Manufacturing method of fiber bragg grating and vibration event detection method

Abstract

The invention relates to the technical field of fiber bragg gratings, and particularly provides a fiber bragg grating manufacturing method and a vibration event detection method, and the manufacturing method comprises the steps: constructing a first transmission matrix and a first grating period distribution function of a first sub-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 and the first sub-grating are complementary and symmetrical; 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 of 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 in series to obtain the target fiber bragg grating. The invention aims to solve the problem that the fiber bragg grating in the prior art cannot give consideration to high sensitivity, signal-to-noise ratio, stability and relatively simple design and manufacturing process.

Description

Technical Field

[0001] The present invention relates to the technical field of fiber Bragg gratings, and particularly to a manufacturing method of a fiber Bragg grating and a vibration event detection method. Background Art

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

[0003] The existing FBG sensing technology mainly relies on uniformly etched fiber Bragg gratings. Although basic signal detection can be achieved, when detecting weak vibration events, the sensitivity and signal-to-noise ratio are often insufficient, which limits its application in complex environments. To solve this problem, the design of FBG can be improved by non-uniform etching to adjust the reflection spectrum and enhance the sensing performance. However, a simple non-uniform etching design is difficult to optimize the sensitivity, signal-to-noise ratio, and stability simultaneously. In addition, due to the complexity of the etching process, how to simplify the design and manufacturing process while improving the performance is still an urgent problem to be solved.

[0004] Aiming at the problem that the fiber Bragg grating in the related technology cannot balance high sensitivity, signal-to-noise ratio, stability, and a relatively simple design and manufacturing process, no effective solution has been proposed yet. Summary of the Invention

[0005] A manufacturing method of a fiber Bragg grating and a vibration event detection method provided by an embodiment of the present invention at least solve the problem that the fiber Bragg grating in the related technology cannot balance high sensitivity, signal-to-noise ratio, stability, and a relatively simple design and manufacturing process.

[0006] A manufacturing method of a fiber Bragg grating provided by an embodiment of the present invention includes: constructing a first transmission matrix and a first grating period distribution function of a first sub-grating, where 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, where the second sub-grating is complementary and symmetric 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 manufacturing method of the fiber Bragg grating provided by the embodiment of the present invention creates a second transfer matrix of a second sub-grating based on a first transfer matrix, including: if the first transfer matrix is T1, the second transfer matrix T2 is: ; wherein, , T1 -1 represents the inverse matrix of T1.

[0008] After the manufacturing method of the fiber Bragg grating provided by the embodiment of the present invention creates a second transfer matrix of a second sub-grating based on a first transfer matrix, the above 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 as: ; wherein, represents solving for the minimum value, T1 represents the first transfer matrix, T2 represents the second transfer matrix, represents the transfer matrix of the first fiber Bragg grating, and 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.

[0009] After the manufacturing method of the fiber Bragg grating provided by the embodiment of the present invention creates a joint optimization problem based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix, the above method further includes: determining an objective function J based on the joint optimization problem as: ; , , ; Determining the first gradient J of the first transfer matrix J, the second gradient J of the second transfer matrix, and the third gradient , ; , ; ; Among them, the objective function J, the first gradient J, the second gradient J, the third gradient J are used to solve the joint optimization problem.

[0010] The manufacturing method of the fiber Bragg grating provided by the embodiment of the present invention creates a solution to the joint optimization problem through a gradient algorithm, and obtains 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 zero vectors, where 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 each momentum term and each gradient corresponding to the current iteration count value, calculating each momentum term corresponding to the next iteration count value; based on a preset learning rate, the first transmission matrix, the second transmission matrix, and the first grating period distribution function corresponding to the current iteration count value, and each momentum term corresponding to the next iteration count value, calculating the first transmission matrix, the second transmission matrix, and the first grating period distribution function 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, calculating the iteration residual corresponding to the next iteration count value, and in the case where the iteration residual corresponding to the next iteration count value is less than or equal to a preset iteration residual threshold, taking the first transmission matrix, the second transmission matrix, and the first grating period distribution function corresponding to the next iteration count value as the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function.

