Detection method, device and equipment for antistatic high-performance resin line pipe

Abstract

The invention discloses a detection method, device and equipment for an antistatic high-performance resin line pipe, and the detection method for the antistatic high-performance resin line pipe comprises the following steps: carrying out multidirectional sampling on the resin line pipe to obtain an original sample strip group and a pretreated sample strip group; carrying out multi-wavelength Raman spectrum analysis; calculating estimated conductivity and specific surface area; differential electrostatic charge injection is carried out; monitoring a charge dissipation process; and calculating the comprehensive anisotropic conductivity index. According to the technical scheme, the conductivity of the antistatic high-performance resin line pipe in different directions can be effectively detected and evaluated, and it is ensured that the antistatic high-performance resin line pipe dissipates static electricity comprehensively and evenly in practical application.

Description

Technical Field

[0001] The present invention relates to the technical field of material performance detection, and in particular to a detection method, device and equipment for an antistatic high-performance resin wire tube. Background Art

[0002] Antistatic high-performance resin wire tube is a special wire tube that adds conductive fillers, such as carbon black or carbon nanotubes, to polymer materials to achieve the function of static dissipation. This wire tube can promptly conduct static charges and avoid static accumulation by forming a conductive path in the resin matrix. When the content of the conductive filler reaches the permeation threshold, a continuous conductive network will be formed between the filler particles, giving the material conductive properties. This wire tube is widely used in electronics, chemicals, medical and other fields that are sensitive to static electricity, and is of great significance for protecting sensitive equipment and improving production safety.

[0003] However, there are some problems with the existing production and detection technologies for antistatic high-performance resin wire tubes. In the production process, in order to make the conductive filler evenly dispersed, a high-speed mixing process is often used, which leads to the orientation of the polymer chain and the conductive filler, resulting in differences in the conductive properties of the material in different directions. Traditional detection methods mainly focus on testing the surface resistivity of materials, and fail to fully consider the anisotropy of conductive properties. The limitations of this detection method may lead to the risk of local static electricity accumulation during the actual use of the product, affecting the effect of static electricity dissipation. Therefore, how to effectively detect and evaluate the conductive properties of antistatic high-performance resin wire tubes in different directions to ensure that they can dissipate static electricity comprehensively and evenly in actual applications has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing detection method cannot effectively detect and evaluate the conductive performance of the antistatic high-performance resin wire tube in different directions.

[0005] A first aspect of the present invention provides a method for detecting an antistatic high-performance resin wire tube, the method comprising: The resin wire tube is sampled in the axial, radial and tangential directions respectively to obtain an initial spline group; The initial spline group is equally divided into two parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group; Performing multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-treated spline group respectively to determine the D4 band position and D3 band intensity that characterize the polyolefin structure; Based on the D4 band position and the D3 band intensity, respectively calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group; According to the estimated conductivity and specific surface area, differential electrostatic charge injection is performed on the original spline group and the pre-processed spline group respectively to form an initial charge distribution that is compatible with the estimated performance in the axial, radial and tangential directions; Under dynamic conditions simulating actual applications, the charge dissipation processes of the original spline group and the pre-processed spline group are monitored respectively to obtain dynamic charge distribution curves in the axial, radial and tangential directions; Based on the dynamic charge distribution curve, initial Raman spectrum parameters and comparison data between the original spline group and the pre-processed spline group, the comprehensive anisotropic conductive index of the resin wire tube is calculated.

[0006] Optionally, the initial spline group is divided into two equal parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group, including: Performing a three-dimensional conductivity scan on the initial spline group to obtain a conductivity distribution map; According to the conductivity distribution map, the initial spline group is divided into two groups A and B using a cross-layered algorithm to ensure that the conductivity distribution characteristic deviations of the two groups A and B in the axial, radial and tangential directions do not exceed a preset threshold; Keep group A of splines as the original group of splines; The B group of splines were subjected to gradient temperature treatment to form microstructure gradients in different temperature intervals; The B group of specimens that had been treated with gradient temperature were placed in a controlled humidity environment for humidity cycle treatment to regulate the surface hydrophilicity of the specimens; The treated group B specimens were subjected to surface plasma activation to obtain a pre-treated specimen group.

[0007] Optionally, the performing multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-processed spline group respectively to determine the D4 band position and D3 band intensity characterizing the polyolefin structure comprises: The original spline group and the pre-processed spline group are respectively subjected to Raman spectrum scanning using a 532nm and 780nm dual-wavelength laser source to obtain original spectrum data; Performing adaptive baseline correction and wavelet denoising on the raw spectral data to obtain an optimized Raman spectrum; Performing time-frequency analysis on the optimized Raman spectrum using Hilbert-Huang transform to obtain an instantaneous frequency distribution diagram; In the instantaneous frequency distribution diagram, the characteristic frequencies of the D4 band and the D3 band are located by a peak tracking algorithm; According to the characteristic frequency, local curve fitting is performed on the optimized Raman spectrum to extract the center position of the D4 band and the integral area of ​​the D3 band; The center position of the D4 band is defined as the D4 band position parameter, and the integrated area of ​​the D3 band is defined as the D3 band intensity parameter.

[0008] Optionally, the calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group respectively based on the D4 band position and the D3 band intensity includes: Constructing a two-dimensional feature space of D4 band position-D3 band intensity, and mapping the data points of the original spline group and the preprocessed spline group into the space; Performing adaptive fuzzy clustering analysis on the data points in the two-dimensional feature space to obtain dynamic feature clusters; Calculate the centroid coordinates, discreteness and topological features of each dynamic feature cluster to generate a multi-dimensional cluster feature vector; Using deep neural networks and transfer learning methods, a nonlinear mapping relationship from multidimensional cluster feature vectors to conductivity is established to obtain the estimated conductivity of the original spline group and the preprocessed spline group. Wavelet packet transform and multi-scale entropy analysis methods are used to extract the time-frequency domain features of multi-dimensional cluster feature vectors. Based on the time-frequency domain characteristics and fractal network theory, a specific surface area estimation equation is constructed to calculate the estimated specific surface areas of the original spline group and the preprocessed spline group.

[0009] Optionally, the step of performing differentiated electrostatic charge injection on the original spline group and the pre-processed spline group respectively according to the estimated conductivity and specific surface area to form an initial charge distribution that is compatible with the estimated performance in the axial, radial and tangential directions includes: Based on the estimated conductivity and specific surface area, the charge accommodation coefficients of the original spline group and the preprocessed spline group in the axial, radial and tangential directions are calculated; designing a pulse modulated electrostatic charge injection sequence according to the charge accommodation coefficient; Using the electrostatic charge injection sequence, differential electrostatic charge injection is performed on the original spline group and the pre-processed spline group; The electric field intensity gradient method is used to measure the charge distribution on the spline surface after electrostatic charge injection; Performing Fourier transformation on the measured charge distribution to obtain a spatial spectrum of the charge distribution; Based on the spatial spectrum, the charge distribution uniformity in the axial, radial and tangential directions is calculated to determine the initial charge distribution characteristics.

