Multi-parameter test optical fiber prefabricated member quality detection and grading method and device

Abstract

The invention discloses a multi-parameter test optical fiber preform quality detection and grading method and device, and the method comprises the steps: carrying out the electron probe microscopic analysis test, Raman spectrum test and electron paramagnetic resonance test of an optical fiber preform sample, and obtaining element distribution data, Raman spectrum data and paramagnetic resonance spectrum data; performing preprocessing and feature extraction on the element distribution data, the Raman spectrum data and the paramagnetic resonance spectrum data to obtain a plurality of key quality parameters; inputting the plurality of key quality parameters into a pre-trained comprehensive quality evaluation model, and calculating to obtain a comprehensive quality evaluation coefficient of the optical fiber preform; and comparing the comprehensive quality evaluation coefficient with a preset grading threshold value, and carrying out quality grading on the optical fiber prefabricated member. According to the method, three testing modes are adopted, a plurality of key quality parameters are obtained, the detection dimension is enriched, the comprehensive quality evaluation coefficient is calculated through the model, and subjective errors of operators are reduced.

Description

Technical Field

[0001] This application relates to the field of quality inspection technology, and more specifically, to a method and apparatus for quality inspection and grading of optical fiber preforms using multi-parameter testing. Background Technology

[0002] As the core raw material for manufacturing communication optical fibers, the quality of optical fiber preforms directly determines the transmission performance (such as attenuation and dispersion) and mechanical reliability of the final optical fiber product. Accurate and comprehensive quality assessment and grading of preforms before the fiber drawing process is a crucial step in improving optical fiber production yield and reducing production costs, and is of great significance to the optical fiber manufacturing industry.

[0003] Currently, the industry's quality inspection of optical fiber preforms mainly relies on independent measurement of single-dimensional parameters and human experience. Conventional practices mainly include the following categories: geometric parameter inspection, such as using laser scanning or machine vision to measure the diameter, ellipticity, and curvature of the preform; optical performance inspection, such as measuring the refractive index profile distribution through near-field refraction or determining the hydroxyl content using Fourier transform infrared spectroscopy; and defect detection, such as using dark-field imaging technology to observe surface defects or using X-ray tomography to detect macroscopic defects such as internal bubbles.

[0004] However, the aforementioned existing technologies have significant limitations and defects: the detection dimension is singular, lacking multi-parameter correlation analysis. Existing methods often measure physical, optical, or chemical parameters in isolation, failing to establish quantitative prediction models between multiple parameters and the final fiber performance. They also fail to quantitatively assess intrinsic factors affecting the long-term reliability of optical fibers, such as paramagnetic defects, leading to biased assessment results. Furthermore, the grading process is highly subjective, with quality grading heavily dependent on the operator's experience, resulting in low consistency between grading results from different operators. Summary of the Invention

[0005] To address at least one defect or improvement need in the prior art, this invention provides a method and apparatus for quality inspection and grading of optical fiber preforms using multi-parameter testing. This addresses the problems in the prior art where the inspection of optical fiber preforms has a single dimension, leading to one-sided quality assessment results, and where the grading process is highly subjective due to reliance on operator experience, different operators using different inspection standards, and resulting in significant differences in inspection results.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for quality inspection and grading of optical fiber preforms using multi-parameter testing is provided, comprising: Electron probe microscopy, Raman spectroscopy, and electron paramagnetic resonance (EPR) were performed on the fiber optic preform samples to obtain elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data, respectively. Preprocessing and feature extraction were performed on elemental distribution data, Raman spectroscopy data, and paramagnetic resonance spectroscopy data to obtain several key quality parameters; Multiple key quality parameters are input into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. The quality grade of optical fiber preforms is classified by comparing the comprehensive quality assessment coefficient with the preset grading threshold.

[0007] In one possible implementation, the steps of preprocessing and feature extraction of elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain several key quality parameters include: Spatial registration and outlier removal are performed on the element distribution data; The processed element concentration data were averaged along the radial direction of the preform, and a continuous function of element content as a function of radius was obtained by fitting the data using spline interpolation. The Rayleigh scattering concentration parameter is calculated based on a continuous function of element content as a function of radius.

[0008] In one possible implementation, the steps of preprocessing and feature extraction of elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain multiple key quality parameters also include: Baseline correction and normalization were performed on the Raman spectral data to obtain several Raman characteristic peaks; Identify and fit the Raman characteristic peak of the first preset wavenumber, and use its peak intensity as the virtual temperature; The Raman characteristic peaks of the second preset wavenumber are identified and fitted, and the structural relaxation coefficients are calculated based on their full width at half maximum (FWHM).

