Prediction method for weather resistance degradation of curing agent based on finite element analysis

Abstract

The invention relates to the technical field of finite element analysis, in particular to a method for predicting degradation of weather resistance of a curing agent based on finite element analysis. The method comprises the following steps: performing finite element analysis processing on a curing agent reaction molecular structure based on curing agent material characteristic data and curing agent application scene environment data to obtain finite element curing agent reaction molecular structure data; according to the finite element curing agent reaction molecular structure data, performing curing agent broken bond behavior characteristic analysis of environmental influence, and generating environmental influence curing agent broken bond behavior characteristic data; establishing a context stress tensor-broken bond evaluation model through the environment influence curing agent broken bond behavior characteristic data; and performing bond breaking and weather-resistant degradation relation mapping optimization on the environmental stress tensor-bond breaking evaluation model to obtain an environmental stress tensor-weather-resistant degradation evaluation model. According to the method, the weather resistance degradation of the curing agent is efficiently predicted through a finite element analysis technology.

Description

Technical Field

[0001] This invention relates to the field of finite element analysis technology, and in particular to a method for predicting the weather resistance degradation of curing agents based on finite element analysis. Background Technology

[0002] With the development of computer simulation technology, the finite element method has been gradually introduced into the field of materials performance research, simulating the response of materials under different loads by constructing mechanical models. As a core additive for the molding and performance assurance of polymer materials, the stability of the weather resistance of curing agents directly determines the service life and reliability of end products in complex environments. Among the many types of curing agents, methyltetrahydrophthalic anhydride, with its low toxicity, high reactivity, and excellent curing effect, is widely used in key areas such as electronic device packaging, composite material preparation, and architectural coatings. These applications are often accompanied by harsh environmental tests, such as continuous temperature fluctuations and humid heat cycling in electronic packaging, long-term exposure of outdoor coatings to ultraviolet radiation and wind and rain erosion, and the environmental stress coupling effect of composite materials under mechanical loads. These factors continuously affect the molecular structure of the curing agent, causing microscopic changes such as chemical bond breakage and cross-linking network destruction, which in turn lead to the degradation of the macroscopic properties of the material, causing equipment failure or safety hazards. However, existing techniques for analyzing the weather resistance degradation of curing agents mainly rely on natural aging tests or accelerated aging tests. These tests infer the weather resistance life of materials by testing their performance under prolonged exposure or intensified environmental conditions. Natural aging tests can take years or even decades, which is insufficient to meet the needs of rapid research and development and application of new materials. Furthermore, there is a lack of in-depth research into the relationship between the evolution of the microscopic molecular structure of curing agents and environmental factors. In particular, it is difficult to quantify the molecular bond breaking behavior under the interaction of multiple factors such as temperature, humidity, ultraviolet radiation, and mechanical stress, thus failing to fundamentally reveal the intrinsic laws governing the degradation of weather resistance. Summary of the Invention

[0003] Based on this, the present invention provides a method for predicting the weather resistance degradation of curing agents based on finite element analysis, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for predicting the weather resistance degradation of curing agents based on finite element analysis is provided, comprising the following steps: Step S1: Obtain curing agent material property data and curing agent application scenario environment data; perform material performance clustering processing based on curing agent component differences according to the curing agent material property data to generate clustered curing agent material performance data; Step S2: Based on the performance data of the clustered curing agent material and the environmental data of the curing agent application scenario, perform finite element analysis of the curing agent reaction molecular structure to obtain finite element curing agent reaction molecular structure data; Step S3: Analyze the microscopic bond breaking behavior of the curing agent unit based on the finite element molecular structure data of the curing agent reaction, and generate microscopic bond breaking behavior data of the curing agent unit; based on the microscopic bond breaking behavior data of the curing agent unit, analyze the environmental impact of the curing agent bond breaking behavior characteristics, and generate environmental impact curing agent bond breaking behavior characteristic data. Step S4: Establish the mapping relationship between the environmental stress tensor and bond breakage assessment of each cluster of curing agents by using the characteristic data of the bond breakage behavior of curing agents affected by the environment, so as to obtain the environmental stress tensor-bond breakage assessment model. Step S5: Optimize the mapping between bond breakage and weathering degradation relationship of the curing agent in the environmental stress tensor-bond breakage assessment model to obtain the environmental stress tensor-weathering degradation assessment model. Step S6: Based on the environmental data of the curing agent application scenario, perform trend time-series change element environmental stress tensor characteristic analysis on the finite element curing agent reaction molecular structure data to generate trend time-varying element environmental stress tensor data; transmit the trend time-varying element environmental stress tensor data to the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent and generate weathering performance degradation trend data of the curing agent.

[0005] Furthermore, step S1 includes the following steps: Step S11: Obtain curing agent material property data and curing agent application scenario environment data; Step S12: Perform material composition analysis based on the curing agent material characteristic data to obtain curing agent material composition data; Step S13: Perform phase difference tensor analysis on the differences in curing agent composition based on the curing agent material composition data to obtain the phase difference tensor data of the differences in curing agent composition; Step S14: Perform clustering processing on the curing agent material properties based on the curing agent material composition data and the phase difference tensor data of the curing agent composition differences to generate clustered curing agent material property data.

[0006] Furthermore, step S2 includes the following steps: Step S21: Analyze the heterogeneous environment of the curing agent reaction based on the environmental data of the curing agent application scenario, and generate heterogeneous environment data of the curing agent reaction; Step S22: Perform environmental field intensity gradient characteristic analysis on the environmental data of the curing agent reaction heterogeneity to generate environmental field intensity gradient characteristic data; Step S23: Design the environmental dynamic unit grid density based on the environmental field intensity gradient characteristic data to obtain the environmental dynamic unit grid density data; Step S24: Design the finite element unstructured mesh nodes for the curing agent reaction using the environmental dynamic unit mesh density data to obtain the curing agent finite element mesh node data. Then, use the clustered curing agent material performance data and the heterogeneous environment data of the curing agent reaction as the mesh attribute nodes of the curing agent finite element mesh node data to perform finite element analysis processing of the curing agent reaction to obtain finite element curing agent reaction data. Step S25: Perform molecular structure analysis of the finite element curing agent reaction data to obtain the molecular structure data of the finite element curing agent reaction.

[0007] Furthermore, step S3 includes the following steps: Step S31: Analyze the microscopic bond breaking behavior of the curing agent unit based on the finite element molecular structure data of the curing agent reaction, and generate microscopic bond breaking behavior data of the curing agent unit; Step S32: Based on the heterogeneous environment data of the curing agent reaction, perform correlation processing on the micro-bond breaking behavior data of the curing agent unit to generate curing agent unit environment-bond breaking behavior data. Step S33: Extract the curing agent reaction environment influence factor from the curing agent unit environment-bond breaking behavior data; perform curing agent micro-bond breaking behavior analysis on the curing agent unit environment-bond breaking behavior data based on the curing agent reaction environment influence factor, and generate single environment influence factor-bond breaking behavior data; Step S34: Analyze the microscopic bond breaking behavior of the curing agent based on the environmental impact factor interaction effect using curing agent unit environment-bond breaking behavior data and single environmental impact factor-bond breaking behavior data, and generate interactive environmental impact factor-bond breaking behavior data; Step S35: Based on single environmental impact factor-bond breaking behavior data and interactive environmental impact factor-bond breaking behavior data, perform a characteristic analysis of the bond breaking behavior of curing agents in relation to environmental impact, and generate characteristic data of bond breaking behavior of curing agents in relation to environmental impact.

[0008] Furthermore, the single environmental impact factor-bond breaking behavior data mentioned in step S33 includes temperature conduction impact factor-bond breaking behavior data, humidity diffusion-bond breaking behavior data, ultraviolet absorption characteristics-bond breaking behavior data, and mechanical stress characteristics-bond breaking behavior data.

[0009] Furthermore, step S4 includes the following steps: Step S41: Based on the characteristic data of bond breaking behavior of environmental impact curing agent, perform bond breaking probability distribution analysis of environmental impact curing agent to generate bond breaking probability distribution data of environmental impact curing agent; Step S42: Analyze the bond breakage probability distribution data of the environmental stress tensor based on the environmental impact curing agent bond breakage probability distribution data to generate environmental stress tensor-bond breakage probability distribution data; Step S43: Based on the clustered curing agent material performance data, classify the environmental stress tensor-bond breakage probability distribution data according to the bond breakage probability distribution of material performance differences, and generate classified environmental stress tensor-bond breakage probability distribution data. Step S44: Establish the mapping relationship between the environmental stress tensor and bond breakage assessment of each clustered curing agent by classifying the environmental stress tensor-bond breakage probability distribution data, so as to obtain the environmental stress tensor-bond breakage assessment model.

[0010] Furthermore, step S44 includes the following steps: A pre-defined random forest algorithm is used to establish various tree structure mapping relationships between environmental stress tensor and bond breakage probability to obtain an environmental stress tensor-bond breakage tree structure model. Then, by classifying the environmental stress tensor-bond breakage probability distribution data, the environmental stress tensor-bond breakage tree structure model is used to set the bond breakage evaluation threshold parameters corresponding to the environmental stress tensor to obtain an environmental stress tensor-bond breakage evaluation model.

[0011] Furthermore, step S5 includes the following steps: Step S51: Perform curing agent bond breaking behavior node analysis based on the micro-bond breaking behavior data of the curing agent unit to generate curing agent bond breaking behavior node data; Step S52: Based on the node data of curing agent bond breaking behavior, perform correlation analysis between curing agent unit bond breaking and weathering degradation to generate curing agent unit bond breaking-weathering degradation correlation data; Step S53: Perform a mapping feature analysis of bond breakage and weathering degradation of the curing agent unit based on the correlation data of bond breakage and weathering degradation of the curing agent, and generate the mapping feature data of bond breakage and weathering degradation of the curing agent. Step S54: Transmit the curing agent bond breakage-weathering degradation mapping feature data to the environmental stress tensor-bond breakage evaluation model to optimize the mapping of the bond breakage and weathering degradation relationship of the curing agent, so as to obtain the environmental stress tensor-weathering degradation evaluation model.

