High-precision mode decomposition method and device for curved fiber laser, equipment and medium

Abstract

A high-precision mode decomposition method, apparatus, device, and medium for bent fiber lasers include: arranging the grayscale data of the distorted light spot image to be decomposed under a certain fiber bending radius from a two-dimensional matrix distribution into vector data form to obtain vector I; and sequentially rearranging N distorted eigenmodes into m... 2 I is used to generate N distorted eigenmode vectors from a 1×1 dimensional vector. These N distorted eigenmode vectors are then multiplied pairwise to obtain a distortion coefficient matrix A. Linear equations are solved using the distortion coefficient matrix A and vector I to obtain a mode information vector. A system of nonlinear equations is established based on the elements of the mode information vector to obtain the mode coefficients and mode phases recovered from the N eigenmodes. The recovered mode coefficients and mode phases, combined with the N distorted eigenmodes, are used to generate a reconstructed beam pattern, which serves as the mode decomposition result. This invention improves the measurement accuracy of mode coefficients and mode phases when the fiber bends and the beam pattern is distorted.

Description

Technical Field

[0001] This invention relates to the field of fiber laser mode decomposition technology, and in particular to a high-precision mode decomposition method, apparatus, equipment and medium for bent fiber lasers. Background Technology

[0002] Mode decomposition is a method for measuring the proportion of specific mode components and intermode phase in fiber lasers containing multiple modes. Mode decomposition is mainly used for the complete spatial characterization of fiber lasers and beam quality measurement. It is of great significance to fiber communication based on mode division multiplexing, nonlinear effect dynamics research, and high-power fiber lasers.

[0003] Existing centralized pattern decomposition techniques all have certain limitations, specifically as follows:

[0004] Spatial spectral imaging (JW Nicholson, et al., Opt. Express 16, 7233-7243 (2008)) is a method that analyzes mode information by Fourier transform of the light intensity distribution at different locations. This method is only suitable for light sources with a certain spectral width and is not applicable to mode decomposition applications of narrow linewidth or single-frequency fiber lasers.

[0005] Correlation filtering (T. Kaiser, et al., Opt. Express 17, 9347-9356 (2009)) can achieve all-optical mode weighting without additional computation; however, this method requires additional reference light to determine the mode phase. Furthermore, this method requires the design and fabrication of different computational holograms for different application conditions, resulting in poor compatibility and a lack of cost advantage.

[0006] The two methods described above are direct measurement methods, which typically require complex experimental procedures and rely on specific devices. Another mode decomposition method is the indirect measurement method, which mainly uses a simple imaging system to output fiber laser light to an imaging camera, and then uses computer numerical calculations to obtain mode weights or mode phase information. This type of method has lower requirements for experimental instruments and does not rely on specific devices; the key point lies in the choice of decomposition algorithm. The first algorithm applied to mode decomposition is the Gerchber-Saxton algorithm (O. Shapira, et al., Phys. Rev. Lett. 94, 143902 (2005)), which is a phase retrieval algorithm, but it suffers from slow convergence speed and low accuracy. The second mode decomposition algorithm is the line search algorithm (R. Brüning, et al., Appl. Opt. 52, 7769-7777 (2013)). This algorithm searches step by step by enumerating mode proportions and inter-mode phases, resulting in extremely slow speed and low accuracy, and has not been widely adopted.

[0007] The first pattern decomposition algorithm with practical application value is the stochastic parallel gradient descent algorithm (L. Huang, et al., Opt. Express 23, 4620-4629 (2015)). This method can achieve a decomposition speed of about 10 times per second, and the decomposition accuracy is significantly improved compared with the line search algorithm and the phase retrieval algorithm. However, the pattern decomposition method based on the stochastic parallel gradient descent algorithm is prone to getting trapped in local optima. The pattern decomposition effect is highly dependent on the choice of initial values, and as the number of patterns increases, the accuracy and speed of this method are severely limited.

[0008] The above-mentioned algorithm-based mode decomposition methods are all iterative algorithms, requiring significant machine time. Non-iterative mode decomposition algorithms can greatly improve the speed of mode decomposition. With the rapid development of artificial intelligence-related algorithms, they have also received widespread attention in the field of fiber laser mode decomposition. Machine learning-based mode decomposition methods are a typical example. This method can perform mode decomposition in milliseconds; however, it requires a significant amount of time and high-performance computers to train the network beforehand.