[0010] Optionally, under dynamic conditions simulating actual applications, the charge dissipation processes of the original spline group and the pre-processed spline group are monitored respectively to obtain axial, radial and tangential dynamic charge distribution curves, including: Applying periodic mechanical stress and temperature and humidity cycles to the original spline group and the pre-treated spline group to simulate dynamic conditions of actual applications; Using non-contact electric field scanning technology, the spline surface is scanned at high speed to obtain real-time data on charge distribution; Perform wavelet denoising and singular value decomposition on the acquired real-time data to extract the main features of charge distribution; Based on the main features, an adaptive time series analysis method is used to calculate the charge dissipation rates in the axial, radial and tangential directions; The nonlinear least square method is used to fit the charge dissipation rate curve and obtain the dynamic charge distribution function in each direction. The dynamic charge distribution function is subjected to a time-space joint analysis to generate axial, radial and tangential dynamic charge distribution curves.

[0011] Optionally, the calculating of the comprehensive anisotropic conductive index of the resin wire tube based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group includes: Performing wavelet multi-resolution analysis on the dynamic charge distribution curve to extract characteristic scale coefficients in the axial, radial and tangential directions; The initial Raman spectrum parameters are subjected to nonlinear dimension reduction processing to obtain a compressed feature vector; Performing differential entropy analysis on the comparison data of the original spline group and the preprocessed spline group to obtain heterogeneity indicators between the spline groups; Constructing a three-dimensional tensor space based on the characteristic scale coefficient, the compressed eigenvector and the heterogeneity index; Performing high-order singular value decomposition on the three-dimensional tensor space to extract core tensors and factor matrices; The comprehensive anisotropic conductive index of the resin conduit is calculated by a tensor network algorithm using the elements of the core tensor and the factor matrix.

[0012] A second aspect of the present invention provides a detection device for an antistatic high-performance resin wire tube, the detection device for an antistatic high-performance resin wire tube comprising: The sampling module is used to sample the resin line tube in the axial direction, radial direction and tangential direction respectively to obtain an initial spline group; A spline processing module, used for dividing the initial spline group into two equal parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group; A Raman analysis module, used to perform multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-processed spline group, respectively, to determine the D4 band position and D3 band intensity that characterize the polyolefin structure; a performance estimation module, for calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group respectively based on the D4 band position and the D3 band intensity; An electrostatic charge injection module, for performing differentiated electrostatic charge injection on the original spline group and the pre-processed spline group respectively according to the estimated conductivity and specific surface area, so as to form an initial charge distribution in the axial, radial and tangential directions that is compatible with the estimated performance; A charge monitoring module, used to monitor the charge dissipation process of the original spline group and the pre-processed spline group respectively under dynamic conditions simulating actual applications, and obtain dynamic charge distribution curves in axial, radial and tangential directions; The index calculation module is used to calculate the comprehensive anisotropic conductive index of the resin wire tube based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group.

[0013] The third aspect of the present invention provides an anti-static high-performance resin wire tube detection device, comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected through lines; the at least one processor calls the instructions in the memory to enable the anti-static high-performance resin wire tube detection device to perform the steps of the above-mentioned anti-static high-performance resin wire tube detection method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned method for detecting antistatic high-performance resin wire tubes.

[0015] The technical solution provided by the embodiments of the present application has at least the following advantages: This scheme obtains the initial spline group by sampling the resin wire tube along the axial, radial and tangential directions respectively, and divides it into the original spline group and the pre-treated spline group. This multi-directional sampling method fully considers the anisotropic characteristics of the material. Under the high-speed mixing process, the polymer chain and the conductive filler will produce an oriented arrangement, resulting in differences in the conductive properties in different directions. Through multi-directional sampling, this anisotropy can be fully captured, laying the foundation for subsequent analysis. Secondly, this scheme uses multi-wavelength Raman spectroscopy analysis technology to determine the D4 band position and D3 band intensity that characterize the polyolefin structure. Raman spectroscopy can provide information on the microstructure of the material, especially the D4 band and D3 band are closely related to the polyolefin structure. By analyzing these characteristics, we can gain a deep understanding of the distribution and interaction of the conductive filler in the resin matrix, which is crucial to understanding the conductive mechanism of the material. Thirdly, this scheme calculates the estimated conductivity and specific surface area based on the D4 band position and D3 band intensity. This step establishes the connection between the microstructural characteristics of the material and the macroscopic conductive properties. The estimated conductivity directly reflects the conductivity of the material, while the specific surface area is closely related to the electrostatic dissipation efficiency. In this way, the conductive properties of the material can be quickly evaluated without destructive testing. Next, the scheme performs differential electrostatic charge injection on the spline group according to the estimated conductivity and specific surface area. This step simulates the static accumulation process under actual use conditions. By forming an initial charge distribution that is compatible with the estimated performance in different directions, the electrostatic dissipation ability of the material in all directions can be more accurately evaluated. In addition, the scheme monitors the charge dissipation process under dynamic conditions simulating actual applications to obtain a dynamic charge distribution curve. This step considers not only the static conductive performance but also the dynamic response of the material in the actual use environment. By analyzing the dynamic charge distribution curve, the electrostatic dissipation rate and efficiency of the material in different directions can be evaluated. Finally, the scheme calculates the comprehensive anisotropic conductivity index based on the dynamic charge distribution curve, the initial Raman spectroscopy parameters, and the comparison data between the original spline group and the pre-processed spline group. This index combines the microstructural characteristics, static conductive properties and dynamic response of the material, and comprehensively reflects the anisotropic conductive properties of the material.

[0016] Through the above steps, this solution realizes the effective detection and evaluation of the conductivity of anti-static high-performance resin conduits in different directions. It not only overcomes the limitation of traditional methods that only focus on surface resistivity, but also considers the microstructure, static properties and dynamic response of the material. This comprehensive evaluation method can effectively identify the anisotropy of the conductive properties of the material, thereby preventing the risk of local static electricity accumulation in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of an embodiment of a method for detecting an antistatic high-performance resin wire tube in an embodiment of the present invention; Figure 2 It is a schematic diagram of an embodiment of a detection device for an antistatic high-performance resin wire tube in an embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of a detection device for antistatic high-performance resin wire tube in an embodiment of the present invention.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0022] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0023] An embodiment of the present application provides a method for detecting an anti-static high-performance resin conduit. Figure 1 A flow chart of a method for detecting an antistatic high-performance resin wire tube provided in an embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , the resin wire tube is sampled in the axial, radial and tangential directions respectively to obtain an initial spline group; The initial spline group is equally divided into two parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group; In one embodiment of the present invention, the initial spline group is divided into two parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group, including: performing a three-dimensional conductivity scan on the initial spline group to obtain a conductivity distribution map; according to the conductivity distribution map, using a cross-layering algorithm to divide the initial spline group into two groups A and B, ensuring that the conductivity distribution characteristic deviations of the two groups A and B in the axial, radial and tangential directions do not exceed a preset threshold; retaining the splines of group A as the original spline group; performing gradient temperature treatment on the splines of group B to form a microstructure gradient in different temperature ranges; placing the splines of group B that have undergone gradient temperature treatment in a controllable humidity environment for humidity cycle treatment to regulate the surface hydrophilicity of the splines; and performing surface plasma activation on the treated splines of group B to obtain a preprocessed spline group.