[0012] Furthermore, step S53 includes the following steps: Step S531: Based on the bond breakage-weathering degradation correlation data of the curing agent unit, perform weathering degradation impact analysis on the bond breakage nodes, generate bond breakage node-weathering degradation impact data, and perform weathering degradation impact characteristic analysis on the bond breakage central node based on the bond breakage node-weathering degradation impact data, generate bond breakage central node-weathering degradation impact characteristic data. Step S532: Perform unit weighted coefficient analysis of bond breakage and weathering degradation based on the correlation data of curing agent unit bond breakage and weathering degradation, and generate unit volume weighted coefficients of bond breakage and weathering degradation. Step S533: Perform a mapping characteristic analysis of bond breakage and weathering degradation of the curing agent by using the volume weighting coefficient of the bond breakage-weathering degradation unit and the data on the influence characteristics of the bond breakage central node on weathering degradation, and generate the mapping characteristic data of bond breakage and weathering degradation of the curing agent.

[0013] Furthermore, step S6 includes the following steps: Step S61: Based on the environmental data of the curing agent application scenario, perform trend time-series change analysis of the unit environment boundary conditions of the finite element curing agent reaction molecular structure data to generate trend time-varying unit environment boundary condition data; Step S62: Perform trend-time-varying element environmental boundary condition data analysis on element environmental stress tensor characteristics to generate trend-time-varying element environmental stress tensor data; Step S63: Transmit the environmental stress tensor data of the trend time-varying unit to the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent and generate weathering performance degradation trend data of the curing agent.

[0014] The beneficial effects of this application are as follows: This invention, through the systematic processing of data on the material properties of curing agents and the environmental conditions of application scenarios, lays a precise and efficient foundation for subsequent analysis. By acquiring material property data of curing agents (such as the purity and molecular weight distribution of methyltetrahydrophthalic anhydride) and environmental data (such as temperature, humidity, ultraviolet radiation, and external pressure), it ensures a close fit between the analyzed object and the actual application scenario, avoiding prediction bias caused by missing data. The combination of material composition analysis and phase difference tensor analysis can quantify the potential impact of different curing agent compositional differences (such as impurity content and isomer ratio) on performance. Based on the clustering processing of compositional data and phase difference tensors, curing agents with similar performance are categorized, which not only reduces the computational load of subsequent finite element analysis but also allows for the development of differentiated prediction strategies for different cluster groups, significantly improving analysis efficiency and relevance, especially suitable for the diverse application needs of curing agents in electronic packaging, coatings, and other scenarios. The construction of a curing agent reaction molecular structure model through finite element analysis achieves a precise mapping from the macroscopic environment to the microscopic molecular level, providing crucial support for revealing the weathering degradation mechanism. Heterogeneous environment analysis addresses the complex environments encountered in curing agent applications (such as localized high-temperature zones in electronic packaging and differences in UV radiation in outdoor scenarios), accurately capturing the impact of environmental heterogeneity on molecular reactions and avoiding errors caused by the traditional assumption of a uniform environment. Combining environmental field gradient characteristic analysis with dynamic mesh density design ensures computational accuracy in critical regions (such as areas with severe temperature gradients) while simplifying the mesh in gentler regions, balancing simulation accuracy and computational cost. Furthermore, by using clustered material property data and heterogeneous environment data as mesh attributes and combining them with potential energy surface scanning technology to analyze molecular structures, the microscopic characteristics such as molecular bond energies and bond lengths of different clustered curing agents under complex environments can be accurately reflected. Focusing on the correlation analysis between environmental factors and the microscopic bond-breaking behavior of curing agents, the core mechanism of weather resistance degradation is profoundly revealed, providing crucial evidence for subsequent model construction. Through unit microscopic bond-breaking behavior analysis, vulnerable bond sites in curing agent molecules are accurately identified, and a "environment-bond breaking" correlation is established in conjunction with heterogeneous environment data, clarifying the bond-breaking patterns in different environmental regions. The combination of single environmental impact factor analysis and interaction effect analysis quantifies the bond-breaking characteristics under the independent action of each factor, and reveals the enhancing effect of multi-factor synergy (such as the combined acceleration of bond breaking by high temperature and high humidity). The generated data on environmental impact bond-breaking behavior characteristics integrates the correlation patterns between microscopic bond-breaking laws and environmental factors. By establishing a mapping relationship between environmental stress tensor and bond-breaking assessment, a quantitative assessment of the bond-breaking behavior of different clusters of curing agents is achieved, providing a universal model basis for the weather resistance performance analysis of various curing agents (including anhydride curing agents such as methyltetrahydrophthalic anhydride). Bond-breaking probability distribution analysis transforms the bond-breaking behavior under environmental influences into quantifiable probability data, breaking through the limitations of qualitative description of the bond-breaking laws of curing agents and making the bond-breaking characteristics of different types of curing agents more statistically comparable.The correlation analysis between environmental stress tensor and bond breakage probability integrates the combined effects of multiple environmental factors such as temperature, humidity, and ultraviolet radiation, avoiding the limitations of single-factor analysis. This approach is applicable to curing agents and other curing agents that are affected by multiple factors in complex environments. Combining clustering of material properties for classification allows the model to adapt to the differences in characteristics of curing agents with different compositions (such as methyltetrahydrophthalic anhydride of different purities and composite curing agents containing different accelerators), improving the model's universality for various curing agents. The application of the random forest algorithm and the setting of threshold parameters not only enhance the model's ability to fit nonlinear mapping relationships but also ensure the accuracy of bond breakage assessment for different clustered curing agents through classification data calibration. By constructing a correlation mapping between bond breakage and weathering degradation, microscopic bond breakage behavior is closely linked to macroscopic performance degradation. Bond breakage behavior node analysis accurately locates key bond breakage sites that significantly impact performance, avoiding interference from irrelevant bond breakage information and making correlation analysis more targeted. For example, it clarifies the significant impact of crosslinking point bond breakage on mechanical strength in anhydride-based curing agents such as methyltetrahydrophthalic anhydride, or the effect of NH bond breakage on corrosion resistance in amine-based curing agents. Analysis of the influence characteristics of central bond breakage nodes and calculation of unit weighting coefficients further quantifies the influence weights and spatial distribution differences of different bond breakage nodes, making the mapping characteristics more closely match the actual performance degradation patterns of various curing agents. For instance, bond breakage in methyltetrahydrophthalic anhydride at high temperatures has a higher weight on overall weather resistance. The environmental stress tensor-weather degradation assessment model obtained through mapping characteristic optimization enables direct prediction from environmental stress to macroscopic weather degradation. By introducing time-varying environmental stress analysis, dynamic prediction of the long-term weather resistance degradation trend of various curing agents is achieved. Trend-time-varying unit environmental boundary condition analysis simulates the time-varying changes of future environmental parameters based on actual application scenario data, making the prediction scenarios closer to the real-world usage of various curing agents. The generation of time-varying environmental stress tensors transforms dynamic environmental changes into stress inputs that the model can recognize, capturing the cumulative effect of environmental factors over time and avoiding the limitations of short-term predictions for various curing agents caused by static environmental assumptions. Furthermore, inputting the time-varying stress tensor into the weathering degradation assessment model generates degradation trend data that intuitively reflects the performance degradation patterns of various curing agents during long-term use, such as the tensile strength decrease curve of methyltetrahydrophthalic anhydride over time and the insulation performance degradation trend of epoxy curing agents. This provides quantitative basis for engineering decisions such as the selection of various curing agents, application scenario adaptation, and maintenance cycle formulation, effectively solving the problem that traditional accelerated testing is unable to simulate long-term time-series changes.

[0015] Therefore, the finite element analysis-based method for predicting the weather resistance degradation of curing agents in this invention can reduce the complexity of the analysis object through material property clustering, and improve computational efficiency by combining finite element analysis with dynamic mesh design, thus significantly shortening the prediction cycle and meeting the needs of rapid research and development and application of new materials. Furthermore, it delves into the correlation mechanism between the microscopic molecular structure evolution of curing agents and environmental factors, establishing the relationship between microscopic structure evolution and environmental factors. It accurately quantifies the molecular bond-breaking behavior under the interaction of multiple factors such as temperature, humidity, ultraviolet radiation, and mechanical stress, fundamentally revealing the intrinsic law of weather resistance degradation and achieving accurate prediction from microscopic mechanisms to macroscopic performance degradation trends. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps in the method for predicting the weather resistance degradation of a curing agent using finite element analysis according to the present invention. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S5. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0018] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. Functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods. The term "and / or" as used herein includes any and all combinations of one or more of the associated items listed.

[0019] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for predicting the weather resistance degradation of curing agents using finite element analysis. In the embodiments of this invention, please refer to... Figure 1The diagram shown is a flowchart illustrating the steps of a finite element analysis method for predicting the weather resistance degradation of a curing agent according to the present invention. The method includes the following steps: Step S1: Obtain curing agent material property data and curing agent application scenario environment data; perform material performance clustering processing based on curing agent component differences according to the curing agent material property data to generate clustered curing agent material performance data; In this embodiment of the invention, in a laboratory application scenario, a gas chromatography-mass spectrometry (GC-MS) instrument is used to analyze a curing agent sample (such as a methyltetrahydrophthalic anhydride sample) to obtain material characteristic data, including molecular weight distribution, acid value, purity, and types and contents of impurities. Simultaneously, environmental data is continuously collected using temperature sensors, humidity sensors, ultraviolet radiometers, and stress sensors deployed in the laboratory application scenario. This data covers daily temperature variations, relative humidity fluctuations, ultraviolet radiation intensity, and mechanical stress values, and is used for finite element simulation testing of the weather resistance degradation of the curing agent in practical applications. Based on the material characteristic data obtained from GC-MS, chemometric methods are used to quantitatively analyze each component in the curing agent, determining the content of the main component of methyltetrahydrophthalic anhydride, the proportion of isomers, and the percentage of impurities, thus forming curing agent material composition data. Based on the material composition data, a tensor decomposition algorithm is used to calculate the phase difference tensor of the component differences between different samples. Each element of this tensor corresponds to the degree of difference in the content of a specific component between the two samples, thereby obtaining the curing agent component difference phase difference tensor data. Using material composition data and phase difference tensor data as input variables, the K-means clustering algorithm was employed, with composition similarity and phase difference tensor distance as clustering indices. Samples with composition differences of less than 5% and phase difference tensor distances of less than 0.1 were grouped into the same class, ultimately generating multiple sets of clustered curing agent material performance data. Each set of data includes common characteristic parameters such as the average molecular weight, average acid value, and main impurity content of the curing agent in that class.