[0009] Recently, Manuylovich et al. proposed a semi-analytical mode decomposition method (ESManuylovich, et al., Nat. Commun. 11(1), 5507(2020).), which divides the complex nonlinear mode decomposition problem into a linear equation system solution and a simple nonlinear equation system solution problem. This greatly improves the speed of mode decomposition (the decomposition speed of spot images containing 3-8 modes can reach 100,000 frames per second), and can effectively decompose spot images containing nearly 50 modes (ESManuylovich, et al., Opt. Express 29(22), 36769(2021).).

[0010] However, in practical applications, high-power fiber lasers are often bent and coiled around a water-cooling plate for easier thermal management, or the fiber is bent for a compact spatial structure. This causes bending distortion of the output laser field, resulting in a distorted intensity distribution, which reduces the effectiveness of mode decomposition. For example... Figure 3 This is a comparison chart of the classic mathematical semi-analytical mode decomposition results under ideal conditions with and without bending distortion. (See attached image.) Figure 3 As shown, the classical mathematical version of analytical mode decomposition can accurately decompose modes when there is no fiber bending distortion. However, when there is fiber bending distortion, the difference between the reconstructed spot and the spot to be measured will increase. This means that when the fiber is bent and coiled, the measurement accuracy of the classical mode decomposition method for mode weights and mode phases will deteriorate. Summary of the Invention

[0011] To address the technical problems existing in the prior art, this invention proposes a method, apparatus, device, and medium for predicting the Raman gain coefficient of the gain fiber in a fiber laser. This invention is based on a semi-analytical mode decomposition method, replacing the ideal, undistorted eigenmode basis with a distorted eigenmode basis, thereby improving the measurement accuracy of mode coefficients and mode phase when the fiber bends and the light spot is distorted.

[0012] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0013] On one hand, the present invention provides a high-precision mode decomposition method for bent fiber lasers, comprising:

[0014] Input the grayscale data of the distorted light spot image to be decomposed in the optical fiber under a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distorted light spot image to be decomposed contains m×m pixels of N eigenmodes, and vector I is m 2 ×1-dimensional vector;

[0015] Input N distorted eigenmodes of the optical fiber under test at a certain fiber bending radius, and rearrange the N distorted eigenmodes sequentially into m. 2 ×1-dimensional vectors are used to obtain N distortion eigenmode vectors. The N distortion eigenmode vectors are multiplied pairwise to obtain the distortion coefficient matrix A.

[0016] Solving the linear equation X = A based on the distortion coefficient matrix A and vector I. -1 I, obtain the pattern information vector X;

[0017] A system of nonlinear equations is established based on the elements in the mode information vector X to obtain the mode coefficients and mode phases recovered from N eigenmodes.

[0018] The reconstructed light spot is generated based on the mode coefficients and mode phases recovered from N intrinsic modes and combined with N distorted intrinsic modes, and is used as the result of mode decomposition.

[0019] The distortion eigenmode described in this invention is a linear polarization distortion eigenlateral mode.

[0020] The grayscale data of the distortion spot image of the optical fiber under test under a certain optical fiber bending radius described in this invention is generated by simulation according to the given optical fiber parameters and optical fiber bending radius of the optical fiber under test. The optical fiber parameters include optical fiber type, optical fiber core diameter, optical fiber numerical aperture, and laser wavelength.

[0021] The N distortion eigenmodes of a given optical fiber bending radius described in this invention are calculated using the finite element method based on the given optical fiber parameters and the optical fiber bending radius.

[0022] On the other hand, this invention proposes a mode decomposition accuracy evaluation method for high-precision mode decomposition methods for bent fiber lasers. The method uses the correlation coefficient C between the sample spot and the reconstructed spot to evaluate the mode decomposition accuracy. The expression for the correlation coefficient C is as follows:

[0023]

[0024] In the formula, (x,y) represents spatial coordinates, I m I represents the light intensity distribution of the light spot in the sample to be tested. r This represents the reconstructed light intensity distribution of the light spot. The average light intensity of the light spot in the sample to be tested. To reconstruct the average light intensity of the light spot.