[0011] An optical fiber Bragg grating-based vibration event detection method provided by an embodiment of the present invention, where the optical fiber Bragg grating is a target optical fiber Bragg grating manufactured according to any of the above methods. The above detection method includes: obtaining a first sampling observation signal of a first sub-grating and a second sampling observation signal of a second sub-grating; based on a wavelength offset dictionary, respectively representing the first sampling observation signal and the second sampling observation signal as a linear combination of a sparse feature vector and a noise vector to obtain a group sparse model, and determining a group sparse optimization problem, where 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 to obtain a first optimal sparse feature vector of the first sampling observation signal and a second optimal sparse feature vector of the second sampling 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 performing vibration event detection based on a detection algorithm and the integrated feature vector.

[0012] The group sparse optimization problem for the optical fiber Bragg grating-based vibration event detection method provided by an embodiment of the present invention is: ; where represents the sparse feature vector of the first sampling observation signal , represents the sparse feature vector of the second sampling observation signal , D represents the wavelength offset dictionary, represents the square of the Euclidean norm, represents the first sparsity regularization parameter, represents norm.

[0013] The sparse optimization problem obtained by introducing a symmetric offset constraint into the group sparse optimization problem for the optical fiber Bragg grating-based vibration event detection method provided by an embodiment of the present invention is: ; where represents the second sparsity regularization parameter.

[0014] The method for performing feature domain integration on the first optimal sparse feature vector and the second optimal sparse feature vector to obtain an integrated feature vector in the optical fiber Bragg grating-based vibration event detection method provided by an embodiment of the present invention includes: reversing the order of the elements in the second optimal sparse feature vector 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.

[0015] An electronic device provided by an embodiment of the present invention includes: a processor, and a memory storing a program, where the program includes instructions that cause the processor to execute any of the above detection methods when executed by the processor.

[0016] A manufacturing method of a fiber Bragg grating provided by an embodiment of the present invention. The grating period of the first sub-grating is variable, and the second sub-grating is complementary symmetric 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, enabling the optimization of wavelength selectivity and bandwidth while maintaining the grating reflection characteristics, further suppressing environmental noise, and improving the robustness of the system; by solving the joint optimization problem, the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function are obtained, and the second grating period distribution function is determined based on the first optimal grating period distribution function, which can improve the symmetry of the target fiber Bragg grating, reduce the etching amount of the fiber grating while obtaining the equivalent transmission matrix, and simplify the design and manufacturing process; in the case of large-scale grating distribution, it can improve the structural strength of the fiber. To solve the problem in the related art that the fiber Bragg grating cannot take into account high sensitivity, signal-to-noise ratio, stability, and a relatively simple design and manufacturing process.

[0017] In addition, based on the above target fiber Bragg grating for vibration event detection, it can amplify the originally difficult-to-measure small wavelength shift, equivalently enhance the characteristic domain energy of the observation signal, and contribute to improving the detection accuracy of the fiber optic system for weak vibration events. Description of the Drawings

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other embodiments based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of the steps of a manufacturing method of a fiber Bragg grating in an embodiment of the present invention.

[0020] Figure 2 It is a schematic structural diagram of a target fiber Bragg grating in an embodiment of the present invention.

[0021] Figure 3 It is a flowchart of the steps of a vibration event detection method based on a fiber Bragg grating in an embodiment of the present invention.

[0022] Figure 4It is a schematic structural diagram of an electronic device in an embodiment of the present invention. Detailed implementation manners

[0023] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0024] The FBG sensing technology mainly relies on uniformly etched fiber Bragg gratings. Although it can achieve basic signal detection, when detecting weak vibration events, the sensitivity and signal-to-noise ratio are often insufficient, which limits its application in complex environments. To solve this problem, the design of the FBG can be improved by non-uniform etching to adjust the reflection spectrum and enhance the sensing performance. However, a simple non-uniform etching design is difficult to optimize the sensitivity, signal-to-noise ratio, and stability simultaneously. In addition, due to the complexity of the etching process, how to simplify the design and manufacturing process while improving the performance is still an urgent problem to be solved.

