High-reliability yarn elongation at break detection method based on data analysis

Abstract

The application provides a high-reliability yarn breaking elongation detection method based on data analysis, comprising the following steps: a sample preparation and pretreatment step, a sample morphology measurement and mathematical modeling step, a tensile testing equipment calibration and parameter setting step, a tensile testing execution and data collection step, a breaking elongation calculation and preliminary analysis step, a stress-strain relationship analysis step, a data stability analysis step, an outlier detection and processing step, a result comprehensive analysis and report generation step, the method realizes accurate analysis of yarn morphology and stress-strain relationship, improves the accuracy of test results, performs stability and outlier detection analysis on data, and ensures the reliability and stability of test data, and provides strong support for yarn performance evaluation and improvement. The method has feasibility and practicability, and can significantly improve the efficiency and accuracy of yarn breaking elongation detection.

Description

Technical Field

[0001] This invention specifically relates to a highly reliable method for detecting the breaking elongation of spun yarn products based on data analysis. Background Technology

[0002] Elongation at break (EBLD) is the ratio of a material's elongation at break to its initial length, usually expressed as a percentage. It is an important indicator of a material's softness and elasticity. A higher EBLD indicates better softness and elasticity. In textile processing, fibers with high EBLD have a softer feel, can cushion stress, and reduce fuzz and breakage. The EBLD of ordinary textile fibers is generally between 10% and 30%. For high-strength industrial fibers, high breaking strength and low EBLD are required to prevent product deformation.

[0003] Currently, there is no accurate and reliable data analysis-based method for detecting the breaking elongation of spun yarn products in the spinning industry. Therefore, this application proposes a data analysis-based method for detecting the breaking elongation of spun yarn products to solve this problem. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a highly reliable method for detecting the breaking elongation of spun yarn products based on data analysis. This highly reliable method for detecting the breaking elongation of spun yarn products based on data analysis can effectively solve the aforementioned problems.

[0005] To achieve the above requirements, the technical solution adopted by the present invention is: to provide a highly reliable method for detecting the breaking elongation of spun yarn products based on data analysis, which includes the following steps:

[0006] S1: Steps for sample preparation and pretreatment;

[0007] At least 30 samples were randomly selected from the yarn products to be tested, ensuring that the samples covered different batches and different locations to ensure the representativeness of the results. The samples were placed at a temperature of 20±2℃ and a relative humidity of 65±3% for at least 48 hours to eliminate the influence of the environment on the test results.

[0008] S2: Steps for sample morphology measurement and mathematical modeling;

[0009] The diameter and cross-sectional shape parameters of each sample were measured using a high-precision optical microscope. Based on the measurement data, a mathematical model with an elliptical yarn cross-section was established using curve fitting techniques from calculus. The equation is expressed as follows:

[0010]

[0011] in:

[0012] a and b: These are the reference values ​​for the semi-major and semi-minor axes of the fractional-order generalized hyperellipse, respectively.

[0013] α(x,y,θ), β(x,y,θ), γ(x,y,θ), δ(x,y,θ): are fractional functions of x, y and polar angle θ, representing the complexity of the cross-sectional shape as it changes with position;

[0014] x and y: coordinates of any point on the cross section;

[0015] θ: Polar angle on the cross section;

[0016] A n The coefficients of the Bézier curve;

[0017] v n Bessel function of order 1;

[0018] γ: The distance from a point on the cross section to the center.

[0019] c: Scaling factor for the Bézier curve.

[0020] λ(θ): is a fractional function of polar angle θ, representing the complexity of the Bézier curve as the angle changes;

[0021] S3: Steps for calibrating and setting parameters of tensile testing equipment;

[0022] The tensile testing equipment was calibrated using standard samples to ensure its accuracy and stability. The tensile speed was set to a constant value of 50 mm / min, and the sampling frequency of the data acquisition system was set to 100 Hz to ensure that the subtle changes in the yarn during the tensile process could be captured.

[0023] S4: Steps for performing tensile tests and collecting data;

[0024] The pretreated sample is installed on the tensile testing equipment and subjected to tensile testing according to the set parameters. During the test, the data acquisition system records the tensile force, elongation and time data in real time. Each sample is tested at least five times to reduce the impact of random errors.