[0025] On the other hand, the present invention provides a high-precision mode decomposition apparatus for bent fiber lasers, comprising:

[0026] The distortion spot image processing module is used to input the grayscale data of the distortion spot image of the optical fiber under test at a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distortion spot image contains m×m pixels of N eigenmodes, and vector I is m... 2 ×1 dimensional vector;

[0027] The distortion coefficient matrix acquisition module is used to input N distortion eigenmodes of the optical fiber under test at a certain fiber bending radius, and to rearrange the N distortion eigenmodes sequentially into m. 2 ×1-dimensional vectors are used to obtain N distortion eigenmode vectors. The N distortion eigenmode vectors are multiplied pairwise to obtain the distortion coefficient matrix A.

[0028] The pattern information vector acquisition module is used to solve the linear equation X = A based on the distortion coefficient matrix A and the vector I. -1 I, obtain the pattern information vector X;

[0029] The mode information acquisition module is used to establish a system of nonlinear equations based on the elements in the mode information vector X, and obtain the mode coefficients and mode phases recovered from N eigenmodes.

[0030] The mode decomposition module is used to generate a reconstructed light spot based on the mode coefficients and mode phases recovered from N intrinsic modes and combined with N distorted intrinsic modes, as the mode decomposition result.

[0031] On the other hand, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0032] Input the grayscale data of the distorted light spot image to be decomposed in the optical fiber under a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distorted light spot image to be decomposed contains m×m pixels of N eigenmodes, and vector I is m 2 ×1 dimensional vector;

[0033] Input N distorted eigenmodes of the optical fiber under test at a certain fiber bending radius, and rearrange the N distorted eigenmodes sequentially into m. 2 ×1-dimensional vectors are used to obtain N distortion eigenmode vectors. The N distortion eigenmode vectors are multiplied pairwise to obtain the distortion coefficient matrix A.

[0034] Solving the linear equation X = A based on the distortion coefficient matrix A and vector I. -1 I, obtain the pattern information vector X;

[0035] A system of nonlinear equations is established based on the elements in the mode information vector X to obtain the mode coefficients and mode phases recovered from N eigenmodes.

[0036] The reconstructed light spot is generated based on the mode coefficients and mode phases recovered from N intrinsic modes and combined with N distorted intrinsic modes, and is used as the result of mode decomposition.

[0037] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps:

[0038] Input the grayscale data of the distorted light spot image to be decomposed in the optical fiber under a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distorted light spot image to be decomposed contains m×m pixels of N eigenmodes, and vector I is m 2 ×1 dimensional vector;

[0039] Input N distorted eigenmodes of the optical fiber under test at a certain fiber bending radius, and rearrange the N distorted eigenmodes sequentially into m. 2 ×1-dimensional vectors are used to obtain N distortion eigenmode vectors. The N distortion eigenmode vectors are multiplied pairwise to obtain the distortion coefficient matrix A.

[0040] Solving the linear equation X = A based on the distortion coefficient matrix A and vector I. -1 I, obtain the pattern information vector X;

[0041] A system of nonlinear equations is established based on the elements in the mode information vector X to obtain the mode coefficients and mode phases recovered from N eigenmodes.

[0042] The reconstructed light spot is generated based on the mode coefficients and mode phases recovered from N intrinsic modes and combined with N distorted intrinsic modes, and is used as the result of mode decomposition.

[0043] In existing technologies, the distortion-free eigenmodes of the optical fiber are directly calculated based on the wave equation and waveguide boundary conditions, and used as the basis for decomposing the laser spot image. However, this method cannot achieve high-precision mode decomposition for optical fiber laser spots with bending distortion. This invention provides a method for decomposing distorted laser spot images using distorted eigenmodes as the basis. Specifically, based on the classic mathematical semi-analytical mode decomposition method, this method does not limit itself to directly using ideal distortion-free eigenmodes as the basis for mode decomposition. Instead, it first calculates the distorted eigenmodes based on optical fiber parameters and bending radius, and then uses the distorted eigenmode basis to replace the ideal distortion-free eigenmode basis. This significantly improves the mode decomposition accuracy when mode field distortion exists due to optical fiber bending. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a high-precision mode decomposition method for bent fiber lasers provided in one embodiment;

[0046] Figure 2 The intensity distribution of an ideal undistorted eigenmode and the intensity distribution of a distorted eigenmode with a bending radius of 8 cm are shown in one embodiment.