[0025] For this reason, please refer to Figure 1 As shown, a manufacturing method of a fiber Bragg grating provided by an embodiment of the present invention includes steps S101 to S105.

[0026] Step S101, constructing a first transfer matrix and a first grating period distribution function of a first sub-grating, where the first sub-grating is a fiber Bragg grating with a variable grating period.

[0027] Step S102, determining a second transfer matrix of a second sub-grating based on the first transfer matrix, where the second sub-grating is complementary and symmetric to the first sub-grating.

[0028] Step S103, obtaining a first optimal transfer matrix corresponding to the first transfer matrix, a second optimal transfer matrix corresponding to the second transfer matrix, and a first optimal grating period distribution function corresponding to the first grating period distribution function by solving a joint optimization problem.

[0029] Step S104, determining a second grating period distribution function of the second sub-grating based on the first optimal grating period distribution function.

[0030] Step S105, determining the first sub-grating based on the first optimal transfer matrix and the first optimal grating period distribution function, determining the second sub-grating based on the second optimal transfer 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.

[0031] It is understandable that a fiber Bragg grating (FBG) utilizes the photosensitivity of the fiber material to form a structure with a periodic refractive index change inside the fiber through ultraviolet light irradiation or other methods. The etching in this embodiment can be understood as the formation process of the above-mentioned structure with a periodic refractive index change.

[0032] The transmission matrix describes the reflection and transmission characteristics of the FBG for optical signals. The grating period distribution function defines the spatial distribution law of the refractive index modulation in the fiber core and directly affects the filtering performance of the FBG. Therefore, the corresponding FBG can be determined based on the transmission matrix and the grating period distribution function.

[0033] Variable grating period means that the period length of the periodic refractive index change can be altered. The variable grating period of the first sub-grating indicates that the first sub-grating is non-uniform. The second sub-grating is complementary and symmetric to the first sub-grating, so the second sub-grating is also non-uniform, and the target fiber Bragg grating obtained from the first and second sub-gratings is also non-uniform, which can provide more degrees of freedom during grating etching.

[0034] For example, please refer to Figure 2 as shown, the first sub-grating FBG1 and the second sub-grating FBG2 are correspondingly connected in series to obtain the target fiber Bragg grating. Among them, Figure 2 The overall striped shadow shown within the black solid line frame represents the target fiber Bragg grating, and the blank area shown within the black solid line frame represents the area in the fiber core where no refractive index modulation is performed.

[0035] It is understandable that the target fiber Bragg grating is a reverse symmetric cascade structure of two FBG sub-arrays.

[0036] The complementarity between the first sub-grating and the second sub-grating means that they complement each other in function or characteristics. When detecting weak vibration events, the wavelength shifts of the two show opposite change relationships, and combined, they can more comprehensively and accurately reflect vibration information, thus making up for the limitations of single detection.

[0037] Among them, weak vibration events can be judged by those skilled in the art according to prior values and actual situations.

[0038] For example, in the case of using an acceleration sensor 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 a vibration velocity RMS less than 2.8 mm / s can be considered a weak vibration event. For buildings and bridges, a vibration event with a vibration displacement less than 0.1 mm or an acceleration less than 0.01 times the acceleration due to gravity can be considered a weak vibration event.

[0039] The symmetry between the first sub - grating and the second sub - grating means that their structures are symmetric, with similar physical properties. Under the same external conditions, their responses are consistent and comparable, which can simplify the signal - processing process and improve the detection accuracy and reliability.

[0040] 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.

[0041] Preferably, in this embodiment, the joint optimization problem is determined based on the first transfer matrix, the first grating period distribution function and the second transfer matrix, which has the advantages of taking into account both computational efficiency and accuracy. In this case, the second grating period distribution function for determining the second sub - grating is determined according to the first optimal grating period distribution function obtained by solving the optimization problem.

[0042] The algorithms for solving the joint optimization problem include but are not limited to the gradient algorithm, the adaptive moment estimation algorithm, and the adaptive increment algorithm. Preferably, in this embodiment, the joint optimization problem is solved based on the gradient algorithm, which has the advantages of high computational efficiency, few local - optimal traps, and smooth parameter updates, and will be specifically introduced later.

