Method and system for detecting aging of water-based waterproof material in xenon lamp simulation environment

Abstract

The invention relates to the technical field of aging detection, and discloses a xenon lamp simulation environment aging detection method and system for a water-based waterproof material. The method comprises the following steps: testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data; calculating a characteristic spectrum drift parameter combination in combination with the first aging response data, and solving a multi-factor aging dynamics equation set to obtain three-dimensional environment field data; inputting the three-dimensional environment field data into a back propagation neural network to carry out multi-factor environment parameter collaborative optimization to obtain an intelligent control parameter combination; performing test parameter adjustment and detection on the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; and performing spectral attenuation index calculation and aging degree grading based on the second aging response data to obtain an aging state evaluation result of the water-based waterproof material sample. According to the method, multi-dimensional quantitative characterization of the aging state of the water-based waterproof material and scientific prediction of the service life of the water-based waterproof material in an acid pollution environment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of aging detection, and in particular to a method and system for detecting aging of water-based waterproof materials in a xenon lamp simulated environment. Background Art

[0002] Traditional xenon lamp aging detection methods are mainly based on the single light intensity assumption for environmental simulation, ignoring the synergistic coupling effect between acid rain erosion, pollutant gas corrosion and photooxidative degradation. As a result, there is a significant deviation between the aging test results and the material performance attenuation law in the actual environment, and it is impossible to accurately predict the actual service life of water-based waterproof materials.

[0003] Existing xenon lamp aging equipment generally uses traditional proportional-integral-differential controllers to adjust environmental parameters. This requires manual adjustment of parameters such as acid rain concentration and pollutant gas flow. This multi-factor coordinated control has limited accuracy and lacks the ability to monitor and provide feedback on the material's molecular-level response mechanisms. This control method fails to capture the synergistic hysteresis effect between acid corrosion and photooxidation, leading to the accumulation of multi-factor coupling errors, which seriously affects the environmental authenticity and reliability of aging testing results. Summary of the Invention

[0004] The main purpose of the present invention is to provide a method and system for detecting aging of water-based waterproof materials in a xenon lamp simulated environment. The present invention realizes multi-dimensional quantitative characterization of the aging state of water-based waterproof materials and scientific prediction of their service life in an acidic pollution environment.

[0005] To achieve the above object, the present invention provides a method for detecting aging of water-based waterproof materials in a xenon lamp simulated environment, comprising the following steps: Testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data; Calculating a characteristic spectrum drift parameter combination in combination with the first aging response data, and solving a multi-factor aging kinetic equation group to obtain three-dimensional environmental field data; Inputting the three-dimensional environmental field data into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination; adjusting and testing test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; Spectral attenuation index calculation and aging degree classification are performed based on the second aging response data, and an aging status evaluation result of the water-based waterproof material sample is output.

[0006] Optionally, in a first implementation of the first aspect of the present invention, testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data includes: The multi-factor coupling test parameters of the xenon lamp irradiation system, acid rain spray system and polluted gas injection system in the aging detection platform were set to obtain composite environmental exposure conditions; Based on the composite environmental exposure conditions, collaborative parameter setting is performed on the temperature control module, the humidity control module, and the pressure control module in the aging detection platform to obtain an environmental parameter control scheme; According to the environmental parameter control scheme, the water-based waterproof material sample is respectively subjected to initial stage exposure, accelerated stage exposure and stable stage exposure, and the first aging response data is collected at the same time.

[0007] Optionally, in a second implementation of the first aspect of the present invention, calculating a characteristic spectrum drift parameter combination in combination with the first aging response data and solving a multi-factor aging kinetic equation group to obtain three-dimensional environmental field data includes: Performing Fourier transform infrared spectroscopy detection based on the first aging response data to obtain a characteristic peak detection interval of the polymer molecular chain; Based on the characteristic peak detection interval of the polymer molecular chain, the surface of the water-based waterproof material sample is subjected to in-situ detection by an ATR accessory and synchronous monitoring by a pH response sensor array to obtain original detection data; performing signal operation on the original detection data to obtain spectrum extraction parameters; Calculating the spectrum drift amount and the intensity attenuation rate according to the spectrum extraction parameters to obtain a characteristic spectrum drift parameter combination; The multi-factor aging dynamics equations are numerically solved based on the characteristic spectrum drift parameter combination to obtain three-dimensional environmental field data.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the numerical solution of the multi-factor aging dynamics equations based on the characteristic spectrum drift parameter combination to obtain three-dimensional environmental field data includes: Establish a multi-factor aging kinetics equation system including the acid rain penetration equation, the photooxidative degradation equation, and the heat and moisture coupled transfer equation; Inputting the characteristic spectrum drift parameter combination into the multi-factor aging kinetic equation group to analyze the mapping relationship between molecular response characteristics and environmental parameters to obtain a polymer molecular response coefficient matrix; Iteratively numerically solving the multi-factor aging kinetics equations based on the polymer molecular response coefficient matrix to obtain kinetic parameter solution results; Gradient calculation and time evolution law analysis are performed based on the solution results of the kinetic parameters to obtain three-dimensional environmental field data, which includes sulfur dioxide concentration distribution gradient, nitrogen dioxide concentration distribution gradient and acidity value distribution gradient.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, the characteristic spectrum drift parameter combination is input into the multi-factor aging kinetic equation group to perform a mapping relationship analysis between molecular response characteristics and environmental parameters to obtain a polymer molecular response coefficient matrix, including: Classifying the molecular response types based on the characteristic spectral drift parameter combination to obtain a classification spectral response parameter set; Based on the classification spectral response parameter set, a quantitative correlation analysis is performed on the polymer main chain rupture mechanism, the molecular chain crosslinking density change mechanism and the molecular chain rearrangement mechanism to obtain a molecular response sensitivity function combination; Performing numerical coupling calculations on the molecular response sensitivity function combination and the multi-factor aging kinetic equations for the environmental node gradient values ​​and the polymer molecular response characteristics to obtain environmental-molecule coupling correlation data; According to the environment-molecule coupling correlation data, reverse numerical solution and coefficient matrix construction are performed to obtain the polymer molecule response coefficient matrix.

[0010] Optionally, in a fifth implementation of the first aspect of the present invention, inputting the three-dimensional environmental field data into a back propagation neural network to perform multi-factor environmental parameter collaborative optimization to obtain an intelligent control parameter combination includes: fusing the three-dimensional environmental field data with infrared spectrum characteristic peak drift data and pH sensor response time offset data to obtain a target input vector; Inputting the target input vector into the molecular response layer of the back propagation neural network for linear transformation to obtain a molecular response layer output vector; Inputting the output vector of the molecular response layer into the environmental coupling layer of the back propagation neural network to perform weighted linear transformation and Sigmoid activation function calculation of the synergistic effect of acid rain erosion and photooxidation, thermal aging and moisture-heat coupling, and multi-factor interaction to obtain an output vector of the environmental coupling layer; The output vector of the environmental coupling layer is input into the output layer of the back propagation neural network for linear transformation and parameter analysis to obtain an intelligent control parameter combination, which includes a xenon lamp irradiation intensity adjustment value, an acid rain pH adjustment value, a sulfur dioxide flow adjustment value, a nitrogen dioxide flow adjustment value, and a temperature and humidity adjustment value.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, inputting the target input vector into the molecular response layer of the back propagation neural network for linear transformation to obtain a molecular response layer output vector includes: Inputting the target input vector into a molecular response layer of a back propagation neural network, wherein the molecular response layer includes an acid rain erosion response neuron group, a photooxidation degradation response neuron group, a heat and humidity aging response neuron group, and a multi-factor interaction response neuron group; The target input vector is subjected to matrix multiplication of the synergistic effect of acid rain penetration depth and light irradiation intensity and nonlinear coupling function calculation by using an acid rain erosion response neuron group and a photooxidation degradation response neuron group to obtain an acid rain-light synergistic feature; The target input vector is subjected to coupled calculations of temperature gradient and relative humidity gradient and interaction calculations of pollutant gas concentration and environmental parameters by using a heat-humidity aging response neuron group and a multi-factor interaction response neuron group to obtain a temperature-humidity-pollutant gas coupling feature; An environmental coupling layer output vector is generated according to the acid rain-light synergistic feature and the temperature, humidity and polluted gas coupling feature.

