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

Abstract

The present invention discloses a method, device, and equipment for detecting antistatic, high-performance resin conduits. The method includes: multi-directional sampling of the conduits to obtain sets of raw and preprocessed splines; multi-wavelength Raman spectroscopy analysis; calculation of estimated conductivity and specific surface area; differential electrostatic charge injection; monitoring the charge dissipation process; and calculation of a comprehensive anisotropic conductivity index. The technical solution of the present invention can effectively detect and evaluate the conductivity of antistatic, high-performance resin conduits in different directions, ensuring comprehensive and balanced static dissipation in practical applications.

Description

Technical Field

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

[0002] Antistatic high-performance resin conduit is a specialty conduit made by adding conductive fillers, such as carbon black or carbon nanotubes, to a polymer material to achieve static dissipation. This conduit creates a conductive path within the resin matrix, effectively dissipating static charges and preventing static buildup. When the conductive filler concentration reaches the percolation threshold, a continuous conductive network forms between the filler particles, imbuing the material with conductive properties. This conduit is widely used in electrostatically sensitive applications such as electronics, chemicals, and medical applications, playing a crucial role in protecting sensitive equipment and improving production safety.

[0003] However, there are some problems with the existing production and testing technologies for anti-static high-performance resin conduits. During the production process, in order to evenly disperse the conductive filler, a high-speed mixing process is often used, which results in an oriented arrangement of the polymer chains 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 the material, and fail to fully consider the anisotropy of the conductive properties. The limitations of this detection method may lead to the risk of local static electricity accumulation during actual use of the product, affecting the effect of static electricity dissipation. Therefore, how to effectively detect and evaluate the conductive properties of anti-static high-performance resin conduits in different directions to ensure that they can comprehensively and evenly dissipate static electricity 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 methods cannot effectively detect and evaluate the conductive properties of anti-static high-performance resin conduits in different directions.

[0005] A first aspect of the present invention provides a method for detecting an antistatic high-performance resin conduit, the method comprising:

[0006] The resin wire tube is sampled along the axial, radial and tangential directions to obtain an initial spline group;

[0007] 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;

[0008] Performing multi-wavelength Raman spectroscopy analysis on the original spline group and the pre-treated spline group to determine the D4 band position and D3 band intensity that characterize the polyolefin structure;

[0009] 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 preprocessed spline group;

[0010] According to the estimated electrical conductivity and specific surface area, differential electrostatic charge injection is performed on the original spline group and the pre-treated spline group, respectively, to form initial charge distributions in the axial, radial and tangential directions that are compatible with the estimated performance;

[0011] 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;

[0012] 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, a comprehensive anisotropic conductive index of the resin conduit is calculated.

[0013] 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:

[0014] Performing a three-dimensional conductivity scan on the initial spline group to obtain a conductivity distribution map;

[0015] 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;

[0016] Keep group A of splines as the original spline group;

[0017] The splines of group B were subjected to gradient temperature treatment to form microstructure gradients in different temperature ranges;

[0018] The B group of specimens that had undergone gradient temperature treatment were placed in a controlled humidity environment for humidity cycle treatment to regulate the surface hydrophilicity of the specimens;

[0019] The treated group B strips were subjected to surface plasma activation to obtain a pre-treated strip group.

[0020] Optionally, 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 characterizing the polyolefin structure includes:

[0021] Performing Raman spectroscopy scanning on the original spline group and the pre-processed spline group using a 532 nm and 780 nm dual-wavelength laser source, respectively, to obtain original spectral data;

[0022] performing adaptive baseline correction and wavelet denoising on the raw spectral data to obtain an optimized Raman spectrum;

[0023] Performing time-frequency analysis on the optimized Raman spectrum using Hilbert-Huang transform to obtain an instantaneous frequency distribution diagram;

[0024] In the instantaneous frequency distribution diagram, the characteristic frequencies of the D4 band and the D3 band are located by a peak tracking algorithm;

[0025] Performing local curve fitting on the optimized Raman spectrum according to the characteristic frequency to extract the center position of the D4 band and the integral area of ​​the D3 band;

[0026] 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.

[0027] Optionally, the calculating the estimated conductivity and specific surface area of ​​the original spline group and the preprocessed spline group based on the D4 band position and the D3 band intensity respectively includes:

[0028] Constructing a D4 band position-D3 band intensity two-dimensional feature space, and mapping the data points of the original spline group and the preprocessed spline group into the space;

[0029] Performing adaptive fuzzy clustering analysis on the data points in the two-dimensional feature space to obtain dynamic feature clusters;

[0030] Calculate the center of gravity coordinates, discreteness and topological characteristics of each dynamic feature cluster to generate a multi-dimensional cluster feature vector;

[0031] Using deep neural networks and transfer learning methods, a nonlinear mapping relationship between multidimensional cluster feature vectors and conductivity is established to obtain the estimated conductivity of the original spline group and the preprocessed spline group.

[0032] Wavelet packet transform and multi-scale entropy analysis are used to extract the time-frequency domain features of multi-dimensional cluster feature vectors.

[0033] 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.