[0009] In one possible implementation, the steps of preprocessing and feature extraction of elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain multiple key quality parameters also include: Baseline correction and double integration were performed on the paramagnetic resonance spectral data to obtain the absorption spectral area. The spin concentration of the defect peak is calculated based on the absorption spectrum area.

[0010] In one possible implementation, the step of inputting multiple key quality parameters into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform includes: Key quality parameters were extracted from multiple historical optical fiber preform sample data, and the measured attenuation value of each sample was recorded to construct a training dataset. Based on physical mechanisms and data fitting, an empirical formula is established with key quality parameters as independent variables and measured attenuation values ​​as dependent variables. Using the training dataset, a well-trained comprehensive quality evaluation model is obtained by fitting and optimizing the empirical formula through a multivariate nonlinear regression algorithm. By inputting multiple key quality parameters into the trained comprehensive quality evaluation model, the comprehensive quality evaluation coefficients are obtained.

[0011] In one possible implementation, the comprehensive quality evaluation model is as follows: ; in, The structural relaxation coefficient is... Rayleigh scattering density factor Rayleigh scattering concentration factor EPR Refers to the spin concentration of the defect peak. and The ratio of the peak intensity of three-membered and four-membered ring defects in Raman spectroscopy data to the standard intensity. A , B The component contribution coefficient varies depending on the fiber material. This is the quality assessment coefficient.

[0012] In one possible implementation, the step of inputting multiple key quality parameters into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform further includes: The quality assessment coefficient is obtained by performing area-weighted calculation on the quality assessment coefficient; The area-weighted formula is: ; in, a The sampling radius of the prefabricated component.

[0013] According to a second aspect of the present invention, a multi-parameter testing device for quality inspection and grading of optical fiber preforms is also provided, comprising: The sample testing module is configured to perform electron probe microscopy, Raman spectroscopy and electron paramagnetic resonance analysis on the optical fiber preform sample to obtain elemental distribution data, Raman spectral data and paramagnetic resonance spectral data, respectively. The feature extraction module is configured to preprocess and extract features from elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain multiple key quality parameters. The model calculation module is configured to input multiple key quality parameters into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. The quality grading module is configured to compare the comprehensive quality assessment coefficient with a preset grading threshold to classify the quality level of the optical fiber preform.

[0014] According to a third aspect of the present invention, a multi-parameter testing optical fiber preform quality inspection and grading device is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, implements the steps of the multi-parameter testing optical fiber preform quality inspection and grading method as described in any of the preceding claims.

[0015] According to a fourth aspect of the present invention, a storage medium is also provided, which stores a computer program executable by a multi-parameter testing optical fiber preform quality inspection and grading device, wherein when the computer program is run on the multi-parameter testing optical fiber preform quality inspection and grading device, the multi-parameter testing optical fiber preform quality inspection and grading device performs the steps of the multi-parameter testing optical fiber preform quality inspection and grading method described above.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: This invention provides a multi-parameter testing method for the quality inspection and grading of optical fiber preforms. It employs three different testing methods: electron probe microscopy, Raman spectroscopy, and electron paramagnetic resonance (EPR) testing. This approach acquires information about the optical fiber preforms from multiple perspectives, significantly enriching the testing dimensions and avoiding information gaps caused by single testing methods, resulting in more comprehensive and accurate quality assessments. Preprocessing and feature extraction of the data from the three tests yield multiple key quality parameters, providing a more comprehensive reflection of the optical fiber preform's quality status. This allows for more accurate identification of potential quality problems, improving the reliability and accuracy of quality assessments and solving the problem of one-sided quality assessment results caused by single testing dimensions in existing technologies. The unified preprocessing and feature extraction of the acquired data does not rely on the operator's personal experience and judgment, ensuring consistency in data processing among different operators. The comprehensive quality assessment coefficient is calculated using a comprehensive quality evaluation model, exhibiting higher stability and consistency. This reduces data processing differences caused by varying operator experience, thereby reducing reliance on operator experience and addressing the issue of strong subjectivity in the grading process. The quality grade of optical fiber preforms is classified according to preset grading thresholds, providing a unified quality assessment standard. Different operators use the same methods and standards for testing and grading, avoiding the problem of large differences in test results caused by different operators' testing standards, thus improving the reliability and stability of testing. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the multi-parameter testing method for quality inspection and grading of optical fiber preforms provided by the present invention; Figure 2 A flowchart illustrating an embodiment of element distribution data processing provided by the present invention; Figure 3 This is a schematic flowchart of an embodiment of Raman spectroscopy data processing provided by the present invention; Figure 4 Provided by the present invention Figure 1 A schematic flowchart of an embodiment of step S103; Figure 5 A schematic diagram illustrating the relationship between the overall quality assessment coefficient and the fiber attenuation coefficient provided by the present invention; Figure 6 A schematic diagram of an embodiment of the fiber optic preform quality inspection and grading device for multi-parameter testing provided by the present invention; Figure 7 This is a schematic diagram of the structure of a multi-parameter testing equipment for fiber optic preform quality inspection and grading provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0021] This invention provides a method and apparatus for quality inspection and grading of optical fiber preforms using multi-parameter testing, which will be described in detail below.