[0047] Figure 3 This is a schematic diagram of the mode decomposition result of a typical distorted spot in an embodiment with an optical fiber bending radius of 8cm. The three sub-images in the first row, from left to right, are the distorted spot to be decomposed, the reconstructed spot obtained based on the classical mathematical semi-analytical mode decomposition method, and the difference between the distorted spot and the reconstructed spot. The three sub-images in the second row, from left to right, are the distorted spot to be decomposed, the reconstructed spot obtained based on the mode decomposition method of this invention, and the difference between the distorted spot and the reconstructed spot.

[0048] Figure 4 The diagram shows the decomposition accuracy (dashed line) of the classical mathematical semi-analytical mode decomposition method in one embodiment and the decomposition accuracy (solid line) of the mode decomposition method of the present invention as a function of the bending radius.

[0049] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] A high-precision mode decomposition method for bent fiber lasers includes:

[0052] Input the grayscale data of the distorted light spot image to be decomposed in the optical fiber under a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distorted light spot image to be decomposed contains m×m pixels of N eigenmodes, and vector I is m 2×1-dimensional vector;

[0053] Input N distortion eigenmodes ψ1-ψ of the optical fiber under test at a certain fiber bending radius. N Rearrange the N distorted eigenmodes sequentially into m 2 From a 1-dimensional vector, we obtain N distortion eigenmode vectors. Multiplying each of these N distortion eigenmode vectors pairwise yields a product. From the dimensional matrix, we obtain the distortion coefficient matrix A;

[0054] The distortion coefficient matrix A is expressed as follows:

[0055]

[0056] Where ψ1, ψ2...ψ N This represents N distorted eigenmodes.

[0057] Solving the linear equation X = A based on the distortion coefficient matrix A and vector I. -1 I, obtain the pattern information vector X; where A -1 Let A be the pseudo-inverse matrix of the coefficient matrix A. -1 =(A T A) -1 A T , where the superscript T denotes matrix transpose.

[0058] The mode information vector X contains the mode coefficients and mode phase information of N distorted eigenmodes, and its expression is:

[0059]

[0060] In the formula, γ i and θ i Let θ1 and θ2 represent the mode coefficient and mode phase of the i-th distorted intrinsic mode, respectively. The mode phase refers to the relative phase, i.e., the first phase θ1 is 0.

[0061] A system of nonlinear equations is established based on the elements in the mode information vector X to obtain the mode coefficients and mode phases recovered from N eigenmodes.

[0062] The reconstructed light spot is generated based on the mode coefficients and mode phases recovered from N intrinsic modes and combined with N distorted intrinsic modes, and is used as the result of mode decomposition.

[0063] The distortion eigenmode described in this invention is a linear polarization distortion eigenlateral mode.

[0064] The grayscale data of the distortion spot image of the optical fiber under test under a certain optical fiber bending radius described in this invention is generated by simulation according to the given optical fiber parameters and optical fiber bending radius of the optical fiber under test. The optical fiber parameters include optical fiber type, optical fiber core diameter, optical fiber numerical aperture, and laser wavelength.

[0065] The N distortion eigenmodes of a given optical fiber bending radius described in this invention are calculated using the finite element method based on given optical fiber parameters and bending radius. Given the parameters of the optical fiber under test, including core size, numerical aperture, and operating wavelength, and the measured bending radius, the distribution of N distortion eigenmodes at a given optical fiber bending radius is obtained using finite element simulation software.

[0066] In this embodiment, the image of the light spot to be tested is obtained through simulation of an actual optical fiber system, rather than by actually acquiring it through an experimental system. On the one hand, acquiring tens of thousands of distorted light spot images to statistically demonstrate the fiber bending resistance of this embodiment would be cumbersome and require significant preprocessing time. On the other hand, the light spot generated by computer simulation makes it easier to quantify the fiber bending radius, which facilitates a quantitative and intuitive representation of the advantages of this invention.