[0025] S5: Steps for calculating and performing preliminary analysis of elongation at break;

[0026] Based on the data obtained from the tensile test, the elongation at break of each sample was calculated. The formula for calculating the elongation at break is as follows:

[0027]

[0028] A preliminary analysis of the calculated elongation at break was conducted to check the rationality and consistency of the data.

[0029] S6: Steps for performing stress-strain relationship analysis;

[0030] Using the differential method in calculus, the stress-strain relationship of the yarn during the stretching process is analyzed. The stress-strain relationship of the yarn conforms to a fractional-order nonlinear elastic model, and its mathematical expression is:

[0031]

[0032] Where σ is stress, λ is elongation ratio (λ = 1 + ε, ε is strain), C1 and C2 are material constants, and α(λ), β(λ) are constants. -1 ), γ(λ), δ(λ) -1 ) is a fractional function of λ. Using data obtained from stretching tests, and combining fractional nonlinear least squares and variational principles, C1, C2, α(λ), and β(λ) are fitted to obtain... -1 ), γ(λ), δ(λ) -1 The optimal value of ).

[0033] S7: Steps for performing data stability analysis;

[0034] ANOVA analysis was used to analyze the stability of test results across different batches and locations. The total variance was decomposed into between-group and within-group variance. By comparing the proportions of between-group and within-group variance, the significance of variance from different sources was determined. Bayesian mixed-effects ANOVA was employed, and its mathematical model is as follows:

[0035]

[0036] in:

[0037] g(μ ijk ): This is the join function, used to connect the expected mean μ. ijk Associated with linear predictors;

[0038] η: is the intercept term;

[0039] A i : This refers to the batch effect, which indicates the impact of the i-th batch on the result;

[0040] B j : This is the position effect, representing the influence of the j-th position on the result;

[0041] (AB) ij This is the interaction effect between batch and location;

[0042] S ijk: This refers to the intra-subject effect, which indicates the effect of repeated measurements within the same batch and at the same location;

[0043] Z l This is the design matrix, representing other covariates that may affect the outcome;

[0044] β l : These are the coefficients of the covariates;

[0045] ξ(t): is a fractional-order random process, representing a random effect that changes over time;

[0046] T: Time range;

[0047] ∈ ijk : This is the residual term, representing the random error that was not captured by the model;

[0048] S8: Steps for outlier detection and handling;

[0049] Using the box plot method in statistics, outliers in the test data are detected. By calculating the quartiles Q1, Q2, Q3 and the interquartile range IQR = Q3 - Q1, a box plot is drawn and outliers are identified. For the detected outliers, necessary processing is carried out, including deletion and replacement.

[0050] S9: Steps for comprehensive analysis of results and generation of reports;

[0051] The test data is comprehensively analyzed, including the calculation of the average, standard deviation, maximum and minimum values ​​of the breaking elongation, to evaluate the performance of the yarn. Based on the analysis results, a test report is generated.

[0052] The advantages of this data analysis-based, highly reliable method for detecting the breaking elongation of spun yarn products are as follows:

[0053] This method addresses several key issues in existing testing of elongation at break for spun yarn products: First, by applying calculus formulas, it achieves precise analysis of yarn morphology and stress-strain relationships, improving the accuracy of test results. Second, statistical formulas are used to analyze data stability and outlier detection, ensuring the reliability and stability of the test data. Finally, detailed comprehensive analysis and report generation provide strong support for yarn performance evaluation and improvement. This method is feasible and practical, significantly improving the efficiency and accuracy of elongation at break testing for spun yarn products. Attached Figure Description

[0054] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, use the same reference numerals to denote the same or similar parts. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0055] Figure 1 A schematic flowchart of a data analysis-based high-reliability method for detecting the breaking elongation of spun yarn products according to an embodiment of this application is shown. Detailed Implementation

[0056] To make the objectives, technical solutions and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and specific embodiments.

[0057] In the following description, references to "an embodiment," "an embodiment," "an example," "example," etc., indicate that the described embodiment or example may include a particular feature, structure, characteristic, property, element, or limitation, but not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Furthermore, the repeated use of the phrase "an embodiment according to this application," while possibly referring to the same embodiment, does not necessarily refer to the same embodiment.