[0022] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the multi-parameter testing method for quality inspection and grading of optical fiber preforms provided by the present invention. In a specific embodiment of the present invention, a multi-parameter testing method for quality inspection and grading of optical fiber preforms is disclosed, comprising: S101. Perform electron probe microscopy, Raman spectroscopy and electron paramagnetic resonance analysis on the optical fiber preform sample to obtain elemental distribution data, Raman spectral data and paramagnetic resonance spectral data respectively. S102. Preprocess and extract features from elemental distribution data, Raman spectroscopy data and paramagnetic resonance spectroscopy data to obtain several key quality parameters; S103. Input multiple key quality parameters into the pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. S104. Compare the comprehensive quality assessment coefficient with the preset grading threshold to classify the quality level of the optical fiber preform.

[0023] In the above embodiments, firstly, electron probe microscopy is used to test the sample. By focusing an electron beam to excite characteristic X-rays of the sample, two-dimensional distribution information of various elements, including dopants, on the cross-section of the preform is obtained, and the output is a high spatial resolution elemental distribution map. Secondly, Raman spectroscopy is performed, utilizing the inelastic scattering effect of laser light with the vibrational modes of sample molecules to obtain characteristic spectral data characterizing the bonding state, internal stress, and intrinsic defects of the glass network structure. Finally, electron paramagnetic resonance (EPR) testing is performed to detect unpaired electrons in the material, thereby accurately quantifying the concentration and type of paramagnetic defects such as oxygen vacancies and E' centers.

[0024] For elemental distribution data obtained by electron probe microanalysis, spatial registration is first performed to ensure coordinate consistency of different elemental spectra. Then, statistical methods are used to remove outliers to eliminate measurement error interference. The processed data is then fitted with a smooth function of elemental concentration as a function of radius using spline interpolation, and finally, the Rayleigh scattering concentration parameter, characterizing compositional fluctuations, is calculated through numerical integration. For Raman spectroscopy data, baseline correction is performed to eliminate fluorescence background, and spectral normalization is performed to eliminate the influence of test condition fluctuations. Then, a peak deconvolution fitting algorithm is used to accurately extract the intensity, full width at half maximum (FWHM), and other parameters of specific characteristic peaks (such as the D2 peak) from overlapping spectral peaks. Based on this, the structural relaxation coefficient, reflecting the relaxation state of the network structure, and the virtual temperature, characterizing internal stress, are calculated. For paramagnetic resonance spectroscopy data, baseline correction and double integration are required. Finally, by comparing with standard samples, the spin concentration, a core parameter of defect concentration, is quantitatively calculated.

[0025] The comprehensive quality evaluation model is pre-trained and constructed based on a large amount of historical sample data. It uses extracted key quality parameters as input variables and the actual performance of the fiber after drawing from the corresponding prefabricated component as the target output. The model is then fitted and optimized using algorithms such as multivariate nonlinear regression to obtain an empirical formula. In practical applications, the key quality parameters extracted from the current prefabricated component are simply input as independent variables into the model. The model then performs built-in mathematical calculations and outputs a comprehensive quality evaluation coefficient, which comprehensively reflects the potential impact of composition, structure, and defects on the final performance of the fiber, thus achieving a quantitative assessment of the prefabricated component quality.