[0012] Optionally, in a seventh implementation of the first aspect of the present invention, the step of adjusting and detecting test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data includes: Generate xenon lamp irradiation system control instructions, acid rain spray system control instructions and polluted gas injection system control instructions according to the intelligent control parameter combination; Adjusting the irradiation intensity of the xenon lamp irradiation system in the aging detection platform based on the xenon lamp irradiation system control instruction, adjusting the acidity value of the acid rain spraying system in the aging detection platform based on the acid rain spraying system control instruction, and controlling the flow rate of the polluted gas injection system in the aging detection platform based on the polluted gas injection system control instruction; During the test parameter adjustment, the water-based waterproof material sample in the aging detection platform is continuously monitored and multi-parameter synchronously recorded for compensation of the synergistic hysteresis effect of acid corrosion and photooxidation to obtain second aging response data.

[0013] Optionally, in an eighth implementation of the first aspect of the present invention, the calculating of the spectral attenuation index and grading of the aging degree based on the second aging response data, and outputting an aging status assessment result of the water-based waterproof material sample, includes: Extracting characteristic parameters and configuring weighting coefficients on the second aging response data to obtain a spectral attenuation index; Based on the spectral attenuation index, a weighted comprehensive operation is performed on the characteristic peak intensity attenuation rate and the peak position drift, and the spectral attenuation index ADI value is calculated to obtain comprehensive spectral attenuation data; Performing threshold judgment and aging degree classification based on the comprehensive spectral attenuation data to obtain an aging degree grade; Combined with the aging degree grade, the second aging response data is input into a life prediction algorithm based on the Arrhenius-pH coupled aging model to perform a multivariate nonlinear regression calculation to obtain an aging state assessment result.

[0014] The present invention also provides a water-based waterproof material xenon lamp simulated environment aging detection system, comprising: An acquisition module, configured to test a water-based waterproof material sample in an aging detection platform and acquire first aging response data; a solution module, configured to calculate a characteristic spectrum drift parameter combination based on the first aging response data, and solve a multi-factor aging kinetic equation group to obtain three-dimensional environmental field data; A collaborative optimization module is used to input the three-dimensional environmental field data into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination; a parameter adjustment module, configured to adjust and detect test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; An output module is used to calculate the spectral attenuation index and classify the aging degree based on the second aging response data, and output an aging status evaluation result of the water-based waterproof material sample.

[0015] In summary, the present invention realizes the coordinated simulation of acid rain erosion, pollutant gas corrosion and photooxidative degradation through the coupling configuration of the acid rain spray system, the polluted gas injection system and the xenon lamp irradiation system, overcomes the limitations of the traditional single light intensity simulation, and significantly improves the environmental authenticity of the aging test. At the same time, the Fourier transform infrared spectroscopy detection system is used to realize the real-time, continuous and non-destructive monitoring of the polymer molecular chain breakage process, breaking through the technical bottleneck of the traditional periodic sampling and detection. By extracting the characteristic spectral drift parameters of the carbon-oxygen bond, carbonyl and hydroxyl stretching vibration peaks, a quantitative mapping relationship between the material molecular response and the environmental parameters is established, and an innovative inversion algorithm for inferring the distribution of environmental parameters from the material response is realized. In addition, the present invention has developed a back propagation neural network controller specifically for the molecular response mechanism of water-based waterproof materials. Compared with the manual parameter adjustment method of the traditional controller, it realizes adaptive parameter optimization based on the material molecular response. The multi-layer neural network architecture effectively handles the complex nonlinear relationship such as the synergistic effect of acid rain erosion and photooxidation, significantly improving the coordinated control accuracy of multi-factor environmental parameters and effectively compensating for the synergistic hysteresis effect between acid erosion and photooxidation. The present invention establishes a weighted comprehensive evaluation system based on the spectral attenuation index and a five-level evaluation grading system, integrates a life prediction algorithm based on a coupled aging model, and realizes a multi-dimensional quantitative characterization of the aging state of water-based waterproof materials and a scientific prediction of their service life in an acidic pollution environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the steps of a xenon lamp simulated environment aging detection method for water-based waterproof materials in one embodiment of the present invention; Figure 2 This is a structural block diagram of a xenon lamp simulated environment aging detection system for water-based waterproof materials in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Reference Figure 1 This embodiment provides a method for detecting aging of water-based waterproof materials in a xenon lamp simulated environment, comprising the following steps: S1, testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data; Among them, multi-factor coupling test parameter setting is carried out for the xenon lamp irradiation system, acid rain spray system and polluted gas injection system. The xenon lamp irradiation system uses a high-power xenon lamp light source covering the 280-800nm ​​band, with an irradiation intensity of 100-1200W / m 2The system continuously adjusts within a certain range to ensure that it can simulate a composite spectral environment of ultraviolet light, visible light, and even near-infrared light. The acid rain spray system uses a pH adjustment device in conjunction with an atomizing nozzle array to create an acidic rain mist environment with a controllable pH value between 2.5 and 6.5. The spray flow accuracy must reach ±2% to ensure droplet uniformity and reproducibility. The polluted gas injection system is equipped with a high-precision mass flow controller to achieve stable injection of SO2 concentrations from 0.1ppm to 5.0ppm and NO2 concentrations from 0.05ppm to 2.0ppm, and to ensure that the gas mixing uniformity exceeds 95% to truly reproduce the atmospheric corrosion effects in a polluted environment. Based on the above composite environmental exposure conditions, the temperature control module, humidity control module, and pressure control module in the aging detection platform are collaboratively parameterized to form an environmental parameter control scheme. The temperature control module boasts an adjustable range of -40°C to +150°C and a control accuracy of ±0.5°C, adapting to the stringent thermal requirements of different aging stages. The humidity control module maintains an adjustable range of 10%-98% RH with a control accuracy of ±2% RH to fully simulate the heat-moisture coupling effect during the aging process. The pressure control module maintains a stable internal pressure to prevent errors in gas permeation rates and acid rain droplet deposition characteristics caused by pressure fluctuations. Through coordinated multi-factor control, the realistic aging conditions of the heat-humidity-light-chemical multi-field coupling in the natural environment are simulated, significantly improving the environmental reproducibility and scientific nature of the test. After the aforementioned environmental parameter control schemes are established, the water-based waterproofing material specimens are exposed in stages according to the established procedures. In the initial exposure phase (0-144 hours), mild conditions were used to collect baseline data. The xenon lamp irradiation intensity was set to 300W / m², the acid rain pH was controlled at 5.5, the SO2 and NO2 concentrations were set to 0.2ppm and 0.1ppm respectively, the temperature was maintained at 35℃, and the humidity was kept at around 65%, ensuring that the water-based waterproof material was in an initial state close to natural aging. In the accelerated exposure phase (144-1080 hours), the environmental severity was gradually increased, and the xenon lamp irradiation intensity was increased to 600-1000W / m². 2The acid rain pH value was reduced to 3.0-4.5, SO2 and NO2 concentrations were increased to 1.0-3.0 ppm and 0.5-1.5 ppm, respectively. The temperature was raised to 60-80°C, and the humidity was adjusted to 80%-90%. This phase primarily simulated the rapid aging behavior of the material under high-intensity UV radiation, acid rain erosion, and polluted atmosphere. The stabilization phase exposure (1080-1440 hours) maintained a highly harsh environment to verify the stability and ultimate durability of the material's aging performance under long-term, complex conditions. Throughout the exposure process, changes in the molecular chain structure of the material surface were monitored in real time using a Fourier transform infrared spectrometer. Peak position shifts and intensity changes of characteristic absorption peaks such as CO, C=O, and OH were collected, and surface pH changes were recorded in real time using a composite electrode pH sensor array to generate the first aging response data.

