Preparation method of high-temperature-resistant and anti-aging cable sheath material

Abstract

The application provides a preparation method of a high-temperature-resistant and anti-aging cable sheath material, comprising the following steps: obtaining molecular chain distribution data of a polymer matrix and inorganic fillers, and establishing a bonding strength distribution graph; obtaining regional non-uniform points, and optimizing filler distribution parameters; calculating a crosslinking temperature gradient model based on a compatibility improvement coefficient; predicting crosslinking density and correcting reaction parameters according to the model; evaluating an aging rate distribution and optimizing a modifier dosage. The application controls filler dispersion through molecular-level bonding strength, makes the crosslinking temperature field self-adapt to the filler distribution, and optimizes the formula in combination with aging behavior prediction, so that the structural uniformity, heat resistance and service life of the material are significantly improved. The method solves the problems of uneven filler dispersion, mismatched crosslinking thermal field and inaccurate aging evaluation in the traditional process.

Description

Technical Field

[0001] This invention relates to the field of cable sheath material preparation technology, and in particular to a method for preparing a high-temperature resistant and anti-aging cable sheath material. Background Technology

[0002] Cable sheath materials, as an indispensable component of power transmission and communication systems, directly affect the safety and service life of cables. In high-temperature, high-pressure, or extreme environments, such as aerospace, nuclear power plants, or high-temperature industrial furnaces, cable sheaths need to possess excellent high-temperature resistance and anti-aging properties to ensure long-term stable system operation. In recent years, with the increasing demands for equipment reliability and durability in these fields, developing a cable sheath material capable of maintaining performance under ultra-high temperature conditions has become particularly important. This material must not only withstand extreme temperatures but also resist material aging caused by long-term exposure to high-temperature environments; therefore, its preparation technology has become a key focus of industry research.

[0003] In the preparation of high-temperature resistant and anti-aging cable sheath materials, the core technical challenge lies in effectively controlling the cross-linking structure and filler distribution. The cross-linking structure is a key factor determining the material's high-temperature resistance and anti-aging properties. Too low a cross-linking density leads to softening and deformation at high temperatures, while too high a density can cause embrittlement and loss of necessary flexibility. However, traditional methods struggle to precisely control the temperature gradient and residence time during the cross-linking reaction, resulting in uneven cross-linking density distribution and affecting the overall material performance. Simultaneously, while inorganic fillers such as ceramic fibers or alumina can improve the material's thermal stability, their poor compatibility with the polymer matrix makes them prone to agglomeration within the material, causing localized stress concentration. This uneven filler distribution not only reduces the material's mechanical strength but also induces microcracks at high temperatures, accelerating the aging process.

[0004] In existing technologies, the control of crosslinking density and filler distribution often relies on empirical methods, lacking systematic optimization techniques. Especially under high-temperature environments, there is a complex nonlinear relationship between the aging rate of materials and crosslinking density and filler distribution, making it difficult for traditional processes to achieve synergistic optimization of these key parameters. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the difficulty in achieving synergistic optimization of key parameters in the preparation of cable sheath materials in the prior art, which leads to the inability of the material to meet the requirements. The present invention provides a method for preparing high-temperature resistant and anti-aging cable sheath materials, which can optimize the filler distribution by optimizing the molecular chain bonding strength, adaptively control the crosslinking temperature gradient and accurately predict the aging behavior, thereby significantly improving the structural uniformity, high-temperature resistance and long-term stability of the material.

[0006] To solve the above technical problems, the present invention provides a method for preparing a high-temperature resistant and aging-resistant cable sheath material, comprising:

[0007] Obtain molecular chain distribution data of the initial mixture of polymer matrix and inorganic filler, determine the bonding strength index in the mixture, and obtain the bonding strength distribution map;

[0008] Based on the bond strength distribution map, regional non-uniformity points are obtained, the addition ratio of inorganic fillers is adjusted, and the optimized filler distribution parameters are determined.

[0009] The compatibility enhancement coefficient is obtained from the optimized filler distribution parameters, and the temperature gradient change during the crosslinking reaction is calculated to obtain the temperature gradient distribution model.

[0010] Based on the temperature gradient distribution model, the predicted value of crosslinking density is obtained, the reaction residence time parameter is corrected, and the corrected crosslinking density control scheme is determined.

[0011] Structural stability indices were obtained from the modified crosslinking density control scheme, and the aging rate distribution under high temperature conditions was evaluated to obtain an aging rate prediction map.

[0012] Potential microcrack formation regions are obtained based on the aging rate prediction map. The amount of modifier in the polymer matrix is ​​adjusted through iterative optimization to determine the final material formulation parameters.

[0013] In one embodiment of the present invention, obtaining molecular chain distribution data of the initial mixture of polymer matrix and inorganic filler, determining the bonding strength index in the mixture, and obtaining a bonding strength distribution map includes the following steps:

[0014] Microstructure images of the polymer matrix and inorganic filler mixture were obtained by scanning electron microscopy to determine the properties of the mixture;

[0015] Image processing algorithms were used to analyze microstructure images to obtain data on material mixing uniformity.

[0016] The molecular chain distribution characteristics are determined by acquiring spectral data of the mixture using an infrared spectrometer.

[0017] The chemical bond characteristics were obtained by processing the spectral data using Fourier transform.

[0018] Based on the characteristics of chemical bonds, principal component analysis algorithm is used to extract bond strength indices;

[0019] A bond strength distribution map is generated using data visualization tools based on the bond strength index.

[0020] In one embodiment of the present invention, regional non-uniformity points are obtained based on the bonding strength distribution map, the addition ratio of inorganic fillers is adjusted, and the optimized filler distribution parameters are determined, including the following steps:

[0021] The coordinates of regional non-uniform points are extracted from the bond strength distribution map using data visualization tools to obtain the non-uniform point distribution data;

[0022] If the number of unevenly distributed data exceeds a preset threshold, statistical analysis methods are used to calculate the spatial density of the uneven points and obtain the density distribution characteristics.

[0023] Based on the density distribution characteristics, the proportion of inorganic packing material is adjusted using a particle swarm optimization algorithm to determine the optimized packing material distribution parameters.

[0024] In one embodiment of the present invention, the compatibility enhancement coefficient is obtained from the optimized filler distribution parameters, and the temperature gradient change during the crosslinking reaction is calculated to obtain a temperature gradient distribution model, including the following steps:

[0025] Optimization parameters are obtained from the packing distribution, and compatibility improvement coefficients are extracted using data processing techniques to determine the coefficient values.

[0026] Based on the compatibility enhancement coefficient, a finite element simulation model of the crosslinking reaction is constructed, boundary conditions of the reaction process are set, and initial simulation results are generated.

[0027] Temperature gradient data are extracted from the initial simulation results, and the gradient change is calculated using numerical analysis methods to obtain the gradient distribution characteristics.

[0028] If the gradient distribution characteristics exceed the preset threshold, adjust the packing distribution parameters, re-execute the simulation calculation, and obtain updated temperature gradient data.

[0029] Using the updated temperature gradient data, an interpolation algorithm is employed to construct a temperature gradient distribution model and generate model parameters.

[0030] Based on the model parameters, the accuracy of the temperature gradient distribution model was verified. The model structure was adjusted using grid optimization techniques to obtain the final temperature gradient distribution model.

[0031] In one embodiment of the present invention, the process of obtaining a predicted crosslinking density value based on a temperature gradient distribution model, correcting the reaction residence time parameter, and determining a corrected crosslinking density control scheme includes the following steps:

[0032] The crosslinking density prediction value was calculated using a temperature gradient distribution model to obtain the initial prediction results;

[0033] If the initial prediction results deviate from the target range, the deviation value is extracted from the prediction results to determine the adjustment direction;

[0034] The gradient descent algorithm is used to iteratively optimize the reaction residence time parameter based on the deviation value to obtain the adjusted parameter value;

[0035] The temperature gradient distribution model is updated with the adjusted parameter values ​​to generate new predicted crosslinking density values.