[0067] In another embodiment, a method for evaluating the mode decomposition accuracy of a high-precision mode decomposition method for bent fiber lasers is provided. The method uses the correlation coefficient C between the sample spot and the reconstructed spot to evaluate the mode decomposition accuracy. The expression for the correlation coefficient C is as follows:

[0068]

[0069] In the formula, (x,y) represents spatial coordinates, I m I represents the light intensity distribution of the light spot in the sample to be tested. r This represents the reconstructed light intensity distribution of the light spot. The average light intensity of the light spot in the sample to be tested. To reconstruct the average light intensity of the light spot.

[0070] In one embodiment, the fiber parameters of the fiber under test are selected as follows: the fiber is a step-index fiber with a core diameter of 30 μm, a numerical aperture of 0.06, a laser wavelength of 1.064 μm, and a fiber bending radius of 8 cm. The method of this invention and the classical mathematical semi-analytical mode decomposition method in the prior art are applied to both. The comparison illustrates the superiority of the method proposed in this invention, which uses distorted eigenmodes as a basis to decompose distorted light spot images, over the classical mathematical semi-analytical mode decomposition method in the prior art (i.e., the method of decomposing light spot images using undistorted eigenmodes as a basis).

[0071] The distorted light field output by the optical fiber can be obtained by the linear superposition of the distorted eigenmodes. The light intensity distribution, i.e., the sample of the bent distorted light spot to be tested, is the mode square of the light field. Specifically, the eigenmode field distribution of the optical fiber under test at a set fiber bending radius is calculated using finite element simulation software (such as COMSOL software used in this embodiment). The number of eigenmodes included in the sample is set (not exceeding the maximum number of modes supported by the optical fiber under this parameter; in this embodiment, the optical system fiber selected supports a maximum of 8 linearly polarized eigenmodes, and the first 3 distorted eigenmodes are taken as the modes in this embodiment), namely LP01 mode, LP11e mode, and LP11o mode. Then, the mode coefficients and mode phases of each eigenmode are randomly generated, thereby generating different light spot image samples of the bent optical fiber.

[0072] In this embodiment, the intensity distribution of the distorted light spot with a bending radius of 8cm is discretized into 256×256 data points, represented by a 256×256 matrix. In specific implementation, the distorted light spot image represented by this 256×256 matrix is ​​rearranged into a 65536×1-dimensional column vector, denoted as vector I.

[0073] Classical mathematical semi-analytical mode decomposition methods directly employ ideal, distortion-free eigenmodes. As the basis for mode decomposition, these distortion-free eigenmodes can be directly obtained numerically using semi-analytical formulas. In this embodiment, the basis of classical mathematical semi-analytical mode decomposition is expressed as: The specific two-dimensional intensity distributions of the distortion-free intrinsic modes LP01, LP11e, and LP11o are as follows: Figure 2 As shown in the first row. At this point, the distortion-free coefficient matrix A0 can be expressed as a pairwise product of these bases, i.e.

[0074]

[0075] Then, based on vector I and the pseudo-inverse matrix A0 of coefficient matrix A0 -1 =(A0) T A0) -1 A0 T The solution is obtained by directly solving the matrix equation, i.e., X = A0. -1 I, the vector X contains mode coefficients and phase information. In specific implementation, the mode coefficients and mode phases corresponding to each element of the mode information vector X are:

[0076]

[0077] Among them, γ1-γ3 are the modes. The mode coefficients, θ1-θ3 are the mode coefficients. The mode phase.

[0078] This invention provides a method to replace ideal, distortion-free eigenmodes with ψ-basis eigenmodes that exhibit distortion. The basic technical method. Specifically, in this embodiment, the distortion eigenmodes ψ1, ψ2, ψ3 are calculated using the finite element simulation software COMSOL when the bending radius is 8cm. The specific two-dimensional intensity distribution of the distortion eigenmodes is as follows: Figure 2 As shown, the distortion coefficient matrix A in this embodiment can be represented as

[0079]

[0080] Similarly, the solution to the matrix equation using the distorted eigenmodes as the basis can be directly obtained, i.e., X = A0. -1 I. Finally, by using formula (2), the pattern decomposition result based on the method of the present invention in this embodiment can be obtained.

[0081] It should be noted that the above-described method for replacing ideal intrinsic modes with bending distortion intrinsic modes is only one embodiment of the present invention. For those skilled in the art, without creative effort, the method of this embodiment can also be used to obtain the corresponding distortion decomposition basis under any optical fiber parameters and any bending radius.