[0020] Step S2: Based on the performance data of the clustered curing agent material and the environmental data of the curing agent application scenario, perform finite element analysis of the curing agent reaction molecular structure to obtain finite element curing agent reaction molecular structure data; In this embodiment of the invention, based on the collected environmental data of the curing agent application scenario, regions with temperature differences greater than 10°C, humidity differences greater than 20%, ultraviolet radiation intensity differences greater than 30%, and mechanical stress value differences greater than 15% are divided into heterogeneous environment regions. These regions are defined as heterogeneous environment regions, and the environmental parameter ranges of each region are summarized to generate heterogeneous environment data for the curing agent reaction. Gradient calculations are performed on the temperature, humidity, ultraviolet radiation, and mechanical stress parameters in the heterogeneous environment data. Spatial interpolation is used to obtain the rate of change of each parameter at different locations. For example, the ratio of the temperature difference to the distance between two adjacent points is calculated to determine the magnitude and direction of the environmental field strength gradient, forming environmental field strength gradient feature data. Based on the environmental field strength gradient characteristic data, a grid density of 0.1cm×0.1cm is used in regions with a gradient change rate greater than 5% / cm; a grid density of 0.5cm×0.5cm is used in regions with a gradient change rate between 2% / cm and 5% / cm; and a grid density of 1cm×1cm is used in regions with a gradient change rate less than 2% / cm. This completes the design of the environmental dynamic unit grid density, obtaining the environmental dynamic unit grid density data. According to the above grid density data, the Delaunay triangulation method is used to construct the finite element unstructured mesh for the curing agent reaction, determining the spatial coordinates of each grid node, forming the curing agent finite element mesh node data. Parameters such as molecular weight and acid value from the clustered curing agent material performance data and temperature and humidity from the heterogeneous environment data are assigned to the corresponding grid nodes as grid attributes. The displacement method in finite element analysis is used to establish the equilibrium equations for the grid nodes, solving for the stress, strain, and energy data of each node, forming the finite element curing agent reaction data. Based on potential surface scanning technology, energy calculations are performed on the molecular structure of different conformations in finite element curing agent reaction data in molecular dynamics simulation software. By changing the molecular bond length and bond angle, the potential energy value corresponding to each conformation is recorded, and the stable conformation with the lowest potential energy is found. In this way, the spatial structure, bond length, bond angle and bond energy of the molecule are determined, and the finite element curing agent reaction molecular structure data is obtained.

[0021] Step S3: Analyze the microscopic bond breaking behavior of the curing agent unit based on the finite element molecular structure data of the curing agent reaction, and generate microscopic bond breaking behavior data of the curing agent unit; based on the microscopic bond breaking behavior data of the curing agent unit, analyze the environmental impact of the curing agent bond breaking behavior characteristics, and generate environmental impact curing agent bond breaking behavior characteristic data. In this embodiment of the invention, based on the bond length, bond angle, and bond energy parameters recorded in the finite element method (FEM) molecular structure data of the curing agent reaction, the breaking energy barrier of each chemical bond in the methyltetrahydrophthalic anhydride curing agent molecule is calculated using molecular dynamics simulation equipment. It is defined that when the external input energy exceeds this energy barrier, a chemical bond breakage is determined. Simultaneously, the specific location and time of bond breakage, as well as the corresponding bond energy changes, are recorded, thereby generating microscopic bond-breaking behavior data of the curing agent unit containing information such as the bond-breaking frequency and average breakage time of each chemical bond. Next, the environmental parameters of each region in the curing agent reaction heterogeneous environment data are matched one-to-one with the bond-breaking positions in the microscopic bond-breaking behavior data of the curing agent unit. By statistically analyzing the bond-breaking types and quantities in different environmental regions, such as the proportion of CO bond breaks in the high-temperature region, a correspondence between heterogeneous environments and microscopic bond-breaking behaviors is established, generating curing agent unit environment-bond-breaking behavior data. Temperature, humidity, ultraviolet radiation, and mechanical stress were extracted as independent environmental impact factors from the data. Other factors were kept constant, while only the values ​​of individual factors were changed. For example, humidity, ultraviolet radiation, and mechanical stress were fixed, and only the temperature was gradually increased from room temperature to a specific high temperature. The bond-breaking behavior changes at each temperature were recorded, thus obtaining temperature conduction impact factor-bond-breaking behavior data. Using the same method, humidity diffusion-bond-breaking behavior data, ultraviolet absorption characteristics-bond-breaking behavior data, and mechanical stress characteristics-bond-breaking behavior data were obtained. Then, the curing agent unit environment-bond-breaking behavior data were compared with the single environmental impact factor-bond-breaking behavior data. The difference between the bond-breaking rate when multiple factors act together and the sum of the bond-breaking rates when each single factor acts alone was calculated to quantify the interaction effect of environmental impact factors, generating interactive environmental impact factor-bond-breaking behavior data. Finally, by combining the above two types of data, the bond-breaking probability, bond-breaking rate, and bond-breaking position distribution patterns of each chemical bond under different environmental conditions were statistically analyzed to generate environmental impact curing agent bond-breaking behavior characteristic data.

[0022] Step S4: Establish the mapping relationship between the environmental stress tensor and bond breakage assessment of each cluster of curing agents by using the characteristic data of the bond breakage behavior of curing agents affected by the environment, so as to obtain the environmental stress tensor-bond breakage assessment model. In this embodiment of the invention, based on the characteristic data of bond breakage behavior of curing agents under environmental impact, a probabilistic statistical method is used to statistically analyze the frequency of bond breakage events under different environmental conditions, calculate the probability of bond breakage occurring for each environmental combination, and form the probability distribution data of bond breakage probability of curing agents under environmental impact, which includes probability density functions for different bond breakage types. Environmental parameters such as temperature, humidity, ultraviolet radiation, and mechanical stress are integrated into an environmental stress tensor, each component of which corresponds to the intensity of an environmental factor. A mathematical relationship between each component of the environmental stress tensor and the bond breakage probability is established through multiple regression analysis, generating environmental stress tensor-bond breakage probability distribution data. Combined with the clustering of curing agent material performance data, the environmental stress tensor-bond breakage probability distribution data is grouped according to cluster categories, so that each group of data corresponds to the bond breakage probability characteristics of a type of curing agent, forming categorized environmental stress tensor-bond breakage probability distribution data. A random forest algorithm is used, with the classified environmental stress tensor as the input variable and the bond breakage probability as the output variable, to construct multiple decision trees. Each decision tree is trained and generated based on different sample subsets and feature subsets. The final output result is determined by majority voting, forming an environmental stress tensor-bond breakage tree structure model. Then, based on the classified environmental stress tensor-bond breakage probability distribution data, a bond breakage threshold value is set for different clustered curing agents under a specific environmental stress tensor. When the bond breakage probability exceeds the threshold value, it is judged as significant bond breakage. This completes the threshold parameter setting of the model, resulting in an environmental stress tensor-bond breakage evaluation model.

[0023] Step S5: Optimize the mapping between bond breakage and weathering degradation relationship of the curing agent in the environmental stress tensor-bond breakage assessment model to obtain the environmental stress tensor-weathering degradation assessment model. In this embodiment of the invention, based on the microscopic bond-breaking behavior data of the curing agent units, the specific molecular locations where bond breaking occurs are identified, and bond-breaking sites that significantly affect the overall performance of the curing agent, such as key connection points in the crosslinking network, are marked, generating bond-breaking behavior node data of the curing agent. Performance testing of curing agent samples with different degrees of bond breaking is conducted using mechanical property testing equipment to obtain the correspondence between bond breaking rate and weathering performance parameters such as tensile strength, hardness, and insulation resistance, establishing bond-breaking-weathering degradation correlation data of the curing agent units. Based on the bond-breaking-weathering degradation correlation data of the curing agent units, sensitivity analysis is used to calculate the influence weight of each bond-breaking node on weathering performance degradation. The bond-breaking nodes with the top 20% influence weights are selected as bond-breaking central nodes, and their influence on weathering performance under different degrees of bond breaking is analyzed, generating bond-breaking central node-weathering degradation influence characteristic data. Simultaneously, based on the spatial distribution of the curing agent units, the volume proportion of units at different locations is calculated. Combined with the degree of influence of bond breaking on overall performance, the bond-breaking-weathering degradation unit volume weighting coefficient is determined. This coefficient is positively correlated with both the unit volume proportion and the influence weight. The volume weighting coefficient of the bond breakage-weathering degradation unit is fused with the data on the influence characteristics of the bond breakage central node on weathering degradation. By weighted summation, a quantitative mapping relationship between bond breakage behavior and weathering performance degradation is established, generating curing agent bond breakage-weathering degradation mapping characteristic data. This data is then substituted into the environmental stress tensor-bond breakage evaluation model, and the model's output parameters are corrected so that the model can directly output weathering performance degradation indicators, thus obtaining the environmental stress tensor-weathering degradation evaluation model.

[0024] Step S6: Based on the environmental data of the curing agent application scenario, perform trend time-series change element environmental stress tensor characteristic analysis on the finite element curing agent reaction molecular structure data to generate trend time-varying element environmental stress tensor data; transmit the trend time-varying element environmental stress tensor data to the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent and generate weathering performance degradation trend data of the curing agent.