[0036] If the new predicted value still deviates from the target range, repeat the iterative optimization steps until the predicted value meets the target range and a stable parameter set is obtained;

[0037] A crosslinking density control scheme is generated based on the stable parameter set, and the final control parameters are output.

[0038] The temperature gradient distribution model was validated using the final control parameters, confirming the consistency of the predicted crosslinking density.

[0039] In one embodiment of the present invention, structural stability indices are obtained from a modified crosslinking density control scheme, and the aging rate distribution under high temperature conditions is evaluated to obtain an aging rate prediction map, including the following steps:

[0040] Structural stability parameters were obtained from the optimized crosslinking density control scheme, and key indicators were extracted using a pre-set molecular dynamics model to obtain a set of structural stability parameters.

[0041] The aging rate of the structural stability parameter set under high temperature environment was calculated by Monte Carlo simulation, and an aging rate distribution dataset was generated.

[0042] If the variance of the aging rate distribution dataset exceeds a preset threshold, the data is normalized to obtain a standardized aging rate distribution.

[0043] Based on the standardized aging rate distribution, a continuous probability density function is generated using a kernel density estimation algorithm to obtain the aging rate prediction curve.

[0044] For the aging rate prediction curve, key inflection points and trend features are extracted to generate an aging rate prediction distribution map.

[0045] In one embodiment of the present invention, potential microcrack formation regions are obtained based on aging rate prediction maps, and the final material formulation parameters are determined by iteratively optimizing and cyclically adjusting the amount of modifier in the polymer matrix, including the following steps:

[0046] Obtain a predicted map of material aging rate, segment the potential microcrack generation area using image processing technology, and obtain regional distribution data;

[0047] The proportion of microcrack formation areas was calculated from the regional distribution data, and the regional proportion values ​​were obtained by statistical analysis.

[0048] If the regional proportion is higher than the preset threshold, an iterative optimization loop is started to adjust the amount of matrix modifier and obtain the adjusted amount data.

[0049] Based on the adjusted usage data, the material formulation parameters were updated, and the linear regression algorithm was used to predict the impact of the formulation parameters on the aging rate, thus obtaining the prediction results.

[0050] The aging rate trend is extracted from the prediction results, and combined with the crack formation probability, it is determined whether the formulation parameters meet the requirements, and the optimized formulation parameters are obtained.

[0051] In one embodiment of the present invention, the following steps are also included:

[0052] Extrusion molding simulation data were obtained from the final material formulation parameters, and the balance between mechanical strength and thermal stability was verified by finite element analysis, resulting in a verified sheath material preparation protocol.

[0053] For the validated sheath material preparation protocol, long-term reliability indicators are obtained. All simulation results are integrated through data fusion methods to determine whether the overall performance meets the requirements for high temperature resistance and anti-aging, and to obtain an optimized cable sheath material design scheme.

[0054] In one embodiment of the present invention, extrusion molding simulation data is obtained from the final material formulation parameters, and the balance between mechanical strength and thermal stability is verified using the finite element analysis method to obtain a verified sheath material preparation protocol, including the following steps:

[0055] Initial formulation parameters are extracted from the material formulation parameter database, and outliers are removed using data cleaning techniques to obtain a standardized set of formulation parameters;

[0056] Based on a standardized set of formula parameters, extrusion molding simulation software is used to generate simulation data of the molding process and obtain extrusion molding data.

[0057] For the extrusion molding data, the geometric model of the sheath material is constructed using the finite element analysis method, and a finite element analysis mesh model is generated;

[0058] Stress distribution data and temperature distribution data are extracted from the finite element analysis mesh model to verify mechanical strength and perform thermal stability analysis, thereby determining the balance between strength and thermal performance.

[0059] Based on the balance data of strength and thermal properties, a sheath material preparation protocol is generated, and a standardized protocol document is output using template matching technology.

[0060] In one embodiment of the present invention, long-term reliability indicators are obtained for the validated sheath material preparation protocol. All simulation results are integrated using a data fusion method to determine whether the overall performance meets the requirements for high-temperature resistance and anti-aging, thereby obtaining an optimized cable sheath material design scheme. This includes the following steps:

[0061] Long-term reliability index data were obtained from the validated sheath material preparation protocol, and the data was structured using a pre-defined standardized format to obtain a reliability index dataset.

[0062] For the reliability index dataset, principal component analysis algorithm is used to extract the main feature vectors and determine the key reliability parameters;

[0063] Performance data under ultra-high temperature conditions are obtained from simulation results. Key reliability parameters and simulation performance data are then fused using a weighted average method to obtain a comprehensive performance dataset.

[0064] If the index value in the comprehensive performance dataset is greater than the preset ultra-high temperature threshold, the material is judged to meet the ultra-high temperature requirements, and a preliminary qualified material solution is obtained.

[0065] Based on the preliminary qualified material scheme, the genetic algorithm was used to optimize the sheath material formula parameters to obtain the optimized material formula scheme.

[0066] The anti-aging performance of the optimized material formulation was simulated by finite element analysis under ultra-high temperature environment to determine the stability of the material in long-term use.

[0067] If the stability assessment results meet the preset reliability threshold, then the final cable sheath material design scheme is determined.

[0068] The technical solution of the present invention has the following advantages compared with the prior art:

[0069] The method for preparing high-temperature resistant and anti-aging cable sheath material described in this invention significantly improves the performance and reliability of cable sheath material from a mechanistic perspective through multi-dimensional synergistic optimization.

[0070] By acquiring molecular chain distribution data and establishing a bonding strength distribution map, weak areas of interfacial bonding between the polymer matrix and inorganic fillers can be accurately identified. Dynamically adjusting the filler ratio can effectively eliminate local stress concentration, thereby enhancing the overall structural stability of the material.

[0071] The temperature gradient change is calculated based on the compatibility enhancement coefficient, which makes the thermal field distribution of the crosslinking reaction more consistent with the actual dispersion state of the filler. This avoids the problem of crosslinking degree difference caused by uneven thermal conduction in traditional processes and ensures the uniformity of the internal network structure of the material.

[0072] By correcting reaction parameters using predicted crosslinking density values, the formation process of crosslinked networks can be precisely controlled, reducing areas of excessive or insufficient crosslinking, thereby improving the high-temperature resistance and mechanical strength of the material.

[0073] The aging rate prediction map, combined with iterative optimization of the microcrack formation region, makes the addition of modifiers more targeted and effectively inhibits the breakage of molecular chains and oxidative degradation under high temperature conditions.

[0074] Through the synergistic effect of the above-mentioned technical features, the final sheath material can maintain more stable performance during long-term use at high temperatures, delay the aging process, and reduce the risk of early failure caused by microstructural defects. Attached Figure Description

[0075] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein:

[0076] Figure 1 This is a flowchart of the preparation method of the high-temperature resistant and anti-aging cable sheath material of the present invention;

[0077] Figure 2 This is a flowchart of the steps in obtaining the bonding strength distribution map according to the present invention;

[0078] Figure 3 This is a flowchart of the steps in determining the optimized packing distribution parameter distribution map according to the present invention;

[0079] Figure 4 This is a flowchart of the steps in obtaining the temperature gradient distribution model according to the present invention;

[0080] Figure 5 This is a flowchart of the steps in determining the modified crosslinking density control scheme of the present invention;

[0081] Figure 6 This is a flowchart of the steps in obtaining the aging rate prediction map according to the present invention;

[0082] Figure 7 This is a flowchart of the steps in determining the final material formulation parameters according to the present invention;

[0083] Figure 8 This is a flowchart illustrating the steps of the sheath material preparation protocol that has been verified and approved in this invention;

[0084] Figure 9 This is a flowchart illustrating the steps involved in obtaining the reliability index dataset according to the present invention. Detailed Implementation

[0085] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0086] In the traditional cable sheath material preparation process, there is a dynamic coupling problem between crosslinking density control and inorganic filler distribution optimization. Due to the spatial heterogeneity of the interfacial bonding strength between the polymer matrix and the filler, the temperature field distribution and the filler dispersion state during the crosslinking reaction form a nonlinear interaction. This interaction makes it difficult to achieve a synergistic match between the crosslinking network structure and the filler dispersion morphology under traditional fixed process parameters, resulting in an imbalance in the internal stress distribution of the material. This leads to the initiation and propagation of microcracks under high temperature conditions, ultimately causing the mechanical strength of the sheath material to decrease and its anti-aging performance to deteriorate.