[0082] Figure 3 This is a schematic diagram illustrating the mode decomposition results of a typical distorted laser spot with a fiber bending radius of 8 cm in one embodiment of the invention. The first row of three sub-figures, from left to right, represents the distorted laser spot to be decomposed; the reconstructed laser spot obtained based on the classical semi-analytical mode decomposition method; and the difference between the distorted laser spot and the reconstructed laser spot. The second row of three sub-figures, from left to right, represents the distorted laser spot to be decomposed; the reconstructed laser spot obtained based on the mode decomposition method of this invention; and the difference between the distorted laser spot and the reconstructed laser spot. Comparing the figures, it can be seen that the mode decomposition method of this invention has higher decomposition accuracy for bent and distorted fiber lasers. Specifically, the correlation coefficient of the classical method is 0.948, while the correlation coefficient of the method of this invention is 1.000.

[0083] Furthermore, Figure 4 In this embodiment, the decomposition accuracy (i.e., correlation coefficient) of the classical mathematical semi-analytical mode decomposition method (dashed line) and the mode decomposition method of the present invention (solid line) varies with the bending radius of the optical fiber. The decomposition accuracy data for each bending radius is the average of the correlation coefficients after decomposing 2000 randomly generated light spot images at that bending radius. Figure 4It can be seen that as the bending radius decreases (the bending curvature increases), the decomposition accuracy (correlation coefficient) of the classical mathematical semi-analytical mode decomposition method decreases rapidly. However, the method of this invention replaces the ideal eigenmode with the distorted eigenmode as the basis for mode decomposition, and the decomposition accuracy does not change with the bending radius. Therefore, this invention is an effective and high-precision mode decomposition method for fiber laser mode decomposition when there is mode field bending distortion.

[0084] In one embodiment, a high-precision mode decomposition apparatus for bent fiber lasers is provided, comprising:

[0085] The distortion spot image processing module is used to input the grayscale data of the distortion spot image of the optical fiber under test at a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distortion spot image contains m×m pixels of N eigenmodes, and vector I is m... 2 ×1-dimensional vector;

[0086] The distortion coefficient matrix acquisition module is used to input N distortion eigenmodes of the optical fiber under test at a certain fiber bending radius, and to rearrange the N distortion eigenmodes sequentially into m. 2 ×1-dimensional vectors are used to obtain N distortion eigenmode vectors. The N distortion eigenmode vectors are multiplied pairwise to obtain the distortion coefficient matrix A.

[0087] The pattern information vector acquisition module is used to solve the linear equation X = A based on the distortion coefficient matrix A and the vector I. -1 I, obtain the pattern information vector X;

[0088] The mode information acquisition module is used to establish a system of nonlinear equations based on the elements in the mode information vector X, and obtain the mode coefficients and mode phases recovered from N eigenmodes.

[0089] The mode decomposition module is used to generate a reconstructed light spot based on the mode coefficients and mode phases recovered from N intrinsic modes and combined with N distorted intrinsic modes, as the mode decomposition result.

[0090] The implementation methods of the above modules and the construction of the model can all adopt the methods described in any of the foregoing embodiments, and will not be repeated here.

[0091] On the other hand, the present invention provides a computer device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the high-precision mode decomposition method for bent fiber lasers provided in any of the above embodiments. The computer device may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store sample data. The network interface of the computer device is used for communication with external terminals via a network connection.

[0092] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the high-precision mode decomposition method for bent fiber lasers provided in any of the above embodiments.

[0093] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0094] Matters not covered in this invention are common knowledge.

[0095] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-precision mode decomposition method for bent fiber lasers, characterized in that, include: Input the grayscale data of the distortion spot image to be decomposed in the optical fiber under a certain bending radius, and arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I. The grayscale data of the distortion spot image to be decomposed contains a total of N Individual eigenmodes m × m There are pixels, and vector I is... m 2 ×1 dimensional vector; Input the optical fiber under test at a certain fiber bending radius N Each aberration intrinsic mode will N The aberration eigenmodes are rearranged in sequence as follows: m 2 A ×1 dimensional vector is obtained N Each distorted eigenmode vector will N Multiply each pair of the distorted eigenmode vectors by a product to obtain the distortion coefficient matrix A; Solving the linear equation X=A based on the distortion coefficient matrix A and vector I. -1 I, obtain the pattern information vector X; A system of nonlinear equations is established based on the elements in the pattern information vector X, yielding... N The mode coefficients and mode phases recovered from each eigenmode; based on N The mode coefficients and mode phases recovered from each eigenmode are combined with... N Each distorted eigenmode generates a reconstructed light spot as the result of mode decomposition.