[0026] The grading thresholds are not fixed values, but are predetermined based on statistical process control principles or product performance standards. For example, by analyzing the distribution of historical qualified product evaluation coefficients, their mean and standard deviation are calculated, and the numerical range boundaries corresponding to each level, such as "Excellent," "Good," "Qualified," and "Unqualified," are set accordingly. The current comprehensive quality evaluation coefficient is checked to see which preset threshold range it falls into, thus assigning a clear quality grade label to the prefabricated component. The entire process is completed entirely by established rules, eliminating interference from subjective human factors, ensuring the objectivity, consistency, and traceability of the grading results, and allowing direct connection to the Manufacturing Execution System to guide production flow.

[0027] Compared with existing technologies, this embodiment provides a multi-parameter testing method for the quality inspection and grading of optical fiber preforms. It employs three different testing methods: electron probe microscopy, Raman spectroscopy, and electron paramagnetic resonance (EPR) testing. This approach acquires information about the optical fiber preforms from multiple perspectives, significantly enriching the testing dimensions and avoiding information gaps caused by single testing methods. This results in more comprehensive and accurate quality assessments. Preprocessing and feature extraction of the data from the three tests yield multiple key quality parameters, which more comprehensively reflect the quality status of the optical fiber preforms. This allows for more accurate identification of potential quality problems, improving the reliability and accuracy of quality assessments and resolving the problem of one-sided quality assessment results due to a single testing dimension in existing technologies. The unified preprocessing and feature extraction of the acquired data does not rely on the personal experience and judgment of operators, ensuring consistency in data processing among different operators. The comprehensive quality assessment coefficient is calculated using a comprehensive quality evaluation model, exhibiting higher stability and consistency. This reduces data processing differences caused by varying operator experience, thereby reducing reliance on operator experience and addressing the issue of strong subjectivity in the grading process. The quality grade of optical fiber preforms is classified according to preset grading thresholds, providing a unified quality assessment standard. Different operators use the same methods and standards for testing and grading, avoiding the problem of large differences in test results caused by different operators' testing standards, thus improving the reliability and stability of testing.

[0028] Please see Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of elemental distribution data processing provided by the present invention. In some embodiments of the present invention, elemental distribution data, Raman spectroscopy data, and paramagnetic resonance spectroscopy data are preprocessed and feature extracted to obtain several key quality parameters, including: S201. Perform spatial registration and outlier removal on the element distribution data; S202. The processed element concentration data is averaged along the radial direction of the preform, and the continuous function of element content as a function of radius is obtained by spline interpolation. S203. The Rayleigh scattering concentration parameter is calculated based on a continuous function of element content as a function of radius.

[0029] In the above embodiments, when electron probe microscopy scans different elements, pixel-level misalignments may occur between element distribution maps due to instrument drift or minute sample displacement. To address this issue, a feature-point-based image registration algorithm can be employed. This algorithm selects significant features present in all element maps as reference points and uses rigid or affine transformation models to precisely align the two-dimensional distribution data of all elements to the same spatial coordinate system. The outlier removal logic involves identifying and removing non-physically interpretable data points caused by measurement errors. Statistical discrimination methods can be used, such as calculating the Z-score of each pixel's element concentration relative to its surrounding local concentration. Pixels with absolute values ​​exceeding a preset threshold are identified as outliers and replaced using the mean or median of their neighborhood concentrations, effectively eliminating noise interference and ensuring the purity of the original data.

[0030] Fiber optic preforms typically possess rotationally symmetric waveguide structures, whose performance primarily depends on parameter variations along the radial direction. Therefore, two-dimensional surface distribution data can be compressed into a more representative and operable radial one-dimensional distribution. First, the registered elemental concentration data is divided into equally spaced annular zones in a polar coordinate system according to radial distance. The arithmetic mean of the concentration of all pixels within each annular zone is calculated, resulting in a discrete data point sequence showing the elemental concentration varying with the radius. Subsequently, cubic spline interpolation is used to fit these discrete data points. Cubic spline interpolation generates a smooth curve passing through all data points and ensures that the fitted function has continuous first and second derivatives at the nodes, thus accurately reproducing the true trend of the concentration distribution.

[0031] Rayleigh scattering density factor is: ; in, n For refractive index, P The average photoelastic coefficient, β T For isothermal compression, E a For activation energy, I D2 Peak D2 (approximately 606 cm) -1 The peak strength of ) I 0 is the standard peak intensity value, which can be set according to the actual situation.

[0032] Please see Figure 3 , Figure 3 This is a flowchart illustrating an embodiment of Raman spectroscopy data processing provided by the present invention. In some embodiments of the present invention, elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data are preprocessed and feature extracted to obtain multiple key quality parameters, and the process also includes: S301. Baseline correction and normalization are performed on the Raman spectral data to obtain several Raman characteristic peaks; S302. Identify and fit the Raman characteristic peak of the first preset wavenumber, and use its peak intensity as the virtual temperature. S303. Identify and fit the Raman characteristic peak of the second preset wavenumber, and calculate the structural relaxation coefficient based on its full width at half maximum (FWHM).