[0087] For example, in the continuous extrusion molding process of cable sheaths for high-temperature industrial furnaces, when the mass fraction of alumina filler reaches 15%, the traditional static mixing process causes the filler to form agglomerates with a diameter exceeding 5 μm in the polyethylene matrix. These agglomerates hinder the uniform diffusion of the DCP crosslinking agent during the crosslinking reaction stage, resulting in a crosslinking density fluctuation of ±12% across the material's cross-section. Under continuous operation at 135℃, a residual stress gradient of 0.5-1.2 MPa is generated in the region of crosslinking density difference, causing microcrack propagation at the silane coupling agent modified interface after 300 hours of operation. The crack density reaches 8-12 cracks / mm², leading to a two-order-of-magnitude decrease in the volume resistivity of the sheath material.

[0088] If the above problems are not addressed, the spatial mismatch between the cross-linked network and the filler distribution will lead to anisotropic degradation of the material's thermomechanical properties. Cyclic thermal stress at high temperatures will cause microcracks to propagate directionally along the filler agglomeration interface, forming through-hole defect channels and accelerating the penetration of oxygen and moisture into the conductor interface. This degradation process will cause insulation failure of the sheath material before its rated service life, resulting in partial discharge in the power transmission system and, in severe cases, short circuits. Simultaneously, the uneven filler distribution leading to localized differences in thermal conductivity will create hot spot effects, further accelerating the thermo-oxidative aging rate of the material and forming a positive feedback loop of performance degradation.

[0089] Faced with the aforementioned problems, this application first recognizes the dynamic coupling challenge between crosslinking density control and filler distribution optimization in traditional processes. Specifically, the spatial heterogeneity of the interfacial bonding strength between the polymer matrix and the filler leads to a nonlinear interaction between the temperature field distribution and the filler dispersion state. To address this, this application attempts to explore a dynamic synergistic optimization path by establishing a correlation mechanism between the material's microstructure and macroscopic properties. On one hand, it considers obtaining the spatial distribution characteristics of bonding strength through molecular chain distribution data to provide a basis for adjusting the filler ratio. On the other hand, it attempts to construct a feedback mechanism between a temperature gradient distribution model and crosslinking density prediction to achieve dynamic correction of reaction parameters. After comparing multiple schemes, it was found that simply relying on static mixing process improvements cannot eliminate stress concentration caused by filler agglomeration, while optimizing the crosslinking reaction temperature alone is insufficient to solve the thermal conductivity differences caused by uneven filler distribution. Ultimately, it was determined that a closed-loop optimization system needs to be established, achieving synergistic improvement of material properties through multi-level linkage of bonding strength distribution identification, dynamic filler adjustment, temperature gradient modeling, and crosslinking density prediction.

[0090] Reference Figure 1 As shown, this application proposes a method for preparing a high-temperature resistant and aging-resistant cable sheath material, comprising:

[0091] Obtain molecular chain distribution data of the initial mixture of polymer matrix and inorganic filler, determine the bonding strength index in the mixture, and obtain the bonding strength distribution map;

[0092] Based on the bond strength distribution map, regional non-uniformity points are obtained, the addition ratio of inorganic fillers is adjusted, and the optimized filler distribution parameters are determined.

[0093] The compatibility enhancement coefficient is obtained from the optimized filler distribution parameters, and the temperature gradient change during the crosslinking reaction is calculated to obtain the temperature gradient distribution model.

[0094] Based on the temperature gradient distribution model, the predicted value of crosslinking density is obtained, the reaction residence time parameter is corrected, and the corrected crosslinking density control scheme is determined.

[0095] Structural stability indices were obtained from the modified crosslinking density control scheme, and the aging rate distribution under high temperature conditions was evaluated to obtain an aging rate prediction map.

[0096] Potential microcrack formation regions are obtained based on the aging rate prediction map. The amount of modifier in the polymer matrix is ​​adjusted through iterative optimization to determine the final material formulation parameters.

[0097] Among them, the bonding strength distribution map refers to the spatial distribution map reflecting the interfacial bonding strength generated by analyzing the molecular chain distribution data of the polymer matrix and inorganic filler mixture. Specifically, it can be generated by acquiring spectral data with an infrared spectrometer, extracting chemical bond characteristics with principal component analysis algorithm, and then using data visualization tools to locate weak areas of bonding between the filler and the matrix.

[0098] Regional non-uniformity points refer to spatial coordinates points in the bond strength distribution map where the local bond strength is significantly lower than the average value. Specifically, image processing algorithms can be used to analyze the microstructure image and then combined with statistical methods to calculate the spatial density, which can be used to identify areas of filler agglomeration or poor dispersion.

[0099] The adjustment of the inorganic filler addition ratio refers to a global optimization method based on swarm intelligence. Specifically, the uniformity of filler distribution can be evaluated by setting a fitness function, and the filler ratio parameters can be iteratively updated to dynamically correct the filler addition ratio in order to eliminate local stress concentration.

[0100] The temperature gradient distribution model is a mathematical model that characterizes the temperature field changes during the cross-linking reaction. Specifically, it can be used to calculate the heat conduction process by combining finite element simulation with numerical analysis methods to predict the cross-linking reaction rate distribution.

[0101] The predicted crosslinking density is a quantitative indicator of the degree of crosslinking network formation calculated based on a temperature gradient distribution model. Specifically, it can be derived by combining the Arrhenius equation with reaction kinetic parameters and is used to guide the dynamic adjustment of reaction residence time.

[0102] The aging rate prediction map is a probability distribution map generated by Monte Carlo simulation that reflects the high-temperature aging process of materials. Specifically, it can be generated by combining molecular dynamics models with kernel density estimation algorithms to correlate microscopic defects with macroscopic aging behavior.

[0103] Iterative optimization and cyclic adjustment refers to the process of continuously correcting the amount of modifier through a feedback mechanism. Specifically, a linear regression algorithm can be used to establish a mapping relationship between formulation parameters and aging rate, which is used to balance anti-aging performance and mechanical strength.

[0104] The core innovation of this application lies in establishing a dynamic synergistic mechanism for crosslinking density control and filler distribution optimization. By locating micro-defect regions through a bond strength distribution map, and by combining a particle swarm optimization algorithm to adjust the filler ratio in real time to eliminate uneven dispersion, the crosslinking reaction process is predicted using a temperature gradient model, and the formulation parameters are reversely corrected through an aging rate prediction map, thus forming a closed-loop optimization system for material performance.

[0105] The working process and principle of this application are as follows: First, molecular chain distribution data of the initial mixture of polymer matrix and inorganic filler are obtained. Then, the bonding strength index in the mixture is determined through analysis, and a bonding strength distribution map is generated. This step identifies weak regions at the filler-matrix interface at the molecular level, providing spatial positioning basis for subsequent optimization.

[0106] Based on the bond strength distribution map, regional non-uniformity points can be identified, and the addition ratio of inorganic fillers can be dynamically adjusted using the Particle Swarm Optimization (PSO) algorithm. This process directionally corrects regional non-uniformity points in the bond strength distribution, avoiding local stress concentrations caused by traditional uniform addition, and determining the optimized filler distribution parameters.

[0107] The compatibility enhancement coefficient is obtained from the optimized filler distribution parameters, and the temperature gradient change during the crosslinking reaction is calculated. By utilizing the coupling relationship between the compatibility enhancement coefficient and the temperature gradient distribution, a temperature field model of the crosslinking reaction process is constructed, resulting in a temperature gradient distribution model. This allows the control of the crosslinking density to respond to changes in the filler distribution.

