetric shift constraint can be introduced into the group sparse optimization problem, further expanding the group sparse optimization problem into the aforementioned sparse optimization problem. This ensures that the first sampled observation signal and the second sampled observation signal have an opposite variation relationship, thereby reducing the degrees of freedom of the group sparse model and improving its robustness.

[0157] The above sparse optimization problem is solved by iterative soft threshold algorithm or alternating direction multiplier method (ADMM) algorithm to obtain the first optimal sparse feature vector of the first sampled observation signal , and the second optimal sparse feature vector of the second sampled observation signal , which belongs to the prior art and will not be described in detail in this embodiment.

[0158] Preferably, the first optimal sparse feature vector and the second optimal sparse feature vector are integrated to obtain an integrated feature vector, specifically: the second optimal sparse feature vector Arrange the elements in reverse order to obtain the vector to be integrated; compare the vector to be integrated with the first optimal sparse feature vector Add together to get the integrated eigenvector :

[0159] ;

[0160] in, Represents the vector reversal function, which is used to reverse the order of the elements of the vector.

[0161] It can be understood that the optimal sparse feature vector obtained by optimization is integrated in the feature domain, which realizes the energy accumulation of the feature domain and improves the sensing sensitivity to weak vibration signals.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] refer to Figure 4 , 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.

[0166] like Figure 4 As shown, the electronic device includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. RAM 403 can also store various programs and data required for the operation of the electronic device. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0167] Multiple components in the electronic device are connected to the I / O interface 405, including an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 can be any type of device capable of inputting information into the electronic device. The input unit 406 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 407 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 408 can include, but is not limited to, a magnetic disk and an optical disk. The communication unit 409 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.

[0168] Computing unit 401 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing unit 401 include, but are not limited to, a CPU, a graphics processing unit (GPU), various specialized 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. Computing unit 401 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 storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device via ROM 402 and / or communication unit 409. In some embodiments, computing unit 401 may be configured to perform the above-described methods by any other suitable means (e.g., via firmware).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0173] 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.

[0174] 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.

[0175] The above-described embodiments merely represent several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that a person of ordinary skill in the art would be able to make various modifications and improvements without departing from the scope of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for manufacturing a fiber Bragg grating, characterized in that: include: Constructing a first transmission matrix and a first grating period distribution function of a first sub-grating, wherein the first sub-grating is a fiber Bragg grating with a variable grating period; determining a second transmission matrix for a second sub-grating based on the first transmission matrix, wherein the second sub-grating is complementary to the first sub-grating; Obtaining a first optimal transmission matrix corresponding to the first transmission matrix, a second optimal transmission matrix corresponding to the second transmission matrix, and a first optimal grating period distribution function corresponding to the first grating period distribution function by solving a joint optimization problem; determining a second grating period distribution function of the second sub-grating based on the first optimal grating period distribution function; The first sub-grating is determined based on the first optimal transmission matrix and the first optimal grating period distribution function, the second sub-grating is determined based on the second optimal transmission matrix and the second grating period distribution function, and the first sub-grating and the second sub-grating are connected in series to obtain a target fiber Bragg grating.

2. The method according to claim 1, characterized in that Determining a second transmission matrix of a second sub-grating based on the first transmission matrix includes: The first transmission matrix is ​​T1, and the second transmission matrix T2 is: ； in, , T1 -1 Represents the inverse matrix of T1.

3. The method according to claim 1, characterized in that After determining a second transmission matrix of a second sub-grating based on the first transmission matrix, the method further includes: Based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix, the joint optimization problem is determined as: ； in, represents solving the minimum value, T1 represents the first transmission matrix, T2 represents the second transmission matrix, represents a transmission matrix of a first fiber Bragg grating, wherein the first fiber Bragg grating is a uniform fiber Bragg grating equivalent to the target fiber Bragg grating, represents the first grating period distribution function, z represents the etching position of the first sub-grating, represents the square of the F norm, is the side mode suppression ratio of the cascaded grating, is the first optimization weight coefficient, is the second optimization weight coefficient, is the third optimization weight coefficient.