[0033] In the above embodiments, the original Raman spectral signal, in addition to containing characteristic Raman scattered light originating from molecular vibrations, inevitably contains a strong fluorescence background signal, usually manifested as a slowly changing broad band, which can severely interfere with or even obscure the true Raman characteristic peaks. Furthermore, experimental factors such as laser power fluctuations and differences in focusing conditions can also lead to significant variations in the acquired spectral intensity, making it unsuitable for direct quantitative comparison. First, baseline correction is performed using the least squares method to fit the morphology of the fluorescence background and subtract it from the original spectrum, resulting in a Raman spectrum with a flat baseline and prominent characteristic peaks. Subsequently, spectral normalization is performed. Typically, a characteristic peak with stable intensity, unaffected by sample conditions, is selected as an internal standard. The intensity of the entire spectrum is divided by the intensity of this internal standard peak, thereby normalizing all spectra to the same intensity benchmark, eliminating systematic errors caused by testing conditions, and preparing for subsequent accurate quantitative analysis.

[0034] During the glass formation process, its structure fails to reach a fully relaxed equilibrium state, but is instead "frozen" in a certain high-temperature state within the liquid state. The degree of this state determines the magnitude of its structural stress and stability. The system identifies and fits values ​​at a first preset wavenumber (preferred to be 606 cm⁻¹). -1 The Raman characteristic peak at the wavenumber was deconvolved using a Gaussian-Lorentz hybrid peak fitting algorithm to separate it from potentially overlapping spectral peaks, and its peak intensity (i.e., the maximum intensity value of the peak) was extracted as a virtual temperature. The intensity of this characteristic peak has a specific correlation with the relaxation degree of the glass network; the higher the intensity, the higher the energy state of the glass network during solidification, and the greater the retained internal stress.

[0035] The broadening of Raman characteristic peaks directly reflects the damping of molecular vibrations and the range of atomic bond angle distribution, and is closely related to the short-range order of the glass network. The peaks located at a second preset wavenumber (preferred to be 805 cm⁻¹) are identified and fitted. -1The Raman characteristic peak at the wavenumber was determined. A nonlinear least squares fitting algorithm was used to accurately fit the peak shape and extract the full width at half maximum (FWHM), which is the spectral peak width corresponding to half the maximum peak intensity. A larger FWHM value indicates a more dispersed bond angle distribution and a more disordered and unstable glass network structure. For quantitative evaluation, the experimentally measured FWHM value was divided by a reference value (which can be set according to actual needs). The resulting ratio is the structural relaxation coefficient, a dimensionless number. A larger value indicates a lower degree of relaxation in the glass network structure of the current preform, indicating a higher energy state.

[0036] The structural relaxation coefficient is: ; in, These are the standard half-height and width values; the specific values ​​can be set according to the actual situation. This is the actual value of half-width.

[0037] In some embodiments of the present invention, elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data are preprocessed and feature extracted to obtain multiple key quality parameters, and the method further includes: Baseline correction and double integration were performed on the paramagnetic resonance spectral data to obtain the absorption spectral area. The spin concentration of the defect peak is calculated based on the absorption spectrum area.

[0038] In the above embodiments, the acquired paramagnetic resonance spectrum data is in the form of a first-order differential curve, and inevitably contains non-ideal background signals generated by the resonant cavity, sample tube, etc. This background signal usually manifests as a slowly changing baseline drift, which, if not eliminated, will directly lead to severe distortion of the integration results. Baseline correction is required. This is done by acquiring a spectrum of a blank region without paramagnetic signals, or by using a mathematical algorithm to estimate and fit the shape of the background baseline. Then, this fitted baseline is subtracted from the original first-order differential spectrum to obtain a first-order differential spectral line with a straight baseline and clear signal characteristics.

[0039] Subsequently, the corrected first differential spectral line is subjected to double integration. The intensity of the paramagnetic resonance spectral data is proportional to the area of ​​its integral over the magnetic field, and the first differential spectrum is the derivative of the absorption spectrum. Therefore, a first numerical integration is performed on the baseline-corrected first differential spectrum to reconstruct the shape of the corresponding absorption spectral line. Then, a second numerical integration is performed on this absorption spectral line to calculate the area it covers within the entire magnetic field range, which is proportional to the total number of unpaired electrons in the sample, i.e., the total number of paramagnetic centers.