[0025] In this embodiment of the invention, based on environmental data of the curing agent application scenario, a time series analysis method is used to fit the trends of environmental parameters such as temperature, humidity, ultraviolet radiation, and mechanical stress over a long period of time, predict the future changes in environmental parameters, determine the range of environmental parameters at each time point, and generate trend-varying unit environmental boundary condition data. This data includes the maximum, minimum, and average values ​​of each environmental factor at different times. Based on the trend-varying unit environmental boundary condition data and combined with the spatial distribution of elements in the finite element curing agent reaction molecular structure data, the stress calculation method in finite element analysis is used to calculate the environmental stress tensor borne by each element at each time point. The components of this tensor change dynamically with time, thereby generating trend-varying unit environmental stress tensor data, which includes the stress tensor values ​​of each element at different times. The environmental stress tensor data of the trend time-varying unit are input into the environmental stress tensor-weathering degradation assessment model. The model calculates the weathering performance parameters of the curing agent at each time node according to the preset mapping relationship, such as tensile strength retention rate and insulation resistance decay rate. By continuously calculating the performance parameters at different times, the curve of weathering performance changing with time is obtained, and the weathering performance degradation trend data of the curing agent is generated. This data can intuitively reflect the performance degradation law of the curing agent in future use.

[0026] Furthermore, step S1 includes the following steps: Step S11: Obtain curing agent material property data and curing agent application scenario environment data; In this embodiment of the invention, in a laboratory application scenario, for the analyzed curing agent (such as methyltetrahydrophthalic anhydride), gas chromatography-mass spectrometry (GC-MS) is used to detect material properties. The chromatographic column separates the components in the sample, and the mass spectrometer determines the molecular weight and structure of each component, obtaining material property data including the content of the main component, the proportion of isomers, the types and contents of impurities, and the acid value. Simultaneously, in the laboratory setting where the curing agent is applied, temperature sensors, humidity sensors, ultraviolet radiometers, and stress sensors are arranged at specific intervals, and data is collected continuously for 30 days. Daily records are made of temperature fluctuation range, relative humidity variation range, peak ultraviolet radiation intensity, and average mechanical stress, forming environmental data for the curing agent application scenario. All testing instruments are calibrated, and the sensor acquisition frequency is set to once per hour to ensure data accuracy and continuity.

[0027] Step S12: Perform material composition analysis based on the curing agent material characteristic data to obtain curing agent material composition data; In this embodiment of the invention, based on the acquired curing agent material characteristic data, an infrared spectrometer is used to analyze the curing agent sample. The type and number of functional groups in the molecule are determined by the position and intensity of characteristic absorption peaks. Combined with the component separation results obtained by gas chromatography-mass spectrometry, each component is quantitatively calculated. For example, by comparing the characteristic peak area of ​​a standard sample, the content of the main component of the curing agent, methyltetrahydrophthalic anhydride, and the proportion of isomers (such as cis and trans structures) are calculated. At the same time, components such as residual phthalic anhydride and moisture in the impurities are identified and their proportions are determined. Finally, curing agent material composition data containing the name, chemical structure, and mass fraction of each component are formed, with the quantitative result of each component accurate to two decimal places.

[0028] Step S13: Perform phase difference tensor analysis on the differences in curing agent composition based on the curing agent material composition data to obtain the phase difference tensor data of the differences in curing agent composition; In this embodiment of the invention, based on the curing agent material composition data, 10 different batches of curing agent samples are selected, and a component vector is constructed using the component mass fraction of each sample as the basic data. A tensor decomposition algorithm is used to transform the component vector of each sample into a three-dimensional tensor, where the first dimension represents the component type, the second dimension represents the sample number, and the third dimension represents the mass fraction. The phase difference between any two sample tensors is calculated, and each element of the phase difference tensor is solved through matrix operations. This element value reflects the degree of difference between the two samples in a specific component. For example, the difference between curing agent sample A and curing agent sample B in the principal component methyltetrahydrophthalic anhydride is reflected by the value at the corresponding position in the tensor, ultimately forming a curing agent component difference phase difference tensor data containing all component differences between samples.

[0029] Step S14: Perform clustering processing on the curing agent material properties based on the curing agent material composition data and the phase difference tensor data of the curing agent composition differences to generate clustered curing agent material property data.

[0030] In this embodiment of the invention, K-means clustering algorithm is used to process the curing agent material composition data and component difference phase difference tensor data (reflecting the degree of component difference between samples). First, the number of clusters is determined, and each curing agent sample is transformed into a multi-dimensional vector containing component features and phase difference features. The component features are taken from the mass fractions of each component in the material composition data, and the phase difference features are taken from the element values ​​in the phase difference tensor data. The Euclidean distance between any two sample vectors is calculated; the smaller the distance value, the closer the sample performance. Initially, multiple samples are randomly selected as cluster centers, and each sample is assigned to the category of the nearest cluster center. Then, the new center for each category (i.e., the average of all sample vectors in that category) is recalculated. This assignment and update process is repeated until the change in cluster centers is less than 0.001 in three consecutive iterations, at which point the iteration stops. Finally, multiple sets of cluster data are obtained, each set containing the average component mass fraction, average phase difference tensor, common functional group features, and sample number for that category. The data for each group are summarized to identify the commonalities in the composition and performance differences of similar curing agents. Clustered curing agent material performance data are generated to ensure that the compositional similarity among similar samples is not less than 90% and the phase difference tensor distance does not exceed 0.05, providing a classification basis for targeted modeling in subsequent finite element analysis.

[0031] Furthermore, step S2 includes the following steps: Step S21: Analyze the heterogeneous environment of the curing agent reaction based on the environmental data of the curing agent application scenario, and generate heterogeneous environment data of the curing agent reaction; In this embodiment of the invention, based on environmental data of the curing agent application scenario, environmental monitoring areas are divided according to the application scenario of the curing agent in electronic packaging. Areas with daily temperature fluctuations exceeding 15°C are marked as temperature heterogeneous areas, areas with relative humidity fluctuations exceeding 30% are marked as humidity heterogeneous areas, areas with ultraviolet radiation intensity differences exceeding 50% are marked as radiation heterogeneous areas, and areas with mechanical stress value changes exceeding 20% ​​are marked as stress heterogeneous areas. Spatial coordinate positioning is performed for each heterogeneous area, and the maximum, minimum, and change cycles of environmental parameters within each area are recorded, such as the temperature range between the chip vicinity and the packaging edge area in the temperature heterogeneous area. The spatial distribution and parameter characteristics of all heterogeneous areas are summarized to generate curing agent reaction heterogeneous environmental data. This data includes the boundary coordinates of each heterogeneous area, the dominant environmental factors, and the parameter fluctuation range, ensuring that the division accuracy of each area is controlled within ±0.5 cm.

[0032] Step S22: Perform environmental field intensity gradient characteristic analysis on the environmental data of the curing agent reaction heterogeneity to generate environmental field intensity gradient characteristic data; In this embodiment of the invention, environmental parameter monitoring points are selected within each heterogeneous region for the heterogeneous environment data of the curing agent reaction, with the distance between adjacent monitoring points set to 1 cm. The environmental field intensity gradient is calculated using the numerical difference method. For the temperature field, the ratio of the temperature difference between two adjacent points to the spatial distance is calculated to obtain the temperature gradient value; for the humidity field, the ratio of the relative humidity difference to the distance is used to calculate the humidity gradient; for the ultraviolet radiation field and the mechanical stress field, the radiation gradient and stress gradient are calculated using the same method, respectively. The gradient magnitude and direction at each monitoring point are recorded, and points with the same gradient value are connected using the contour line drawing method to form a gradient field distribution map. Regions with drastic gradient changes (gradient values ​​exceeding 5 units / cm) are marked as high gradient regions, and regions with gentle gradient changes (gradient values ​​below 2 units / cm) are marked as low gradient regions. Environmental field intensity gradient feature data containing gradient values, directions, and region divisions are generated, with all gradient values ​​calculated to two decimal places.

[0033] Step S23: Design the environmental dynamic unit grid density based on the environmental field intensity gradient characteristic data to obtain the environmental dynamic unit grid density data; In this embodiment of the invention, based on environmental field strength gradient characteristic data, a grid density division standard is set: a 0.2 cm × 0.2 cm grid cell size is used in high gradient regions (gradient values ​​exceeding 5 units / cm) to capture subtle gradient changes; a 0.5 cm × 0.5 cm grid cell size is used in medium gradient regions (gradient values ​​between 2 and 5 units / cm); and a 1 cm × 1 cm grid cell size is used in low gradient regions (gradient values ​​below 2 units / cm). Based on the boundary coordinates of each region, transition grids are set at the boundaries between high and medium gradient regions, and between medium and low gradient regions. The size of the transition grids changes gradually in a linear proportion to avoid calculation errors caused by abrupt changes in grid size. The grid density is correlated with the coordinate range of the corresponding region to form dynamic environmental unit grid density data. The data clearly defines the grid cell size within each coordinate interval, ensuring that the grid covers all heterogeneous regions without overlap.

[0034] Step S24: Design the finite element unstructured mesh nodes for the curing agent reaction using the environmental dynamic unit mesh density data to obtain the curing agent finite element mesh node data. Then, use the clustered curing agent material performance data and the heterogeneous environment data of the curing agent reaction as the mesh attribute nodes of the curing agent finite element mesh node data to perform finite element analysis processing of the curing agent reaction to obtain finite element curing agent reaction data. In this embodiment of the invention, the Delaunay triangulation method is used for finite element unstructured mesh node design based on the environmental dynamic element mesh density data. First, the boundary contour of the curing agent reaction region is determined. Discrete points on the contour are used as initial boundary nodes. The spacing between adjacent boundary nodes is determined according to the mesh density of the region: 0.1 cm for high gradient regions, 0.25 cm for medium gradient regions, and 0.5 cm for low gradient regions. Internal nodes are generated within the region according to the mesh density requirements. Internal nodes in high gradient regions are evenly distributed at intervals of 0.2 cm × 0.2 cm, in medium gradient regions at 0.5 cm × 0.5 cm, and in low gradient regions at 1 cm × 1 cm. All nodes are connected to form triangular elements, ensuring that the interior angles of each element are between 30 and 120 degrees to avoid distorted elements. The three-dimensional coordinates of each node (accurate to 0.01 mm) are recorded to form curing agent finite element mesh node data, including node number and corresponding coordinate information. The material properties of the clustered curing agent are assigned to the mesh nodes as material attributes. Parameters such as average molecular weight and acid value are assigned according to the cluster category to which the node belongs, and functional group density is distributed to each node using interpolation. The heterogeneous environment data of the curing agent reaction are assigned to the nodes as environmental attributes. Parameters such as temperature and humidity are determined based on the parameter range of the heterogeneous region where the node is located, and the node parameters at the region boundary are obtained through linear transition calculations. The displacement method in finite element analysis is used for reaction analysis, establishing the equilibrium equation for each node. The equation includes the stiffness coefficient corresponding to the material properties and the load values ​​corresponding to the environmental properties. The equation system is solved using Gaussian elimination to obtain the displacement of each node. Strain is then calculated based on the geometric equations, stress is obtained through the physical equations, and the energy values ​​at the nodes (including elastic potential energy and thermal energy) are calculated simultaneously to generate finite element curing agent reaction data.