[0058] For simplicity, certain technical features known to those skilled in the art are omitted in the following description.

[0059] According to one embodiment of this application, a highly reliable method for detecting the breaking elongation of spun yarn products based on data analysis is provided, such as... Figure 1 As shown, it includes the following steps:

[0060] S1: Steps for sample preparation and pretreatment;

[0061] At least 30 samples were randomly selected from the yarn products to be tested, ensuring that the samples covered different batches and locations to guarantee the representativeness of the results. The samples were placed at a temperature of 20±2℃ and a relative humidity of 65±3% for at least 48 hours to eliminate the influence of the environment on the test results. Sample pretreatment is a key step to ensure the accuracy of the test. By placing the samples under standard temperature and humidity conditions, fluctuations in yarn performance caused by environmental changes can be eliminated. At the same time, the cleaning step removed impurities that might affect the test results.

[0062] S2: Steps for sample morphology measurement and mathematical modeling;

[0063] The diameter and cross-sectional shape parameters of each sample were measured using a high-precision optical microscope. Based on the measurement data, a mathematical model with an elliptical yarn cross-section was established using curve fitting techniques from calculus. The equation is expressed as follows:

[0064]

[0065] Where a and b are the major and minor axes of the fractional hyperellipse, respectively;

[0066] α(x), β(y), γ(y), and δ(x) are fractional functions of x and y;

[0067] x and y are the coordinates of any point on the cross section;

[0068] By combining variational methods with fractional calculus, the optimal values ​​of a, b, α(x), β(y), γ(y), and δ(x) are obtained by fitting actual measurement data.

[0069] S3: Steps for calibrating and setting parameters of tensile testing equipment;

[0070] The tensile testing equipment was calibrated using standard samples to ensure its accuracy and stability. The tensile speed was set to a constant 50 mm / min, and the sampling frequency of the data acquisition system was set to 100 Hz to ensure that subtle changes in the yarn during the stretching process could be captured. Equipment calibration is fundamental to ensuring test accuracy, and testing with standard samples verifies the equipment's accuracy and stability. The settings for tensile speed and sampling frequency are determined based on the characteristics of the yarn material and the testing requirements.

[0071] S4: Steps for performing tensile tests and collecting data;

[0072] The pretreated samples are mounted on a tensile testing device and subjected to tensile testing according to the set parameters. During the test, the data acquisition system records the tensile force, elongation, and time data in real time. Each sample is tested at least five times to reduce the impact of random errors. Tensile testing is a key step in evaluating the breaking elongation of yarn. By recording key data during the tensile process in real time, the breaking elongation of the yarn can be calculated. Repeated testing can improve the reliability of the test results.

[0073] S5: Steps for calculating and performing preliminary analysis of elongation at break;

[0074] Based on the data obtained from the tensile test, the elongation at break of each sample was calculated. The formula for calculating the elongation at break is as follows:

[0075]

[0076] A preliminary analysis of the calculated elongation at break is performed to check the rationality and consistency of the data. Elongation at break is an important indicator for evaluating yarn performance. It is obtained by calculating the ratio of the elongation at break to the initial length. This preliminary analysis helps to identify abnormal data or potential problems in the testing process.

[0077] S6: Steps for performing stress-strain relationship analysis;

[0078] Using the differential method in calculus, the stress-strain relationship of the yarn during the stretching process is analyzed. The stress-strain relationship of the yarn conforms to a fractional-order nonlinear elastic model, and its mathematical expression is:

[0079]

[0080] Where σ is stress, λ is elongation ratio (λ = 1 + ε, ε is strain), C1 and C2 are material constants, and α(λ), β(λ) are constants. -1 ), γ(λ), δ(λ) -1 ) is a fractional function of λ. Using data obtained from stretching tests, and combining fractional nonlinear least squares and variational principles, C1, C2, α(λ), and β(λ) are fitted to obtain... -1 ), γ(λ), δ(λ) -1 The optimal value of ).