[0024] Specifically, when achieving comprehensive testing of anti-static high-performance resin wire tubes, it is first necessary to sample the resin wire tubes in multiple directions. This step is completed by cutting sample strips in the axial, radial and tangential directions. For example, for a resin wire tube with a length of 1 meter and a diameter of 10 cm, a 5-cm-long sample strip can be cut every 20 cm along the length direction (axial direction), a sample strip can be taken every 1 cm from the outside to the inside along the radial direction (radial direction), and a sample strip can be taken every 45 degrees along the circumferential direction (tangential direction). This multi-directional sampling method ensures that the initial set of sample strips obtained fully represents the characteristics of the resin wire tube in all directions.

[0025] After obtaining the initial set of splines, the next step is to perform a three-dimensional conductivity scan to obtain a conductivity distribution map. This step can be done using a four-probe method or a non-contact conductivity measurement instrument to scan multiple points on each spline. For example, for a 5 cm long spline, a measurement can be made every 0.5 cm to form a 10 10 10. This high-resolution scan can accurately reflect the distribution and anisotropic characteristics of the conductive filler in the resin conduit.

[0026] According to the obtained conductivity distribution map, the initial spline group is divided into two groups, A and B, using a cross-stratification algorithm. The purpose of this step is to ensure that the deviation of the conductivity distribution characteristics of groups A and B in the axial, radial and tangential directions does not exceed the preset threshold, thereby ensuring the comparability and reliability of subsequent experiments. In specific operations, the average conductivity and standard deviation in each direction can be calculated first, and then the splines can be allocated to groups A and B in an iterative manner until the difference in the conductivity distribution characteristics of the two groups in each direction is less than the preset threshold, such as 5%. This precise grouping method can minimize experimental errors and improve the accuracy of test results.

[0027] After retaining the A group of splines as the original spline group, a series of pretreatments are performed on the B group of splines. The first is the gradient temperature treatment, the purpose of which is to form a microstructure gradient in different temperature intervals. For example, the B group of splines can be placed in a gradient heating furnace with a temperature range of 30°C to 150°C, with a temperature interval set every 10°C and each interval maintained for 30 minutes. This treatment method can simulate the performance changes of resin wire tubes under different temperature environments, which helps to evaluate the thermal stability of the material and the temperature dependence of the conductive properties.

[0028] Next, the B group of splines that have been treated with gradient temperature are placed in a controlled humidity environment for humidity cycling treatment to adjust the surface hydrophilicity of the splines. The specific operation can be to place the splines in an environmental chamber with adjustable humidity and cycle in the range of 30% to 90% relative humidity, with each humidity level maintained for 2 hours. The significance of this step is to evaluate the changes in the conductive properties of the resin conduit under different humidity conditions, especially for application scenarios exposed to humid environments.

[0029] Finally, the treated group B splines are subjected to surface plasma activation to obtain a pre-treated spline group. Plasma activation can be accomplished by low-temperature plasma treatment equipment. This step can change the chemical composition and physical structure of the spline surface, enhance its interaction with the environment, and thus improve the electrostatic dissipation efficiency.

[0030] Through the above series of carefully designed sampling and pretreatment steps, the conductivity and static dissipation capabilities of antistatic high-performance resin conduits in different directions and under different environmental conditions can be comprehensively evaluated. This method not only takes into account the anisotropy of the material, but also simulates various environmental factors that may be encountered in actual applications, providing a comprehensive and reliable sample for subsequent performance analysis. This comprehensive method is significantly superior to the traditional method of measuring only surface resistivity, and can more accurately predict the performance of the material in actual applications, effectively prevent the risk of local static electricity accumulation, and ensure that the resin conduit can maintain excellent antistatic properties in various complex environments.

[0031] Please continue reading Figure 1, performing multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-treated spline group respectively to determine the D4 band position and D3 band intensity that characterize the polyolefin structure; In one embodiment of the present invention, the original spline group and the pre-processed spline group are subjected to multi-wavelength Raman spectroscopy analysis respectively to determine the D4 band position and D3 band intensity characterizing the polyolefin structure, including: using 532nm and 780nm dual-wavelength laser sources to perform Raman spectroscopy scanning on the original spline group and the pre-processed spline group respectively to obtain original spectral data; performing adaptive baseline correction and wavelet denoising on the original spectral data to obtain an optimized Raman spectrum; using Hilbert-Huang transform to perform time-frequency analysis on the optimized Raman spectrum to obtain an instantaneous frequency distribution diagram; in the instantaneous frequency distribution diagram, locating the characteristic frequencies of the D4 band and the D3 band by a peak tracking algorithm; according to the characteristic frequencies, performing local curve fitting on the optimized Raman spectrum to extract the D4 band center position and the D3 band integral area; defining the D4 band center position as a D4 band position parameter, and defining the D3 band integral area as a D3 band intensity parameter.

[0032] Specifically, Raman spectroscopy plays a key role in the in-depth analysis of antistatic high-performance resin wire tubes. First, the original spline group and the pre-treated spline group are scanned by Raman spectroscopy using a dual-wavelength laser source of 532nm and 780nm to obtain the original spectral data. The advantage of this dual-wavelength scanning method is that more comprehensive material information can be obtained. For example, 532nm laser (green light) is suitable for detecting surface information, while 780nm laser (near-infrared light) can penetrate deep into the sample and provide more information about the internal structure. In actual operation, a Raman spectrometer equipped with a dual-wavelength laser source can be used to fix the spline on the sample stage. At least 5 different positions of each spline are selected for scanning. The scanning time for each position is 60 seconds, and the scanning range is 100-3200. This approach ensures high-quality, representative Raman spectral data.

[0033] After obtaining the raw spectral data, adaptive baseline correction and wavelet denoising are required to obtain the optimized Raman spectrum. Adaptive baseline correction is to eliminate the influence of background fluorescence and improve the accuracy of the spectral peak. This step can be implemented using polynomial fitting or rolling ball algorithm. For example, using the 5th order polynomial fitting method, the fitting parameters are iteratively optimized until the deviation between the baseline and the actual spectrum is less than the preset threshold (such as 0.1%). Wavelet denoising is to reduce random noise in the spectrum and improve the signal-to-noise ratio. The soft threshold denoising method in wavelet transform can be used to select the appropriate wavelet basis function (such as Daubechies wavelet) and the number of decomposition layers (usually 3-5 layers) to perform multi-scale decomposition and reconstruction of the spectrum. These processing steps can significantly improve the accuracy of subsequent analysis.

[0034] Next, the optimized Raman spectrum is subjected to time-frequency analysis using the Hilbert-Huang transform to obtain the instantaneous frequency distribution diagram. The Hilbert-Huang transform is an adaptive signal processing method that is suitable for processing nonlinear and non-stationary signals. In actual operation, the spectral signal is first subjected to empirical mode decomposition to decompose it into several eigenmode functions. Each eigenmode function is then subjected to a Hilbert transform to calculate the instantaneous frequency. Finally, the instantaneous frequency information of all eigenmode functions is combined to obtain a complete instantaneous frequency distribution diagram. This method can accurately capture the local features in the Raman spectrum and provide an important basis for the subsequent determination of the band position.

[0035] In the instantaneous frequency distribution diagram obtained, the characteristic frequencies of the D4 and D3 bands are located by the peak tracking algorithm. The peak tracking algorithm can use the local maximum search method to locate the characteristic frequencies of the D4 and D3 bands within a specific frequency range (about 1200 , D3 with about 1500 ) Search for local maximum points. To improve accuracy, you can set threshold conditions for peak recognition, such as the peak intensity must be greater than 1.5 times the average intensity and the peak width must be between 20-50 This method can accurately identify the positions of the D4 and D3 bands and works effectively even in complex spectral backgrounds.