[0034] Optionally, the step of performing differentiated electrostatic charge injection on the original spline group and the pre-processed spline group based on the estimated conductivity and specific surface area to form initial charge distributions in the axial, radial, and tangential directions that are compatible with the estimated performance includes:

[0035] Based on the estimated conductivity and specific surface area, calculating the charge accommodation coefficients of the original spline group and the preprocessed spline group in the axial, radial and tangential directions;

[0036] designing a pulse-modulated electrostatic charge injection sequence according to the charge accommodation coefficient;

[0037] Using the electrostatic charge injection sequence, differential electrostatic charge injection is performed on the original spline group and the preprocessed spline group;

[0038] The electric field intensity gradient method is used to measure the charge distribution on the spline surface after electrostatic charge injection;

[0039] Performing Fourier transform on the measured charge distribution to obtain the spatial spectrum of the charge distribution;

[0040] 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.

[0041] Optionally, the step of monitoring the charge dissipation processes of the original spline group and the pre-processed spline group respectively under dynamic conditions simulating actual applications to obtain dynamic charge distribution curves in the axial, radial, and tangential directions includes:

[0042] 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 application;

[0043] Using non-contact electric field scanning technology, the spline surface is scanned at high speed to obtain real-time data on charge distribution;

[0044] Perform wavelet denoising and singular value decomposition on the acquired real-time data to extract the main features of the charge distribution;

[0045] Based on the main characteristics, the adaptive time series analysis method is used to calculate the charge dissipation rates in the axial, radial and tangential directions;

[0046] The charge dissipation rate is curve-fitted using the nonlinear least squares method to obtain the dynamic charge distribution function in each direction.

[0047] The dynamic charge distribution function is subjected to a time-space joint analysis to generate axial, radial and tangential dynamic charge distribution curves.

[0048] Optionally, the calculating of the comprehensive anisotropic conductive index of the resin conduit 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:

[0049] Performing wavelet multi-resolution analysis on the dynamic charge distribution curve to extract characteristic scale coefficients in the axial, radial and tangential directions;

[0050] The initial Raman spectrum parameters are subjected to nonlinear dimensionality reduction processing to obtain compressed feature vectors;

[0051] Performing differential entropy analysis on the comparative data of the original spline group and the preprocessed spline group to obtain heterogeneity indicators between the spline groups;

[0052] constructing a three-dimensional tensor space based on the characteristic scale coefficient, the compressed eigenvector, and the heterogeneity index;

[0053] Performing high-order singular value decomposition on the three-dimensional tensor space to extract core tensors and factor matrices;

[0054] 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.

[0055] A second aspect of the present invention provides a device for detecting an antistatic high-performance resin conduit, the device comprising:

[0056] A sampling module is used to sample the resin pipe in the axial, radial and tangential directions to obtain an initial spline group;

[0057] A spline processing module 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;

[0058] A Raman analysis module, configured 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;

[0059] a performance estimation module for calculating the estimated conductivity and specific surface area of ​​the original spline group and the preprocessed spline group respectively based on the D4 band position and the D3 band intensity;

[0060] an electrostatic charge injection module, configured to perform differentiated 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;

[0061] A charge monitoring module is 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 the axial, radial and tangential directions;

[0062] An index calculation module is used to calculate the comprehensive anisotropic conductive index of the resin conduit 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.

[0063] 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, wherein instructions are stored in the memory, and the memory and the at least one processor are 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.

[0064] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, causes the computer to execute the steps of the above-mentioned method for detecting antistatic high-performance resin conduits.

[0065] The technical solution provided by the embodiments of the present application has at least the following advantages:

[0066] This protocol samples the resin conduit along the axial, radial, and tangential directions to obtain an initial set of splines, which are then divided into a raw spline set and a pre-treated spline set. This multi-directional sampling method fully accounts for the anisotropic properties of the material. Under high-speed mixing, polymer chains and conductive fillers align, resulting in differences in conductivity along different directions. Multi-directional sampling fully captures this anisotropy, laying the foundation for subsequent analysis. Secondly, this protocol uses multi-wavelength Raman spectroscopy to determine the position and intensity of the D4 band, which characterize the polyolefin structure. Raman spectroscopy provides information about the material's microstructure, particularly the D4 and D3 bands, which are closely related to the polyolefin structure. Analyzing these features provides insight into the distribution and interactions of the conductive filler within the resin matrix, which is crucial for understanding the material's conductivity mechanism. Finally, this protocol uses the D4 band position and D3 band intensity to calculate estimated conductivity and specific surface area. This step establishes a link between the material's microstructural characteristics and its macroscopic conductive properties. The estimated conductivity directly reflects the material's electrical conductivity, while the specific surface area is closely related to the static dissipation efficiency. This method allows for rapid assessment of the material's electrical conductivity without destructive testing. Next, this approach differentially injects static charges into the spline group based on the estimated conductivity and specific surface area. This step simulates the static accumulation process under actual use conditions. By creating initial charge distributions in different directions that align with the estimated performance, the material's static dissipation capability in various directions can be more accurately assessed. Furthermore, this approach monitors the charge dissipation process under dynamic conditions simulating actual applications to generate dynamic charge distribution curves. This step considers not only the static conductivity but also the dynamic response of the material in actual use. By analyzing the dynamic charge distribution curves, the static dissipation rate and efficiency of the material in different directions can be assessed. Finally, this approach calculates a comprehensive anisotropic conductivity index based on the dynamic charge distribution curves, initial Raman spectral parameters, and comparative data between the original and pre-processed spline groups. This index comprehensively reflects the material's anisotropic conductivity by integrating its microstructural characteristics, static conductivity, and dynamic response.