[0043] In summary, for the manufacturing method of the fiber Bragg grating provided in this embodiment, the grating period of the first sub - grating is variable, and the second sub - grating is complementary and symmetric 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, enabling the optimization of wavelength selectivity and bandwidth while maintaining the grating reflection characteristics, further suppressing environmental noise, and improving the robustness of the system.

[0044] By obtaining the first optimal transfer matrix, the second optimal transfer matrix, and the first optimal grating period distribution function through solving the joint optimization problem, and 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 the equivalent transfer matrix, the etching amount of the fiber grating is reduced, and the design and manufacturing process is simplified. In the case of large - scale grating distribution, the structural strength of the fiber can be improved. This solves the problem in the related art that the fiber Bragg grating cannot take into account high sensitivity, signal - to - noise ratio, stability, and a relatively simple design and manufacturing process.

[0045] It can be understood that two gratings having equivalent transfer matrices means that their phase and amplitude modulation effects on optical signals are exactly the same, although their physical structures may be different. For example, the periodic distribution and length are different.

[0046] Specifically, in step S101, constructing the first transfer matrix and the first grating periodic distribution function of the first sub-grating includes: Let the first transfer matrix be T1, and the first grating periodic distribution function be , where z represents the etching position of the first sub-grating.

[0047] Preferably, in step S102, determining the second transfer matrix of the second sub-grating based on the first transfer matrix includes: If the first transfer matrix is T1, then the second transfer matrix T2 is: ; where , and T1 -1 represents the inverse matrix of the first transfer matrix T1.

[0048] It can be understood that determining T2 based on T1 provides a basis for the subsequent manufacturing of the target fiber Bragg grating.

[0049] Further, after determining the second transfer matrix of the second sub-grating based on the first transfer matrix, the above method further includes determining the joint optimization problem based on the first transfer matrix, the first grating periodic distribution function, and the second transfer matrix as: ; where represents solving for the minimum value, represents the transfer matrix of the first fiber Bragg grating, and the first fiber Bragg grating is a uniform and 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.

[0050] It can be 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 to design the periodic distribution function so that the variable-period grating achieves the same transmission characteristics as the fixed-period grating under preset conditions.

[0051] The specific definition of the side mode suppression ratio is: ; where represents the corresponding Bragg wavelength The reflection intensity at represents the reflection intensity at the corresponding non-Bragg wavelength, which is the secondary wavelength.

[0052] 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 above method further includes: Determining the objective function J based on the joint optimization problem as: ; , , ; Determining the first gradient J of the first transfer matrix, the second gradient J of the second transfer matrix, and the third gradient J of the first grating period distribution function are respectively: , ; , ; ; wherein, the objective function J, the first gradient J, the second gradient J, and the third gradient J are used to solve the joint optimization problem.

[0053] It can be understood that the gradient of is , the gradients of and the gradients of are both zero. Therefore, the total gradient of the first transfer matrix is the first gradient .

[0054] the gradient of is , the gradients of and the gradients of are both zero. Therefore, the total gradient of the second transfer matrix is the second gradient .

[0055] the gradient of is zero, the gradient of The gradient of is For the gradient of is Therefore, the total gradient of the first grating period distribution function is

[0056] Furthermore, by solving the joint optimization problem through the gradient algorithm, the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function are obtained, including steps S1031 to S1035.

[0057] Step S1031: Set the first initial value of the first transmission matrix , the second initial value of the second transmission matrix , and the third initial value of the first grating period distribution function . And set 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 zero vectors for accumulating gradients, where the initial iteration count value is zero.

[0058] Meanwhile, set the learning rate and the momentum factor .

[0059] Specifically, the value range of the learning rate is from 0.001 to 0.1, such as 0.01 or 0.05.

[0060] The value range of the momentum factor is from 0.85 to 0.95, such as 0.85 or 0.9.

[0061] Step S1032: Based on the first transmission matrix , the second transmission matrix , and the first grating period distribution function corresponding to the current iteration count value k, calculate the first gradient , the second gradient , and the third gradient corresponding to the current iteration count value k.