[0020] S2, calculating a characteristic spectrum drift parameter combination based on the first aging response data, and solving a multi-factor aging kinetic equation group to obtain three-dimensional environmental field data; Specifically, Fourier transform infrared spectroscopy is performed based on the first aging response data. The detection process uses a wave number range covering 400-4000cm -1 , resolution of 4cm -1 The high-precision infrared spectrometer is equipped with an ATR accessory to achieve in-situ detection, thereby directly obtaining the characteristic peak detection range of the polymer molecular chain without pretreatment of the material surface, focusing on the CO stretching vibration peak (1050-1150cm -1 ), C=O stretching vibration peak (1680-1750cm -1 ) and OH stretching vibration peaks (3200-3600 cm -1) and other absorption peak position changes closely related to molecular chain breakage and crosslinking. After obtaining the characteristic peak detection interval, the surface of the waterborne waterproofing material sample was subjected to in situ surface ATR testing using a surface ATR accessory. Simultaneously, a pH-responsive sensor array was used to continuously monitor changes in the sample's surface microenvironmental acidity and base concentration, generating a raw data set. This pH-responsive sensor array utilizes a highly sensitive glass electrode and reference electrode composite structure, offering a response time of less than 30 seconds and a detection accuracy of ±0.02 pH units. This ensures time synchronization with the spectral data, thereby reflecting the synergistic effects of changes in the material's molecular structure and the evolution of the surface microenvironment during aging. Signal processing is then performed based on these raw data. Polynomial baseline correction is used to eliminate background interference. Spectral peak separation is then used to extract key information such as peak position, peak intensity, and half-maximum width of each characteristic absorption peak. Peak area changes are then calculated through integration to obtain spectral extraction parameters. These parameters, including characteristic peak shift, intensity change, and peak shape change, can quantitatively describe the microstructural evolution of waterborne waterproofing materials under multi-factor aging conditions, including molecular chain degradation, crosslink density changes, and hydrogen bond breakage. Based on the extracted spectral parameters, spectral drift and intensity decay rates were calculated. Spectral drift, obtained by comparing the wavenumber changes of characteristic peaks at different time points, reflects the breakage rate and bond energy changes of the polymer backbone. Intensity decay, calculated by calculating the ratio of the characteristic peak intensity to the initial intensity, reveals the rate of loss of molecular structural integrity. The drift and intensity decay rates corresponding to each characteristic peak were combined to construct a characteristic spectral drift parameter combination, forming a complete characteristic vector describing the material aging process. A multi-factor aging kinetic equation system was numerically solved based on the characteristic spectral drift parameter combination. The kinetic equation system comprehensively considers the effects of acid rain penetration, photooxidative degradation, and coupled thermal and humidity transfer. The Fick diffusion law correction equation, quantum yield theory equation, and Arrhenius-humidity correction equation were used to describe the diffusion of SO2 and NO2 concentration fields within the material, the coupled changes in the photooxidative reaction rate, and the temperature and humidity acceleration effects. The space and time domains are discretized using the finite element method, and the characteristic spectrum drift parameters are input as inversion constraints. The solution is solved step by step through iteration, and finally the distribution gradient data of SO2 concentration, NO2 concentration and pH value in the three-dimensional space and time evolution inside the material are obtained.

[0021] A multi-factor aging kinetics system was constructed, including the acid rain penetration equation, the photooxidative degradation equation, and the coupled thermal and moisture transfer equation. The acid rain penetration equation, based on a modified form of Fick's diffusion law, describes the diffusion and reaction processes of acidic ions within the material, taking into account the effects of pH and temperature on the diffusion coefficient and reaction rate constant, reflecting the dynamic evolution of acidic corrosion. The photooxidative degradation equation, constructed based on quantum yield theory, correlates the photodegradation rates of polymer molecules under irradiation at different wavelengths, describing the mechanism of light-induced chemical bond breakage and polymer concentration decay. The coupled thermal and moisture transfer equation uses the Arrhenius formula to introduce a humidity correction term to quantitatively characterize the combined effects of temperature and relative humidity on the material aging reaction rate. This coupled mechanism can reflect the combined impact of environmental thermal and humidity changes on the material lifespan. After the above kinetics system was modeled, characteristic spectral drift parameters obtained through Fourier transform infrared spectroscopy were combined into the equations. These characteristic spectral drift parameters include the wavenumber drift and intensity decay rate of key vibrational peaks such as CO, C=O, and OH. These parameters can sensitively reflect the molecular chain breakage, changes in crosslinking degree, and the evolution of functional group content within the material. By inputting these spectral drift data, the mapping relationship between the molecular response characteristics and environmental parameters involved in the equations, such as diffusion coefficients, reaction rate constants, and activation energies, is analyzed. This establishes a direct quantitative relationship between the characteristic spectral response and environmental loads, and further solves for the polymer molecular response coefficient matrix. This coefficient matrix quantitatively describes the sensitivity of the material's molecular structure evolution to various environmental factors under the influence of different environmental variables. Based on the polymer molecular response coefficient matrix, an iterative numerical method is used to solve the multi-factor aging kinetic equations. The material's internal spatial domain is discretized using the finite element method, while step-by-step integration is performed in the time domain. The stable solution of the equations is gradually approached through nonlinear iterations. During each iteration, the solution is corrected and updated based on the current environmental variable distribution and molecular response data to ensure convergence and physical plausibility. The final kinetic parameter solution contains important information such as the diffusion depth distribution of acid rain ions in the material, the distribution of photooxidative degradation rates, and the distribution of heat-moisture coupled reaction rates. Based on the kinetic parameter solutions, gradient calculations and time evolution analysis are performed, and three-dimensional environmental field data are further derived and visualized. By calculating the gradient of the rate of change of environmental parameters at each spatial node, we obtain the sulfur dioxide concentration distribution gradient, the nitrogen dioxide concentration distribution gradient, and the acidity distribution gradient. The sulfur dioxide and nitrogen dioxide concentration distribution gradients reveal the rate and direction of pollutant diffusion and penetration within the material, while the acidity distribution gradient reflects the spatial heterogeneity of local pH changes within the material. Simultaneously, combined with time evolution analysis, we obtain the dynamic process of pollutant penetration and material degradation over time, revealing the environmental response characteristics of the material at different aging stages.