[0067] Through the above steps, this solution effectively detects and evaluates the electrical conductivity of high-performance, anti-static resin conduit in different directions. It not only overcomes the limitations of traditional methods that focus solely on surface resistivity, but also considers the material's microstructure, static properties, and dynamic response. This comprehensive assessment method effectively identifies the anisotropy of the material's electrical conductivity, thereby preventing the risk of localized static electricity accumulation in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0069] Figure 1 Schematic diagram of an embodiment of a method for detecting an antistatic high-performance resin conduit according to an embodiment of the present invention;

[0070] Figure 2 A schematic diagram of an embodiment of a detection device for an antistatic high-performance resin conduit according to an embodiment of the present invention;

[0071] Figure 3 This is a schematic diagram of an embodiment of a detection device for anti-static high-performance resin conduit in an embodiment of the present invention.

[0072] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0074] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0075] In addition, the descriptions of "first", "second", etc. in the present invention are only 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, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0076] 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 antistatic high-performance resin conduit provided in one embodiment of the present application. In this embodiment, the method includes:

[0077] See also Figure 1 , the resin wire tube is sampled along the axial, radial and tangential directions respectively to obtain the initial spline group;

[0078] 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;

[0079] In one embodiment of the present invention, 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; based on the conductivity distribution map, using a cross-layering algorithm to divide the initial spline group into two groups A and B, ensuring that the deviation of the conductivity distribution characteristics of groups A and B in the axial, radial and tangential directions does not exceed a preset threshold; retaining the splines in group A as the original spline group; performing a gradient temperature treatment on the splines in group B to form a microstructure gradient in different temperature ranges; placing the splines in group B that have undergone the gradient temperature treatment in a controllable humidity environment for humidity cycling treatment to regulate the surface hydrophilicity of the splines; and performing surface plasma activation on the treated splines in group B to obtain the preprocessed spline group.

[0080] Specifically, when achieving comprehensive testing of high-performance anti-static resin conduits, it is first necessary to sample the conduits in multiple directions. This step is accomplished by cutting splines in the axial, radial, and tangential directions. For example, for a 1-meter-long and 10-cm-diameter resin conduit, a 5-cm-long spline can be cut every 20 cm along the length direction (axial direction), a spline can be taken every 1 cm from the outside to the inside along the radial direction (radial direction), and a spline can be taken every 45 degrees along the circumferential direction (tangential direction). This multi-directional sampling method ensures that the initial set of spline strips obtained fully represents the characteristics of the resin conduit in all directions.

[0081] After obtaining the initial set of splines, a three-dimensional conductivity scan is then performed to obtain a conductivity distribution map. This step can be performed using a four-probe method or a non-contact conductivity measuring instrument to scan multiple points on each spline. For example, for a 5 cm long spline, a measurement can be taken every 0.5 cm to form a 10 10 10. This high-resolution scan accurately reflects the distribution and anisotropic characteristics of the conductive filler in the resin conduit.

[0082] Based on the obtained conductivity distribution map, a cross-stratification algorithm is used to divide the initial spline group into two groups, A and B. This step ensures that the deviations in the conductivity distribution characteristics of Groups A and B in the axial, radial, and tangential directions do not exceed preset thresholds, thereby ensuring comparability and reliability in subsequent experiments. Specifically, the average conductivity and standard deviation in each direction are calculated. Then, the splines are iteratively assigned to Groups A and B until the difference in the conductivity distribution characteristics of the two groups in each direction is less than a preset threshold, such as 5%. This precise grouping method minimizes experimental error and improves the accuracy of test results.

[0083] After retaining Group A as the original spline group, Group B splines undergo a series of pretreatments. The first is a gradient temperature treatment, aimed at creating a microstructural gradient across different temperature ranges. For example, Group B splines can be placed in a gradient heating furnace with a temperature range of 30°C to 150°C, with each temperature range set at 10°C intervals and maintained for 30 minutes. This treatment simulates the performance changes of resin conduit under different temperature environments, helping to evaluate the material's thermal stability and the temperature dependence of its electrical conductivity.

[0084] Next, the temperature-graded B specimens were placed in a controlled humidity environment for humidity cycling to adjust the surface hydrophilicity of the specimens. This was accomplished by placing the specimens in an environmental chamber with adjustable humidity and cycling through a relative humidity range of 30% to 90%, maintaining each humidity level for 2 hours. This step is crucial for evaluating the changes in the conductivity of the resin conduit under varying humidity conditions, particularly for applications exposed to humid environments.