[0081] S7: Steps for performing data stability analysis;

[0082] ANOVA analysis was used to analyze the stability of test results across different batches and locations. The total variance was decomposed into between-group and within-group variance. By comparing the proportions of between-group and within-group variance, the significance of variance from different sources was determined. Bayesian mixed-effects ANOVA was employed, and its mathematical model is as follows:

[0083] Y ijk ~Normal(μ+A) i +B j +(AB) ij +S ijk , σ 2 );

[0084] Among them, Y ijk It is the observation value of the i-th batch, the j-th location, and the k-th repeated measurement;

[0085] μ is the population mean;

[0086] A i It's a batch effect;

[0087] B j It is a positional effect;

[0088] (AB) ij It is the interaction effect between batch and location;

[0089] S ijk It is an intra-subject effect, that is, the effect of repeated measurements within the same batch and the same location;

[0090] σ 2 It is the error variance;

[0091] S8: Steps for outlier detection and handling;

[0092] Box plots, a statistical method, are used to detect outliers in test data. By calculating the quartiles Q1, Q2, Q3, and the interquartile range (IQR) = Q3 - Q1, a box plot is drawn to identify outliers. For detected outliers, necessary processing, including deletion and replacement, is performed. Outlier detection is a crucial step in ensuring the accuracy of test data. Box plots provide a visual way to identify outliers and allow for appropriate handling.

[0093] S9: Steps for comprehensive analysis of results and generation of reports;

[0094] The test data undergoes comprehensive analysis, including calculations of the average, standard deviation, maximum, and minimum values ​​of breaking elongation. This process evaluates the yarn's performance and generates a test report based on the analysis results. The comprehensive analysis and report generation constitute the summary phase of the testing work. By comprehensively analyzing the test data, a full understanding of the yarn's performance characteristics can be obtained, providing a basis for subsequent improvements and optimizations.

[0095] According to one embodiment of this application, in the data analysis-based high-reliability yarn breaking elongation detection method, S2 is the step of sample morphology measurement and mathematical modeling; the diameter and cross-sectional shape parameters of each sample are measured using a high-precision optical microscope, and based on the measurement data, a mathematical model of an elliptical yarn morphology is established using curve fitting techniques in calculus, the equation of which is expressed as:

[0096]

[0097] in:

[0098] a and b: These are the reference values ​​for the semi-major and semi-minor axes of the fractional-order generalized hyperellipse, respectively.

[0099] α(x,y,θ), β(x,y,θ), γ(x,y,θ), δ(x,y,θ): are fractional functions of x, y and polar angle θ, representing the complexity of the cross-sectional shape as it changes with position;

[0100] x and y: coordinates of any point on the cross section;

[0101] θ: Polar angle on the cross section;

[0102] A n The coefficients of the Bézier curve;

[0103] v n Bessel function of order 1;

[0104] γ: The distance from a point on the cross section to the center.

[0105] c: Scaling factor for the Bézier curve.

[0106] λ(θ): is a fractional function of the polar angle θ, representing the complexity of the Bézier curve as the angle changes.

[0107] According to one embodiment of this application, the step S1 of the high reliability yarn breaking elongation detection method based on data analysis, which involves sample preparation and pretreatment, further includes cleaning the sample to remove surface impurities and ensure the accuracy of the test.

[0108] According to one embodiment of this application, the mathematical model established in step S2 of the data analysis-based high-reliability yarn product breaking elongation detection method is expressed by the following equation:

[0109]

[0110] Where a and b are the major and minor axes of the fractional hyperellipse, respectively;

[0111] α(x), β(y), γ(y), and δ(x) are fractional functions of x and y;

[0112] x and y are the coordinates of any point on the cross section;

[0113] By combining variational methods with fractional calculus, the optimal values ​​of a, b, α(x), β(y), γ(y), and δ(x) are obtained by fitting actual measurement data.

[0114] According to one embodiment of this application, the mathematical model established in step S2 of the data analysis-based high-reliability yarn product breaking elongation detection method is expressed by the following equation:

[0115]

[0116] in:

[0117] a and b: These are the reference values ​​for the semi-major and semi-minor axes of the fractional-order generalized hyperellipse, respectively.