[0108] The crosslinking density is predicted using a temperature gradient distribution model, and the reaction residence time parameter is corrected. Through iterative correction of the predicted crosslinking density value and the actual parameter, a dynamic control strategy for the reaction residence time is formed, overcoming the crosslinking structure defects caused by traditional fixed process parameters, and determining the corrected crosslinking density control scheme.

[0109] Structural stability indices were obtained from the modified crosslinking density control scheme to assess the aging rate distribution under high-temperature conditions. An aging rate prediction model was established based on the structural stability indices, correlating microscopic defects in the material with macroscopic aging behavior to obtain an aging rate prediction map.

[0110] Potential microcrack formation regions are identified based on aging rate prediction maps, and the amount of modifier in the polymer matrix is ​​adjusted through iterative optimization cycles. This step guides the precise control of the modifier dosage. Ultimately, through multi-parameter iterative optimization cycles, a balance between anti-aging performance and mechanical strength is achieved in the formulation design, determining the final material formulation parameters.

[0111] In the process of obtaining molecular chain distribution data of the initial mixture of polymer matrix and inorganic filler, determining the bonding strength index in the mixture, and obtaining the bonding strength distribution map in the above embodiments, there are problems such as inaccurate evaluation of mixing uniformity, incomplete analysis of chemical bond characteristics, and inaccurate method of bonding strength index extraction. As a result, it is impossible to reliably generate a bonding strength distribution map that reflects the true interface bonding of the material.

[0112] Reference Figure 2As shown, this application further proposes to obtain molecular chain distribution data of the initial mixture of polymer matrix and inorganic filler, determine the bonding strength index in the mixture, and obtain a bonding strength distribution map, including the following steps: obtaining microstructure images of the polymer matrix and inorganic filler mixture using a scanning electron microscope to determine the mixture characteristics; analyzing the microstructure images using image processing algorithms to obtain material mixing uniformity data; obtaining spectral data of the mixture using an infrared spectrometer to determine the molecular chain distribution characteristics; processing the spectral data using Fourier transform to obtain chemical bond characteristics; extracting the bonding strength index using principal component analysis based on the chemical bond characteristics; and generating a bonding strength distribution map using a data visualization tool based on the bonding strength index.

[0113] The scanning electron microscope (SEM) has an imaging resolution of 5-10 nanometers to capture the microscopic morphology of the interface between the filler and the matrix. Image processing algorithms can utilize gray-level co-occurrence matrix analysis to analyze texture features, converting the microstructure image into a quantitative index of mixing uniformity. The infrared spectrometer covers a detection band of 4000-400 cm⁻¹, identifying molecular chain distribution characteristics by detecting characteristic absorption peaks of CO or Si-O bonds. Spectral data with peak intensities exceeding a preset threshold in a specific band are acquired, triggering Fourier transform processing to convert the time-domain spectral data into a frequency-domain signal, analyzing chemical bond vibrational modes. The preset threshold should be determined based on a standard spectral database of characteristic chemical bonds between the polymer matrix and the inorganic filler. Principal component analysis (PCA) algorithms retain principal components with a contribution rate greater than 85% through dimensionality reduction, eliminating noise interference in the spectral data. Data visualization tools can use 3D heatmaps to present the spatial distribution of bonding intensity, with different color levels corresponding to intensity differences.

[0114] Through the above technical solutions, this application achieves multi-dimensional characterization and analysis of mixtures of polymer matrix and inorganic fillers. The microstructure images obtained by scanning electron microscopy intuitively reflect the physical morphology of the mixture, providing a reliable basis for subsequent homogeneity analysis. Image processing algorithms transform visual information into quantitative data, avoiding subjective judgment errors. Infrared spectroscopy analysis reveals the molecular chain distribution characteristics, and combined with Fourier transform processing, accurately resolves the differences in chemical bond types and strengths. Principal component analysis algorithms extract key indicators from multi-dimensional data, eliminating redundant information interference. The final generated bond strength distribution map transforms abstract indicators into intuitive spatial distribution information, providing a scientific basis for subsequent filler distribution optimization. This multi-dimensional, multi-scale analysis method significantly improves the accuracy and reliability of bond strength indicator extraction, laying the foundation for the preparation of high-performance cable sheath materials.

[0115] In the process of obtaining regional non-uniform points based on the bond strength distribution map, adjusting the addition ratio of inorganic fillers, and determining the optimized filler distribution parameters in the above embodiments, if the spatial distribution characteristics of the non-uniform points cannot be accurately quantified, the optimization algorithm may not be able to effectively adjust the filler ratio, and thus may not be able to eliminate the stress concentration and accelerated aging problems caused by the uneven distribution of fillers inside the material.

[0116] Reference Figure 3 As shown, this application further proposes to extract the coordinates of regional non-uniform points from the bond strength distribution map using a data visualization tool to obtain non-uniform point distribution data; if the number of non-uniform point distribution data exceeds a preset threshold, a statistical analysis method is used to calculate the spatial density of non-uniform points to obtain density distribution characteristics; based on the density distribution characteristics, a particle swarm optimization algorithm is used to adjust the proportion of inorganic fillers to determine the optimized filler distribution parameters.

[0117] The data visualization tools can convert the intensity differences in the bond strength distribution map into spatial coordinate data using heatmaps or scatter plots. For example, the edge detection algorithm in the OpenCV library can be used to identify the boundary coordinates of regions with abrupt changes in intensity. When the number of non-uniform points detected exceeds a preset threshold, such as more than 5 non-uniform points per square millimeter, a kernel density estimation algorithm is used to calculate the spatial density distribution and generate a density contour map to characterize the degree of filler aggregation. The preset threshold is determined experimentally based on a dynamic standard established by the correlation between material properties and bond strength distribution. When the number of non-uniform points detected does not exceed the preset threshold, the original parameters are maintained. The Particle Swarm Optimization (PSO) algorithm defines high-density regions in the density distribution characteristics, for example, regions with density values ​​exceeding 0.8 as key adjustment regions. A fitness function is used to establish a mapping relationship between the density gradient change rate and the filler ratio adjustment amount, thereby reducing the filler addition amount corresponding to high-density regions.

[0118] Through the above technical solution, this application achieves precise quantification and effective control of the non-uniformity of filler distribution within materials. By employing data visualization and statistical analysis methods, the spatial distribution characteristics of non-uniform points are accurately captured, providing precise input data for particle swarm optimization algorithms. This optimization strategy based on spatial density characteristics can selectively adjust the filler ratio, effectively eliminating stress concentration areas within the material and significantly improving its uniformity and stability. Furthermore, the method, through the application of intelligent algorithms, achieves automated optimization of filler distribution, not only improving optimization efficiency but also adapting to material preparation processes under different formulations and process conditions, demonstrating broad applicability. Ultimately, this optimization method effectively suppresses stress concentration and accelerated aging problems caused by uneven filler distribution, significantly improving the high-temperature resistance and anti-aging properties of cable sheath materials.

[0119] In the embodiments of this application, a technical solution is proposed to improve material compatibility by determining optimized filler distribution parameters through adjusting the addition ratio of inorganic fillers. However, in this process, changes in filler distribution parameters may lead to uneven temperature distribution during the crosslinking reaction, thereby affecting the accuracy of crosslinking density control. If the temperature gradient change cannot be accurately calculated and a reliable distribution model cannot be constructed, the crosslinking reaction conditions may deviate from expectations, ultimately affecting the high-temperature resistance and anti-aging properties of the material.

[0120] Reference Figure 4 As shown, this application further proposes to obtain a compatibility enhancement coefficient from the optimized filler distribution parameters, calculate the temperature gradient change during the crosslinking reaction, and obtain a temperature gradient distribution model, including the following steps: obtaining optimized parameters from the filler distribution, extracting the compatibility enhancement coefficient using data processing technology, and determining the coefficient value; constructing a finite element simulation model of the crosslinking reaction based on the compatibility enhancement coefficient, setting the boundary conditions of the reaction process, and generating initial simulation results; extracting temperature gradient data from the initial simulation results, calculating the gradient change using numerical analysis methods, and obtaining gradient distribution characteristics; if the gradient distribution characteristics exceed a preset threshold, adjusting the filler distribution parameters, re-executing the simulation calculation, and obtaining updated temperature gradient data; constructing a temperature gradient distribution model using an interpolation algorithm based on the updated temperature gradient data, and generating model parameters; verifying the accuracy of the temperature gradient distribution model based on the model parameters, adjusting the model structure using mesh optimization technology, and obtaining the final temperature gradient distribution model.