[0036] According to the determined characteristic frequency, the optimized Raman spectrum is locally curve fitted to extract the center position of the D4 band and the integral area of ​​the D3 band. The curve fitting can use a Gaussian-Lorentzian mixed function, which can well describe the peak shape of the Raman spectrum. The fitting range can be selected around the characteristic frequency ±50 During the fitting process, the least squares method can be used to optimize the fitting parameters until the correlation coefficient between the fitting curve and the actual data is greater than 0.99. Through this precise fitting, the center position of the D4 band can be accurately determined and the integral area of ​​the D3 band can be calculated.

[0037] Finally, the center position of the D4 band is defined as the D4 band position parameter, and the integrated area of ​​the D3 band is defined as the D3 band intensity parameter. These two parameters are key indicators for characterizing the structure of polyolefins. The D4 band position reflects the carbon atoms in the material. / The ratio of D2 is 0.04, while the intensity of D3 band is related to the content of amorphous carbon. Through this definition, complex spectral information can be converted into numerical parameters with clear physical meaning, providing a reliable data basis for subsequent performance evaluation and analysis.

[0038] This series of Raman spectroscopy analysis steps, from data collection to parameter extraction, has been carefully designed and optimized. Through dual-wavelength scanning, signal optimization, time-frequency analysis and precise fitting, we can deeply understand the characteristics of antistatic high-performance resin wire tubes from the microstructural level. This method can not only accurately reflect the structural information of the material, but also reveal the distribution and interaction of conductive fillers in the resin matrix, providing a strong scientific basis for evaluating the antistatic performance and anisotropy of the material.

[0039] Please continue reading Figure 1 , based on the D4 band position and the D3 band intensity, respectively calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group; In one embodiment of the present invention, the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group are calculated based on the D4 band position and the D3 band intensity, respectively, including: constructing a two-dimensional feature space of D4 band position-D3 band intensity, and mapping the data points of the original spline group and the pre-processed spline group to the space; performing adaptive fuzzy clustering analysis on the data points in the two-dimensional feature space to obtain dynamic feature clusters; calculating the centroid coordinates, discreteness and topological features of each dynamic feature cluster to generate a multidimensional cluster feature vector; using a deep neural network and a transfer learning method to establish a nonlinear mapping relationship from the multidimensional cluster feature vector to the conductivity, and obtain the estimated conductivity of the original spline group and the pre-processed spline group; using a wavelet packet transform and a multi-scale entropy analysis method to extract the time-frequency domain features of the multidimensional cluster feature vector; based on the time-frequency domain features and fractal network theory, constructing a specific surface area estimation equation to calculate the estimated specific surface area of ​​the original spline group and the pre-processed spline group.

[0040] Specifically, in the performance evaluation process of antistatic high-performance resin wire tube, the D4 band position-D3 band strength two-dimensional feature space is first constructed. This space takes the D4 band position as the horizontal coordinate and usually ranges from 1150-1250 The vertical coordinate is the D3 band intensity, ranging from 0 to 100 relative units. For example, a 200×200 grid can be created to represent this two-dimensional space, with each grid cell representing 2.5 The data points of the original spline group and the preprocessed spline group are mapped into this space, and each spline forms a unique point in the space. This mapping method can intuitively display the distribution of the structural characteristics of the material and help identify the microstructural differences and change trends of the material.

[0041] Adaptive fuzzy clustering analysis is performed on the data points in the two-dimensional feature space to obtain dynamic feature clusters. The fuzzy C-means algorithm can be used here, and the initial number of clusters can be set to 3-7. Then the number of clusters and the membership function are automatically adjusted through an iterative process. For example, the maximum number of iterations can be set to 200 and the convergence threshold can be set to 0.0001. In each iteration, the membership of each data point to each cluster is calculated, and the cluster center is updated. This method can automatically form the optimal clustering structure according to the actual data distribution, effectively capture the subtle differences in material properties, and is particularly advantageous when dealing with materials with complex structures or continuous performance changes.

[0042] The centroid coordinates, dispersion and topological features of each dynamic feature cluster are calculated to generate a multidimensional cluster feature vector. The centroid coordinates can be obtained by calculating the weighted average position of all points in the cluster, and the weight can be set as the membership of each point. The dispersion can be expressed as the standard deviation, reflecting the degree of dispersion of points in the cluster. For topological features, the shape factors of the cluster can be calculated, such as roundness (defined as 4π×area / ), aspect ratio (ratio of maximum Feret diameter to minimum Feret diameter), etc. The Hausdorff distance between clusters can also be calculated to reflect the degree of separation between clusters. These features together form a multidimensional vector, usually 15-25 dimensions, which fully describes the properties of each feature cluster.

[0043] Using deep neural networks and transfer learning methods, a nonlinear mapping relationship from multidimensional cluster feature vectors to conductivity is established. A six-layer fully connected neural network can be designed, with the number of input layer nodes equal to the feature vector dimension, 128, 64, 32, and 16 nodes in the hidden layers, and 1 node (conductivity) in the output layer. The activation function can choose the modified linear unit function, the optimizer can use the adaptive moment estimation algorithm, the learning rate is initially set to 0.001, and a learning rate decay strategy is adopted. First, the network is pre-trained using a known material database (containing at least 1,000 samples), and then the network parameters are fine-tuned through transfer learning methods to adapt to the current resin line pipe samples. During the fine-tuning process, the network parameters of the first few layers can be frozen, and only the subsequent layers can be trained. This can effectively utilize the feature extraction capabilities of the pre-trained model while adapting to the characteristics of the new data.

[0044] Wavelet packet transform and multiscale entropy analysis methods are used to extract the time-frequency domain features of multidimensional cluster feature vectors. Wavelet packet transform can use symmetric orthogonal wavelets, and the decomposition level is set to 6. For each decomposition coefficient, its sample entropy, fuzzy entropy and permutation entropy are calculated to form a multiscale entropy feature vector. Sample entropy reflects the complexity of the time series, fuzzy entropy takes into account the fuzziness of the data, and permutation entropy describes the dynamic behavior of the time series. This method can capture the complexity and uncertainty of feature vectors at different scales and provide richer information.

[0045] Based on the time-frequency domain characteristics and fractal network theory, the specific surface area estimation equation is constructed. First, the concept of generalized fractal dimension is used to map the time-frequency domain characteristics to the fractal dimension space. The capacity dimension, correlation dimension and information dimension can be calculated to construct a three-dimensional fractal feature space. Then, a kernel regression method, such as Gaussian process regression, is used to establish a mapping relationship from fractal characteristics to specific surface area. The kernel function can select the radial basis function, and the length scale parameter can be determined by maximum likelihood estimation. This method takes into account the multi-scale structural characteristics of the material and can more accurately estimate the specific surface area, especially for materials with complex pore structures.