[0118] α(x,y), β(x,y), γ(x,y), δ(x,y): are fractional functions of x and y, representing the complexity of the cross-sectional shape as it changes with position;

[0119] x and y: coordinates of any point on the cross section;

[0120] By combining variational methods with fractional calculus, the optimal values ​​of a, b, α(x,y), β(x,y), γ(x,y), and δ(x,y) are obtained by fitting actual measurement data. The periodic characteristics of the cross-sectional shape are analyzed using Fourier transform, and the nonlinear characteristics of the cross-sectional shape are extracted by combining Hilbert-Huang transform.

[0121] According to one embodiment of this application, S7 of the data analysis-based high-reliability yarn breaking elongation detection method is a step of performing data stability analysis. Using ANOVA analysis, the stability of test results between different batches and locations is analyzed, and the total variation is decomposed into between-group variation and within-group variation. By comparing the proportions of between-group variation and within-group variation, the significance of variations from different sources is determined. The mathematical model is as follows:

[0122]

[0123] in:

[0124] g(μ ijk ): This is the join function, used to connect the expected mean μ. ijk Associated with linear predictors;

[0125] η: is the intercept term;

[0126] A i : This refers to the batch effect, which indicates the impact of the i-th batch on the result;

[0127] B j : This is the position effect, representing the influence of the j-th position on the result;

[0128] (AB) ij This is the interaction effect between batch and location;

[0129] S ijk : This refers to the intra-subject effect, which indicates the effect of repeated measurements within the same batch and at the same location;

[0130] Z l This is the design matrix, representing other covariates that may affect the outcome;

[0131] β l : These are the coefficients of the covariates;

[0132] ξ(t): is a fractional-order random process, representing a random effect that changes over time;

[0133] T: Time range;

[0134] ∈ ijk : is the residual term, representing the random error that was not captured by the model.

[0135] According to one embodiment of this application, the stress-strain relationship of the yarn in step S6 of the data analysis-based high-reliability yarn breaking elongation detection method conforms to the mathematical expression of a fractional-order nonlinear elastic model as follows:

[0136]

[0137] Where σ is stress, λ is elongation ratio (λ = 1 + ε, ε is strain), C1 and C2 are material constants, and α(λ), β(λ) are constants. -1 ), γ(λ), δ(λ) -1 ) is a fractional function of λ. Using data obtained from stretching tests, and combining fractional nonlinear least squares and variational principles, C1, C2, α(λ), and β(λ) are fitted to obtain... -1 ), γ(λ), δ(λ) -1 The optimal value of ).

[0138] According to one embodiment of this application, the stress-strain relationship of the yarn in step S6 of the data analysis-based high-reliability yarn breaking elongation detection method conforms to the mathematical expression of a fractional-order nonlinear elastic model as follows:

[0139]

[0140] in:

[0141] σ(t): Stress at time t;

[0142] K1(t-τ) and K2(t-τ): These are kernel functions related to time and strain history, respectively, representing the viscoelastic properties of the material;

[0143] λ(τ): Elongation ratio at time τ;

[0144] α(λ(τ)) and β(λ(τ)) -1 ): is a fractional function of elongation ratio, representing the nonlinear characteristics of the stress-strain relationship;

[0145] γ(τ): is a fractional function of time, representing the non-integer derivative of the strain rate;

[0146] d γ(τ) / dτ γ(τ) : Represents the fractional derivative, used to describe the non-integer order variation of strain rate.

[0147] According to one embodiment of this application, in S7 of the high-reliability data analysis-based method for detecting the breaking elongation of spun yarn products, the determination of whether the variation from different sources is significant is performed using Bayesian mixed-effects analysis of variance, and its mathematical model is as follows:

[0148] Y ijk ~Normal(μ+A) i +B j +(AB) ij +S ijk , σ 2 );

[0149] Among them, Y ijk It is the observation value of the i-th batch, the j-th location, and the k-th repeated measurement;

[0150] μ is the population mean;

[0151] A i It's a batch effect;

[0152] B j It is a positional effect;

[0153] (AB) ij It is the interaction effect between batch and location;

[0154] S ijk It is an intra-subject effect, that is, the effect of repeated measurements within the same batch and the same location;

[0155] σ 2 It is the error variance.