[0121] Data processing techniques, such as principal component analysis or multiple regression algorithms, are used to extract key factors related to compatibility from the packing distribution parameters. For example, the improvement coefficient can be determined by calculating the correlation coefficient between the packing spacing and the interfacial bonding energy. The finite element simulation model is constructed in conjunction with reaction kinetic equations, and boundary conditions can be set as the initial temperature field distribution and reaction rate parameters. Numerical analysis methods, such as the finite difference method or the finite volume method, are used to calculate the rate of change of the temperature gradient. A preset threshold can be set to ensure the temperature gradient does not exceed 5℃ / mm (adjusted according to actual needs). When the gradient distribution characteristics exceed the threshold, the packing distribution parameters are dynamically adjusted. Cubic spline interpolation or radial basis function interpolation is preferred for interpolation algorithms to convert discrete simulation data into a continuous temperature field distribution. Mesh optimization techniques eliminate distorted elements by adjusting the mesh density and shape.

[0122] Specifically, during the crosslinking reaction, a compatibility enhancement coefficient is first extracted from the optimized filler distribution parameters using principal component analysis. This coefficient, quantified to a value range of 0.5-0.8, reflects the degree of optimization of the interface bonding between the filler and the matrix. When constructing the finite element simulation model based on this coefficient, the boundary conditions of the reaction process are set as the initial temperature field distribution and reaction rate parameters, for example, setting the decomposition rate of the crosslinking agent to 0.02-0.05 mol / (m³·s). After the initial simulation results are generated, the temperature gradient change is calculated using the finite difference method. If a local gradient exceeding 5℃ / mm is detected, a filler distribution parameter adjustment mechanism is triggered, for example, reducing the filler concentration in the agglomerated region by 2-4%. The updated temperature gradient data is then used to generate a continuous distribution model through radial basis function interpolation, with the interpolation node spacing controlled within 0.5 mm to ensure accuracy. During the model verification stage, mesh optimization techniques are used to eliminate temperature field distortion caused by mesh distortion, for example, refining the boundary layer mesh to 0.1 mm through adaptive mesh refinement. The final temperature gradient distribution model can accurately predict temperature fluctuations during the crosslinking reaction process, providing data support with an error of less than ±2% for subsequent crosslinking density control, thereby ensuring the structural stability and anti-aging performance of the material under high temperature conditions.

[0123] Through the above technical solutions, this application achieves accurate calculation of the crosslinking reaction temperature gradient and construction of a reliable distribution model. By extracting the compatibility enhancement coefficient from the filler distribution parameters, a key input is provided for the crosslinking reaction simulation, improving the model's accuracy. Using finite element simulation and numerical analysis methods, the temperature gradient changes during the crosslinking reaction process can be comprehensively captured, identifying potential regions of uneven temperature distribution. A feedback optimization mechanism that dynamically adjusts the filler distribution parameters and re-simulates ensures the consistency between the model parameters and actual reaction conditions. The application of interpolation algorithms transforms discrete simulation data into a continuous temperature field distribution, more accurately reflecting the internal temperature change patterns of the material. The introduction of mesh optimization technology further eliminates simulation errors caused by unreasonable mesh partitioning, improving the model's predictive accuracy. This series of steps enables dynamic monitoring and active control of the crosslinking reaction temperature gradient, providing a reliable data foundation for subsequent crosslinking density control. This effectively solves the problem of uneven crosslinking reaction temperature distribution that may be caused by filler distribution adjustments, laying the foundation for improving the material's high-temperature resistance and anti-aging properties.

[0124] In the embodiments of this application, a technical solution is proposed to obtain the predicted value of crosslinking density through a temperature gradient distribution model to control the material properties. However, when the initial prediction result deviates from the target range, the traditional parameter adjustment method is difficult to converge to the optimal solution quickly, resulting in the crosslinking density control scheme having the problems of response lag and parameter inaccuracy, which in turn affects the structural stability of the material in a high-temperature environment.

[0125] Reference Figure 5 As shown, this application further proposes to calculate the predicted value of crosslinking density using a temperature gradient distribution model to obtain an initial prediction result; if the initial prediction result deviates from the target range, the deviation value is extracted from the prediction result to determine the adjustment direction; the gradient descent algorithm is used to iteratively optimize the reaction residence time parameter based on the deviation value to obtain the adjusted parameter value; the temperature gradient distribution model is updated using the adjusted parameter value to generate a new predicted value of crosslinking density; if the new predicted value still deviates from the target range, the iterative optimization steps are repeated until the predicted value meets the target range to obtain a stable parameter set; a crosslinking density control scheme is generated based on the stable parameter set, and the final control parameters are output; the temperature gradient distribution model is verified using the final control parameters to confirm the consistency of the predicted value of crosslinking density.

[0126] The calculation process of the temperature gradient distribution model can be constructed by combining the material's thermal conductivity coefficient and reaction kinetic parameters. For example, when using the finite difference method to solve the heat conduction equation, the mesh generation accuracy can be controlled within the range of 0.1-0.5 mm. Deviation values ​​are extracted by comparing the difference between the predicted value and the target range boundary. When the absolute value of the deviation exceeds 5%, a parameter adjustment mechanism is triggered. The step size parameter of the gradient descent algorithm can be set to 0.01-0.1, and the reaction residence time parameter is adjusted incrementally by 0.5-2 seconds after each iteration. The maximum number of iterations is set to 10-20 to avoid infinite loops. The generation of a stable parameter set must satisfy the condition that the error of the predicted value is less than 1% for three consecutive iterations. Finally, the effectiveness of the control parameters is verified through orthogonal experiments.

[0127] Through the above technical solution, this application achieves precise control of crosslinking density. A closed-loop control system is constructed by establishing a dynamic feedback mechanism between the temperature gradient distribution model and the predicted crosslinking density. The gradient descent algorithm is used to iteratively optimize the reaction residence time parameter. Utilizing the local optimum search characteristic of this algorithm, it can quickly converge to a stable parameter set that meets the target range. The temperature gradient distribution model is updated synchronously after each parameter adjustment, forming a collaborative optimization mechanism between model parameters and process parameters. This closed-loop control method based on deviation feedback effectively solves the problem of parameter adjustment lag in traditional open-loop control. Simultaneously, through dynamic matching of model parameters and process parameters, the accuracy and stability of crosslinking density control are improved. This, in turn, improves the structural stability of the material under high-temperature environments and extends the service life of the cable sheath.

[0128] Specifically, traditional methods for assessing the aging rate of materials under high-temperature conditions suffer from high data dispersion and discontinuous prediction models, making it impossible to accurately capture the dynamic changes in the aging rate and effectively identify potential aging risk areas.

[0129] Reference Figure 6As shown, this application further proposes to obtain structural stability indicators from the modified crosslinking density control scheme, evaluate the aging rate distribution under high temperature conditions, and obtain an aging rate prediction map, including the following steps: obtaining structural stability parameters from the optimized crosslinking density control scheme, extracting key indicators using a preset molecular dynamics model to obtain a structural stability parameter set; calculating the aging rate under high temperature conditions using Monte Carlo simulation on the structural stability parameter set to generate an aging rate distribution dataset; if the variance of the aging rate distribution dataset exceeds a preset threshold, normalizing the data to obtain a standardized aging rate distribution; generating a continuous probability density function using a kernel density estimation algorithm based on the standardized aging rate distribution to obtain an aging rate prediction curve; and extracting key inflection points and trend features from the aging rate prediction curve to generate an aging rate prediction distribution map.