[0022] Molecular response types are classified based on combinations of characteristic spectral drift parameters. In the collected spectral data, the wavenumber drift and intensity decay of key vibrational peaks, such as CO, C=O, and OH, represent different molecular reaction mechanisms. Therefore, based on the chemical bonds corresponding to the characteristic peaks and their evolutionary paths during aging, the characteristic spectral data are divided into subsets reflecting main chain rupture, crosslink density changes, and molecular chain rearrangement, forming a set of classified spectral response parameters. This step uses algorithms such as peak separation and principal component analysis to extract features and cluster them, ensuring that the spectral parameter set is highly discriminative and representative of the mechanisms. Based on the set of classified spectral response parameters, quantitative correlation analysis is performed for each molecular response mechanism. For the main chain rupture mechanism, the focus is on the cleavage rates of CO and C=O bonds and their corresponding wavenumber drift trends. For the crosslink density change mechanism, the focus is on the attenuation of the characteristic absorption peak intensity and the impact of crosslink formation on the vibrational modes. For the molecular chain rearrangement mechanism, the shift and intensity changes of the OH absorption band are combined to reflect the reconstruction of the hydrogen bond network between molecular chains. By performing regression analysis and fitting on the mathematical relationships between these mechanistic properties and spectral parameters, a combination of molecular response sensitivity functions was constructed, forming a system describing the sensitivity of polymer molecular responses to environmental changes. Each sensitivity function uses the rate of change of the spectral parameter as the response variable and environmental loads (such as irradiation intensity, SO2 / NO2 concentration, temperature, and humidity) as the independent variable, quantifying the dependence of molecular-scale reactions on environmental changes. The molecular response sensitivity function combination was numerically coupled to a multi-factor aging kinetic system to calculate the environmental node gradient values ​​and the polymer molecular response characteristics. The SO2 concentration gradient, NO2 concentration gradient, and pH distribution gradient at each discrete node in the kinetic system were used as environmental variable inputs and numerically coupled point-by-point with the molecular response sensitivity function. The reaction intensities of molecular chain breakage, crosslinking changes, and rearrangements under specific environmental conditions were calculated, thereby generating environmental-molecule coupling correlation data. Based on this environmental-molecule coupling correlation data, an inverse numerical solution method was employed, involving multiple nonlinear regression and matrix construction, to gradually fit the relationships between environmental variables and molecular response variables, ultimately yielding a polymer molecular response coefficient matrix. Each element of this matrix quantifies the intensity of the impact of changes in specific environmental factors on specific molecular response mechanisms, and is a set of key parameters that describe the microscopic aging process of materials.

[0023] S3, inputting the three-dimensional environmental field data into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination; It is important to note that deep data fusion is performed on the 3D environmental field data, along with infrared spectral peak drift data and pH sensor response time offset data. The 3D environmental field data, which contains information such as sulfur dioxide concentration gradients, nitrogen dioxide concentration gradients, and acidity gradients, comprehensively reflects the dynamic spatial changes in the material under the influence of polluted gases and the acidic environment. The infrared spectral peak drift data reveals the trends of polymer chain breakage, crosslinking, or rearrangement during aging, while the pH sensor response time offset data further captures the dynamic characteristics of the acid-base changes in the sample surface microenvironment. By normalizing and vectorizing these multi-source heterogeneous data, data of varying dimensions and sampling frequencies are unified to form a high-dimensional, dense, and highly representative target input vector. This target input vector is then fed into the first processing module of the backpropagation neural network, the molecular response layer. Within the molecular response layer, a linear transformation is performed. By performing matrix multiplication on the input vector and a weight matrix, and adding a bias vector, a characteristic subspace directly related to the material's molecular response mechanism is extracted. The ReLU (rectified linear unit) activation function is used to enhance the nonlinear representation of feature extraction and effectively avoid the vanishing gradient problem. The output vector of the molecular response layer is obtained, which captures the sensitivity and response trends of material microstructural changes to external environmental perturbations. This output vector is then passed to the environmental coupling layer of the back-propagation neural network. As the core module of the neural network, the environmental coupling layer is responsible for modeling the complex relationships between the synergistic effects of acid rain erosion and photooxidation, the coupled effects of thermal aging and moisture-heat, and the interactions of multiple factors. Within this layer, a weighted linear transformation is performed, assigning different weights to each input feature to reflect its importance and interaction strength in the multifactorial aging process. The sigmoid activation function is then introduced, leveraging its excellent nonlinear mapping properties to compress the weighted sum to the range of 0 to 1, thereby representing the intensity distribution of the synergistic effects of environmental variables. Through this weighted and nonlinear processing, the environmental coupling layer effectively captures the high-order interactions between various aging mechanisms, constructing a complex and detailed mapping between the aging environment and the material response, and generating the output vector. This output vector is then input into the output layer of the neural network. The output layer uses a simple linear transformation to compress the high-dimensional environmental interaction features into a final set of control parameters. By analyzing these output parameters, an intelligent control parameter combination is derived. This parameter combination includes a xenon lamp irradiation intensity adjustment value, which reflects the need for real-time regulation of ultraviolet irradiation intensity; an acid rain pH adjustment value, which is used to control the severity of the acidic corrosive environment; sulfur dioxide and nitrogen dioxide flow rate adjustment values, which correspond to the input concentration control of the main pollutants, respectively, to ensure high consistency between the simulated environment and the actual application environment; and a temperature and humidity adjustment value, which dynamically adjusts the temperature and humidity fields in the aging environment to maintain optimal accelerated aging conditions.

[0024] The target input vector is fed into the molecular response layer of the neural network architecture. This layer is structurally divided into four functional subunits: an acid rain erosion response neuron group, a photo-oxidative degradation response neuron group, a heat-humidity aging response neuron group, and a multi-factor interaction response neuron group. Each neuron group is tailored to a specific aging mechanism, enabling targeted extraction of the complex relationships between different environmental factors and the material's molecular responses. In the initial data flow, the acid rain erosion response neuron group and the photo-oxidative degradation response neuron group work together to perform matrix multiplication operations on parameters related to acid rain penetration depth (such as acidity gradient and pH change rate) in the target input vector and parameters related to light irradiation intensity (such as irradiation intensity distribution and photon energy density), respectively, to form a basic feature weighted mapping matrix. This matrix is ​​then processed using a nonlinear coupling function to capture the higher-order nonlinear effects of molecular chain breakage and crosslinking changes in the material under the synergistic effects of acid rain and light. The nonlinear coupling function employs multi-level polynomial mapping or a radial basis function network based on a Gaussian kernel to enhance the model's ability to represent complex interactive phenomena. It outputs an acid rain-light synergistic signature, describing the accelerated aging characteristics caused by the combined effects of acid attack and ultraviolet radiation in the environment. Simultaneously, the heat-humidity aging response neuron group and the multi-factor interaction response neuron group process the target input vector in parallel. The heat-humidity aging response neuron group focuses on the coupling relationship between temperature and relative humidity gradients, performing matrix cross-product operations on the temperature and humidity features. The coupling function then calculates the corrective effect of temperature changes on the humidity diffusion coefficient and the internal moisture migration dynamics of the material, generating a temperature-humidity aging signature. The multi-factor interaction response neuron group models the interactions between pollutant gas concentrations (such as SO2 and NO2 concentration gradients) and environmental parameters (temperature, humidity, and acidity). Feature mapping and cross-product operations capture the complex coupling behavior of each pollutant factor with thermal, humidity, light, and chemical environmental factors. A sigmoid activation function is used to enhance the depth and stability of nonlinear feature extraction, resulting in a temperature-humidity-pollutant gas coupling signature. By integrating the acid rain-light synergy characteristics with the temperature-humidity-pollution gas coupling characteristics, a final output vector for the molecular response layer is generated through vector splicing or weighted fusion. This output vector encompasses the basic aging behavior characteristics under the influence of each single factor and fully reflects the material response mechanism under the influence of multiple factors and the quantitative characterization of various coupling effects.