[0156] According to one embodiment of this application, in step S7 of the high-reliability data analysis-based method for detecting the breaking elongation of spun yarn products, the determination of whether the variation from different sources is significant is performed using Bayesian mixed-effects analysis of variance, and its mathematical model is as follows:

[0157] g(μ ijk )=η+A i +B j +(AB) ij +S ijk +Z l β l ;

[0158] in:

[0159] g(μ ijk ): A join function used to connect the expected mean μ ijk Associated with linear predictors;

[0160] η: Intercept term;

[0161] A iBatch effect: This refers to the impact of the i-th batch on the result.

[0162] B j Position effect: representing the influence of the j-th position on the result;

[0163] (AB) ij The interaction effect between batch and location;

[0164] S ijk Intra-subject effect: refers to the effect of repeated measurements within the same batch and at the same location;

[0165] Z l Design a matrix to represent other covariates that may affect the outcome;

[0166] β l : is the coefficient of the covariate.

[0167] According to one embodiment of this application, in step S5 of the high-reliability data analysis-based method for detecting the breaking elongation of yarn products, the breaking elongation is calculated and preliminarily analyzed based on the data obtained from the tensile test. The breaking elongation is calculated using the following formula:

[0168]

[0169] in:

[0170] a n and b m : These represent the coefficients of yarn length variation under breaking elongation and initial length, respectively;

[0171] and These represent the nonlinear changes in yarn at breaking elongation and initial length, respectively;

[0172] ε random : Represents the random error term;

[0173] A preliminary analysis was conducted on the calculated elongation at break to check the rationality and consistency of the data.

[0174] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A high reliability data analysis based method for detecting the elongation at break of a spun yarn product, characterized in that, Comprising the following steps: S1: Perform sample preparation and pretreatment steps; Randomly select at least 30 samples from the spinning products to be tested, ensuring that the samples cover different batches and different positions to ensure the representativeness of the results, and place the samples at a temperature of 20±2℃ and a relative humidity of 65±3% for at least 48 hours to eliminate the influence of the environment on the test results; S2: Perform sample shape measurement and mathematical modeling steps; Use a high-precision optical microscope to measure the diameter and cross-sectional shape parameters of each sample, and based on the measurement data, use curve fitting techniques in calculus to establish a mathematical model of the elliptical yarn shape, whose equation is expressed as: Where: a and b: are the reference values of the major and minor semi-axes of the fractional-order generalized super-ellipse, respectively; α(x, y, θ), β(x, y, θ), γ(x, y, θ), δ(x, y, θ): are fractional-order functions related to x, y, and polar angle θ, representing the complexity of the cross-sectional shape changing with position; x and y: coordinates of any point on the cross-section; θ: polar angle on the cross-section; A n : coefficients of a Bezier curve; v n Legendre function γ: distance from the center of the cross-section; c: scaling factor of the Bezier curve; λ(θ): is a fractional-order function related to the polar angle θ, representing the complexity of the Bezier curve changing with angle; S3: Perform calibration and parameter setting steps of the tensile testing equipment; Calibrate the tensile testing equipment using standard samples to ensure the accuracy and stability of the equipment, set the tensile speed to a constant value of 50mm / min, and set the sampling frequency of the data acquisition system to 100Hz to ensure that subtle changes in the yarn during the stretching process can be captured; S4: Perform tensile test execution and data acquisition steps; Install the pretreated samples on the tensile testing equipment and perform tensile testing according to the set parameters, and in the testing process, the data acquisition system records the tensile force, elongation, and time data in real time. Each sample is tested at least five times to reduce the influence of random errors; S5: Perform elongation at break calculation and preliminary analysis steps; According to the data obtained by tensile testing, calculate the elongation at break of each sample, and preliminarily analyze the calculated elongation at break to check the reasonableness and consistency of the data; S6: Perform stress-strain relationship analysis steps; Use the differential method in calculus to analyze the stress-strain relationship of the yarn during the stretching process, and the stress-strain relationship of the yarn conforms to the fractional-order nonlinear elastic model; S7: Perform data stability analysis steps; Use ANOVA analysis method to analyze the stability of test results between different batches and different positions, decompose the total variation into inter-group variation and intra-group variation, and judge whether the variation from different sources is significant by comparing the proportion of inter-group variation and intra-group variation. Its mathematical model is: Where: g(μ ijk ) is a connection function that relates the desired mean μ ijk to the linear predictor; η: is the intercept term; A i : is the batch effect, representing the effect of the ith batch on the outcome; B j : is the position effect, representing the influence of the jth position on the result; (AB) ij : is the interaction effect of batch and location; S ijk : is the within-subjects effect, representing the repeated measures effect within the same batch, same location; Z l : is a design matrix representing other covariates that can potentially influence the outcome; β l : is the coefficient of the covariate; ξ(t): is a fractional-order random process representing the random effect changing with time; T: time range; ∈ ijk : is the residual term, representing the random error not captured by the model; S8: Perform outlier detection and processing steps; The abnormal values in the test data are detected by using the box plot method in statistics, the quartiles Q1, Q2, Q3 and interquartile range IQR=Q3-Q1 of the data are calculated, the box plot is drawn, and the abnormal values are identified, and the detected abnormal values are processed including deletion and replacement; S9: a step of result comprehensive analysis and report generation; The test data are comprehensively analyzed, including the calculation of the average value, the standard deviation, the maximum value and the minimum value of the elongation at break, the performance of the yarn is evaluated, and the test report is generated according to the analysis result.