4. The method according to claim 3, characterized in that After determining the joint optimization problem based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix, the method further includes: Based on the joint optimization problem, the objective function J is determined as: ； ， ， ； Determine a first gradient of the first transmission matrix based on the objective function J J. the second gradient of the second transmission matrix J. The third gradient of the first grating period distribution function J are: ， ； ， ； ； Among them, the objective function J, the first gradient J. The second gradient J. The third gradient J is used to solve the joint optimization problem.

5. The method according to claim 4, characterized in that Solving the joint optimization problem by a gradient algorithm to obtain the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function includes: Setting initial values ​​of the first transfer matrix, the second transfer matrix, and the first grating period distribution function, and setting the first momentum term of the first transfer matrix, the second momentum term of the second transfer matrix, and the third momentum term of the first grating period distribution function corresponding to an initial iteration count value to zero vectors, wherein the initial iteration count value is zero; Calculating a first gradient, a second gradient, and a third gradient corresponding to the current iteration count value based on the first transmission matrix, the second transmission matrix, and the first grating period distribution function corresponding to the current iteration count value; Calculating each momentum term corresponding to a next iteration count value based on a preset momentum factor and each momentum term and each gradient corresponding to the current iteration count value; Calculate the first transfer matrix, the second transfer matrix, and the first grating period distribution function corresponding to the next iteration count value based on a preset learning rate, the first transfer matrix, the second transfer matrix, and the first grating period distribution function corresponding to the current iteration count value, and the momentum terms corresponding to the next iteration count value; Based on the objective function corresponding to the current iteration count value and the objective function corresponding to the next iteration count value, the iteration residual corresponding to the next iteration count value is calculated. When the iteration residual corresponding to the next iteration count value is less than or equal to a preset iteration residual threshold, the first transfer matrix, the second transfer matrix, and the first grating period distribution function corresponding to the next iteration count value are used as the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function.

6. A vibration event detection method based on fiber Bragg grating, characterized in that: The fiber Bragg grating is a target fiber Bragg grating manufactured by the method according to any one of claims 1 to 5, and the detection method comprises: Acquire a first sampled observation signal of the first sub-grating and a second sampled observation signal of the second sub-grating; Based on a wavelength offset dictionary, the first sampled observation signal and the second sampled observation signal are respectively represented as linear combinations of a sparse feature vector and a noise vector to obtain a group sparse model, and a group sparse optimization problem is determined, wherein each atom in the wavelength offset dictionary represents a wavelength offset pattern caused by a corresponding vibration event; Introducing a symmetric offset constraint into the group of sparse optimization problems to obtain a sparse optimization problem, and solving the problem to obtain a first optimal sparse feature vector of the first sampled observation signal and a second optimal sparse feature vector of the second sampled observation signal; Performing feature domain integration on the first optimal sparse feature vector and the second optimal sparse feature vector to obtain an integrated feature vector; Vibration event detection is performed based on a detection algorithm and the integrated feature vector.

7. The method according to claim 6, characterized in that The group sparse optimization problem is: ； in, Represents the first sample observation signal The sparse feature vector of Represents the second sampling observation signal The sparse feature vector of D represents the wavelength offset dictionary. represents the square of the Euclidean norm, represents the first sparsity regularization parameter, express norm.

8. The method according to claim 7, characterized in that Introducing a symmetric offset constraint into the set of sparse optimization problems, the resulting sparse optimization problem is: ； in, represents the second sparsity regularization parameter.

9. The method according to claim 6, characterized in that Performing feature domain integration on the first optimal sparse feature vector and the second optimal sparse feature vector to obtain an integrated feature vector includes: Arrange the elements in the second optimal sparse feature vector in reverse order to obtain a vector to be integrated; The vector to be integrated is added to the first optimal sparse feature vector to obtain the integrated feature vector.

10. 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 6 to 9.

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