2. The high-precision mode decomposition method for bent fiber lasers according to claim 1, characterized in that, The distortion eigenmode is a linear polarization distortion eigenlateral mode.

3. The high-precision mode decomposition method for bent fiber lasers according to claim 1, characterized in that, The grayscale data of the distortion spot image of the optical fiber under test under a certain optical fiber bending radius is generated by simulation according to the given optical fiber parameters and optical fiber bending radius of the optical fiber under test. The optical fiber parameters include optical fiber type, optical fiber core diameter, optical fiber numerical aperture, and laser wavelength.

4. The high-precision mode decomposition method for bent fiber lasers according to claim 1, characterized in that, The certain optical fiber bending radius N Each distortion eigenmode is calculated using the finite element method based on given fiber parameters and fiber bending radius.

5. The high-precision mode decomposition method for bent fiber lasers according to any one of claims 1 to 4, characterized in that, The distortion coefficient matrix A is as follows: in ψ 1. ψ 2... ψ N express N A distorted intrinsic pattern.

6. The mode decomposition accuracy evaluation method for high-precision mode decomposition of bent fiber lasers as described in claim 1, characterized in that, The correlation coefficient between the light spot of the sample under test and the reconstructed light spot is used. C To evaluate the accuracy of mode decomposition, correlation coefficient C A higher correlation coefficient indicates higher accuracy in pattern decomposition, where the correlation coefficient is... C The expression is as follows: In the formula, ( x , y ) represents spatial coordinates, I m This indicates the light intensity distribution of the light spot in the sample to be tested. I r This represents the reconstructed light intensity distribution of the light spot. The average light intensity of the light spot in the sample to be tested. To reconstruct the average light intensity of the light spot.

7. A high-precision mode decomposition device for bent fiber lasers, characterized in that, include: The distortion spot image processing module is used to input the grayscale data of the distortion spot image to be decomposed of the optical fiber under a certain fiber bending radius, and to arrange it from a two-dimensional matrix distribution into vector data form to obtain vector I, in which the grayscale data of the distortion spot image to be decomposed contains a total of N Individual eigenmodes m × m There are pixels, and vector I is... m 2 ×1-dimensional vector; The distortion coefficient matrix acquisition module is used to input the distortion coefficient matrix of the optical fiber under test at a certain optical fiber bending radius. N Each aberration intrinsic mode will N The aberration eigenmodes are rearranged in sequence as follows: m 2 A ×1 dimensional vector is obtained N Each distorted eigenmode vector will N Multiply each pair of the distorted eigenmode vectors by a product to obtain the distortion coefficient matrix A; The pattern information vector acquisition module is used to solve the linear equation X=A based on the distortion coefficient matrix A and the vector I. -1 I, obtain the pattern information vector X; The pattern information acquisition module is used to establish a system of nonlinear equations based on the elements in the pattern information vector X, and obtain... N The mode coefficients and mode phases recovered from each intrinsic mode; Pattern decomposition module, used for... N The mode coefficients and mode phases recovered from each eigenmode are combined with... N Each distorted eigenmode generates a reconstructed light spot, which serves as the result of mode decomposition.

8. The mode decomposition accuracy evaluation device for the high-precision mode decomposition method for bent fiber lasers as described in claim 1, characterized in that, Includes a correlation coefficient calculation module, used to calculate the correlation coefficient between the light spot of the sample under test and the reconstructed light spot. C To evaluate the accuracy of mode decomposition, the correlation coefficient is used. C The expression is as follows: In the formula, ( x , y ) represents spatial coordinates, I m This indicates the light intensity distribution of the light spot in the sample to be tested. I r This represents the reconstructed light intensity distribution of the light spot. The average light intensity of the light spot in the sample to be tested. To reconstruct the average light intensity of the light spot.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the high-precision mode decomposition method for bent fiber lasers as described in claim 1.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the high-precision mode decomposition method for bent fiber lasers as described in claim 1.

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