[0025] S4, adjusting and testing test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; Specifically, the intelligent control parameter combination is parsed and instructions are generated. The intelligent control parameter combination includes key parameters such as xenon lamp irradiation intensity adjustment value, acid rain pH adjustment value, sulfur dioxide flow adjustment value, nitrogen dioxide flow adjustment value and temperature and humidity adjustment value. Based on these parameters, the control instruction generation module generates xenon lamp irradiation system control instructions, acid rain spraying system control instructions and polluted gas injection system control instructions respectively, so that each subsystem can accurately perform environmental condition control tasks according to predetermined strategies. The xenon lamp irradiation system control instructions are used to adjust the output intensity of the xenon lamp light source in real time to ensure that the irradiation intensity covers 100 to 1200W / m 2range, and can achieve light intensity fluctuations to simulate sunlight changes at specific time nodes; the acid rain spray system control instructions adjust the pH value of the spray liquid, with a dynamic regulation range of 2.5 to 6.5, while ensuring that the atomizing nozzle array sprays evenly, simulating the intensity and frequency of the natural acid rain environment; the polluted gas injection system control instructions accurately control the flow rates of SO2 and NO2 as needed, adjusting them within the ranges of 0.1-5.0ppm and 0.05-2.0ppm respectively, and achieving a gas mixing uniformity of more than 95% through the mass flow controller. Based on the generated control instructions, the parameters of each subsystem of the aging detection platform are adjusted sequentially. The xenon lamp irradiation system control module sets the xenon lamp irradiation intensity to the target value, creating an irradiation environment that approximates the natural sunlight spectrum or accelerates aging. The acidity of the acid rain solution is then adjusted based on the acid rain spray system control instructions, maintaining real-time control and stabilization within the set pH range to avoid experimental errors caused by acidity fluctuations. Finally, the injection rates of SO2 and NO2 are adjusted based on the polluted gas injection system control instructions. Fine control is achieved through flow sensors and feedback loops to ensure accurate and repeatable simulation of the polluted environment. After completing the test parameter adjustments, the formal testing phase begins, with continuous monitoring of water-based waterproofing material samples. Because materials are subject to the synergistic effects of acid attack and photooxidation in real environments, and exhibit a delayed response characteristic caused by the cumulative effect of environmental factors, a compensation mechanism for the synergistic hysteresis effect of acid attack and photooxidation is incorporated into the monitoring process. A Fourier transform infrared spectrometer is used to monitor changes in the molecular chain structure of the material in real time, and a pH response sensor array is used to continuously record changes in the acidity of the material surface. At the same time, multi-parameter data such as ambient temperature, humidity, radiation intensity, and pollutant gas concentration are simultaneously recorded. In order to accurately capture the hysteresis effect, the monitoring system introduces a dynamic time warping algorithm to align the response data on the time axis, and uses Kalman filtering technology to perform data denoising and trend extraction to ensure that the causal relationship between the material molecular response characteristics and changes in environmental parameters can be clearly distinguished and quantitatively analyzed. Through the above-mentioned continuous monitoring and multi-parameter synchronous recording process, the second aging response data is finally obtained. This data includes the changing characteristics of the material molecular structure, such as the absorption peak displacement and intensity attenuation trend of CO, C=O, OH, etc., as well as the pH change curve of the sample surface, real-time recording data of environmental parameters, and aging hysteresis response correction data.

[0026] S5, calculating a spectral attenuation index and grading an aging degree based on the second aging response data, and outputting an aging status evaluation result of the water-based waterproof material sample.

[0027] The second aging response data is processed. This data includes information on changes in infrared spectral characteristic peaks, pH response trends, and environmental parameter records obtained through continuous monitoring under a composite aging environment. Spectral data is particularly critical, and the intensity changes and peak position drifts of the CO stretching vibration peak, C=O stretching vibration peak, and OH stretching vibration peak are specifically extracted. Using peak separation and baseline correction techniques, the initial intensity, current intensity, initial wavenumber position, and current wavenumber displacement of each characteristic peak are extracted. These extracted characteristic parameters are then assigned different weighting coefficients. These weighting coefficients, determined based on preliminary statistical analysis, are a weight of 0.3 for the CO peak intensity decay rate, 0.4 for the C=O peak intensity decay rate, 0.2 for the OH peak intensity decay rate, and 0.1 for the peak position drift. This results in a preliminary spectral attenuation index for characterizing aging behavior. Based on this spectral attenuation index, a weighted comprehensive calculation is performed on the intensity decay rates and peak position drifts of each characteristic peak. By multiplying the intensity change rate of each characteristic absorption peak by the corresponding weighting coefficient and proportionally superimposing the drifts of each characteristic peak, an overall spectral attenuation trend curve is formed. The combined results are used to calculate the spectral attenuation index (ADI). This weighted summation method quantifies microscopic changes such as molecular chain breakage, changes in crosslink density, and hydrogen bond network rearrangement into a unified metric, generating comprehensive spectral attenuation data. Threshold determination and aging severity classification are performed based on this comprehensive spectral attenuation data. According to a pre-set aging severity grading standard, ADI values ​​are divided into intervals: an ADI less than 0.1 corresponds to mild aging, an ADI between 0.1 and 0.3 corresponds to mild aging, an ADI between 0.3 and 0.5 corresponds to moderate aging, an ADI between 0.5 and 0.7 corresponds to severe aging, and an ADI greater than 0.7 corresponds to severe aging. Based on the obtained aging severity grades, the secondary aging response data, specifically the spectral attenuation data, and environmental records are combined and input into a lifespan prediction algorithm based on the Arrhenius-pH coupled aging model. This model considers the effect of temperature on reaction rate in the traditional Arrhenius model and introduces pH, SO2 concentration, and NO2 concentration as coupling variables to establish a multivariate nonlinear regression equation that reflects the influence of complex real-world environments. By substituting the second aging response data into the life prediction equation and combining it with the divided aging degree levels, regression analysis and prediction are performed, and finally an aging status assessment result is output.

[0028] In one example, testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data includes: The multi-factor coupling test parameters of the xenon lamp irradiation system, acid rain spray system and polluted gas injection system in the aging detection platform were set to obtain composite environmental exposure conditions; Based on the composite environmental exposure conditions, collaborative parameter setting is performed on the temperature control module, the humidity control module, and the pressure control module in the aging detection platform to obtain an environmental parameter control scheme; According to the environmental parameter control scheme, the water-based waterproof material sample is respectively subjected to initial stage exposure, accelerated stage exposure and stable stage exposure, and the first aging response data is collected at the same time.