[0040] The response sensitivity of a paramagnetic resonance spectrometer is affected by instrument settings, therefore the absorption spectral area of ​​a sample cannot be directly used to represent its absolute concentration. It is necessary to establish a quantitative conversion relationship between "absorption spectral area and spin concentration" by testing and calibrating a standard sample of known concentration under identical instrument conditions.

[0041] By comparing the absorption spectral area of ​​the sample to be tested with that of the standard sample, and taking into account the slight differences in instrument gain settings between the two measurements, and finally multiplying by the known spin concentration of the standard sample, the spin concentration of paramagnetic defects in the sample to be tested can be accurately calculated. This spin concentration is expressed as the number of spins per unit mass or unit volume (e.g., spins / g or spins / cm²). 3 The unit is 100%, which is a core quality indicator that directly quantifies the concentration of paramagnetic point defects in preforms.

[0042] Please see Figure 4 , Figure 4 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S103 shows that, in some embodiments of the present invention, multiple key quality parameters are input into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform, including: S401. Extract key quality parameters from multiple historical optical fiber preform sample data and record the measured attenuation value of each sample to construct a training dataset. S402. Based on physical mechanisms and data fitting, establish an empirical formula with key quality parameters as independent variables and measured attenuation values ​​as dependent variables. S403. Using the training dataset, the empirical formula is fitted and optimized through a multivariate nonlinear regression algorithm to obtain a well-trained comprehensive quality evaluation model. S404. Input multiple key quality parameters into the trained comprehensive quality evaluation model to obtain the comprehensive quality evaluation coefficient.

[0043] In some embodiments of the present invention, the comprehensive quality evaluation model is as follows: ; in, The structural relaxation coefficient is... Rayleigh scattering density factor Rayleigh scattering concentration factor EPR Refers to the spin concentration of the defect peak. and The ratio of the peak intensity of three-membered and four-membered ring defects in Raman spectroscopy data to the standard intensity. A , B The component contribution coefficient varies depending on the fiber material. These are the quality assessment coefficients. In a preferred embodiment, the component contribution coefficient A is 1.2, and the component contribution coefficient B is 1.5.

[0044] In some embodiments of the present invention, multiple key quality parameters are input into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform, and the method further includes: The quality assessment coefficient is obtained by performing area-weighted calculation on the quality assessment coefficient; The area-weighted formula is: ; in, a The sampling radius of the prefabricated component.

[0045] In one specific embodiment of the present invention, the preset grading threshold is dynamically set based on the statistical process control principle, and the mean μ and standard deviation σ of the historical qualified prefabricated component quality assessment values ​​are calculated.

[0046] The grading thresholds are set as follows: Superior grade: ε < μ - σ; Good quality: μ-σ≤ε<μ+σ; Qualified product: μ+σ≤ε<μ+2σ; Non-conforming product: ε≥μ+2σ.

[0047] It should be noted that other grading thresholds can also be used for grading in this invention, and this invention does not impose further restrictions on this.

[0048] In a specific embodiment of the present invention, optical fiber preforms are sampled, and the samples are subjected to EPMA, Raman spectroscopy, and electron spin resonance (EPR) tests. The test results are then incorporated into empirical formulas, allowing for the grading of optical fiber preforms before fiber drawing. The relationship between the comprehensive quality assessment coefficient εave and the optical fiber attenuation coefficient is shown in the table below.

[0049]

[0050] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating the relationship between the overall quality assessment coefficient and the fiber attenuation coefficient according to an embodiment of the present invention. The relationship between the overall quality assessment coefficient and the fiber attenuation coefficient is as follows: Figure 1As shown, there is a positive correlation between the overall quality assessment coefficient of the optical fiber preform and the optical fiber attenuation coefficient. Controlling εave to below 26 and above 24 yields optical fibers with attenuation coefficients between 0.170 and 0.160. Controlling εave to below 20 and above 18 yields optical fibers with attenuation coefficients between 0.155 and 0.150. Controlling it to below 18 yields optical fibers with attenuation coefficients less than 0.150.