[0035] Step S25: Perform molecular structure analysis of the finite element curing agent reaction data to obtain the molecular structure data of the finite element curing agent reaction.

[0036] In this embodiment of the invention, potential surface scanning technology is used to analyze the molecular structure of finite element curing agent reaction data. Key chemical bonds in the curing agent molecule (such as the CO and C-C bonds in methyltetrahydrophthalic anhydride) are selected, and the bond length variation step is set to 0.01 Å, and the bond angle variation step is set to 1 degree. The total potential energy of the molecule is calculated at each step. The potential energy values ​​under different conformations are solved using quantum chemical calculation methods, and a three-dimensional surface is plotted to show the change in potential energy with bond length and bond angle. The lowest point on the surface corresponds to the stable conformation of the molecule. The bond length, bond angle, dihedral angle, and bond energy data under the stable conformation are recorded. Simultaneously, the correlation between stress distribution in the finite element reaction data and molecular conformation is analyzed, for example, the bond length changes corresponding to high stress regions. The molecular structure parameters of all mesh nodes are summarized to generate finite element curing agent reaction molecular structure data, which includes the molecular three-dimensional coordinates, bond parameters, and potential energy values ​​at each node.

[0037] Furthermore, step S3 includes the following steps: Step S31: Analyze the microscopic bond breaking behavior of the curing agent unit based on the finite element molecular structure data of the curing agent reaction, and generate microscopic bond breaking behavior data of the curing agent unit; In this embodiment of the invention, based on the finite element method (FEM) molecular structure data of the curing agent reaction, for example focusing on easily broken bond sites such as CO bonds and C / C bonds in the methyltetrahydrophthalic anhydride molecule, the breaking energy barrier of each chemical bond is calculated using molecular mechanics methods. By analyzing the bond length, bond angle, and bond energy parameters in the molecular structure data, a breaking criterion is set: when the bond length exceeds 15% of the initial bond length, or the bond energy is lower than 30% of the initial bond energy, it is determined that bond breaking has occurred. The molecular structure of each curing agent unit is dynamically monitored, and the specific location of bond breaking (e.g., which CO bond in the molecular chain), the time of bond breaking (in units of the finite element analysis time step), and the change in bond energy before and after bond breaking are recorded. The bond breaking information of all units is summarized, and the bond breaking frequency (number of bond breaks per unit time), average breaking time, and molecular conformational parameters at the time of bond breaking are statistically analyzed to generate microscopic bond breaking behavior data of the curing agent unit.

[0038] Step S32: Based on the heterogeneous environment data of the curing agent reaction, perform correlation processing on the micro-bond breaking behavior data of the curing agent unit to generate curing agent unit environment-bond breaking behavior data. In this embodiment of the invention, based on the heterogeneous environment data of the curing agent reaction, the boundary coordinates of each heterogeneous region are matched with the bond breakage location coordinates in the obtained microscopic bond breakage behavior data of the curing agent unit to determine the heterogeneous environment region to which each bond breakage event belongs. For each heterogeneous region, the types of bond breakage (e.g., the proportion of CO bond breakage), the number of bond breakages, and the bond breakage rate (total number of bond breakages per unit time) are statistically analyzed, and associated with the environmental parameters of that region (e.g., temperature range, humidity value). For example, in the high-temperature sub-region of the temperature heterogeneous region, the correspondence between the number of CO bond breakages and the maximum temperature of that region is recorded; in the high-humidity sub-region of the humidity heterogeneous region, the correlation between the bond breakage rate of C and D bonds and relative humidity is recorded. These associated information are categorized and organized by region to generate curing agent unit environment-bond breakage behavior data. The data includes region number, environmental parameter range, bond breakage type, and corresponding statistical values, ensuring that each bond breakage event can be accurately matched to a unique heterogeneous environment region.

[0039] Step S33: Extract the curing agent reaction environment influence factor from the curing agent unit environment-bond breaking behavior data; perform curing agent micro-bond breaking behavior analysis on the curing agent unit environment-bond breaking behavior data based on the curing agent reaction environment influence factor, and generate single environment influence factor-bond breaking behavior data; In this embodiment of the invention, four independent variables—temperature, humidity, ultraviolet radiation intensity, and mechanical stress—are separated from the environmental-bond-breaking behavior data of the curing agent unit and identified as environmental influencing factors of the curing agent reaction. For each factor, a microscopic bond-breaking behavior analysis of a single environmental influencing factor is conducted using the controlled variable method. Taking the temperature conduction influencing factor analysis as an example, humidity is fixed at the average relative humidity in the environmental data, ultraviolet radiation intensity is set as the average monitored radiation value, and mechanical stress is maintained at the average stress level; only the temperature parameter is changed. The temperature adjustment range covers the lowest to the highest temperature recorded in the application scenario, with a gradient set in every 10°C. The duration for which the environmental parameters remain stable under each gradient is the average duration of that temperature range in the environmental data. Under each temperature gradient, the bond length changes of each chemical bond in the curing agent unit are tracked through molecular dynamics simulation. When the bond length exceeds 12% of the initial bond length, it is recorded as a bond-breaking event. The total number of bond breaks per unit time, the proportion of bond breaks of different chemical bonds, and the location distribution of bond breaks are statistically analyzed to form temperature conduction influencing factor-bond-breaking behavior data. For the humidity diffusion influencing factor analysis, the average temperature was fixed, and the average values ​​of ultraviolet radiation and mechanical stress were maintained. Only the relative humidity was changed, with gradients set at 20% relative humidity intervals. The stabilization time at each gradient matched the average duration of the corresponding humidity range. Bond breaking behavior was monitored and statistically analyzed to generate humidity diffusion-bond breaking behavior data. For the ultraviolet absorption characteristic influencing factor analysis, the average values ​​of temperature, humidity, and mechanical stress were fixed, with gradients set at 20 W / m² ultraviolet radiation intensity intervals. The stabilization time of each gradient matched the average duration of the corresponding radiation intensity. Bond breaking was statistically analyzed to generate ultraviolet absorption characteristic-bond breaking behavior data. For the mechanical stress characteristic influencing factor analysis, the average values ​​of temperature, humidity, and ultraviolet radiation were fixed, with gradients set at 10 MPa mechanical stress intervals. The stabilization time of each gradient corresponded to the average duration of the corresponding stress level. Bond breaking data was statistically analyzed to generate mechanical stress characteristic-bond breaking behavior data. In all analyses, bond breaking statistics at each gradient were repeated five times, and the average of the five results was taken as the final data for that gradient to ensure data reliability.

[0040] Step S34: Analyze the microscopic bond breaking behavior of the curing agent based on the environmental impact factor interaction effect using curing agent unit environment-bond breaking behavior data and single environmental impact factor-bond breaking behavior data, and generate interactive environmental impact factor-bond breaking behavior data; In this embodiment of the invention, combining the environmental-bond-breaking behavior data of the curing agent unit and the single environmental influence factor-bond-breaking behavior data, three typical interaction combinations—temperature and humidity, temperature and ultraviolet radiation, and humidity and mechanical stress—are selected for analysis. Taking the interaction effect of temperature and humidity as an example, a grid-like combination parameter is set with temperature at 5°C intervals and humidity at 10% RH intervals, covering the extreme ranges of both. Under each combination parameter, the difference between the actual bond-breaking rate (taken from the environmental-bond-breaking behavior data) and the sum of the bond-breaking rates under the sole effects of temperature and humidity is calculated; this difference is the interaction effect value. If the difference is positive, it indicates a synergistic enhancement effect; if it is negative, it indicates an antagonistic weakening effect. The interaction effect values, corresponding bond-breaking types, and bond-breaking location distributions under different combination parameters are recorded to generate temperature-humidity interaction environmental influence factor-bond-breaking behavior data. The same logic is used to analyze the other two interaction combinations to generate corresponding interaction environmental influence factor-bond-breaking behavior data. All interaction effect values ​​are calculated to three decimal places.

[0041] Step S35: Based on single environmental impact factor-bond breaking behavior data and interactive environmental impact factor-bond breaking behavior data, perform a characteristic analysis of the bond breaking behavior of curing agents in relation to environmental impact, and generate characteristic data of bond breaking behavior of curing agents in relation to environmental impact.