[0062] Step S1033: Based on the preset momentum factor , and each momentum term and each gradient corresponding to the current iteration count value k, calculate each momentum term corresponding to the next iteration count value k + 1.

[0063] Specifically, the first momentum term of the first transfer matrix corresponding to the next iteration count value k + 1 is: For: ; Wherein, represents the first momentum term of the first transfer matrix corresponding to the current iteration count value k .

[0064] Similarly, the second momentum term of the first transfer matrix corresponding to the next iteration count value k + 1 , and the third momentum term of the first grating period distribution function are respectively: For: For: ; ; Wherein, represents the second momentum term of the second transfer matrix corresponding to the current iteration count value k , represents the third momentum term of the first grating period distribution function corresponding to the current iteration count value k .

[0065] Step S1034, based on the preset learning rate , the first transfer matrix corresponding to the current iteration count value k , the second transfer matrix , the first grating period distribution function , and each momentum term corresponding to the next iteration count value k + 1, calculate the first transfer matrix corresponding to the next iteration count value k + 1 , the second transfer matrix , the first grating period distribution function , specifically: ; ; .

[0066] 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 iteration residual corresponding to the next iteration count value k + 1 , when the iteration residual corresponding to the next iteration count value k + 1 is less than or equal to the preset iteration residual threshold In the case of, the first transmission matrix corresponding to the next iteration count value k + 1 , the second transmission matrix , the first grating period distribution function , are used as the first optimal transmission matrix , the second optimal transmission matrix , the first optimal grating period distribution function .

[0067] Specifically, the iteration residual corresponding to the next iteration count value k + 1 is: .

[0068] In the case of , the iteration is completed, and the first optimal transmission matrix , the second optimal transmission matrix , the first optimal grating period distribution function are output.

[0069] In the case of , continue the iteration, let k = k + 1, and repeat steps S1032 to S1035 until the new iteration residual is less than or equal to the iteration residual threshold . The first transmission matrix, the second transmission matrix, and the first grating period distribution function corresponding to the next iteration count value of the new iteration residual are used as the first optimal transmission matrix, the second optimal transmission matrix, and the first optimal grating period distribution function.

[0070] It can be understood that the method for 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 prominent advantages of faster convergence speed, fewer local optimal traps, and smoother parameter updates compared with the gradient algorithm in the related technology.

[0071] In addition, the gradient algorithm for solving the above joint optimization problem can also be an accelerated gradient algorithm or an adaptive gradient algorithm.

[0072] The accelerated gradient algorithm has a relatively fast convergence speed and has good convergence guarantee for convex optimization problems, but its implementation is relatively complex and its effect on non-convex problems is unstable.

[0073] The adaptive gradient algorithm can reduce hyperparameter tuning, but the learning rate decreases monotonically, the convergence speed is slow, and it may lead to gradient disappearance.

[0074] Those skilled in the art can choose any one of the above gradient algorithms, accelerated gradient algorithms, and adaptive gradient algorithms provided in this embodiment according to the actual situation to solve the joint optimization problem.

[0075] Further, according to the first optimal grating period distribution function the grating etching position of the first sub-grating FBG1 can be determined.

[0076] Let be the inversion of z, then the second grating period distribution function of the second sub-grating FBG2 is , and the grating etching position of the second sub-grating FBG2 can be determined.

[0077] Further, based on the first optimal transmission matrix and the first optimal grating period distribution function the first sub-grating FBG1 is determined.

[0078] Based on the second optimal transmission matrix and the second grating period distribution function the second sub-grating FBG2 is determined.

[0079] The first sub-grating FBG1 and the second sub-grating FBG2 are correspondingly connected in series to obtain the target fiber Bragg grating.

[0080] In addition, specifically manufacturing the target fiber Bragg grating requires multiple steps, including but not limited to fiber preprocessing, phase mask preparation, exposure system setting, exposure writing, annealing treatment, performance testing and packaging, which belong to the prior art and will not be elaborated herein.