[0085] Finally, the treated splines in group B are subjected to surface plasma activation to obtain the pre-treated spline group. Plasma activation can be performed using low-temperature plasma treatment equipment. This step changes the chemical composition and physical structure of the spline surface, enhancing its interaction with the environment and thus improving the static dissipation efficiency.

[0086] Through this series of carefully designed sampling and pretreatment steps, the conductive properties and static dissipation capabilities of high-performance, antistatic resin conduit can be comprehensively evaluated in different directions and under various environmental conditions. This method not only accounts for the anisotropy of the material but also simulates the various environmental factors that may be encountered in actual applications, providing a comprehensive and reliable sample for subsequent performance analysis. This comprehensive approach significantly outperforms the traditional method of measuring only surface resistivity, more accurately predicting the material's performance in actual applications, effectively preventing the risk of localized static electricity accumulation, and ensuring that the resin conduit maintains its excellent antistatic properties in a variety of complex environments.

[0087] 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;

[0088] In one embodiment of the present invention, the multi-wavelength Raman spectroscopy analysis of the original spline group and the pre-processed spline group is performed respectively to determine the D4 band position and D3 band intensity characterizing the polyolefin structure, comprising: performing Raman spectroscopy scanning on the original spline group and the pre-processed spline group using a dual-wavelength laser source of 532 nm and 780 nm to obtain original spectral data; performing adaptive baseline correction and wavelet denoising on the original spectral data to obtain an optimized Raman spectrum; performing time-frequency analysis on the optimized Raman spectrum using a Hilbert-Huang transform to obtain an instantaneous frequency distribution diagram; locating the characteristic frequencies of the D4 band and the D3 band in the instantaneous frequency distribution diagram using a peak tracking algorithm; performing local curve fitting on the optimized Raman spectrum based on the characteristic frequencies 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.

[0089] Specifically, Raman spectroscopy plays a key role in the in-depth analysis of anti-static 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 and representative Raman spectral data.

[0090] After obtaining the raw spectral data, adaptive baseline correction and wavelet denoising are required to obtain an optimized Raman spectrum. Adaptive baseline correction aims to eliminate the influence of background fluorescence and improve the accuracy of spectral peaks. This step can be implemented using polynomial fitting or a rolling ball algorithm. For example, a fifth-order polynomial fitting method can be used to iteratively optimize the fitting parameters until the deviation between the baseline and the actual spectrum is less than a preset threshold (e.g., 0.1%). Wavelet denoising aims to reduce random noise in the spectrum and improve the signal-to-noise ratio. The soft threshold denoising method of the wavelet transform can be used to select an appropriate wavelet basis function (e.g., Daubechies wavelet) and number of decomposition levels (typically 3-5) to perform multi-scale decomposition and reconstruction of the spectrum. These processing steps can significantly improve the accuracy of subsequent analysis.

[0091] Next, the optimized Raman spectrum is subjected to time-frequency analysis using the Hilbert-Huang transform to obtain an instantaneous frequency distribution map. The Hilbert-Huang transform is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. In practice, the spectral signal is first decomposed into several eigenmode functions through empirical mode decomposition. 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 map. This method can accurately capture local features in the Raman spectrum, providing an important basis for subsequent band position determination.

[0092] 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 find the characteristic frequencies of the D4 and D3 bands in a specific frequency range (about 1200 , D3 with about 1500 ) Search for local maximum points. To improve accuracy, you can set the 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.

[0093] 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 the 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 fitted curve and the actual data is greater than 0.99. This precise fitting allows the center position of the D4 band to be accurately determined and the integrated area of ​​the D3 band to be calculated.

[0094] Finally, the D4 band center position is defined as the D4 band position parameter, and the D3 band integral area 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 the D band is related to the amorphous carbon content, while the D3 band intensity is related to the amorphous carbon content. Through this definition, complex spectral information can be converted into numerical parameters with clear physical meanings, providing a reliable data basis for subsequent performance evaluation and analysis.

[0095] Each step in this series of Raman spectroscopy analysis, from data acquisition to parameter extraction, has been meticulously designed and optimized. Through dual-wavelength scanning, signal optimization, time-frequency analysis, and precise fitting, we can gain a deep understanding of the microstructural properties of high-performance, antistatic resin conduit. This method not only accurately reflects the material's structural information but also reveals the distribution and interactions of conductive fillers within the resin matrix, providing a strong scientific basis for evaluating the material's antistatic properties and anisotropy.

[0096] 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 preprocessed spline group;

[0097] In one embodiment of the present invention, the estimated conductivity and specific surface area of ​​the original spline group and the preprocessed spline group are calculated based on the D4 band position and the D3 band intensity, respectively, including: constructing a D4 band position-D3 band intensity two-dimensional feature space, and mapping the data points of the original spline group and the preprocessed 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 characteristics 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 preprocessed spline group; using a wavelet packet transform and a multiscale entropy analysis method to extract the time-frequency domain features of the multidimensional cluster feature vector; constructing a specific surface area estimation equation based on the time-frequency domain features and fractal network theory, and calculating the estimated specific surface area of ​​the original spline group and the preprocessed spline group.