[0042] In this embodiment of the invention, data on bond-breaking behavior of curing agents under environmental impact are integrated from single environmental impact factors and interactive environmental impact factors. Analysis of variance and regression analysis are used to analyze the bond-breaking behavior characteristics of curing agents under environmental impact. First, for single environmental impact factors, the average change in bond-breaking probability for each factor (temperature, humidity, ultraviolet radiation, mechanical stress) within its value range is calculated for every standard unit change. For example, for every 10°C increase in temperature, the increase in CO bond breakage probability is statistically analyzed; for every 15% increase in humidity (RH), the change in C / D bond breakage probability is calculated. The absolute values ​​of these changes are used to determine the influence weight of each factor on different chemical bonds. The weight values ​​are expressed as percentages and summed to 100%. For interactive environmental impact factors, the intensity of the interaction effects of combinations such as temperature and humidity, temperature and ultraviolet radiation, and humidity and mechanical stress are analyzed in different parameter ranges. The difference between the actual bond-breaking rate under interaction and the sum of the bond-breaking rates of each individual factor acting alone is calculated. A positive difference is defined as a synergistic effect, and a negative difference is defined as an antagonistic effect. Parameter ranges with effect strength exceeding 20% ​​are recorded (e.g., at temperatures of 60-80℃ and humidity of 70-90% RH, the synergistic effect increases the bond-breaking rate by 35%). Simultaneously, high-frequency regions of bond breaking under various environmental conditions are determined through spatial distribution statistics. The top 10% of units with the highest bond-breaking frequency are marked as sensitive regions. Structural parameters such as bond length and bond angle of molecules within these regions are measured to clarify the molecular structural characteristics of the sensitive regions. Information such as the influence weight of individual factors, the intensity and parameter range of interaction effects, and the structural characteristics of sensitive regions are integrated and categorized by chemical bond type. For example, the temperature-humidity synergistic effect range for CO bonds and the corresponding bond length range for sensitive regions, and the UV-stress synergistic effect range for CC bonds, etc. All data are verified through three repeated calculations to ensure that the error is controlled within 5%, ultimately forming environmental impact data on the bond-breaking behavior of curing agents, including the environmental sensitivity to bond breaking, interaction patterns, and spatial distribution characteristics of each chemical bond.

[0043] Furthermore, the single environmental impact factor-bond breaking behavior data mentioned in step S33 includes temperature conduction impact factor-bond breaking behavior data, humidity diffusion-bond breaking behavior data, ultraviolet absorption characteristics-bond breaking behavior data, and mechanical stress characteristics-bond breaking behavior data.

[0044] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S4 is shown below. In this embodiment, step S4 includes the following steps: Step S41: Based on the characteristic data of bond breaking behavior of environmental impact curing agent, perform bond breaking probability distribution analysis of environmental impact curing agent to generate bond breaking probability distribution data of environmental impact curing agent; In this embodiment of the invention, based on the characteristic data of bond-breaking behavior of environmentally impacting curing agents, a probabilistic statistical method is used to conduct a bond-breaking probability distribution analysis. Environmental impact factors are divided into several continuous intervals at fixed intervals, each interval containing a specific combination of temperature, humidity, ultraviolet radiation, and mechanical stress. Bond-breaking events within each interval are counted, and the ratio of the number of bond-breaking events to the total number of observations is calculated to obtain the bond-breaking frequency for that interval. A probability density function fitting technique is used to fit the bond-breaking frequency data to a continuous distribution function. The function parameters are adjusted using the least squares method to ensure that the deviation between the fitted curve and the actual data is controlled within 4%. For each type of chemical bond, a corresponding probability distribution function is established, and the function type, parameters, and applicable environmental interval range are recorded. The distribution functions of all intervals and the associated environmental parameters are summarized to generate the probability distribution data of bond-breaking of environmentally impacting curing agents.

[0045] Step S42: Analyze the bond breakage probability distribution data of the environmental stress tensor based on the environmental impact curing agent bond breakage probability distribution data to generate environmental stress tensor-bond breakage probability distribution data; In this embodiment of the invention, an environmental stress tensor is constructed based on the probability distribution data of bond breakage in curing agents influenced by environmental factors. The environmental stress tensor is a four-dimensional vector, with each component corresponding to the standardized values ​​of temperature, humidity, ultraviolet radiation, and mechanical stress. The standardization process is achieved by calculating the ratio of the actual parameter value to the maximum value, ensuring that the numerical range of each component is controlled between 0 and 1. Tensor decomposition is used to convert the combination of environmental parameters into a characteristic expression of the stress tensor, clarifying the weight coefficient of each component in the tensor. The bond breakage probability distribution data for each environmental interval is correlated with the corresponding environmental stress tensor. Multiple regression analysis is used to establish the mathematical relationship between each component of the stress tensor and the bond breakage probability distribution parameters, retaining four decimal places during the calculation. The probability distribution patterns of different chemical bond types under various stress tensor characteristics are recorded, including the distribution function type, parameters, and corresponding tensor component ranges, generating environmental stress tensor-bond breakage probability distribution data.

[0046] Step S43: Based on the clustered curing agent material performance data, classify the environmental stress tensor-bond breakage probability distribution data according to the bond breakage probability distribution of material performance differences, and generate classified environmental stress tensor-bond breakage probability distribution data. In this embodiment of the invention, based on the performance data of the clustered curing agent materials, the environmental stress tensor-bond breakage probability distribution data obtained in step S42 is classified according to cluster categories. Each cluster category corresponds to a group of curing agents with similar material properties. During classification, samples with material composition similarity exceeding 90% and performance parameter difference less than 8% are grouped into the same category based on material composition similarity and performance parameter difference. The statistical characteristics of the bond breakage probability distribution are recalculated for each category of data, including the probability mean, standard deviation, and distribution range under the same stress tensor. The differences in distribution characteristics between different clusters are compared, and the stress tensor intervals with significant differences and the corresponding probability parameters are marked. The classified datasets are organized according to cluster numbers, and each dataset contains details of the bond breakage probability distribution of that type of curing agent under different stress tensors, generating classified environmental stress tensor-bond breakage probability distribution data.

[0047] Step S44: Establish the mapping relationship between the environmental stress tensor and bond breakage assessment of each clustered curing agent by classifying the environmental stress tensor-bond breakage probability distribution data, so as to obtain the environmental stress tensor-bond breakage assessment model.

[0048] In this embodiment of the invention, a random forest algorithm is used to establish a mapping relationship between the environmental stress tensor and bond breakage assessment for each cluster of curing agents, based on the classification environmental stress tensor-bond breakage probability distribution data. For each cluster of curing agent type, a forest model consisting of 100 decision trees is constructed, using the four components of the environmental stress tensor as input variables and the bond breakage probability as the output variable. The training samples for each decision tree are randomly selected from 70% of the cluster data, and three components of the feature variables are randomly selected. By calculating the prediction results of each tree, the final predicted bond breakage probability value is determined by majority voting. After the model training is completed, a bond breakage assessment threshold is set based on the bond breakage probability distribution data of the cluster: when the predicted bond breakage probability exceeds the upper limit of the 90% confidence interval for the cluster, it is judged as "significant bond breakage"; when it is between the upper limit of the 50% confidence interval and the upper limit of the 90% confidence interval, it is judged as "minor bond breakage"; and when it is below the upper limit of the 50% confidence interval, it is judged as "no significant bond breakage". The threshold parameter is embedded in the model to form an environmental stress tensor-bond breakage assessment model for the cluster. Repeat the above process for all clusters to generate multiple sets of models. Each set of models includes forest structure parameters, threshold criteria, and applicable clustering range.

[0049] Furthermore, step S44 includes the following steps: A pre-defined random forest algorithm is used to establish various tree structure mapping relationships between environmental stress tensor and bond breakage probability to obtain an environmental stress tensor-bond breakage tree structure model. Then, by classifying the environmental stress tensor-bond breakage probability distribution data, the environmental stress tensor-bond breakage tree structure model is used to set the bond breakage evaluation threshold parameters corresponding to the environmental stress tensor to obtain an environmental stress tensor-bond breakage evaluation model.

[0050] In this embodiment of the invention, a random forest algorithm is used to construct a tree-structured mapping relationship between the environmental stress tensor and the bond breakage probability for classification environmental stress tensor-bond breakage probability distribution data. First, the algorithm parameters are determined: the input to each decision tree is the four components of the environmental stress tensor (temperature, humidity, ultraviolet radiation, and standardized values ​​of mechanical stress), and the output is the bond breakage probability. The tree is constructed using a top-down recursive splitting method. Each internal node selects three components, and the optimal splitting feature and splitting threshold are determined based on the principle of minimizing node impurity. The maximum depth of the tree is limited to 12 layers, and the minimum number of samples in a leaf node is set to 6 to avoid overfitting. For each cluster dataset, it is divided into a training set and a validation set in a 7:3 ratio. 150 decision trees are repeatedly constructed based on the training set, with each tree using different bootstrap sampling samples. Finally, the environmental stress tensor-bond breakage tree structure model is formed by averaging the prediction results of all trees. Subsequently, the bond breakage evaluation threshold parameter is set using the classification environmental stress tensor-bond breakage probability distribution data. For each cluster corresponding to a curing agent type, the distribution characteristics of all bond breakage probability data under that category are statistically analyzed. The probability values ​​corresponding to the cumulative distribution function reaching 90%, 70%, and 50% are calculated and used as thresholds for "severe bond breakage," "minor bond breakage," and "no obvious bond breakage," respectively. These thresholds are embedded into a tree structure model. When the bond breakage probability output by the model exceeds the "severe bond breakage" threshold, it is marked as high-risk bond breakage; when it is between the "minor bond breakage" and "severe bond breakage" thresholds, it is marked as medium-risk bond breakage; and when it is below the "no obvious bond breakage" threshold, it is marked as low-risk bond breakage. The accuracy of the threshold classification is verified using validation set data to ensure that the three risk categories are consistent with the actual bond breakage situation. Finally, an environmental stress tensor-bond breakage assessment model is formed, which includes tree structure parameters, threshold standards, and risk marking rules.

[0051] Furthermore, as an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S5 is shown below. In this embodiment, step S5 includes the following steps: Step S51: Perform curing agent bond breaking behavior node analysis based on the micro-bond breaking behavior data of the curing agent unit to generate curing agent bond breaking behavior node data; In this embodiment of the invention, based on the microscopic bond-breaking behavior data of the curing agent unit, node identification technology is used to locate and analyze the bond-breaking behavior. The specific molecular location of each bond-breaking event is determined through molecular structure analysis and marked as a bond-breaking node, including the bond number on the molecular chain, the functional group it belongs to, and its spatial coordinates. All bond-breaking nodes are statistically analyzed, and the bond-breaking frequency (number of bond breaks per unit time) and the bond-breaking influence range (the number of structural changes in adjacent bonds triggered by bond breakage) of each node are calculated. Nodes are sorted from high to low bond-breaking frequency, and the top 20% are selected as key bond-breaking nodes, recording their molecular structural parameters (bond length, bond angle) and environmental characteristics associated with the bond-breaking event. The location information, structural parameters, bond-breaking frequency, and influence range of these nodes are summarized to generate curing agent bond-breaking behavior node data.