[0081] Please refer to Figure 3 shown, an embodiment of the present invention also provides a vibration event detection method based on a fiber Bragg grating. The fiber Bragg grating is the target fiber Bragg grating manufactured according to any one of the above manufacturing methods. The above detection method includes steps S301 to S305.

[0082] Step S301, obtaining a first sampled observation signal of the first sub-grating and a second sampled observation signal of the second sub-grating.

[0083] Step S302, based on the wavelength shift dictionary, representing the first sampled observation signal and the second sampled observation signal as linear combinations of sparse feature vectors and noise vectors respectively, obtaining a group sparse model, and determining a group sparse optimization problem, where each atom in the wavelength shift dictionary represents a wavelength shift pattern caused by a corresponding vibration event.

[0084] Step S303, introducing a symmetric shift constraint into the group sparse optimization problem to obtain a sparse optimization problem, and solving 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.

[0085] Step S304: Integrate the first optimal sparse feature vector and the second optimal sparse feature vector in the feature domain to obtain an integrated feature vector.

[0086] Step S305: Detect vibration events based on the detection algorithm and the integrated feature vector.

[0087] The above detection algorithms include, but are not limited to, modal decomposition algorithms and event dictionary learning algorithms, which belong to the prior art and will not be elaborated herein in this embodiment.

[0088] 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. It can take into account relatively high sensitivity, signal-to-noise ratio, stability, and a relatively simple design and manufacturing process.

[0089] In addition, for the vibration event detection method provided in this embodiment, through the reverse symmetric cascaded structure of two FBG sub-arrays, under the influence of the same vibration event, the central wavelengths corresponding to the two FBGs can achieve reverse offsets, which can amplify the originally difficult-to-measure small wavelength offsets, equivalently enhance the energy in the feature domain of the observed signal, and contribute to improving the detection accuracy of the fiber optic system for weak vibration events.

[0090] Specifically, in step S301, obtain the first sampled observation signal of the first sub-grating FBG1 and the second sampled observation signal of the second sub-grating FBG2 , specifically: ; ; where represents the sampling vector of the wavelength offset signal with a wavelength offset of , and represents the sampling vector of the wavelength offset signal with a wavelength offset of . Specifically, there are: ; ; where represents the reflected wavelength of the first sub-grating FBG1, represents the first reference wavelength, represents the reflected wavelength of the second sub-grating FBG2, represents the second reference wavelength.

[0091] It can be understood that the first reference wavelength and the second reference wavelength can be determined by those skilled in the art according to prior values and actual situations.

[0092] The sampling device for scanning the reflected waves of the first sub-grating FBG1 and the second sub-grating FBG2 can be a spectrometer or a fiber Bragg grating sensor demodulation system, or other signal acquisition devices.

[0093] The first sub-grating FBG1 and the second sub-grating FBG2 provided in this embodiment are complementary and symmetric. Therefore, the wavelength shift and the wavelength shift have a symmetric relationship, that is: .

[0094] In other words, the wavelength shifts of the first sub-grating FBG1 and the second sub-grating FBG2 are opposite and have the same offset, which is convenient for processing the wavelength shift signals of the first sub-grating FBG1 and the second sub-grating FBG2 as a group of joint events. Among them, the first sampling observation signal represents the wavelength shift signal of the first sub-grating FBG1, and the second sampling observation signal represents the wavelength shift signal of the second sub-grating FBG2.

[0095] Preferably, in step S302, based on the wavelength shift amount dictionary, the first sampling observation signal and the second sampling observation signal are respectively expressed as a linear combination of a sparse feature vector and a noise vector to obtain a group sparse model, including: The set of all wavelength shift amounts obtained by scanning is , where is the total number of wavelength shift amounts obtained by scanning, then the corresponding wavelength shift amount dictionary D can be expressed as: ; It can be understood that each atom in the wavelength shift amount dictionary D represents a wavelength shift pattern caused by a certain vibration event.

[0096] The group sparse model can be expressed by the first sampling observation signal and the second sampling observation signal as: ; where represents the sparse feature vector of the first sampling observation signal , represents the first noise vector, represents the sparse feature vector of the second sampling observation signal , represents the second noise vector.