[0130] The molecular dynamics model can employ either an all-atom model or a coarse-grained model, such as the LAMMPS or GROMACS software packages. It simulates the molecular motion trajectories of the material's microstructure, extracting bond length fluctuations and free volume fraction as key indicators. The Monte Carlo simulation iteration count can be set to over 10,000, with temperature and stress parameters randomly adjusted in each iteration to calculate the corresponding aging rate. Normalization can be achieved using Box-Cox transformation or Z-score standardization, setting a variance threshold for the aging rate distribution dataset, for example, triggering data processing when the variance exceeds 0.5. The bandwidth parameter for kernel density estimation can be adaptively determined using the Silverman rule, for example, by selecting the Epanechnikov kernel function for nonparametric fitting. Inflection point extraction can employ the second derivative zero-point detection method, for example, setting a curvature change threshold of 0.01 as the inflection point criterion.

[0131] Specifically, structural stability parameters are obtained through molecular dynamics models, where the bond angle distribution and cross-linking network topology of the material's microstructure are quantified into computable physical quantities. Monte Carlo simulations, under high-temperature conditions, generate an aging rate dataset encompassing thermal fluctuations and stress relaxation effects through probabilistic sampling. When the data variance exceeds a threshold, normalization transforms the skewed distribution into a Gaussian distribution, eliminating the interference of extreme values ​​on subsequent modeling. The preset threshold is determined based on a dynamic standard established according to the statistical characteristics of the material's aging behavior and engineering reliability requirements. The kernel density estimation algorithm constructs a continuous probability density function by smoothing discrete data points, for example, employing a local bandwidth adjustment strategy in regions of abrupt changes in aging rate to improve curve fitting accuracy. During inflection point detection, regions where curvature changes exceed a preset threshold are marked as high-risk areas; for example, locations in the predicted curve where the slope abruptly exceeds 15% are identified as potential accelerated aging regions. By mapping the continuous probability density function to the material's three-dimensional structural model, the generated hotspot distribution map visually displays the differences in anti-aging performance across different regions, providing spatially resolved quantitative evidence for formulation optimization.

[0132] Through the above technical solution, this application achieves dynamic evaluation of material aging rates under high-temperature environments. By integrating molecular dynamics models and Monte Carlo simulations, an evaluation system that accurately captures changes in the material's microstructure is constructed. The use of normalization processing and kernel density estimation algorithms effectively solves the problems of high data dispersion and discontinuous prediction models in traditional methods. By generating continuous aging rate prediction curves and visualized prediction distribution maps, the dynamic changes in the aging rate within the material are accurately captured, providing a quantitative basis for identifying potential aging risk areas. This method improves the accuracy and reliability of aging rate evaluation, providing important support for optimizing material structural stability and extending service life.

[0133] Specifically, traditional methods are difficult to effectively quantify the proportion of crack formation risk areas and lack a dynamic formulation adjustment mechanism based on regional distribution characteristics, resulting in a lack of precise correlation between modifier dosage and crack suppression effect.

[0134] Reference Figure 7 As shown, this application further proposes to obtain a material aging rate prediction map, segment the potential microcrack generation region using image processing technology to obtain regional distribution data; calculate the proportion of microcrack generation region from the regional distribution data, and obtain the regional proportion value using statistical analysis methods; if the regional proportion value is higher than a preset threshold, an iterative optimization loop is initiated to adjust the dosage of matrix modifier, obtaining the adjusted dosage data; based on the adjusted dosage data, the material formulation parameters are updated, and the influence of the formulation parameters on the aging rate is predicted using a linear regression algorithm to obtain the prediction result; the aging rate change trend is extracted from the prediction result, and combined with the crack generation probability, it is determined whether the formulation parameters meet the requirements, obtaining the optimized formulation parameters.

[0135] Image processing techniques can employ edge detection or region growing algorithms to segment the aging rate prediction map, such as using the Canny operator to detect microcrack boundaries. Statistical analysis methods can utilize hypothesis testing or analysis of variance to calculate the region proportion; for example, an optimization loop can be triggered when the crack area proportion exceeds 15% (a preset threshold) (set according to actual needs). The dosage of matrix modifier can be adjusted incrementally using gradient descent, with each adjustment controlled within ±5%. Linear regression algorithms can establish a multiple regression equation between formulation parameters and aging rate, for example, constructing a prediction model using modifier concentration and dispersion as independent variables. The crack formation probability can be calculated using Monte Carlo simulation.

[0136] Specifically, after the aging rate prediction map is input into the image processing system, the potential microcrack generation area is segmented into independent data blocks, for example, by marking crack areas as bright color blocks using a pixel clustering algorithm. The regional distribution data is statistically analyzed to generate quantitative indicators, such as calculating the percentage of the bright color block area to the total area. When this percentage exceeds a preset threshold, the formulation optimization program is automatically triggered; for example, when the crack area percentage exceeds 15%, an optimization loop is initiated. The matrix modifier dosage is adjusted in predetermined steps during each iteration, for example, increasing the silane coupling agent content by 0.3% each time. The updated formulation parameters are input into a linear regression model to predict the aging rate trend; for example, the model outputs that a 1% increase in modifier concentration can reduce the aging rate by 0.8%. Finally, the optimized formulation is determined through dual criteria; for example, a parameter combination that simultaneously satisfies an aging rate reduction ≥20% and a crack generation probability ≤7% is selected as the final solution. The entire process achieves dynamic optimization of formulation parameters through a closed-loop feedback mechanism, effectively solving the problem of insufficient correlation between modifier dosage and crack suppression effect in traditional methods.

[0137] Through the above technical solutions, this application achieves accurate identification and quantitative analysis of potential microcrack formation regions and establishes a dynamic formulation adjustment mechanism based on region proportion. This effectively controls the crack formation risk of materials under high-temperature environments and improves the correlation accuracy between modifier dosage and crack suppression effect. Furthermore, by combining iterative optimization and predictive models, closed-loop optimization of formulation parameters is achieved, ensuring that the material maintains its anti-aging properties while maximally suppressing microcrack formation. This method not only improves the high-temperature stability of the material but also provides strong support for the long-term reliability of cable sheath materials.

[0138] To further verify the high-temperature resistance and anti-aging properties of the cable sheath material prepared by the method of this embodiment after determining the final material formulation parameters, this application further verifies the actual processing performance of the material in the extrusion molding process, based on the above embodiments, including the following steps:

[0139] Extrusion molding simulation data were obtained from the final material formulation parameters. Finite element analysis was used to verify the balance between mechanical strength and thermal stability, resulting in a validated sheath material preparation protocol. Long-term reliability indicators were obtained for the validated sheath material preparation protocol. All simulation results were integrated using data fusion methods to determine whether the overall performance meets the requirements for high temperature resistance and anti-aging, thus obtaining an optimized cable sheath material design scheme.

[0140] Specifically, refer to Figure 8 As shown, this application further proposes to extract initial formulation parameters from a material formulation parameter database, remove outliers using data cleaning technology, and obtain a standardized formulation parameter set; based on the standardized formulation parameter set, use extrusion molding simulation software to generate simulation data of the molding process and obtain extrusion molding data; for the extrusion molding data, use the finite element analysis method to construct a geometric model of the sheath material and generate a finite element analysis mesh model; extract stress distribution data and temperature distribution data from the finite element analysis mesh model, perform mechanical strength verification and thermal stability analysis, and determine the balance state between strength and thermal performance; based on the balance state data of strength and thermal performance, generate a sheath material preparation protocol, and use template matching technology to output a standardized protocol document.