[0052] Step S52: Based on the node data of curing agent bond breaking behavior, perform correlation analysis between curing agent unit bond breaking and weathering degradation to generate curing agent unit bond breaking-weathering degradation correlation data; In this embodiment of the invention, correlation analysis is conducted based on the data of bond breakage behavior nodes of the curing agent, combined with the results of weather resistance testing. Macroscopic performance parameters (such as tensile strength, hardness, and insulation resistance) of the curing agent under different bond breakage degrees are measured using mechanical testing equipment. Gradients are defined according to the bond breakage rate of the broken nodes (the proportion of broken nodes to total nodes), and three parallel tests are performed under each gradient, with the average value taken. The bond breakage rate of each broken node and the rate of change of the corresponding performance parameter (the ratio of performance degradation to the initial value) are calculated, and the mathematical relationship between the two is determined through correlation analysis (such as a linear or exponential relationship). For example, for every 10% increase in the bond breakage rate of a critical broken node, the decrease in tensile strength is recorded. The correlation between the bond breakage rate and the rate of change of performance of all nodes is categorized and organized according to node type, generating curing agent unit bond breakage-weather degradation correlation data. The data includes node number, bond breakage rate range, corresponding performance parameter, and rate of change.

[0053] Step S53: Perform a mapping feature analysis of bond breakage and weathering degradation of the curing agent unit based on the correlation data of bond breakage and weathering degradation of the curing agent, and generate the mapping feature data of bond breakage and weathering degradation of the curing agent. In this embodiment of the invention, a weighted analysis and feature extraction method is used to perform mapping feature analysis on the correlation data between bond breakage and weathering degradation of curing agent units. The influence weight of each bond breakage node on weathering degradation is calculated. The weight value is determined based on the correlation strength between the bond breakage rate and the performance change rate, with a total weight of 100%. Nodes with the top 30% influence weights are selected as the bond breakage central nodes, and their contribution to performance degradation at different bond breakage stages (early, middle, and late stages) is analyzed. Simultaneously, based on the spatial distribution of curing agent units, the volume proportion of units at different locations is calculated. Combined with the influence weight of bond breakage nodes within a unit, a unit volume weighting coefficient for bond breakage and weathering degradation is determined. The coefficient is calculated using a weighted average method; the sum of the products of the volume proportion and the influence weight is the coefficient value for that unit. The contribution characteristics of the central nodes and the unit volume weighting coefficient are integrated to generate curing agent bond breakage and weathering degradation mapping feature data.

[0054] Step S54: Transmit the curing agent bond breakage-weathering degradation mapping feature data to the environmental stress tensor-bond breakage evaluation model to optimize the mapping of the bond breakage and weathering degradation relationship of the curing agent, so as to obtain the environmental stress tensor-weathering degradation evaluation model.

[0055] In this embodiment of the invention, the curing agent bond breakage-weathering degradation mapping feature data is combined with the environmental stress tensor-bond breakage assessment model to optimize the relationship mapping. Through feature fusion, parameters such as influence weights and weighting coefficients from the mapping feature data are embedded into the bond breakage assessment model to establish a direct correlation between bond breakage probability and the degree of weathering performance degradation. The model optimization process employs an iterative adjustment method. Based on the actual correlation data between bond breakage probability and performance degradation, the conversion coefficient from bond breakage to degradation in the model is corrected, ensuring that the deviation between the predicted degradation degree output by the model and the measured value is controlled within 4%. The optimized model can directly receive environmental stress tensor input and output degradation assessment results including bond breakage probability and corresponding weathering performance parameters (such as strength retention rate and insulation resistance attenuation rate). The model stability is verified through multiple sets of validation data to ensure that the error fluctuation of five consecutive predictions does not exceed 2%, ultimately yielding the environmental stress tensor-weathering degradation assessment model. The model includes feature parameters, conversion coefficients, and degradation degree grading standards (such as thresholds for mild, moderate, and severe degradation).

[0056] Furthermore, step S53 includes the following steps: Step S531: Based on the bond breakage-weathering degradation correlation data of the curing agent unit, perform weathering degradation impact analysis on the bond breakage nodes, generate bond breakage node-weathering degradation impact data, and perform weathering degradation impact characteristic analysis on the bond breakage central node based on the bond breakage node-weathering degradation impact data, generate bond breakage central node-weathering degradation impact characteristic data. In this embodiment of the invention, based on the correlation data of bond breakage and weathering degradation in the curing agent unit, sensitivity analysis is used to analyze the impact of bond breakage nodes on weathering degradation. The ratio of the change in bond breakage rate to the change in weathering performance parameters for each bond breakage node is calculated. A larger ratio indicates a more significant impact of the node on weathering degradation, and these ratios are defined as influence coefficients. The top 25% of bond breakage nodes are extracted as bond breakage pivot nodes, sorted by influence coefficient from largest to smallest, and their influence coefficients and corresponding performance parameter change patterns are recorded. For each bond breakage pivot node, the continuous change curve of weathering performance under different bond breakage degrees (bond breakage rate from 0% to 100%) is analyzed to determine the critical bond breakage rate (the bond breakage rate value at which performance significantly decreases) and degradation rate (the performance decrease per unit bond breakage rate). The influence coefficients, critical bond breakage rates, and degradation rates of all bond breakage pivot nodes are summarized to generate bond breakage pivot node-weathering degradation impact characteristic data.

[0057] Step S532: Perform unit weighted coefficient analysis of bond breakage and weathering degradation based on the correlation data of curing agent unit bond breakage and weathering degradation, and generate unit volume weighted coefficients of bond breakage and weathering degradation. In this embodiment of the invention, a unit-weighted coefficient analysis of bond breakage and weathering degradation is performed based on the correlation data of bond breakage and weathering degradation of curing agent units, combined with the spatial distribution information of curing agent units. The volume ratio of each curing agent unit in the overall structure is determined by a volume calculation method; the volume ratio is the ratio of the unit volume to the total volume, accurate to four decimal places. Based on the influence coefficients of all bond breakage nodes within the unit, the average influence coefficient of the unit is calculated as the sum of the products of the influence coefficients of each node within the unit and the proportion of bond breakage frequency at that node. The unit volume ratio is multiplied by the average influence coefficient to obtain the bond breakage and weathering degradation weighted coefficient for that unit, with the weighted coefficient value controlled between 0 and 1. The above calculation is repeated for all units to generate the bond breakage and weathering degradation unit volume weighted coefficients.

[0058] Step S533: Perform a mapping characteristic analysis of bond breakage and weathering degradation of the curing agent by using the volume weighting coefficient of the bond breakage-weathering degradation unit and the data on the influence characteristics of the bond breakage central node on weathering degradation, and generate the mapping characteristic data of bond breakage and weathering degradation of the curing agent.

[0059] In this embodiment of the invention, the volume weighting coefficient of the bond breakage-weathering degradation unit is fused with the data on the influence characteristics of the bond breakage central node on weathering degradation. The data on the influence characteristics of the bond breakage central node on weathering degradation determines the effect of the breakage of the main chemical bonds in the curing agent on the weathering degradation of the curing agent, excluding the influence of the breakage of irrelevant chemical bonds in the curing agent. Furthermore, the actual coefficient of the proportion of each broken bond in the overall unit is determined by the volume weighting coefficient of the bond breakage-weathering degradation unit, so as to conduct the actual relationship between the chemical bond breakage of each unit of the curing agent and the weathering performance degradation in the finite element analysis. The weighted parameters of all bond breakage central nodes are summarized, and the overall influence coefficient, overall critical bond breakage rate, and overall degradation rate are analyzed. The variation law of these overall parameters with the bond breakage process is analyzed to determine the quantitative mapping relationship between the bond breakage rate and the degree of weathering performance degradation. The overall parameters and mapping relationship are integrated to generate curing agent bond breakage-weathering degradation mapping characteristic data.

[0060] Furthermore, step S6 includes the following steps: Step S61: Based on the environmental data of the curing agent application scenario, perform trend time-series change analysis of the unit environment boundary conditions of the finite element curing agent reaction molecular structure data to generate trend time-varying unit environment boundary condition data; In this embodiment of the invention, based on environmental data of the curing agent application scenario and finite element data of the curing agent reaction molecular structure, a time series decomposition method is used to analyze the unit environmental boundary conditions of trend-time-varying changes. The environmental data is divided into several time segments according to an annual cycle, each time segment containing continuous monitoring values ​​of temperature, humidity, ultraviolet radiation, and mechanical stress. A trend extraction algorithm is used to separate the long-term changing trends (such as year-on-year increasing or decreasing patterns) and periodic fluctuation characteristics (such as seasonal changes) of each environmental parameter. Combining the unit spatial coordinates in the finite element data of the curing agent reaction molecular structure, each unit is matched to its corresponding environmental monitoring area. Based on the environmental parameter trends in the area where the unit is located, the environmental parameter values ​​at multiple future time points (such as the end of each quarter) are predicted, including maximum, minimum, and average values. The predicted values ​​are correlated with the unit coordinates to generate trend-time-varying unit environmental boundary condition data, which includes the environmental parameter range of each unit at different time points.

[0061] Step S62: Perform trend-time-varying element environmental boundary condition data analysis on element environmental stress tensor characteristics to generate trend-time-varying element environmental stress tensor data; In this embodiment of the invention, based on trend-varying unit environmental boundary condition data, the finite element method (FEM) is used to perform trend-time-varying unit environmental stress tensor characteristic analysis. Environmental parameters (temperature, humidity, ultraviolet radiation, mechanical stress) at each time point are converted into corresponding stress components, based on the physical conversion relationship between parameters and stress (e.g., the linear relationship between temperature change and thermal stress). Each stress component is combined into a unit environmental stress tensor, and the eigenvalues ​​(reflecting stress intensity) and eigenvectors (reflecting stress direction) of each stress tensor are calculated. The stress tensor changes of the same unit at different time points are tracked, recording the increase / decrease of eigenvalues ​​and the deflection angle of eigenvectors, analyzing the evolution of the stress tensor over time (e.g., increasing intensity with stable direction, or fluctuating intensity with periodic direction changes). The eigenvalues, eigenvectors, and rates of change of all units at each time point are summarized to generate trend-varying unit environmental stress tensor data, ensuring that the accuracy of stress calculation is consistent with the accuracy of finite element analysis.