[0046] Through this series of precise analysis steps, rich structural information is extracted from Raman spectroscopy data and converted into quantifiable performance parameters. This method not only takes into account the microstructural characteristics of the material, but also establishes a structure-performance relationship through advanced data analysis technology, providing a scientific and reliable basis for accurately evaluating the conductive properties and specific surface area of ​​antistatic high-performance resin conduits. This comprehensive analysis method is significantly superior to the traditional single parameter evaluation method, and can more comprehensively and accurately predict the performance of materials in practical applications, providing strong support for material optimization design and quality control.

[0047] Please continue reading Figure 1 , according to the estimated conductivity and specific surface area, differential electrostatic charge injection is performed on the original spline group and the pre-treated spline group respectively, so as to form an initial charge distribution adapted to the estimated performance in the axial, radial and tangential directions; In one embodiment of the present invention, the method of performing differential electrostatic charge injection on the original spline group and the pre-processed spline group respectively according to the estimated conductivity and specific surface area to form an initial charge distribution that is compatible with the estimated performance in the axial, radial and tangential directions comprises: calculating the charge accommodation coefficients of the original spline group and the pre-processed spline group in the axial, radial and tangential directions based on the estimated conductivity and specific surface area; designing a pulse-modulated electrostatic charge injection sequence according to the charge accommodation coefficient; performing differential electrostatic charge injection on the original spline group and the pre-processed spline group using the electrostatic charge injection sequence; measuring the charge distribution on the spline surface after electrostatic charge injection using the electric field intensity gradient method; performing Fourier transform on the measured charge distribution to obtain the spatial spectrum of the charge distribution; and calculating the charge distribution uniformity in the axial, radial and tangential directions based on the spatial spectrum to determine the initial charge distribution characteristics.

[0048] Specifically, in the performance evaluation process of antistatic high-performance resin wire tubes, calculating the charge accommodation coefficient based on the estimated conductivity and specific surface area is a key step. For the original spline group and the pre-processed spline group, the charge accommodation coefficient is calculated in the axial, radial and tangential directions respectively. The charge accommodation coefficient reflects the ability of the material to store charge in different directions. The modified Gaussian law can be used in the calculation, taking into account the geometry of the material and the anisotropy of the conductivity. For example, for a cylindrical spline with a length of 10 cm and a diameter of 1 cm, the axial charge accommodation coefficient can be calculated using the following formula: =2πεL / ln(b / a), where ε is the dielectric constant of the material, L is the spline length, and b and a are the outer and inner diameters, respectively. The calculation of radial and tangential directions requires consideration of the electric field distribution in the cylindrical coordinate system. This method can fully reflect the charge storage characteristics of the material in different directions.

[0049] Based on the calculated charge accommodation factor, a pulse modulated electrostatic charge injection sequence is designed. This step is intended to simulate the static charge accumulation process under actual use conditions. A multi-stage injection sequence can be designed, and the injection amount in each stage is proportional to the charge accommodation factor in that direction. For example, a three-stage injection sequence can be designed, each stage lasting 10 seconds, with a pulse frequency of 100Hz and a duty cycle of 50%. The first stage injects axial charges, the second stage injects radial charges, and the third stage injects tangential charges. The injection voltage can be set according to the breakdown strength of the material, usually in the range of 1-5kV. This pulse modulated injection method can better simulate the static charge accumulation process in actual use and help evaluate the performance of materials under dynamic conditions.

[0050] Using the designed electrostatic charge injection sequence, differential electrostatic charge injection is performed on the original spline group and the pre-treated spline group. The injection process can use a high-precision electrostatic generator, such as a Van de Graaff generator or a Wilmshurst generator. During injection, the spline is fixed on an insulating bracket to ensure that the spline is insulated from the surrounding environment. The injection electrode can be a needle tip electrode or a flat electrode, which can be adjusted according to the injection requirements in different directions. During the injection process, the potential on the surface of the spline needs to be monitored in real time to ensure that the amount of injected charge is as expected. This differentiated injection method can simulate various static electricity accumulation situations that the material may encounter in actual use, providing a basis for a comprehensive evaluation of material performance.

[0051] After the injection is completed, the charge distribution on the spline surface is measured using the electric field intensity gradient method. An electric field scanner such as an electrostatic voltmeter or a charge decay analyzer can be used. During the measurement, the probe is moved along the spline surface at a fixed speed (such as 1mm / s) to record the changes in the electric field intensity. In order to obtain a high-resolution distribution map, multiple scans can be performed in the axial, radial and tangential directions, and the interval between each scan can be set to 0.5mm. This method can accurately capture subtle changes in the charge distribution on the spline surface and provide detailed spatially resolved data for subsequent analysis.

[0052] The measured charge distribution data is Fourier transformed to obtain the spatial spectrum of the charge distribution. This step aims to analyze the characteristics of the charge distribution from the frequency domain perspective. The fast Fourier transform (FFT) algorithm can be used to process the charge distribution data in each direction separately. For example, for a spline with a length of 10 cm, if the spatial resolution is 0.5 mm, there are 200 data points and 100 effective frequency components can be obtained. By analyzing the amplitude and phase information of these frequency components, the periodic characteristics and spatial scale dependence of the charge distribution can be revealed.

[0053] Based on the obtained spatial spectrum, the uniformity of the charge distribution in the axial, radial and tangential directions is calculated to determine the characteristics of the initial charge distribution. The uniformity of the charge distribution can be characterized by the energy distribution of the spectrum. For example, the proportion of the energy of the low-frequency component (the first 10% of the frequency components) to the total energy can be calculated. The higher this proportion, the more uniform the charge distribution. In addition, the entropy value of the spectrum can be calculated. The larger the entropy value, the more uniform the charge distribution. For each direction, a uniformity index can be obtained. Finally, the uniformity index and spectrum characteristics in the three directions are comprehensively considered to form a multidimensional vector to describe the initial charge distribution characteristics. This method can not only quantify the uniformity of the charge distribution, but also reflect the spatial characteristics of the distribution, providing an important basis for subsequent performance evaluation.

[0054] Through this series of precise injection and measurement steps, the electrostatic properties of antistatic high-performance resin conduits in different directions can be comprehensively evaluated. This method not only takes into account the anisotropy of the material, but also simulates the static accumulation and distribution process under actual use conditions. Through Fourier analysis and uniformity calculation, the electrostatic properties of the material can be quantitatively described from multiple perspectives, providing a scientific basis for optimizing material design and performance prediction. This comprehensive evaluation method is significantly superior to traditional single-direction or single-parameter testing methods, and can more accurately predict the antistatic performance of materials in complex environments.

[0055] Please continue reading Figure 1 , under dynamic conditions simulating actual applications, respectively monitoring the charge dissipation processes of the original spline group and the pre-processed spline group to obtain axial, radial and tangential dynamic charge distribution curves; In one embodiment of the present invention, the charge dissipation processes of the original spline group and the pre-processed spline group are monitored separately under dynamic conditions simulating actual applications to obtain axial, radial and tangential dynamic charge distribution curves, including: applying periodic mechanical stress and temperature and humidity cycles to the original spline group and the pre-processed spline group to simulate the dynamic conditions of actual applications; using non-contact electric field scanning technology to perform high-speed scanning on the spline surface to obtain real-time data of charge distribution; performing wavelet denoising and singular value decomposition on the acquired real-time data to extract the main features of the charge distribution; based on the main features, using an adaptive time series analysis method to calculate the axial, radial and tangential charge dissipation rates; using a nonlinear least squares method to curve fit the charge dissipation rate to obtain a dynamic charge distribution function in each direction; performing a time-space joint analysis on the dynamic charge distribution function to generate axial, radial and tangential dynamic charge distribution curves.