[0029] In this example, the multi-factor coupling test parameters of the xenon lamp irradiation system, acid rain spray system, and polluted gas injection system in the aging detection platform are set. In the xenon lamp irradiation system, a 6000W xenon lamp light source is selected, which can cover the 280 to 800nm ​​band and the irradiation intensity is between 100 and 1200W / m 2The pH value of the acid rain solution can be adjusted to the range of 2.5 to 6.5 through a precise control system, and the spray flow rate is controlled by a pressure-stable circulation pump, and the spray uniformity is controlled within an error range of ±2% to simulate acid rain erosion conditions of different intensities. In the polluted gas injection system, independent gas sources of SO2 and NO2 are respectively configured, and a high-precision mass flow controller is used to adjust the injection rate. The SO2 concentration range is set at 0.1 to 5.0ppm, and the NO2 concentration is controlled at 0.05 to 2.0ppm. The gas mixing uniformity of more than 95% is achieved through an online gas mixer to ensure the stability and consistency of the polluted gas distribution in the test environment. By centrally scheduling and coordinating the operating parameters of the three aforementioned systems, a multi-factor coupled test parameter combination is formed, achieving the combined effects of three typical environmental factors: acid rain erosion, photooxidative degradation, and pollutant gas corrosion, thereby constructing highly simulated composite environmental exposure conditions. Based on these composite environmental exposure conditions, the temperature, humidity, and pressure control modules within the aging testing platform are collaboratively configured to develop an environmental parameter control scheme. The temperature control module requires an adjustment range of -40°C to +150°C with a control accuracy of ±0.5°C to accommodate the thermal load requirements of the various stages of the aging test. In particular, during the periods of increased light intensity and increased gas reaction rates, it must be able to rapidly respond to temperature fluctuations and maintain stability. The humidity control module can adjust the relative humidity from 10% to 98% RH with a control accuracy of ±2% RH. Precise humidity regulation is particularly important for simulating damp heat aging and acid rain environments, as it significantly impacts the thickness of the water film on the material surface and gas dissolution reactions. The pressure control module maintains a slightly positive or negative pressure within the test chamber to avoid experimental errors caused by external airflow interference and gas leaks. By introducing a temperature and humidity linkage control strategy and a pressure fluctuation buffering mechanism, precise control of the dynamic balance between the light and heat environment, the humidity environment, and the gas environment is achieved, thus forming a highly stable, repeatable, and adjustable comprehensive environmental field. According to the environmental parameter control scheme, a phased exposure experiment was conducted on the water-based waterproof material samples, and the first aging response data was collected simultaneously. The test process is divided into three parts: the initial stage, the acceleration stage, and the stabilization stage. The initial stage (0-144 hours) is mainly used to establish baseline data. The xenon lamp irradiation intensity is set to 300W / m 2The pH value of acid rain spray was maintained at 5.5, the SO2 concentration was set to 0.2ppm, the NO2 concentration was 0.1ppm, the temperature was controlled at 35℃, and the humidity was set at 65% to simulate the slow aging process in the natural environment; in the accelerated stage (144-1080 hours), the environmental severity was gradually increased, and the xenon lamp irradiation intensity was increased to 600-1000W / m 2 The acid rain pH value dropped to 3.0-4.5, the SO2 concentration rose to 1.0-3.0 ppm, the NO2 concentration increased to 0.5-1.5 ppm, the temperature rose to 60-80°C, and the humidity was adjusted to 80-90%. This phase primarily simulated the rapid degradation of materials under extreme weather and heavily polluted environments. The stabilization phase (1080-1440 hours) involved continuous, prolonged aging exposure under highly severe environmental conditions to further verify the durability and stability of the materials under long-term service conditions. Throughout the exposure process, a detection system equipped with a Fourier transform infrared spectrometer and an in-situ ATR accessory monitored changes in the molecular structure of the surface of the water-based waterproofing material samples in real time, focusing on the intensity changes and wavenumber drift trends of the characteristic peaks of CO, C=O, and OH. Combined with a surface pH-responsive sensor array, the changes in the sample surface pH were continuously recorded. Environmental parameters such as temperature, humidity, radiation intensity, and pollutant gas concentrations were simultaneously collected to form the first aging response dataset.

[0030] In one example, the calculating of characteristic spectrum drift parameter combinations in combination with the first aging response data and solving a multi-factor aging dynamics equation group to obtain three-dimensional environmental field data includes: Performing Fourier transform infrared spectroscopy detection based on the first aging response data to obtain a characteristic peak detection interval of the polymer molecular chain; Based on the characteristic peak detection interval of the polymer molecular chain, the surface of the water-based waterproof material sample is subjected to in-situ detection by an ATR accessory and synchronous monitoring by a pH response sensor array to obtain original detection data; performing signal operation on the original detection data to obtain spectrum extraction parameters; Calculating the spectrum drift amount and the intensity attenuation rate according to the spectrum extraction parameters to obtain a characteristic spectrum drift parameter combination; The multi-factor aging dynamics equations are numerically solved based on the characteristic spectrum drift parameter combination to obtain three-dimensional environmental field data.

[0031] In this example, a systematic analysis of the collected first aging response data is performed based on a high-performance Fourier transform infrared spectrometer. The spectrometer is configured to cover the wavenumber range of 400-4000cm -1 Detection module with 4cm -1The high resolution and 32-scan overlay function ensure the sensitivity and signal-to-noise ratio of the detection. Through preliminary scanning, the full spectrum infrared spectrum of the surface of the water-based waterproof material sample is collected. Combined with baseline correction and spectrum denoising preprocessing, high-quality original spectrum data is obtained. Then, the characteristic peak screening algorithm is used to locate the key characteristic peak range in the aging process of the polymer molecular chain. According to the aging mechanism analysis, the CO stretching vibration peak (1050-1150cm -1 ), C=O stretching vibration peak (1680-1750cm -1 ) and OH stretching vibration peak (3200-3600cm -1) as the dominant absorption signature for polymer chain degradation, crosslinking changes, and hydrogen bond breakage, forming a characteristic peak detection range for the polymer chain. After determining the characteristic peak detection range, in situ surface testing of water-based waterproofing material samples was performed using a Fourier transform infrared spectrometer equipped with an ATR (attenuated total reflectance) accessory for real-time scanning. The ATR accessory enables non-destructive testing, avoiding damage to the material surface structure caused by sample preparation, while maintaining a detection depth within the range of several microns, accurately reflecting the evolution of surface molecular structure. Simultaneously, a pH-responsive sensor array was used to monitor changes in surface pH. This sensor array, composed of a highly sensitive glass electrode and a reference electrode, boasts a response time of less than 30 seconds and an accuracy of ±0.02 pH units, continuously recording the effects of environmental acidity on chemical reactions on the material surface. By synchronizing spectral detection with pH monitoring, changes in the material's molecular structure and environmental conditions can be recorded over time, generating a complete raw test data set consisting of spectral signals, pH response, and auxiliary environmental data such as temperature, humidity, and pollutant gas concentrations. The collected raw data undergoes a series of signal processing operations to extract meaningful spectral characteristic parameters. Polynomial baseline correction is performed on the spectral data to eliminate background interference. Spectral peaks are then separated using Gaussian or Lorentzian fitting methods to extract basic parameters such as peak position (wavenumber), peak height (absorbance), and full width at half maximum (FWHM). The integrated area of ​​the characteristic peaks is further calculated to reflect the changing trend of molecular group content. Simultaneously, the pH sensor data is filtered and interpolated to ensure time synchronization and signal continuity. Through these processing steps, a set of spectral extraction parameters is extracted, including the initial peak position, real-time peak position, initial intensity, and real-time intensity of characteristic peaks such as CO, C=O, and OH. Based on this extracted set of spectral parameters, spectral drift and intensity decay are calculated. Spectral drift is determined by comparing the wavenumber change of each characteristic absorption peak before and after aging, reflecting changes in molecular bond energy and main chain cleavage trends. The intensity decay is calculated by comparing the ratio of the initial absorbance to the current absorbance, quantifying the rate of decline in polymer functional group content. A characteristic spectral drift parameter combination was constructed by weightedly combining the drift and intensity decay rates of the three characteristic peaks (CO, C=O, and OH). This characteristic spectral drift parameter combination was then used to numerically solve a pre-established multi-factor aging kinetic equation system. This system comprehensively considers the three dominant aging mechanisms: acid rain penetration (based on a modified Fick diffusion law), photooxidative degradation (based on quantum yield theory), and coupled heat and moisture transfer (based on the Arrhenius humidity correction model). The solution was solved using the finite element method using spatial and temporal discretization. The spatial meshing accuracy was controlled to below 100 μm, and the time step was set to 1 hour to ensure both spatial resolution and temporal continuity of the solution.During the numerical solution process, the characteristic spectral drift parameters serve as initial conditions and constraint boundaries, participating in the parameter fitting and dynamic updating of the equation system. This ensures that the model can correct the prediction path in real time during the iteration process, improving the physical rationality and computational convergence of the simulation. Through continuous iteration and collaborative solution of multivariate parameters, three-dimensional environmental field data such as the SO2 concentration distribution gradient, NO2 concentration distribution gradient, and pH value distribution gradient within the material are ultimately obtained.