[0098] Specifically, in the performance evaluation process of antistatic high-performance resin conduit, 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 to 1250. The vertical axis 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 and preprocessed spline groups are mapped into this space, with each spline forming a unique point in the space. This mapping method can intuitively display the distribution of structural features of a material and help identify differences and trends in its microstructure.

[0099] Adaptive fuzzy clustering analysis is performed on data points in a two-dimensional feature space to obtain dynamic feature clusters. The fuzzy C-means algorithm can be used here, with the initial number of clusters set to 3-7. The number of clusters and the membership function are then automatically adjusted through an iterative process. For example, the maximum number of iterations can be set to 200, with a convergence threshold of 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 an optimal clustering structure based on the actual data distribution, effectively capturing subtle differences in material properties. It is particularly advantageous when dealing with materials with complex structures or continuously changing properties.

[0100] 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 factor of the cluster can be calculated, such as roundness (defined as 4π×area / ), aspect ratio (the ratio of the maximum Feret diameter to the minimum Feret diameter), and so on. The Hausdorff distance between clusters can also be calculated, reflecting the degree of separation between clusters. These features together form a multidimensional vector, typically 15-25 dimensions, that comprehensively describes the properties of each feature cluster.

[0101] 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, hidden layers with 128, 64, 32, and 16 nodes respectively, and an output layer with 1 node (conductivity). The activation function can choose the rectified linear unit function, and 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 using transfer learning methods to adapt to the current resin 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 effectively utilizes the feature extraction capabilities of the pre-trained model while adapting to the characteristics of the new data.

[0102] Wavelet packet transform and multiscale entropy analysis are used to extract the time-frequency domain features of multidimensional cluster eigenvectors. The wavelet packet transform uses symmetric orthogonal wavelets, with a decomposition level of six. For each decomposition coefficient, the 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 accounts for the ambiguity of the data, and permutation entropy describes the dynamic behavior of the time series. This method captures the complexity and uncertainty of the eigenvector at different scales, providing richer information.

[0103] Based on the time-frequency domain characteristics and fractal network theory, an equation for estimating specific surface area 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 be selected as 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.

[0104] Through this series of precise analytical steps, rich structural information is extracted from the Raman spectral data and converted into quantifiable performance parameters. This approach not only considers the material's microstructural characteristics but also establishes a structure-performance relationship through advanced data analysis techniques, providing a scientific and reliable basis for accurately evaluating the conductivity and specific surface area of ​​antistatic high-performance resin conduit. This comprehensive analysis method significantly outperforms traditional single-parameter evaluation methods, enabling more comprehensive and accurate predictions of material performance in practical applications, providing strong support for material optimization design and quality control.

[0105] Please continue reading Figure 1 , performing differentiated electrostatic charge injection on the original spline group and the pre-treated spline group according to the estimated conductivity and specific surface area, respectively, to form initial charge distributions in the axial, radial and tangential directions that are compatible with the estimated performance;

[0106] In one embodiment of the present invention, the method of performing differentiated electrostatic charge injection on the original spline group and the pre-processed spline group based on the estimated conductivity and specific surface area to form initial charge distributions that are compatible with the estimated performance in the axial, radial, and tangential directions includes: 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 based on the charge accommodation coefficients; performing differentiated 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 an electric field intensity gradient method; performing Fourier transform on the measured charge distribution to obtain a 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 characteristics of the initial charge distribution.

[0107] Specifically, in the performance evaluation process of antistatic high-performance resin conduit, 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 to take into account the geometric shape and anisotropy of the conductivity of the material. 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 material's dielectric constant, L is the spline length, and b and a are the outer and inner diameters, respectively. Calculations in the radial and tangential directions require consideration of the electric field distribution in cylindrical coordinates. This method comprehensively reflects the material's charge storage properties in different directions.

[0108] Based on the calculated charge accommodation coefficient, a pulse-modulated electrostatic charge injection sequence is designed. This step is intended to simulate the static electricity 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 coefficient in that direction. For example, a three-stage injection sequence can be designed, with each stage lasting 10 seconds, 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 electricity accumulation process in actual use and help evaluate the performance of materials under dynamic conditions.

[0109] Using a 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 of the spline surface needs to be monitored in real time to ensure that the amount of injected charge meets expectations. 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 properties.

[0110] 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 1 mm / 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 0.5 mm. This method can accurately capture subtle changes in the charge distribution on the spline surface and provide detailed spatially resolved data for subsequent analysis.

[0111] 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 a frequency domain perspective. The charge distribution data in each direction can be processed separately using the Fast Fourier Transform (FFT) algorithm. For example, for a spline with a length of 10 cm, if the spatial resolution is 0.5 mm, there are 200 data points, resulting in 100 valid frequency components. 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.

[0112] 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 ratio 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 ratio, 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 of the three directions are comprehensively considered to form a multidimensional vector to describe the characteristics of the initial charge distribution. 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.