[0056] Specifically, in the performance evaluation process of antistatic high-performance resin wire tubes, applying periodic mechanical stress and temperature and humidity cycles to the original spline group and the pre-treated spline group is a key step in simulating the dynamic conditions of actual applications. A composite cycle test scheme can be designed. For example, the mechanical stress cycle can be loaded with alternating tension-compression, with a stress range of 0-50MPa and a frequency of 1Hz; the temperature cycle range can be set to -20℃ to 60℃, with a heating and cooling rate of 2℃ / minute; the humidity cycle range can be set to 20% to 90% relative humidity, with a change rate of 5% / minute. These three cycles can be carried out simultaneously, and the total test time is set to 24 hours to fully simulate the various complex environmental conditions that the material may encounter in actual use. This comprehensive testing method can comprehensively evaluate the antistatic performance and durability of the material under dynamic conditions.

[0057] The surface of the spline is scanned at high speed using non-contact electric field scanning technology to obtain real-time data of charge distribution. An electrostatic voltage probe array can be used, such as setting up a 16×16 probe matrix to cover an area of ​​100mm×100mm on the spline surface. The resolution of each probe can reach 0.1V, and the scanning frequency can reach 1000Hz. The distance between the probe and the spline surface is maintained at 1-2mm to ensure measurement accuracy. The dynamic changes of charge distribution can be captured in real time through a high-speed data acquisition system. This high-resolution, high-frequency scanning method can accurately record the instantaneous changes in the charge distribution of the material under dynamic conditions, providing a rich data basis for subsequent analysis.

[0058] The acquired real-time data is subjected to wavelet denoising and singular value decomposition to extract the main features of the charge distribution. Wavelet denoising can use discrete wavelet transform, select the Dobsey wavelet basis, and set the number of decomposition layers to 5. The wavelet coefficients are processed by the soft threshold method, and the threshold can be set to 3 times the standard deviation of the noise. After denoising, the data matrix is ​​subjected to singular value decomposition, and the features corresponding to the first K largest singular values ​​are retained (K can be selected as the minimum value that can explain 95% of the variance). This method can effectively remove measurement noise while retaining the key features of the charge distribution, greatly improving the accuracy and efficiency of subsequent analysis.

[0059] Based on the extracted main features, an adaptive time series analysis method is used to calculate the charge dissipation rates in the axial, radial and tangential directions. An adaptive autoregressive moving average model can be used, and the model order is automatically selected by the Bayesian information criterion. An autoregressive moving average model is established for the charge distribution time series in each direction, and the model parameters are determined by maximum likelihood estimation. The charge dissipation rate can be characterized by the characteristic roots of the model. The larger the absolute value of the real part of the characteristic root, the faster the dissipation rate. This adaptive method can adapt to the dynamic characteristics of charge dissipation in different directions and different time periods, and provide more accurate dissipation rate estimates.

[0060] Using the nonlinear least squares method, the charge dissipation rate is curve-fitted to obtain the dynamic charge distribution function in each direction. An exponential decay function can be selected as the fitting model, such as: ; Where Q(t) is the charge at time t, is the initial charge, k is the dissipation rate constant, and C is the residual charge. The Levenberg-Marquardt algorithm is used for parameter optimization, and the initial value can be set based on the estimation results of the previous step. During the fitting process, a weight function can be set so that the fitting pays more attention to the rapid changes in the initial stage. This nonlinear fitting method can accurately describe the dynamic process of charge dissipation and provide a reliable mathematical model for subsequent analysis.

[0061] The dynamic charge distribution function is analyzed in time and space to generate dynamic charge distribution curves in the axial, radial and tangential directions. The time and space dimensions can be combined by using the time-space spectrum analysis method. For example, the time-space correlation function can be calculated to reflect the correlation characteristics of the charge distribution in time and space. The Hilbert-Huang transform can also be used to analyze the instantaneous frequency and local energy density of the charge distribution. Through these analyses, a three-dimensional surface diagram of the charge distribution in each direction changing with time and space can be obtained. This joint analysis method can fully display the charge distribution characteristics of the material under dynamic conditions and intuitively reflect the anisotropy and time-varying characteristics of the material.

[0062] Through this series of complex and precise analysis steps, the charge distribution and dissipation characteristics of antistatic high-performance resin conduits under dynamic conditions can be fully evaluated. This method not only takes into account the anisotropy of the material, but also simulates the complex environmental factors under actual use conditions, and can provide more accurate and reliable performance evaluation results. Through high-resolution real-time data acquisition, advanced signal processing technology and precise mathematical models, this method greatly improves the accuracy and depth of antistatic performance evaluation, and provides a strong scientific basis for material optimization design and performance prediction.

[0063] Please continue reading Figure 1 , based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group, the comprehensive anisotropic conductive index of the resin wire tube is calculated.

[0064] In one embodiment of the present invention, the comprehensive anisotropic conductive index of the resin wire tube is calculated based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group, including: performing wavelet multi-resolution analysis on the dynamic charge distribution curve to extract the characteristic scale coefficients of the axial, radial and tangential directions; performing nonlinear dimensionality reduction processing on the initial Raman spectrum parameters to obtain compressed eigenvectors; performing differential entropy analysis on the comparison data between the original spline group and the pre-processed spline group to obtain heterogeneity indicators between the spline groups; constructing a three-dimensional tensor space based on the characteristic scale coefficients, compressed eigenvectors and heterogeneity indicators; performing high-order singular value decomposition on the three-dimensional tensor space to extract core tensors and factor matrices; and calculating the comprehensive anisotropic conductive index of the resin wire tube through a tensor network algorithm using the elements of the core tensor and factor matrix.

[0065] Specifically, in the performance evaluation process of antistatic high-performance resin wire tubes, wavelet multi-resolution analysis of the dynamic charge distribution curve is a key step. This step aims to extract the characteristic scale coefficients in the axial, radial and tangential directions. The continuous wavelet transform method can be used, and the Mexican hat wavelet is selected as the mother wavelet function. For the dynamic charge distribution curve in each direction, wavelet transform is performed at different scales, and the scale range can be set to 1 to 64, increasing in powers of 2. By calculating the energy distribution of the wavelet coefficients, the characteristic scale coefficient in each direction can be obtained. For example, the scale with the top 80% of the energy can be selected as the characteristic scale, which may usually correspond to 3 to 5 different scales. This multi-resolution analysis method can effectively capture the characteristics of charge distribution at different time and space scales, providing important information for subsequent comprehensive analysis.

[0066] The initial Raman spectral parameters are subjected to nonlinear dimensionality reduction to obtain a compressed eigenvector. This step can be performed using the kernel principal component analysis (KPCA) method. First, the radial basis function is selected as the kernel function, and the kernel parameters can be determined by the cross-validation method. Then, the kernel matrix is ​​constructed and the eigenvalue decomposition is performed. The first N principal components are selected so that they can explain more than 95% of the total variance. Through these principal components, the original high-dimensional Raman spectral parameters are mapped to a low-dimensional space to obtain a compressed eigenvector. For example, if the original Raman parameters are 100-dimensional, a 10-20-dimensional compressed eigenvector may be obtained after KPCA processing. This nonlinear dimensionality reduction method can effectively retain the key characteristic information of the Raman spectrum, while greatly reducing the dimension of the data for subsequent analysis.