[0032] In one example, the numerical solution of the multi-factor aging dynamics equations based on the characteristic spectrum drift parameter combination to obtain three-dimensional environmental field data includes: Establish a multi-factor aging kinetics equation system including the acid rain penetration equation, the photooxidative degradation equation, and the heat and moisture coupled transfer equation; Inputting the characteristic spectrum drift parameter combination into the multi-factor aging kinetic equation group to analyze the mapping relationship between molecular response characteristics and environmental parameters to obtain a polymer molecular response coefficient matrix; Iteratively numerically solving the multi-factor aging kinetics equations based on the polymer molecular response coefficient matrix to obtain kinetic parameter solution results; Gradient calculation and time evolution law analysis are performed based on the solution results of the kinetic parameters to obtain three-dimensional environmental field data, which includes sulfur dioxide concentration distribution gradient, nitrogen dioxide concentration distribution gradient and acidity value distribution gradient.

[0033] In this example, a kinetic description framework is established based on a systematic understanding of material aging mechanisms. The acid rain penetration equation is constructed using a modified form of the diffusion law. This equation takes into account that the diffusion of acidic ions within a material is driven not only by concentration gradients but also by the synergistic effects of pH changes and temperature gradients. Therefore, the diffusion coefficient is set as a function of pH and temperature. The acid corrosion reaction rate constant is also introduced to describe the ion reactions and degradation behavior within the material under acidic conditions. The photooxidative degradation equation is constructed based on quantum yield theory, focusing on the chemical degradation of polymers under light. It considers the wavelength-dependent light irradiation energy, the photon absorption capacity of the polymer molecular chain, and the quantum efficiency of the photoinduced reaction to comprehensively reflect the intensity and rate of the photooxidative reaction. The coupled heat and moisture transfer equation is expanded based on empirical formulas to integrate the joint regulation of temperature and humidity on the material aging reaction rate. Specifically, a sensitivity index for the combined effects of temperature and humidity is introduced to reflect the changing trends in the material's aging reaction activity under different heat and humidity environments. By unifying these three sets of equations, a multi-factor aging kinetic equation system is established. After the equations were established, the characteristic spectral drift parameter combinations extracted from previous experiments were input into the equations to analyze the mapping relationship between molecular response characteristics and environmental parameters. These characteristic spectral drift parameter combinations include the wavenumber shift and intensity decay rate of the characteristic absorption peaks of major functional groups (such as CO, C=O, and OH), reflecting the microstructural evolution trends of the material's backbone breakage, changes in crosslink density, and hydrogen bond network rearrangement, respectively. Nonlinear multivariate regression analysis was performed between these spectral change characteristics and environmental variables (acid rain concentration, pollutant gas concentration, temperature, and humidity), establishing the response relationship between each characteristic parameter and each environmental factor. This matrix then formed a polymer molecular response coefficient matrix. This matrix consists of characteristic spectral parameters as columns and environmental variables as rows. Each element represents the sensitivity of a specific molecular response to a specific environmental factor, quantitatively characterizing the polymer structure's response to complex environmental loads. Based on this polymer molecular response coefficient matrix, an iterative numerical solution of the multi-factor aging kinetic equations was performed. Finite element discretization technology is used in the solution process to divide the internal space of the material into three-dimensional grid cells. The grid scale is set at the order of 100 microns based on the sample size and the gradient fineness of environmental changes to ensure calculation accuracy and spatial resolution. The time dimension is discretized using the time integration method, with the time step set to 1 hour to capture the dynamic evolution of the aging reaction. The initial conditions are set by the initial environmental state of the material, and the boundary conditions are defined by combining the xenon lamp irradiation intensity distribution, acid rain spray coverage, and pollutant gas concentration field. Through iterative updates, parameters such as the diffusion coefficient, photooxidation reaction rate, and heat and humidity aging rate are dynamically adjusted according to the molecular response coefficient matrix to achieve a synchronous coupling solution of the evolution of the material microstructure and the dynamic changes of environmental variables.After each iteration, the spatial distribution and temporal evolution of key physical quantities (such as SO2 concentration, NO2 concentration, and pH value) are output in real time to ensure the physical rationality and numerical stability of the model solution. After the kinetic parameters are solved, gradient calculation and temporal evolution analysis are performed based on the results. The SO2 concentration gradient, NO2 concentration gradient, and pH value gradient are calculated in the spatial domain to reveal the penetration depth, diffusion rate, and localized accumulation of pollutant gases within the material, while also reflecting the evolution of the acidic environment on the surface and within the material. The temporal curves of various physical quantities are analyzed in the temporal domain to extract characteristic indicators such as the diffusion front's velocity, the aging reaction rate trend, and the critical degradation time point, providing an important basis for material service life prediction. Through a comprehensive analysis of spatial gradients and temporal evolution, three-dimensional environmental field data is obtained, covering the continuous distribution of various physical variables within the material in three-dimensional space and time.

[0034] In one example, the characteristic spectrum drift parameter combination is input into the multi-factor aging kinetic equation group to perform a mapping relationship analysis between molecular response characteristics and environmental parameters to obtain a polymer molecular response coefficient matrix, including: Classifying the molecular response types based on the characteristic spectral drift parameter combination to obtain a classification spectral response parameter set; Based on the classification spectral response parameter set, a quantitative correlation analysis is performed on the polymer main chain rupture mechanism, the molecular chain crosslinking density change mechanism and the molecular chain rearrangement mechanism to obtain a molecular response sensitivity function combination; Performing numerical coupling calculations on the molecular response sensitivity function combination and the multi-factor aging kinetic equations for the environmental node gradient values ​​and the polymer molecular response characteristics to obtain environmental-molecule coupling correlation data; According to the environment-molecule coupling correlation data, reverse numerical solution and coefficient matrix construction are performed to obtain the polymer molecule response coefficient matrix.

[0035] In this example, data processing is performed on a combination of characteristic spectral drift parameters. This combination, derived from Fourier transform infrared spectroscopy, encompasses the wavenumber shifts and intensity decay rates of absorption peaks for key functional groups, such as CO, C=O, and OH, reflecting the microstructural changes that occur during polymer aging. When classifying molecular response types, the characteristic spectral drift parameters are categorized according to their physicochemical significance. The intensity change and wavenumber drift of the CO peak are classified as main chain cleavage response parameters, as CO bond cleavage is often a key characteristic of the initial stages of polymer degradation. The intensity change and peak position drift of the C=O peak are classified as molecular chain crosslink density change response parameters, as the C=O group exhibits a significant change trend during the crosslinking reaction. The drift and intensity decay of the OH absorption peak are classified as molecular chain rearrangement response parameters, reflecting the reconstruction of the hydrogen bond network under heat, moisture, and chemical corrosion. This classification process yields a set of classified spectral response parameters, clarifying the spectral characteristic change ranges corresponding to different molecular response mechanisms. Based on a set of classified spectral response parameters, quantitative correlation analyses were performed on the polymer backbone rupture mechanism, the molecular crosslink density change mechanism, and the molecular chain rearrangement mechanism. The quantitative analysis of the backbone rupture mechanism involved quantifying the influence of environmental factors such as temperature, humidity, acidity, and pollutant concentration on the backbone rupture rate based on the trends in CO peak drift and intensity decay rates using multivariate linear regression or principal component analysis. The analysis of the molecular crosslink density change mechanism involved correlating the C=O peak intensity changes with environmental variables to determine the quantitative relationship between crosslinking reaction kinetic parameters and environmental conditions. The analysis of the molecular chain rearrangement mechanism relied on the OH peak variation characteristics, combined with parameters such as temperature and humidity gradients and acid rain penetration depth, to establish a mathematical correlation model between the hydrogen bond network rearrangement rate and the intensity of external environmental stimuli. Through these analyses, sensitivity functions were established for the backbone rupture, crosslink density change, and molecular chain rearrangement mechanisms, forming a comprehensive molecular response sensitivity function combination. Each function quantitatively describes the sensitivity of a specific aging mechanism to external environmental stimuli. The molecular response sensitivity function combination was then numerically coupled to the multi-factor aging kinetic equations to calculate the environmental node gradient values ​​and the polymer molecular response characteristics. During the specific operation, the SO2 concentration gradient, NO2 concentration gradient, and acidity gradient values ​​of each environmental node are obtained in three-dimensional space based on the finite element mesh discretization, and the temperature and humidity distribution data at the node are also obtained. The environmental variable gradient of each node is then used as input and substituted into the molecular response sensitivity function combination to calculate the corresponding molecular response change rate, completing the coupling mapping between environmental variables and molecular responses. Through this node-level calculation, environmental-molecule coupling correlation data is obtained. The data content includes the sensitivity of the main chain breakage rate, crosslink density change rate, and molecular chain rearrangement rate at each node to the change of environmental variable gradient, revealing the local aging behavior characteristics within the material under the synergistic action of multiple factors.Based on the obtained environmental-molecule coupling correlation data, an inverse numerical solution and coefficient matrix construction are performed. During the inverse solution process, multivariate nonlinear regression analysis is used, using the least squares method or other optimization algorithms to trace the quantitative mapping relationship between changes in environmental variables and changes in molecular responses. The optimal fit coefficients are extracted and a polymer molecular response coefficient matrix is ​​constructed. This matrix has environmental variables as rows and molecular response characteristics as columns. Each matrix element represents the contribution rate of a unit change in an environmental factor to the change in a specific molecular response, thus characterizing the degradation patterns of polymer materials under the influence of multiple factors and complex environments.