[0113] This series of precise injection and measurement steps allows for a comprehensive evaluation of the static properties of high-performance, antistatic resin conduit in various directions. This method not only accounts for the material's anisotropy but also simulates the static accumulation and distribution process under actual usage conditions. Through Fourier analysis and uniformity calculation, the material's static properties can be quantitatively described from multiple perspectives, providing a scientific basis for optimizing material design and predicting performance. This comprehensive evaluation method significantly outperforms traditional single-direction or single-parameter testing methods, enabling more accurate prediction of a material's antistatic performance in complex environments.

[0114] 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 dynamic charge distribution curves in the axial, radial and tangential directions;

[0115] 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 rates to obtain dynamic charge distribution functions in each direction; and performing a time-space joint analysis on the dynamic charge distribution function to generate axial, radial and tangential dynamic charge distribution curves.

[0116] Specifically, in the performance evaluation process of antistatic high-performance resin conduit, applying periodic mechanical stress and temperature and humidity cycling to the original and pretreated spline groups is a key step in simulating the dynamic conditions of actual applications. A composite cyclic test scheme can be designed. For example, the mechanical stress cycle can use alternating tension and compression loading, with a stress range of 0-50 MPa and a frequency of 1 Hz; the temperature cycle range can be set to -20°C to 60°C, with a heating and cooling rate of 2°C / minute; and 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 performed simultaneously, with a total test time of 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.

[0117] The spline surface is scanned at high speed using non-contact electric field scanning technology to obtain real-time data on the charge distribution. An electrostatic voltage probe array can be used, for example, a 16×16 probe matrix can be set up 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 in 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 foundation for subsequent analysis.

[0118] Wavelet denoising and singular value decomposition are performed on the acquired real-time data to extract the key features of the charge distribution. Wavelet denoising can be performed using a discrete wavelet transform, with the Dobsey wavelet basis selected and the decomposition layer set to 5. The wavelet coefficients are processed using a soft thresholding method, with the threshold set to 3 times the standard deviation of the noise. After denoising, the data matrix is ​​subjected to singular value decomposition, retaining the features corresponding to the first K largest singular values ​​(K can be selected as the minimum value that explains 95% of the variance). This method effectively removes measurement noise while preserving the key features of the charge distribution, greatly improving the accuracy and efficiency of subsequent analysis.

[0119] Based on the extracted key 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, with the model order automatically selected using 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 model's characteristic roots; 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 time periods, providing more accurate dissipation rate estimates.

[0120] 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:

[0121] ;

[0122] 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. Parameter optimization is performed using the Levenberg-Marquardt algorithm, with initial values ​​set based on the estimates from the previous step. During the fitting process, a weighting function can be set to prioritize rapid changes in the initial phase. This nonlinear fitting method accurately describes the dynamics of charge dissipation, providing a reliable mathematical model for subsequent analysis.

[0123] A joint time-space analysis of the dynamic charge distribution function generates dynamic charge distribution curves in the axial, radial, and tangential directions. Space-time spectral analysis can be used to combine the time and space dimensions. For example, space-time correlation functions can be calculated to reflect the temporal and spatial correlation characteristics of the charge distribution. The Hilbert-Huang transform can also be used to analyze the instantaneous frequency and local energy density of the charge distribution. These analyses produce a three-dimensional surface plot of the charge distribution in each direction as it varies with time and space. This joint analysis method comprehensively demonstrates the charge distribution characteristics of a material under dynamic conditions, intuitively reflecting the material's anisotropy and time-varying properties.

[0124] This complex and sophisticated series of analytical steps allows for a comprehensive assessment of the charge distribution and dissipation characteristics of high-performance, antistatic resin conduit under dynamic conditions. This approach not only accounts for material anisotropy but also simulates the complex environmental factors of actual use, providing more accurate and reliable performance evaluation results. Through high-resolution, real-time data acquisition, advanced signal processing techniques, and sophisticated mathematical models, this approach significantly improves the accuracy and depth of antistatic performance evaluation, providing a strong scientific basis for optimized material design and performance prediction.

[0125] 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.

[0126] 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 comparative 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 axial, radial and tangential characteristic scale coefficients; performing nonlinear dimensionality reduction processing on the initial Raman spectrum parameters to obtain compressed eigenvectors; performing differential entropy analysis on the comparative data between the original spline group and the pre-processed spline group to obtain heterogeneity indicators between 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.

[0127] Specifically, in the performance evaluation process of anti-static high-performance resin conduits, 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 the charge distribution at different time and space scales, providing important information for subsequent comprehensive analysis.

[0128] 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 cross-validation. Then, the kernel matrix is ​​constructed and 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, after KPCA processing, a compressed eigenvector of 10-20 dimensions may be obtained. This nonlinear dimensionality reduction method can effectively retain the key characteristic information of the Raman spectrum while greatly reducing the dimensionality of the data, facilitating subsequent analysis.