[0067] The differential entropy analysis is performed on the comparative data of the original spline group and the pre-processed spline group to obtain the heterogeneity index between the spline groups. The differential entropy analysis can adopt a multi-scale method. First, the difference sequence of the original spline group and the pre-processed spline group on each test parameter is calculated. Then, these difference sequences are multi-scale decomposed, and the empirical mode decomposition (EMD) method can be used, usually decomposed into 5-7 intrinsic mode functions (IMFs). For each IMF, its sample entropy is calculated to obtain a multi-scale entropy value sequence. Finally, by weighted averaging these entropy values ​​(the weight can be set to the energy proportion of each IMF), a comprehensive heterogeneity index is obtained. This index reflects the overall degree of difference between the original spline group and the pre-processed spline group. The larger the value, the more significant the performance difference between the two groups of splines.

[0068] Based on the characteristic scale coefficient, compressed eigenvector and heterogeneity index, a three-dimensional tensor space is constructed. This step integrates information from different sources into a unified mathematical structure. The characteristic scale coefficient can be composed into a third-order tensor of I×J×K, where I, J, and K represent the number of characteristic scales in the axial, radial, and tangential directions, respectively. The compressed eigenvector can be composed into an L×M matrix, where L is the number of splines and M is the characteristic dimension after compression. The heterogeneity index is a vector of length N, where N is the number of comparison parameters. Through the tensor outer product operation, these three parts of information are combined into a high-order tensor with dimensions of I×J×K×L×M×N. This tensor representation method can retain the intrinsic correlation structure between data and lay the foundation for subsequent high-order analysis.

[0069] Perform high-order singular value decomposition on the three-dimensional tensor space to extract the core tensor and factor matrix. Here, the Tucker decomposition method can be used to decompose the high-order tensor into the product of a core tensor and multiple factor matrices. The order of decomposition can be determined by the cross-validation method, usually 2 to 4 orders. For example, for a 6-order tensor, you may get a 2×2×2×2×2×2×2 core tensor and 6 factor matrices. The core tensor reflects the main structure and interaction of the data, while the factor matrix represents the main pattern in each dimension. This decomposition method can effectively capture the key information in high-dimensional data and greatly reduce the complexity of subsequent calculations.

[0070] The comprehensive anisotropic conductivity index of the resin wire tube is calculated by the tensor network algorithm using the elements of the core tensor and the factor matrix. The core tensor and each factor matrix can be contracted by using the tensor series contraction method. Specifically, a contraction sequence can be defined, such as contracting along the characteristic scale dimension first, then the spline dimension, and finally the parameter dimension. After each contraction, the intermediate results obtained can be regarded as a new feature representation. Finally, a three-dimensional result tensor can be obtained, and its three dimensions correspond to the axial, radial and tangential directions respectively. The norm of this result tensor can be defined as the comprehensive anisotropic conductivity index. For example, the Frobenius norm can be calculated, or a more complex weighted norm can be used, and the weights can be determined according to the physical meaning of each dimension. This tensor network-based calculation method can fully consider the interaction of various factors and obtain a comprehensive index that can accurately reflect the anisotropic conductivity of the material.

[0071] Through this series of complex and precise mathematical processing steps, we can comprehensively analyze the performance characteristics of antistatic high-performance resin conduits from multiple angles and scales, and ultimately obtain a comprehensive index that can accurately reflect the anisotropic conductive properties of the material. This method not only takes into account the microstructural characteristics and macroscopic performance of the material, but also captures the complex interactions between various factors through high-order tensor analysis. This comprehensive analysis method is significantly superior to traditional single parameter or simple comprehensive evaluation methods, and can more comprehensively and accurately characterize the performance of materials, providing a strong scientific basis for material optimization design and performance prediction.

[0072] The above describes the detection method of the antistatic high-performance resin wire tube in the embodiment of the present invention. The following describes the detection device of the antistatic high-performance resin wire tube in the embodiment of the present invention. Figure 2 , an embodiment of the detection device of the antistatic high-performance resin wire tube in the embodiment of the present invention includes: The sampling module 201 is used to sample the resin wire tube in the axial direction, radial direction and tangential direction respectively to obtain an initial spline group; A spline processing module 202 is used to divide the initial spline group into two equal parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group; A Raman analysis module 203 is used to perform multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-processed spline group to determine the D4 band position and D3 band intensity that characterize the polyolefin structure; a performance estimation module 204 for calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group respectively based on the D4 band position and the D3 band intensity; The electrostatic charge injection module 205 is used to perform differential electrostatic charge injection on the original spline group and the pre-processed spline group according to the estimated conductivity and specific surface area, so as to form an initial charge distribution in the axial, radial and tangential directions that is compatible with the estimated performance; A charge monitoring module 206, for respectively monitoring the charge dissipation process of the original spline group and the pre-processed spline group under dynamic conditions simulating actual applications, and obtaining axial, radial and tangential dynamic charge distribution curves; The index calculation module 207 is used to calculate the comprehensive anisotropic conductive index of the resin wire tube based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group.

[0073] above Figure 2 The detection device for the anti-static high-performance resin wire tube in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The detection equipment for the anti-static high-performance resin wire tube in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0074] Figure 3It is a structural schematic diagram of an anti-static high-performance resin wire tube detection device provided by an embodiment of the present invention. The anti-static high-performance resin wire tube detection device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the anti-static high-performance resin wire tube detection device 300. Furthermore, the processor 310 can be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the anti-static high-performance resin wire tube detection device 300 to implement the steps of the above-mentioned anti-static high-performance resin wire tube detection method.

[0075] The antistatic high-performance resin wire tube detection device 300 may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as WindowsServe, MacOSX, Unix, Linux, FreeBSD, etc. It can be understood by those skilled in the art that Figure 3 The structure of the anti-static high-performance resin wire tube detection equipment shown does not constitute a limitation on the anti-static high-performance resin wire tube detection equipment provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0076] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the anti-static high-performance resin wire tube detection method.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0078] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially 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. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0079] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for detecting an antistatic high-performance resin wire tube, characterized in that: include: The resin wire tube is sampled in the axial, radial and tangential directions respectively to obtain an initial spline group; The initial spline group is equally divided into two parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group; Performing multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-treated spline group respectively to determine the D4 band position and D3 band intensity that characterize the polyolefin structure; Based on the D4 band position and the D3 band intensity, respectively calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group; According to the estimated conductivity and specific surface area, differential electrostatic charge injection is performed on the original spline group and the pre-processed spline group respectively to form an initial charge distribution that is compatible with the estimated performance in the axial, radial and tangential directions; Under dynamic conditions simulating actual applications, the charge dissipation processes of the original spline group and the pre-processed spline group are monitored respectively to obtain dynamic charge distribution curves in the axial, radial and tangential directions; Based on the dynamic charge distribution curve, initial Raman spectrum parameters and comparison data between the original spline group and the pre-processed spline group, the comprehensive anisotropic conductive index of the resin wire tube is calculated.