[0051] To better implement the multi-parameter testing method for fiber optic preform quality inspection and grading in the embodiments of the present invention, based on the multi-parameter testing method for fiber optic preform quality inspection and grading, please refer to the corresponding... Figure 6 , Figure 6 This is a schematic diagram of an embodiment of the fiber optic preform quality inspection and grading device for multi-parameter testing provided by the present invention. The embodiment of the present invention provides a multi-parameter testing device 600 for fiber optic preform quality inspection and grading, comprising: The sample testing module 610 is configured to perform electron probe microscopy, Raman spectroscopy and electron paramagnetic resonance analysis on the optical fiber preform sample to obtain elemental distribution data, Raman spectral data and paramagnetic resonance spectral data, respectively. The feature extraction module 620 is configured to preprocess and extract features from elemental distribution data, Raman spectral data and paramagnetic resonance spectral data to obtain multiple key quality parameters. The model calculation module 630 is configured to input multiple key quality parameters into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. The quality grading module 640 is configured to compare the comprehensive quality assessment coefficient with a preset grading threshold to classify the quality level of the optical fiber preform.

[0052] It should be noted that the device 600 provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0053] Please see Figure 7 , Figure 7This is a schematic diagram of the structure of a multi-parameter testing equipment for fiber optic preform quality inspection and grading provided in an embodiment of the present invention. Based on the above-described multi-parameter testing method for fiber optic preform quality inspection and grading, the present invention also provides a multi-parameter testing equipment for fiber optic preform quality inspection and grading. This equipment can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The multi-parameter testing equipment 700 includes a processor 710, a memory 720, and a display 730. Figure 7 Only a portion of the components of the fiber optic preform quality inspection and grading equipment for multi-parameter testing are shown. However, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0054] In some embodiments, the memory 720 may be an internal storage unit of the multi-parameter testing fiber optic preform quality inspection and grading equipment 700, such as a hard disk or memory of the multi-parameter testing fiber optic preform quality inspection and grading equipment 700. In other embodiments, the memory 720 may be an external storage device of the multi-parameter testing fiber optic preform quality inspection and grading equipment 700, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the multi-parameter testing fiber optic preform quality inspection and grading equipment 700. Furthermore, the memory 720 may include both internal storage units and external storage devices of the multi-parameter testing fiber optic preform quality inspection and grading equipment 700. The memory 720 is used to store application software and various types of data installed in the multi-parameter testing fiber optic preform quality inspection and grading equipment 700, such as the program code of the multi-parameter testing fiber optic preform quality inspection and grading equipment 700. The memory 720 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 720 stores a multi-parameter test fiber optic preform quality inspection and grading program 740, which can be executed by the processor 710 to implement the multi-parameter test fiber optic preform quality inspection and grading method of the various embodiments of this application.

[0055] In some embodiments, processor 710 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 720 or process data, such as performing a method for quality inspection and grading of fiber optic preforms with multiple parameters.

[0056] In some embodiments, display 730 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 730 is used to display information from the multi-parameter testing fiber optic preform quality inspection and grading equipment 700 and to display a user interface for visualization. Components 710-730 of the multi-parameter testing fiber optic preform quality inspection and grading equipment 700 communicate with each other via a system bus.

[0057] In one embodiment, when the processor 710 executes the fiber optic preform quality inspection and grading program 740 for multi-parameter testing stored in the memory 720, the steps in the fiber optic preform quality inspection and grading method for multi-parameter testing described above are implemented.

[0058] This embodiment also provides a computer-readable storage medium storing a multi-parameter test program for quality inspection and grading of optical fiber preforms. When executed by a processor, this multi-parameter test program for quality inspection and grading of optical fiber preforms implements the following steps: Electron probe microscopy, Raman spectroscopy, and electron paramagnetic resonance (EPR) were performed on the fiber optic preform samples to obtain elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data, respectively. Preprocessing and feature extraction were performed on elemental distribution data, Raman spectroscopy data, and paramagnetic resonance spectroscopy data to obtain several key quality parameters; Multiple key quality parameters are input into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. The quality grade of optical fiber preforms is classified by comparing the comprehensive quality assessment coefficient with the preset grading threshold.

[0059] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0060] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0061] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0063] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0066] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0067] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0068] 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.

[0069] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements 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 method for quality inspection and grading of optical fiber preforms using multi-parameter testing, characterized in that, include: Electron probe microscopy, Raman spectroscopy, and electron paramagnetic resonance (EPR) were performed on the fiber optic preform samples to obtain elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data, respectively. The elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data were preprocessed and feature extracted to obtain several key quality parameters. The multiple key quality parameters are input into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. The comprehensive quality assessment coefficient is compared with a preset grading threshold to classify the quality level of the optical fiber preform.