[0129] Differential entropy analysis is performed on the comparative data of the original and preprocessed spline groups to obtain an index of heterogeneity between the spline groups. This differential entropy analysis can be performed using a multi-scale approach. First, a series of differences between the original and preprocessed spline groups on various test parameters is calculated. These difference series are then subjected to a multi-scale decomposition, typically using empirical mode decomposition (EMD), into 5-7 intrinsic mode functions (IMFs). For each IMF, the sample entropy is calculated to obtain a multi-scale entropy value series. Finally, a comprehensive heterogeneity index is obtained by weightedly averaging these entropy values ​​(the weights can be set to the energy contribution of each IMF). This index reflects the overall degree of difference between the original and preprocessed spline groups, with larger values ​​indicating more significant performance differences between the two groups.

[0130] A three-dimensional tensor space is constructed based on characteristic scale coefficients, compressed eigenvectors, and heterogeneity indicators. This step integrates information from different sources into a unified mathematical structure. The characteristic scale coefficients can be combined 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 eigenvectors can be combined into an L×M matrix, where L is the number of splines and M is the dimension of the compressed features. The heterogeneity indicator is a vector of length N, where N is the number of comparison parameters. Through the tensor outer product operation, these three pieces of information are combined into a high-order tensor of dimensions I×J×K×L×M×N. This tensor representation method can preserve the inherent correlation structure between data, laying the foundation for subsequent high-level analysis.

[0131] Perform high-order singular value decomposition on the three-dimensional tensor space to extract the core tensor and factor matrix. Tucker decomposition can be used here 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 cross-validation and is typically between 2 and 4. For example, for a 6th-order tensor, a 2×2×2×2×2×2 core tensor and 6 factor matrices may be obtained. The core tensor reflects the main structure and interactions of the data, while the factor matrix represents the main pattern in each dimension. This decomposition method can effectively capture key information in high-dimensional data and greatly reduce the complexity of subsequent calculations.

[0132] The comprehensive anisotropic conductivity index of the resin conduit is calculated using a tensor network algorithm using the elements of the core tensor and factor matrices. A tensor train contraction method can be used to perform contraction operations on the core tensor and each factor matrix. Specifically, a contraction sequence can be defined, for example, first contracting along the characteristic scale dimension, then the spline dimension, and finally the parameter dimension. After each contraction, the intermediate results can be considered a new feature representation. Ultimately, a three-dimensional result tensor is obtained, with the three dimensions corresponding to the axial, radial, and tangential directions. 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, with weights determined based on the physical meaning of each dimension. This tensor network-based calculation method comprehensively considers the interaction of various factors, resulting in a comprehensive index that accurately reflects the anisotropic conductivity of the material.

[0133] Through this complex and sophisticated mathematical process, we are able to comprehensively analyze the performance characteristics of antistatic, high-performance resin conduit from multiple perspectives and scales, ultimately deriving a comprehensive index that accurately reflects the material's anisotropic conductive properties. This approach not only considers the material's microstructural characteristics and macroscopic performance, but also captures the complex interactions between various factors through high-order tensor analysis. This comprehensive analysis method significantly outperforms traditional single-parameter or simple comprehensive evaluation methods, enabling a more comprehensive and accurate characterization of material properties, providing a strong scientific basis for material optimization design and performance prediction.

[0134] 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 In one embodiment of the present invention, a device for detecting an antistatic high-performance resin conduit includes:

[0135] The sampling module 201 is used to sample the resin pipe in the axial, radial and tangential directions to obtain an initial spline group;

[0136] A spline processing module 202 is configured to divide the initial spline group into two equal parts, wherein one part is retained as the original spline group and the other part is preprocessed to obtain a preprocessed spline group;

[0137] A Raman analysis module 203 is configured 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;

[0138] 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;

[0139] an electrostatic charge injection module 205 for performing differentiated 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;

[0140] A charge monitoring module 206 is configured 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 the axial, radial and tangential directions;

[0141] The index calculation module 207 is configured to calculate the comprehensive anisotropic conductive index of the resin conduit 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.

[0142] 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.

[0143] Figure 3 This is a schematic diagram of the structure of a device for testing anti-static, high-performance resin conduit provided by an embodiment of the present invention. This device 300, which can vary significantly depending on configuration or performance, may include one or more processors (central processing units, CPUs) 310 (e.g., one or more processors), memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage media 330 may be either transient or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions for operating in the device 300 for testing anti-static, high-performance resin conduit. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, executing the series of instructions stored in the storage medium 330 on the device 300 for testing anti-static, high-performance resin conduit, thereby implementing the steps of the aforementioned method for testing anti-static, high-performance resin conduit.

[0144] The antistatic high-performance resin conduit detection device 300 may further 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 Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will 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.

[0145] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are run on a computer, the computer executes the steps of the anti-static high-performance resin wire pipe detection method.