[0062] Step S63: Transmit the environmental stress tensor data of the trend time-varying unit to the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent and generate weathering performance degradation trend data of the curing agent.

[0063] In this embodiment of the invention, the environmental stress tensor data of the trend-varying unit are input into the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent. After receiving different environmental stress tensor data for each unit at various time points, the model calculates the corresponding bond breakage probability according to the built-in mapping relationship, and then converts the bond breakage probability into weathering performance parameters (such as tensile strength retention rate and insulation resistance attenuation rate) through the bond breakage-weathering degradation conversion coefficient. A spatially weighted average (weighted by the unit volume percentage) is performed on the performance parameters of all units at the same time point to obtain the overall weathering performance parameter value. The overall parameter values ​​at all time points are continuously calculated to form a curve showing the weathering performance changing over time. The time point where the performance parameter first falls below 70% of the initial value is marked on the curve (defined as a significant performance degradation point). The curve data, the parameter values ​​at each time point, and the information on significant degradation points are summarized to generate weathering performance degradation trend data for the curing agent.

[0064] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0065] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for predicting the weather resistance degradation of curing agents based on finite element analysis, characterized in that, Includes the following steps: Step S1: Obtain data on the properties of the curing agent material and the environmental data of the curing agent application scenario; Based on the curing agent material characteristic data, the material properties of materials with different curing agent compositions are clustered to generate clustered curing agent material property data; Step S2: Based on the performance data of the clustered curing agent material and the environmental data of the curing agent application scenario, perform finite element analysis of the curing agent reaction molecular structure to obtain finite element curing agent reaction molecular structure data; Step S3: Analyze the microscopic bond breaking behavior of the curing agent unit based on the finite element molecular structure data of the curing agent reaction, and generate microscopic bond breaking behavior data of the curing agent unit; Based on the microscopic bond-breaking behavior data of curing agent units, we conduct a characteristic analysis of the bond-breaking behavior of curing agents that affects the environment, and generate characteristic data of the bond-breaking behavior of curing agents that affect the environment. Step S4: Establish the mapping relationship between the environmental stress tensor and bond breakage assessment of each cluster of curing agents by using the characteristic data of the bond breakage behavior of curing agents affected by the environment, so as to obtain the environmental stress tensor-bond breakage assessment model. Step S5: Optimize the mapping between bond breakage and weathering degradation relationship of the curing agent in the environmental stress tensor-bond breakage assessment model to obtain the environmental stress tensor-weathering degradation assessment model. Step S6: Based on the environmental data of the curing agent application scenario, perform trend time-series variation element environmental stress tensor characteristic analysis on the finite element curing agent reaction molecular structure data to generate trend time-varying element environmental stress tensor data; The environmental stress tensor data of the trend time-varying unit is transmitted to the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent and generate weathering performance degradation trend data of the curing agent.

2. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain curing agent material property data and curing agent application scenario environment data; Step S12: Perform material composition analysis based on the curing agent material characteristic data to obtain curing agent material composition data; Step S13: Perform phase difference tensor analysis on the differences in curing agent composition based on the curing agent material composition data to obtain the phase difference tensor data of the differences in curing agent composition; Step S14: Perform clustering processing on the curing agent material properties based on the curing agent material composition data and the phase difference tensor data of the curing agent composition differences to generate clustered curing agent material property data.

3. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Analyze the heterogeneous environment of the curing agent reaction based on the environmental data of the curing agent application scenario, and generate heterogeneous environment data of the curing agent reaction; Step S22: Perform environmental field intensity gradient characteristic analysis on the environmental data of the curing agent reaction heterogeneity to generate environmental field intensity gradient characteristic data; Step S23: Design the environmental dynamic unit grid density based on the environmental field intensity gradient characteristic data to obtain the environmental dynamic unit grid density data; Step S24: Design the finite element unstructured mesh nodes for the curing agent reaction using the environmental dynamic unit mesh density data to obtain the curing agent finite element mesh node data. Then, use the clustered curing agent material performance data and the heterogeneous environment data of the curing agent reaction as the mesh attribute nodes of the curing agent finite element mesh node data to perform finite element analysis processing of the curing agent reaction to obtain finite element curing agent reaction data. Step S25: Perform molecular structure analysis of the finite element curing agent reaction data to obtain the molecular structure data of the finite element curing agent reaction.

4. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 3, characterized in that, Step S3 includes the following steps: Step S31: Analyze the microscopic bond breaking behavior of the curing agent unit based on the finite element molecular structure data of the curing agent reaction, and generate microscopic bond breaking behavior data of the curing agent unit; Step S32: Based on the heterogeneous environment data of the curing agent reaction, perform correlation processing on the micro-bond breaking behavior data of the curing agent unit to generate curing agent unit environment-bond breaking behavior data. Step S33: Extract the curing agent reaction environment influence factor from the curing agent unit environment-bond breaking behavior data; perform curing agent micro-bond breaking behavior analysis on the curing agent unit environment-bond breaking behavior data based on the curing agent reaction environment influence factor, and generate single environment influence factor-bond breaking behavior data; Step S34: Analyze the microscopic bond breaking behavior of the curing agent based on the environmental impact factor interaction effect using curing agent unit environment-bond breaking behavior data and single environmental impact factor-bond breaking behavior data, and generate interactive environmental impact factor-bond breaking behavior data; Step S35: Based on single environmental impact factor-bond breaking behavior data and interactive environmental impact factor-bond breaking behavior data, perform a characteristic analysis of the bond breaking behavior of curing agents in relation to environmental impact, and generate characteristic data of bond breaking behavior of curing agents in relation to environmental impact.

5. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 4, characterized in that, The single environmental impact factor-bond breaking behavior data mentioned in step S33 includes temperature conduction impact factor-bond breaking behavior data, humidity diffusion-bond breaking behavior data, ultraviolet absorption characteristics-bond breaking behavior data, and mechanical stress characteristics-bond breaking behavior data.

6. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Based on the characteristic data of bond breaking behavior of environmental impact curing agent, perform bond breaking probability distribution analysis of environmental impact curing agent to generate bond breaking probability distribution data of environmental impact curing agent; Step S42: Analyze the bond breakage probability distribution data of the environmental stress tensor based on the environmental impact curing agent bond breakage probability distribution data to generate environmental stress tensor-bond breakage probability distribution data; Step S43: Based on the clustered curing agent material performance data, classify the environmental stress tensor-bond breakage probability distribution data according to the bond breakage probability distribution of material performance differences, and generate classified environmental stress tensor-bond breakage probability distribution data. Step S44: Establish the mapping relationship between the environmental stress tensor and bond breakage assessment of each clustered curing agent by classifying the environmental stress tensor-bond breakage probability distribution data, so as to obtain the environmental stress tensor-bond breakage assessment model.

7. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 6, characterized in that, Step S44 includes the following steps: A pre-defined random forest algorithm is used to establish various tree structure mapping relationships between environmental stress tensor and bond breakage probability to obtain an environmental stress tensor-bond breakage tree structure model. Then, by classifying the environmental stress tensor-bond breakage probability distribution data, the environmental stress tensor-bond breakage tree structure model is used to set the bond breakage evaluation threshold parameters corresponding to the environmental stress tensor to obtain an environmental stress tensor-bond breakage evaluation model.

8. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Perform curing agent bond breaking behavior node analysis based on the micro-bond breaking behavior data of the curing agent unit to generate curing agent bond breaking behavior node data; Step S52: Based on the node data of curing agent bond breaking behavior, perform correlation analysis between curing agent unit bond breaking and weathering degradation to generate curing agent unit bond breaking-weathering degradation correlation data; Step S53: Perform a mapping feature analysis of bond breakage and weathering degradation of the curing agent unit based on the correlation data of bond breakage and weathering degradation of the curing agent, and generate the mapping feature data of bond breakage and weathering degradation of the curing agent. Step S54: Transmit the curing agent bond breakage-weathering degradation mapping feature data to the environmental stress tensor-bond breakage evaluation model to optimize the mapping of the bond breakage and weathering degradation relationship of the curing agent, so as to obtain the environmental stress tensor-weathering degradation evaluation model.

9. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 8, characterized in that, Step S53 includes the following steps: Step S531: Based on the bond breakage-weathering degradation correlation data of the curing agent unit, perform weathering degradation impact analysis on the bond breakage nodes, generate bond breakage node-weathering degradation impact data, and perform weathering degradation impact characteristic analysis on the bond breakage central node based on the bond breakage node-weathering degradation impact data, generate bond breakage central node-weathering degradation impact characteristic data. Step S532: Perform unit weighted coefficient analysis of bond breakage and weathering degradation based on the correlation data of curing agent unit bond breakage and weathering degradation, and generate unit volume weighted coefficients of bond breakage and weathering degradation. Step S533: Perform a mapping characteristic analysis of bond breakage and weathering degradation of the curing agent by using the volume weighting coefficient of the bond breakage-weathering degradation unit and the data on the influence characteristics of the bond breakage central node on weathering degradation, and generate the mapping characteristic data of bond breakage and weathering degradation of the curing agent.

10. The method for predicting the weather resistance degradation of curing agents based on finite element analysis according to claim 1, characterized in that, Step S6 includes the following steps: Step S61: Based on the environmental data of the curing agent application scenario, perform trend time-series change analysis of the unit environment boundary conditions of the finite element curing agent reaction molecular structure data to generate trend time-varying unit environment boundary condition data; Step S62: Perform trend-time-varying element environmental boundary condition data analysis on element environmental stress tensor characteristics to generate trend-time-varying element environmental stress tensor data; Step S63: Transmit the environmental stress tensor data of the trend time-varying unit to the environmental stress tensor-weathering degradation assessment model to predict the weathering performance degradation trend of the curing agent and generate weathering performance degradation trend data of the curing agent.

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