[0141] The data cleaning technique employs the Z-score algorithm to identify and remove outlier parameter values ​​that deviate from the mean ±3σ range. For example, if a filler ratio parameter exceeds this outlier value, the parameter is marked as invalid data and removed from the dataset. The extrusion molding simulation software can be built based on ANSYS's Polyflow module, simulating parameters including screw speed, melt temperature, and pressure distribution. The finite element analysis mesh model uses hexahedral elements for discretization, with element sizes set to 0.1-0.5 mm. Local mesh refinement is performed in stress concentration areas, such as reducing the mesh size to 0.05 mm at material edges. Mechanical strength verification is achieved by comparing the maximum stress value with the material's yield strength; thermal stability analysis is performed by monitoring whether the temperature distribution exceeds the material's glass transition temperature. Template matching technology automatically fills the verification results into the corresponding fields using a predefined XML format protocol template.

[0142] Specifically, after the initial formulation parameters are extracted from the database, outliers are first filtered using data cleaning techniques. For example, if the proportion of inorganic filler exceeds 15-25%, the parameter is discarded and resampled to ensure the reliability of the input data. The standardized parameter set is then input into the extrusion molding simulation software to generate simulation data including melt flow rate, pressure distribution, and temperature field changes. For instance, at a screw speed of 300 rpm, the pressure fluctuation at the melt front end is controlled within ±5 MPa. In the finite element mesh model constructed based on the simulation data, stress distribution data is obtained through nodal displacement calculations, while temperature distribution data is solved using the heat conduction equation. For example, in the crosslinking region, the transient thermal analysis module is used to calculate the temperature gradient change. During the mechanical strength verification stage, if the maximum stress concentration area reaches 25 MPa and exceeds 75% of the material's yield strength, a parameter adjustment command is triggered. In the thermal stability analysis, if the area exceeding 160℃ accounts for 8% of the total area, the thermal performance is deemed substandard. When both strength and thermal performance meet the requirements, the protocol document is automatically generated using a template engine, for example, by writing a verification pass flag.<validation_status> The field is configured, and the process parameters are output in the ISO standard format. This closed-loop verification mechanism ensures that the material formulation parameters simultaneously meet the dual requirements of mechanical strength and thermal stability during the extrusion molding process, eliminating performance imbalances caused by data deviations or parameter anomalies.

[0143] Through the above technical solutions, this application effectively solves the technical problem of imbalance between mechanical strength and thermal stability during extrusion molding caused by abnormal formulation parameters. Data cleaning technology ensures the reliability of input parameters and avoids simulation distortion caused by outliers; the combination of extrusion molding simulation and finite element analysis achieves accurate mapping between process parameters and material properties, where stress distribution data can identify weak areas in the material structure, and temperature distribution data directly reflects thermal conductivity; the judgment logic of simultaneous verification of mechanical strength and thermal stability can promptly detect the risk of coupled failure between high-temperature softening and stress concentration; the standardized protocol document generated by template matching ensures the consistency and repeatability of process parameter transmission, ultimately ensuring that the sheath material meets both structural reliability and high-temperature resistance requirements during the molding process.

[0144] Specifically, refer to Figure 9As shown, this application further proposes to obtain long-term reliability index data from the verified sheath material preparation protocol, perform structured processing using a preset standardized format to obtain a reliability index dataset; for the reliability index dataset, principal component analysis algorithm is used to extract the main feature vectors to determine key reliability parameters; performance data under ultra-high temperature environment is obtained from simulation results, and the key reliability parameters and simulated performance data are fused using a weighted average method to obtain a comprehensive performance dataset; if the index value in the comprehensive performance dataset is greater than the preset ultra-high temperature threshold, the material is judged to meet the ultra-high temperature requirements, and a preliminary qualified material scheme is obtained; based on the preliminary qualified material scheme, the sheath material formulation parameters are optimized using a genetic algorithm to obtain an optimized material formulation scheme; the anti-aging performance of the optimized material formulation scheme under ultra-high temperature environment is simulated through finite element analysis to determine the stability of the material in long-term use; if the stability judgment result meets the preset reliability threshold, the final cable sheath material design scheme is determined.

[0145] The preset standardized format can be JSON or XML data structure to unify reliability index data fields from different sources; the principal component analysis algorithm filters feature vectors by calculating the variance contribution rate, for example, setting a variance contribution rate threshold of ≥85% to determine key reliability parameters; in the weighted average method, the weight coefficients are dynamically adjusted according to the signal-to-noise ratio of the data source, for example, the weight of ultra-high temperature performance data is set to 0.6-0.8, and the weight of key reliability parameters is set to 0.2-0.4; the genetic algorithm adopts a tournament selection strategy, with the crossover probability set to 0.7-0.9 and the mutation probability set to 0.01-0.05, and generates optimized formula parameter combinations through iterative calculation; in the finite element analysis, the simulation time step for the material's anti-aging performance is set to 1-10 years, and the temperature load application range is controlled within 800-1200℃.

[0146] Specifically, after structuring, long-term reliability index data eliminates format differences between different data sources, providing a unified input for principal component analysis. When extracting key feature vectors using the principal component analysis algorithm, redundant features are automatically filtered out, such as reducing the original 20-dimensional data to 3-5 principal components, improving data processing efficiency. When fusing key reliability parameters with ultra-high temperature performance data, a dynamic weight allocation mechanism adjusts the fusion ratio according to data quality; for example, when the signal-to-noise ratio of ultra-high temperature performance data is below 2.0, its weight is automatically reduced to below 0.5. When the comprehensive performance index exceeds a preset threshold (set according to requirements), a genetic algorithm initiates a global search, generating an optimized combination of formulation parameters by simulating the biological evolution process; for example, after 100 iterations, a formulation with a 15% improvement in thermal stability is obtained. When finite element analysis simulates the anti-aging performance of the optimized formulation, a nonlinear material model is used to calculate stress relaxation and creep effects; for example, the material deformation rate after 10 years at 1000℃ is simulated to be ≤3%. Finally, the effectiveness of the design scheme is determined based on the reliability threshold, forming a closed-loop optimization mechanism; for example, when the error between the anti-aging performance simulation results and experimental data is ≤5%, the scheme is deemed to have passed verification.

[0147] Through the above technical solutions, this application effectively solves the problem of inaccurate weight allocation during the fusion of multi-source heterogeneous data. It eliminates redundant features through principal component analysis, thereby improving data processing efficiency. It uses a dynamic weight allocation mechanism to balance the differences in importance between reliability parameters and high-temperature performance data, ensuring the objectivity of the evaluation results. It combines the global search capability of genetic algorithms to overcome the limitations of local optima and achieve precise optimization of formulation parameters. It verifies long-term anti-aging performance through finite element analysis, forming a closed-loop optimization mechanism, which significantly improves the accuracy of predicting the stability of materials under ultra-high temperature environments.

[0148] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A process for the preparation of a high temperature resistant and ageing resistant cable jacket material, characterized in that, The application relates to a method for determining material formula parameters of a polymer matrix and an inorganic filler, and belongs to the technical field of material formula design. The method comprises the following steps: Obtaining molecular chain distribution data of an initial mixture of a polymer matrix and an inorganic filler, determining a bonding strength index in the mixture, and obtaining a bonding strength distribution graph. Obtaining regional uneven points from the bonding strength distribution graph, adjusting an adding proportion of the inorganic filler, and determining an optimized filler distribution parameter. Obtaining a compatibility improvement coefficient from the optimized filler distribution parameter, calculating a temperature gradient change in a cross-linking reaction process, and obtaining a temperature gradient distribution model. Obtaining a cross-linking density prediction value for the temperature gradient distribution model, correcting a reaction residence time parameter, and determining a corrected cross-linking density control scheme. Obtaining a structure stability index from the corrected cross-linking density control scheme, evaluating an aging rate distribution under a high-temperature environment, and obtaining an aging rate prediction graph.

2. The process for the preparation of high temperature resistant and ageing resistant cable jacket material as claimed in claim 1, wherein: Obtaining a potential micro-crack generation area from the aging rate prediction graph, adjusting a modifier amount in the polymer matrix through an iterative optimization cycle, and determining a final material formula parameter. The method comprises the following steps of obtaining molecular chain distribution data of an initial mixture of a polymer matrix and an inorganic filler, determining a bonding strength index in the mixture, and obtaining a bonding strength distribution graph. Obtaining a microstructure image of the mixture of the polymer matrix and the inorganic filler through a scanning electron microscope, and determining characteristics of the mixture. Obtaining material mixing uniformity data by analyzing the microstructure image through an image processing algorithm. Obtaining spectral data of the mixture through an infrared spectrometer, and determining molecular chain distribution characteristics. Obtaining chemical bond characteristics by processing the spectral data through a Fourier transform. Extracting the bonding strength index by using a principal component analysis algorithm according to the chemical bond characteristics.