[0097] It can be understood that introducing the noise vector can fully consider the influence of noise and improve the detection accuracy.

[0098] Furthermore, the group sparse optimization problem is: ; wherein, represents the Euclidean norm, represents the first sparsity regularization parameter, represents norm.

[0099] It can be understood that the above group sparse optimization problem is constructed according to the above group sparse model. The goal of constructing the above group sparse optimization problem is to find a sparse solution such that the first sampled observation signal and the second sampled observation signal can be composed of a small number of dictionary atoms.

[0100] Among them, the specific value of the small number can be determined by those skilled in the art according to prior values, or the proportion of the total number of dictionary atoms, or the sparsity level of the signal. For example, the specific value of the small number is 10%-20% of the total number of dictionary atoms.

[0101] Furthermore, introducing a symmetric offset constraint into the group sparse optimization problem, the obtained sparse optimization problem is: ; wherein, represents the second sparsity regularization parameter.

[0102] It can be understood that by adding a symmetric offset constraint condition in the process of constructing the wavelength offset dictionary D, the symmetric offset constraint can be introduced into the group sparse optimization problem, and the group sparse optimization problem can be further extended to the above sparse optimization problem. This can ensure that the first sampled observation signal and the second sampled observation signal have an opposite change relationship, thereby reducing the degrees of freedom of the group sparse model and improving the robustness of the group sparse model.

[0103] Solving the above sparse optimization problem through the iterative soft threshold algorithm or the Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the first optimal sparse eigenvector of the first sampled observation signal, and the second optimal sparse eigenvector

[0104] of the second sampled observation signal belongs to the prior art, and this embodiment will not elaborate herein. Preferably, the first optimal sparse eigenvector and the second optimal sparse eigenvector are integrated in the feature domain to obtain an integrated eigenvector, specifically: reversing the order of the elements in the second optimal sparse eigenvector to obtain a vector to be integrated; adding the vector to be integrated to the first optimal sparse eigenvector to obtain the integrated eigenvector ; Among them, represents a vector reverse function, which is used to reverse the elements of a vector.

[0105] It can be understood that integrating the optimized optimal sparse feature vector in the feature domain realizes the energy accumulation in the feature domain and improves the induction sensitivity to weak vibration signals.

[0106] The embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the above computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present invention.

[0107] The embodiment of the present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present invention.

[0108] The embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The above memory stores a computer program capable of being executed by the at least one processor, and the above computer program, when executed by the at least one processor, is used to cause the electronic device to execute the method of the embodiment of the present invention.

[0109] Refer to Figure 4 , and now the structural block diagram of an electronic device that can be used as a server or a client in the embodiment of the present invention will be described. It is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0110] As Figure 4As shown, the electronic device includes a computing unit 401, which can perform various appropriate actions and processes according to computer programs stored in a read-only memory (ROM) 402 or computer programs loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device can also be stored. The computing unit 401, the ROM 402, and the 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.

[0111] 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 controls 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, 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.

[0112] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include but are not limited to a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as a computer program, which is tangibly included in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 402 and / or the communication unit 409. In some embodiments, the computing unit 401 can be configured to execute the above methods in any other appropriate manner (for example, by means of firmware).

[0113] The computer programs for implementing the methods of the embodiments of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processors or controllers of general-purpose computers, special-purpose computers, or other programmable data processing devices, such that when the computer programs are executed by the processors or controllers, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0114] In the context of the embodiments of the present invention, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0115] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more". The descriptions of the terms "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features.

[0116] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data that have been authorized by the user or fully authorized by all parties. And the collection, use, and processing of the relevant data need to comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entrances are provided for the user to select to authorize or refuse.

[0117] In the method embodiments provided by the embodiments of the present invention, the steps recorded can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The protection scope of the present invention is not limited in this regard.

[0118] The term "embodiment" in this specification means that the specific features, structures or characteristics described in combination with the embodiments may be included in at least one embodiment of the present invention. The phrase appears in various positions in the specification does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referred to each other. In particular, for 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 embodiments.