[0146] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0147] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0148] 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 transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for detecting antistatic high-performance resin conduit, characterized in that: include: The resin wire tube is sampled along the axial, radial and tangential directions to obtain an initial spline group; 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; The method comprises the following steps: 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; specifically comprising: performing Raman spectroscopy scanning on the original spline group and the pre-processed spline group, respectively, using a dual-wavelength laser source of 532 nm and 780 nm to obtain original spectral data; performing adaptive baseline correction and wavelet denoising on the original spectral data to obtain an optimized Raman spectrum; performing time-frequency analysis on the optimized Raman spectrum using a Hilbert-Huang transform to obtain an instantaneous frequency distribution diagram; locating the characteristic frequencies of the D4 band and the D3 band in the instantaneous frequency distribution diagram by a peak tracking algorithm; performing local curve fitting on the optimized Raman spectrum according to the characteristic frequencies 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; Based on the D4 band position and the D3 band intensity, the estimated conductivity and specific surface area of ​​the original spline group and the pre-processed spline group are calculated respectively; specifically comprising: constructing a D4 band position-D3 band intensity two-dimensional feature space, 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 characteristics of each dynamic feature cluster to generate a multi-dimensional cluster feature vector; using a deep neural network and a transfer learning method, establishing a nonlinear mapping relationship from the multi-dimensional cluster feature vector to the conductivity, and obtaining 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 multi-dimensional cluster feature vector; based on the time-frequency domain features and the fractal network theory, constructing a specific surface area estimation equation, and calculating the estimated specific surface area of ​​the original spline group and the pre-processed spline group; According to the estimated electrical conductivity and specific surface area, differential electrostatic charge injection is performed on the original spline group and the pre-treated spline group, respectively, to form initial charge distributions in the axial, radial and tangential directions that are compatible with the estimated performance; 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, 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; specifically, the method includes: 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 the heterogeneity index between the spline groups; constructing a three-dimensional tensor space based on the characteristic scale coefficients, compressed eigenvectors and heterogeneity indexes; performing high-order singular value decomposition on the three-dimensional tensor space to extract the core tensor and factor matrix; and using the elements of the core tensor and factor matrix to calculate the comprehensive anisotropic conductive index of the resin wire tube through a tensor network algorithm.

2. The method for detecting antistatic high-performance resin conduit 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 spline group; The splines of group B were subjected to gradient temperature treatment to form microstructure gradients in different temperature ranges; The B group of specimens that had undergone gradient temperature treatment were placed in a controlled humidity environment for humidity cycle treatment to regulate the surface hydrophilicity of the specimens; The treated group B strips were subjected to surface plasma activation to obtain a pre-treated strip group.

3. The method for detecting antistatic high-performance resin conduit 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 based on the estimated conductivity and specific surface area to form initial charge distributions in the axial, radial, and tangential directions that are compatible with the estimated performance includes: Based on the estimated conductivity and specific surface area, calculating the charge accommodation coefficients of the original spline group and the preprocessed spline group in the axial, radial and tangential directions; 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 preprocessed 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 transform 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.

4. The method for detecting antistatic high-performance resin conduit according to claim 1, characterized in that: The method comprises monitoring the charge dissipation processes of the original spline group and the pre-processed spline group respectively under dynamic conditions simulating actual applications to obtain dynamic charge distribution curves in the axial, radial and tangential directions, 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 application; 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 the charge distribution; Based on the main characteristics, the adaptive time series analysis method is used to calculate the charge dissipation rates in the axial, radial and tangential directions; The charge dissipation rate is curve-fitted using the nonlinear least squares method to 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.

5. A detection device for antistatic high-performance resin wire tube, characterized in that: The device for detecting antistatic high-performance resin conduits is applied to the method for detecting antistatic high-performance resin conduits according to any one of claims 1 to 4, comprising: A sampling module is used to sample the resin pipe in the axial, radial and tangential directions to obtain an initial spline group; A spline processing module 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 is configured 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 characterizing the polyolefin structure; specifically comprising: performing Raman spectroscopy scanning on the original spline group and the pre-processed spline group using a dual-wavelength laser source of 532 nm and 780 nm to obtain original spectral data; performing adaptive baseline correction and wavelet denoising on the original spectral data to obtain an optimized Raman spectrum; performing time-frequency analysis on the optimized Raman spectrum using a Hilbert-Huang transform to obtain an instantaneous frequency distribution diagram; locating the characteristic frequencies of the D4 band and the D3 band in the instantaneous frequency distribution diagram using a peak tracking algorithm; performing local curve fitting on the optimized Raman spectrum based on the characteristic frequencies 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; A performance estimation module is used to calculate 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; specifically comprising: constructing a D4 band position-D3 band intensity two-dimensional feature space, 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 characteristics of each dynamic feature cluster to generate a multi-dimensional cluster feature vector; using a deep neural network and transfer learning method to establish a nonlinear mapping relationship from the multi-dimensional cluster feature vector to the conductivity, and obtain the estimated conductivity of the original spline group and the pre-processed spline group; using wavelet packet transform and multi-scale entropy analysis methods to extract the time-frequency domain features of the multi-dimensional cluster feature vector; constructing a specific surface area estimation equation based on the time-frequency domain features and fractal network theory to calculate the estimated specific surface area of ​​the original spline group and the pre-processed spline group; an electrostatic charge injection module, configured to perform differentiated 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 is 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 the axial, radial and tangential directions; An 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; specifically comprising: 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 the core tensor and factor matrix; 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. 6.An antistatic high performance resin conduit detection device, characterized in that, The antistatic high-performance resin conduit detection device includes: 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 1-4.

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