3. The process for the preparation of high temperature resistant and ageing resistant cable jacketing material as claimed in claim 1 wherein: Generating the bonding strength distribution graph by using a data visualization tool through the bonding strength index. The method comprises the following steps of obtaining regional uneven points from the bonding strength distribution graph, adjusting an adding proportion of the inorganic filler, and determining an optimized filler distribution parameter. Extracting regional uneven point coordinates from the bonding strength distribution graph through a data visualization tool, and obtaining uneven point distribution data. If the number of the uneven point distribution data exceeds a preset threshold, calculating a spatial density of the uneven points by using a statistical analysis method, and obtaining density distribution characteristics.

4. The process for the preparation of high temperature and ageing resistant cable jacketing material as claimed in claim 1 wherein: Adjusting the proportion of the inorganic filler by using a particle swarm optimization algorithm according to the density distribution characteristics, and determining the optimized filler distribution parameter. The method comprises the following steps of obtaining an optimization parameter from the filler distribution, extracting a compatibility improvement coefficient by using a data processing technology, and determining a coefficient value. Constructing a cross-linking reaction finite element simulation model according to the compatibility improvement coefficient, setting boundary conditions of the reaction process, and generating an initial simulation result. Extracting temperature gradient data from the initial simulation result, calculating gradient changes by using a numerical analysis method, and obtaining gradient distribution characteristics. If the gradient distribution characteristics exceed a preset threshold, adjusting the filler distribution parameter, re-executing simulation calculation, and obtaining updated temperature gradient data. Constructing a temperature gradient distribution model by using an interpolation algorithm through the updated temperature gradient data, and generating model parameters. According to the model parameters, verify the accuracy of the temperature gradient distribution model, and adjust the model structure by using the grid optimization technology to obtain the final temperature gradient distribution model.

5. The process for the preparation of high temperature and ageing resistant cable jacketing material as claimed in claim 1 wherein: To obtain the crosslinking density prediction value from the temperature gradient distribution model, correct the reaction residence time parameter, and determine the corrected crosslinking density control scheme, including the following steps: Calculate the crosslinking density prediction value through the temperature gradient distribution model to obtain the initial prediction result; If the initial prediction result deviates from the target range, extract the deviation value from the prediction result and determine the adjustment direction; Use the gradient descent algorithm to iteratively optimize the reaction residence time parameter according to the deviation value to obtain the adjusted parameter value; Update the temperature gradient distribution model with the adjusted parameter value to generate a new crosslinking density prediction value; If the new prediction value still deviates from the target range, repeat the iterative optimization step until the prediction value meets the target range to obtain a stable parameter set; Generate a crosslinking density control scheme according to the stable parameter set and output the final control parameters; Verify the temperature gradient distribution model with the final control parameters to confirm the consistency of the crosslinking density prediction value.

6. The process for the preparation of high temperature and ageing resistant cable jacketing material as claimed in claim 1 wherein: Obtain the structure stability index from the corrected crosslinking density control scheme to evaluate the aging rate distribution under high temperature environment and obtain the aging rate prediction graph, including the following steps: Obtain the structure stability parameter from the optimized crosslinking density control scheme, extract the key index using the preset molecular dynamics model, and obtain the structure stability parameter set; Calculate the aging rate under high temperature environment for the structure stability parameter set through Monte Carlo simulation to generate an aging rate distribution data set; If the variance of the aging rate distribution data set exceeds the preset threshold, normalize the data to obtain the standardized aging rate distribution; According to the standardized aging rate distribution, generate a continuous probability density function using the kernel density estimation algorithm to obtain the aging rate prediction curve; For the aging rate prediction curve, extract the key inflection point and trend characteristics to generate the aging rate prediction distribution graph.

7. The process for the preparation of high temperature and ageing resistant cable jacketing material as claimed in claim 1 wherein: According to the aging rate prediction graph, obtain the potential microcrack generation area, adjust the amount of modifier in the polymer matrix through an iterative optimization cycle, and determine the final material formulation parameters, including the following steps: Obtain the material aging rate prediction graph, segment the potential microcrack generation area through image processing technology to obtain the area distribution data; Calculate the microcrack generation area ratio from the area distribution data and use statistical analysis methods to obtain the area ratio value; If the area ratio value is higher than the preset threshold, start the iterative optimization cycle, adjust the amount of matrix modifier, and obtain the adjusted amount data; According to the adjusted amount data, update the material formulation parameters, use the linear regression algorithm to predict the influence of the formulation parameters on the aging rate, and obtain the prediction result; Extract the aging rate trend from the prediction result and combine the crack generation probability to determine whether the formulation parameters meet the requirements to obtain the optimized formulation parameters.

8. The process for the preparation of high temperature and ageing resistant cable jacketing material as claimed in claim 1 wherein: Further including the following steps: Obtain the extrusion molding simulation data from the final material formulation parameters, verify the balance of mechanical strength and thermal stability using finite element analysis method to obtain the verified sheath material preparation protocol; The long-term reliability index is obtained for the jacket material preparation protocol that passes the verification, and all simulation results are integrated by a data fusion method to determine whether the overall performance meets the high-temperature resistance and aging resistance requirements, and an optimized cable jacket material design scheme is obtained.

9. The process for the preparation of high temperature and ageing resistant cable jacket material as claimed in claim 8, wherein the said process is characterized by: Extrusion molding simulation data is obtained from the final material formula parameters, the balance of mechanical strength and thermal stability is verified by a finite element analysis method, and a jacket material preparation protocol that passes the verification is obtained, including the following steps: Extract initial formula parameters from the material formula parameter database, remove outliers by data cleaning technology, and obtain a standardized formula parameter set; According to the standardized formula parameter set, generate simulation data of the molding process by using an extrusion molding simulation software, and obtain extrusion molding data; For the extrusion molding data, a finite element analysis method is used to build a geometric model of the jacket material, and a finite element analysis grid model is generated; Stress distribution data and temperature distribution data are extracted from the finite element analysis grid model, mechanical strength verification and thermal stability analysis are performed, and the balance state of strength and thermal performance is determined; According to the balance state data of strength and thermal performance, a jacket material preparation protocol is generated, and a template matching technology is used to output a standardized protocol document.

10. The process for the preparation of high temperature and ageing resistant cable jacket material as claimed in claim 8, wherein the said process is characterized by: The long-term reliability index is obtained for the jacket material preparation protocol that passes the verification, and all simulation results are integrated by a data fusion method to determine whether the overall performance meets the high-temperature resistance and aging resistance requirements, and an optimized cable jacket material design scheme is obtained, including the following steps: The long-term reliability index data is obtained from the jacket material preparation protocol that passes the verification, and is structured by using a pre-set standardized format to obtain a reliability index data set; For the reliability index data set, the main feature vector is extracted by using a principal component analysis algorithm to determine the key reliability parameters; Performance data under ultra-high temperature environment is obtained from the simulation results, and the key reliability parameters and simulation performance data are fused by a weighted average method to obtain a comprehensive performance data set; If the index value in the comprehensive performance data set is greater than the pre-set ultra-high temperature threshold value, it is determined that the material meets the ultra-high temperature requirement, and a preliminary qualified material scheme is obtained; According to the preliminary qualified material scheme, a genetic algorithm is used to optimize the jacket material formula parameters to obtain an optimized material formula scheme; The anti-aging performance of the optimized material formula scheme under ultra-high temperature environment is simulated by a finite element analysis, and the stability of the material in long-term use is determined; If the stability determination result meets the pre-set reliability threshold value, the final cable jacket material design scheme is determined.

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