2. The high reliability data analysis based spun yarn elongation at break detection method according to claim 1, characterized in that: The step S1 performs the sample preparation and pretreatment, and further includes: cleaning the sample to remove surface impurities, and ensuring the accuracy of the test.

3. The high reliability data analysis based spun yarn elongation at break detection method according to claim 1, characterized in that, The mathematical expression of the stress-strain relationship of the yarn in the step S6 conforms to the fractional order nonlinear elastic model, and is as follows: where σ is the stress, λ is the stretch ratio (λ = 1 + ∈, ∈ is the strain), C1 and C2 are material constants, a(λ), β(λ -1 ), γ(λ), δ(λ -1 ) are fractional functions of λ, and the optimal values of C1, C2, a(λ), β(λ -1 ), γ(λ), δ(λ -1 ) are obtained by fitting the data obtained from the tensile test, combined with fractional nonlinear least squares and variational principle.

4. The data analysis based high reliability spun yarn product elongation at break detection method according to claim 1, characterized in that: The mathematical expression of the stress-strain relationship of the yarn in the step S6 conforms to the fractional order nonlinear elastic model, and is as follows: Wherein: σ(t): stress at time t; K1(t-τ) and K2(t-τ): kernel functions related to time and strain history, respectively, representing the viscoelastic properties of the material; λ(τ): elongation ratio at time τ; a(λ(τ)) and β(λ(τ)) -1 ): is a fractional order function with respect to the elongation ratio, indicating the nonlinear characteristics of the stress-strain relationship; γ(τ): fractional order function with respect to time, representing the non-integer order derivative of the strain rate; d γ(τ) / d γ(τ) : represents fractional derivative, used to describe the non-integer order change of strain rate.

5. The data analysis based high reliability spun yarn product elongation at break detection method according to claim 1, characterized in that Step S5: a step of calculating and preliminarily analyzing the elongation at break, the elongation at break of each sample is calculated according to the data obtained by the tensile test, and the calculation formula of the elongation at break is as follows: The calculated elongation at break is preliminarily analyzed to check the rationality and consistency of the data.

6. The high reliability data analysis based spun yarn elongation at break detection method according to claim 1, characterized in that, Step S5: a step of calculating and preliminarily analyzing the elongation at break, the elongation at break of each sample is calculated according to the data obtained by the tensile test, and the calculation formula of the elongation at break is as follows: Wherein: a n and b m : represent the coefficient of the yarn length change at the elongation at break and the initial length, respectively; and denote the non-linear change of the yarn at the elongation at break and the initial length, respectively; ε random : represents a random error term; The calculated elongation at break is preliminarily analyzed to check the rationality and consistency of the data.

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