2. The method for detecting antistatic high-performance resin wire tube according to claim 1, characterized in that: The initial spline group is divided into two equal parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group, including: Performing a three-dimensional conductivity scan on the initial spline group to obtain a conductivity distribution map; According to the conductivity distribution map, the initial spline group is divided into two groups A and B using a cross-layered algorithm to ensure that the conductivity distribution characteristic deviations of the two groups A and B in the axial, radial and tangential directions do not exceed a preset threshold; Keep group A of splines as the original group of splines; The B group of splines were subjected to gradient temperature treatment to form microstructure gradients in different temperature intervals; The B group of specimens that had been treated with gradient temperature were placed in a controlled humidity environment for humidity cycle treatment to regulate the surface hydrophilicity of the specimens; The treated group B specimens were subjected to surface plasma activation to obtain a pre-treated specimen group.

3. The method for detecting antistatic high-performance resin wire tube according to claim 1, characterized in that: The method of performing multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-processed spline group to determine the D4 band position and D3 band intensity that characterize the polyolefin structure comprises: The original spline group and the pre-processed spline group are respectively subjected to Raman spectrum scanning using a 532nm and 780nm dual-wavelength laser source to obtain original spectrum data; Performing adaptive baseline correction and wavelet denoising on the raw spectral data to obtain an optimized Raman spectrum; Performing time-frequency analysis on the optimized Raman spectrum using Hilbert-Huang transform to obtain an instantaneous frequency distribution diagram; In the instantaneous frequency distribution diagram, the characteristic frequencies of the D4 band and the D3 band are located by a peak tracking algorithm; According to the characteristic frequency, local curve fitting is performed on the optimized Raman spectrum to extract the center position of the D4 band and the integral area of ​​the D3 band; The center position of the D4 band is defined as the D4 band position parameter, and the integrated area of ​​the D3 band is defined as the D3 band intensity parameter.

4. The method for detecting antistatic high-performance resin wire tube according to claim 1, characterized in that: The method of calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group based on the D4 band position and the D3 band intensity respectively comprises: Constructing a two-dimensional feature space of D4 band position-D3 band intensity, and mapping the data points of the original spline group and the preprocessed spline group into the space; Performing adaptive fuzzy clustering analysis on the data points in the two-dimensional feature space to obtain dynamic feature clusters; Calculate the centroid coordinates, discreteness and topological features of each dynamic feature cluster to generate a multi-dimensional cluster feature vector; Using deep neural networks and transfer learning methods, a nonlinear mapping relationship from multidimensional cluster feature vectors to conductivity is established to obtain the estimated conductivity of the original spline group and the preprocessed spline group. Wavelet packet transform and multi-scale entropy analysis methods are used to extract the time-frequency domain features of multi-dimensional cluster feature vectors. Based on the time-frequency domain characteristics and fractal network theory, a specific surface area estimation equation is constructed to calculate the estimated specific surface areas of the original spline group and the preprocessed spline group.

5. The method for detecting antistatic high-performance resin wire tube according to claim 1, characterized in that: The step of injecting differentiated electrostatic charges into the original spline group and the pre-processed spline group respectively according to the estimated conductivity and specific surface area to form an initial charge distribution that is compatible with the estimated performance in the axial, radial and tangential directions includes: Based on the estimated conductivity and specific surface area, the charge accommodation coefficients of the original spline group and the preprocessed spline group in the axial, radial and tangential directions are calculated; designing a pulse modulated electrostatic charge injection sequence according to the charge accommodation coefficient; Using the electrostatic charge injection sequence, differential electrostatic charge injection is performed on the original spline group and the pre-processed spline group; The electric field intensity gradient method is used to measure the charge distribution on the spline surface after electrostatic charge injection; Performing Fourier transformation on the measured charge distribution to obtain the spatial spectrum of the charge distribution; Based on the spatial spectrum, the charge distribution uniformity in the axial, radial and tangential directions is calculated to determine the initial charge distribution characteristics.

6. The method for detecting antistatic high-performance resin wire tube according to claim 1, characterized in that: The method of monitoring the charge dissipation process of the original spline group and the pre-processed spline group respectively under dynamic conditions simulating actual applications to obtain axial, radial and tangential dynamic charge distribution curves includes: Applying periodic mechanical stress and temperature and humidity cycles to the original spline group and the pre-treated spline group to simulate dynamic conditions of actual applications; Using non-contact electric field scanning technology, the spline surface is scanned at high speed to obtain real-time data on charge distribution; Perform wavelet denoising and singular value decomposition on the acquired real-time data to extract the main features of charge distribution; Based on the main features, an adaptive time series analysis method is used to calculate the charge dissipation rates in the axial, radial and tangential directions; The nonlinear least square method is used to fit the charge dissipation rate curve and obtain the dynamic charge distribution function in each direction. The dynamic charge distribution function is subjected to a time-space joint analysis to generate axial, radial and tangential dynamic charge distribution curves.

7. The method for detecting antistatic high-performance resin wire tube according to claim 1, characterized in that: The method of calculating the comprehensive anisotropic conductive index of the resin wire tube based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group includes: Performing wavelet multi-resolution analysis on the dynamic charge distribution curve to extract characteristic scale coefficients in the axial, radial and tangential directions; The initial Raman spectrum parameters are subjected to nonlinear dimension reduction processing to obtain a compressed feature vector; Performing differential entropy analysis on the comparison data of the original spline group and the preprocessed spline group to obtain heterogeneity indicators between the spline groups; Constructing a three-dimensional tensor space based on the characteristic scale coefficient, the compressed eigenvector and the heterogeneity index; Performing high-order singular value decomposition on the three-dimensional tensor space to extract core tensors and factor matrices; The comprehensive anisotropic conductive index of the resin conduit is calculated by a tensor network algorithm using the elements of the core tensor and the factor matrix.

8. A detection device for antistatic high-performance resin wire tube, characterized in that: The antistatic high-performance resin wire tube detection device is applied to the antistatic high-performance resin wire tube detection method according to any one of claims 1 to 7, comprising: The sampling module is used to sample the resin line tube in the axial direction, radial direction and tangential direction respectively to obtain an initial spline group; A spline processing module, used for dividing the initial spline group into two equal parts, one part is retained as the original spline group, and the other part is preprocessed to obtain a preprocessed spline group; A Raman analysis module, used to perform multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-processed spline group, respectively, to determine the D4 band position and D3 band intensity that characterize the polyolefin structure; a performance estimation module, for calculating the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group respectively based on the D4 band position and the D3 band intensity; An electrostatic charge injection module, for performing differentiated electrostatic charge injection on the original spline group and the pre-processed spline group respectively according to the estimated conductivity and specific surface area, so as to form an initial charge distribution in the axial, radial and tangential directions that is compatible with the estimated performance; A charge monitoring module, used to monitor the charge dissipation process of the original spline group and the pre-processed spline group respectively under dynamic conditions simulating actual applications, and obtain dynamic charge distribution curves in axial, radial and tangential directions; The index calculation module is used to calculate the comprehensive anisotropic conductive index of the resin wire tube based on the dynamic charge distribution curve, the initial Raman spectrum parameters and the comparison data between the original spline group and the pre-processed spline group. 9.An antistatic high-performance resin wire tube detection device, characterized in that: The antistatic high-performance resin wire tube detection device comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the anti-static high-performance resin wire tube detection device to perform the steps of the anti-static high-performance resin wire tube detection method as described in any one of claims 1-7.

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