[0119] The above-described embodiments only represent several implementation manners of the present invention, and the description is relatively specific and detailed, but it should not be construed as a limitation on the protection scope. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the appended claims.

Claims

1. A manufacturing method of a fiber Bragg grating, characterized in that, Including: Constructing a first transfer 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 transfer matrix of a second sub-grating based on the first transfer matrix, wherein the second sub-grating is complementary symmetric to the first sub-grating; Obtaining a first optimal transfer matrix corresponding to the first transfer matrix, a second optimal transfer matrix corresponding to the second transfer 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 transfer matrix and the first optimal grating period distribution function, determining the second sub-grating based on the second optimal transfer matrix and the second grating period distribution function, and connecting the first sub-grating and the second sub-grating in series correspondingly to obtain a target fiber Bragg grating.

2. The method according to claim 1, characterized in that, Determining a second transfer matrix of a second sub-grating based on the first transfer matrix includes: If the first transfer matrix is T1, then the second transfer matrix T2 is: ； Among them, , T1 -1 represents the inverse matrix of T1.

3. The method according to claim 1, characterized in that, After determining the second transfer matrix of the second sub-grating based on the first transfer matrix, the method further includes: Determining the joint optimization problem based on the first transfer matrix, the first grating period distribution function, and the second transfer matrix as: ； Among them, represents solving for the minimum value, T1 represents the first transmission matrix, and T2 represents the second transmission matrix. represents the transmission matrix of the first fiber Bragg grating, and 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, and 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: Determining the objective function J based on the joint optimization problem as: ； ， ， ； Determine the 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 respectively:[[]]END]] ， ； ， ； ； Among them, the objective function J, the first gradient J, the second gradient J, the third gradient J are 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 transfer matrix, the second optimal transfer matrix, and the first optimal grating period distribution function, including: Setting initial values of the first transfer matrix, the second transfer matrix, and the first grating period distribution function respectively, and setting a first momentum term of the first transfer matrix, a second momentum term of the second transfer matrix, and a 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 transfer matrix, the second transfer matrix, and the first grating period distribution function corresponding to the current iteration count value; Calculating respective momentum terms corresponding to a next iteration count value based on a preset momentum factor, respective momentum terms, and respective gradients corresponding to the current iteration count value; Calculating 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 respective momentum terms corresponding to the next iteration count value; Calculate the iteration residual corresponding to the next iteration count based on the objective function corresponding to the current iteration count and the objective function corresponding to the next iteration count. When the iteration residual corresponding to the next iteration count is less than or equal to a preset iteration residual threshold, use the first transmission matrix, the second transmission matrix, and the first grating period distribution function corresponding to the next iteration count 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 a 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. The detection method includes: Obtain 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, represent the first sampled observation signal and the second sampled observation signal as linear combinations of sparse feature vectors and noise vectors respectively, obtain a group sparse model, and determine a group sparse optimization problem, where each atom in the wavelength offset dictionary represents a wavelength offset pattern caused by a corresponding vibration event; Introduce a symmetric offset constraint into the group sparse optimization problem to obtain a sparse optimization problem, and solve 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; Perform feature domain integration on the first optimal sparse feature vector and the second optimal sparse feature vector to obtain an integrated feature vector; Perform vibration event detection based on a detection algorithm and the integrated feature vector.

7. According to the method of claim 6, wherein The group sparse optimization problem is: ； Among them, represents the sparse feature vector of the first sampled observation signal , represents the sparse feature vector of the second sampled observation signal , D represents the wavelength offset dictionary, represents the square of the Euclidean norm, represents the first sparsity regularization parameter, represents norm.

8. According to the method of claim 7, wherein Introduce a symmetric offset constraint into the group sparse optimization problem, and the obtained sparse optimization problem is: ； Among them, represents the second sparsity regularization parameter.

9. According to the method of claim 6, wherein 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: Reverse the order of the elements in the second optimal sparse feature vector to obtain a vector to be integrated; Add the vector to be integrated 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 includes instructions that, when executed by the processor, cause the processor to execute the method according to any one of claims 6 to 9.

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