2. The method for quality inspection and grading of optical fiber preforms using multi-parameter testing as described in claim 1, characterized in that, The elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data are preprocessed and feature extracted to obtain several key quality parameters, including: Spatial registration and outlier removal are performed on the element distribution data; The processed element concentration data were averaged along the radial direction of the preform, and a continuous function of element content as a function of radius was obtained by fitting the data using spline interpolation. The Rayleigh scattering concentration parameter is calculated based on a continuous function of the element content as a function of radius.

3. The method for quality inspection and grading of optical fiber preforms using multi-parameter testing as described in claim 1, characterized in that, The step of preprocessing and feature extraction of the elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain multiple key quality parameters further includes: The Raman spectral data were baseline corrected and normalized to obtain several Raman characteristic peaks; Identify and fit the Raman characteristic peak of the first preset wavenumber, and use its peak intensity as the virtual temperature; The Raman characteristic peaks of the second preset wavenumber are identified and fitted, and the structural relaxation coefficients are calculated based on their full width at half maximum (FWHM).

4. The method for quality inspection and grading of optical fiber preforms using multi-parameter testing as described in claim 1, characterized in that, The step of preprocessing and feature extraction of the elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain multiple key quality parameters further includes: The paramagnetic resonance spectral data are subjected to baseline correction and double integration to obtain the absorption spectral area; The spin concentration of the defect peak is calculated based on the absorption spectrum area.

5. The method for quality inspection and grading of optical fiber preforms using multi-parameter testing as described in claim 1, characterized in that, The step of inputting the multiple key quality parameters into a pre-trained comprehensive quality evaluation model and calculating the comprehensive quality evaluation coefficient of the optical fiber preform includes: Key quality parameters were extracted from multiple historical optical fiber preform sample data, and the measured attenuation value of each sample was recorded to construct a training dataset. Based on physical mechanisms and data fitting, an empirical formula is established with the key quality parameters as independent variables and the measured attenuation value as dependent variable. Using the training dataset, the empirical formula is fitted and optimized using a multivariate nonlinear regression algorithm to obtain a well-trained comprehensive quality evaluation model. The multiple key quality parameters are input into the trained comprehensive quality evaluation model to obtain the comprehensive quality evaluation coefficients.

6. The method for quality inspection and grading of optical fiber preforms using multi-parameter testing as described in claim 5, characterized in that, The comprehensive quality evaluation model is as follows: ； in, The structural relaxation coefficient is... Rayleigh scattering density factor Rayleigh scattering concentration factor EPR Refers to the spin concentration of the defect peak. and The ratio of the peak intensity of three-membered and four-membered ring defects in Raman spectroscopy data to the standard intensity. A , B The component contribution coefficient varies depending on the fiber material. This is the quality assessment coefficient.

7. The method for quality inspection and grading of optical fiber preforms using multi-parameter testing as described in claim 1, characterized in that, The step of inputting the multiple key quality parameters into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform further includes: The comprehensive quality assessment coefficient is obtained by performing area-weighted calculation on the aforementioned quality assessment coefficient. The area-weighted formula is: ； in, a The sampling radius of the prefabricated component.

8. A multi-parameter testing device for quality inspection and grading of optical fiber preforms, characterized in that, include: The sample testing module is configured to perform electron probe microscopy, Raman spectroscopy and electron paramagnetic resonance analysis on the optical fiber preform sample to obtain elemental distribution data, Raman spectral data and paramagnetic resonance spectral data, respectively. The feature extraction module is configured to preprocess and extract features from the elemental distribution data, Raman spectral data, and paramagnetic resonance spectral data to obtain multiple key quality parameters. The model calculation module is configured to input the multiple key quality parameters into a pre-trained comprehensive quality evaluation model to calculate the comprehensive quality evaluation coefficient of the optical fiber preform. A quality grading module is configured to compare the comprehensive quality assessment coefficient with a preset grading threshold to classify the quality level of the optical fiber preform.

9. A multi-parameter testing device for quality inspection and grading of optical fiber preforms, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, implements the steps of the fiber optic preform quality inspection and grading method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, It stores a computer program that can be executed by a multi-parameter testing fiber optic preform quality inspection and grading device. When the computer program is run on the multi-parameter testing fiber optic preform quality inspection and grading device, the multi-parameter testing fiber optic preform quality inspection and grading device performs the steps of the multi-parameter testing fiber optic preform quality inspection and grading method according to any one of claims 1 to 7.

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