[0036] In one example, the three-dimensional environmental field data is input into a back propagation neural network to perform multi-factor environmental parameter collaborative optimization to obtain an intelligent control parameter combination, including: fusing the three-dimensional environmental field data with infrared spectrum characteristic peak drift data and pH sensor response time offset data to obtain a target input vector; Inputting the target input vector into the molecular response layer of the back propagation neural network for linear transformation to obtain a molecular response layer output vector; Inputting the output vector of the molecular response layer into the environmental coupling layer of the back propagation neural network to perform weighted linear transformation and Sigmoid activation function calculation of the synergistic effect of acid rain erosion and photooxidation, thermal aging and moisture-heat coupling, and multi-factor interaction to obtain an output vector of the environmental coupling layer; The output vector of the environmental coupling layer is input into the output layer of the back propagation neural network for linear transformation and parameter analysis to obtain an intelligent control parameter combination, which includes a xenon lamp irradiation intensity adjustment value, an acid rain pH adjustment value, a sulfur dioxide flow adjustment value, a nitrogen dioxide flow adjustment value, and a temperature and humidity adjustment value.

[0037] In this example, multi-source data is standardized and structured. The three-dimensional environmental field data, derived from the numerical solution of the aging kinetics equations, contains spatially continuous information such as the SO2 concentration distribution gradient, NO2 concentration distribution gradient, and acidity distribution gradient. The infrared spectral characteristic peak drift data includes the wavenumber changes and intensity decay trends of key vibrational modes of the polymer chain, such as CO, C=O, and OH, reflecting the real-time evolution of the material's molecular structure. The pH sensor response time offset data records the impact of changes in the acid-base environment on the material's surface on the aging reaction rate, demonstrating temporal dynamics and high sensitivity. To effectively integrate data from different sources and scales, the environmental field data is spatially averaged, and the infrared spectral data and pH response data are time-synchronized and normalized to ensure consistency in dimensionality and data density across all dimensions. Through data concatenation and feature selection techniques, the processed multi-source information is integrated into a unified high-dimensional vector, forming the target input vector. This target input vector is then fed into the molecular response layer of the backpropagation neural network. The molecular response layer is the first processing module in the entire network architecture, responsible for processing the material's microscopic response characteristics. It contains multiple sub-neuron groups, each of which extracts features specific to four typical aging mechanisms: acid rain erosion, photo-oxidative degradation, thermal aging, and moisture-heat coupling. The molecular response layer applies a linear transformation to the target input vector, performs a matrix product with the input vector using a weight matrix, and adds a bias vector to extract primary features related to each aging mechanism. This process not only effectively reduces dimensionality and extracts the most physically meaningful response information from the input data, but also introduces nonlinear transformations through the ReLU activation function, enhancing the network's ability to model complex microscopic response patterns. The molecular response layer outputs a vector that integrates the material's molecular structure's sensitivity to environmental stimuli, aging rate trends, and response hysteresis. This output vector is input into the environmental coupling layer of the back-propagation neural network. The environmental coupling layer, the core layer of the network responsible for modeling the synergistic effects of multiple factors, includes submodules for the synergistic effects of acid rain erosion and photo-oxidative degradation, thermal aging and moisture-heat coupling, and multiple factor interactions. The environmental coupling layer performs a weighted linear transformation on the output vectors from the molecular response layer, assigning different weight coefficients to different aging mechanisms to reflect the degree of dominance and influence of each factor under specific aging conditions. By introducing the Sigmoid activation function, the linear transformation results are nonlinearly compressed and mapped, improving the model's ability to fit and generalize complex interactions. The Sigmoid function limits the neuron output range to between 0 and 1, effectively avoiding exploding or vanishing gradients while providing a stable input for subsequent control parameter prediction.Through a synergistic analysis of acid rain erosion and photooxidation, the nonlinear enhancement effect of material degradation rates under the combined effects of acidic environments and high irradiation intensity is identified. Thermal aging and hygrothermal coupling modeling reveals the accelerated aging mechanism caused by water molecular diffusion and accelerated polymer chain motion under high temperature and humidity conditions. A multi-factor interaction module comprehensively characterizes the material's true aging behavior under complex environments by comprehensively considering high-order interactions among multiple variables, such as temperature, humidity, acidity, and pollutant concentrations. The environmental coupling layer outputs a vector containing high-order characteristic information about the material's microscopic response under multi-factor environmental coupling. This vector is input into the output layer of a back-propagation neural network, mapping the high-dimensional feature vector to specific control parameters. By performing a linear transformation on the output vector of the environmental coupling layer and combining it with the specified output weight matrix and bias term, specific control parameters related to xenon lamp irradiation intensity, acid rain pH adjustment, sulfur dioxide flow adjustment value, nitrogen dioxide flow adjustment value, and temperature and humidity adjustment are extracted. To improve the physical interpretability and operability of the output results, the output layer performs parameter analysis on the preliminary output results to ensure that each adjustment value complies with the physical parameter limitations of the equipment and the test plan settings. Through this process, a combination of intelligent control parameters is ultimately obtained. Among them, the xenon lamp irradiation intensity adjustment value is used to adjust the light source irradiation intensity in real time to simulate natural sunlight or accelerate changes in the aging environment; the acid rain pH adjustment value is used to precisely control the acidity and alkalinity of the acid rain solution to simulate different acid corrosion intensities; the sulfur dioxide and nitrogen dioxide flow rate adjustment values ​​correspond to the flow control of the pollutant gas source, respectively, to ensure the dynamic controllability of the pollutant concentration in the ambient atmosphere; and the temperature and humidity adjustment value is used to adjust the temperature and humidity field distribution of the aging environment to achieve accurate simulation of the thermal and humidity coupled aging environment.

[0038] In one example, inputting the target input vector into the molecular response layer of the back propagation neural network for linear transformation to obtain the molecular response layer output vector includes: Inputting the target input vector into a molecular response layer of a back propagation neural network, wherein the molecular response layer includes an acid rain erosion response neuron group, a photooxidation degradation response neuron group, a heat and humidity aging response neuron group, and a multi-factor interaction response neuron group; The target input vector is subjected to matrix multiplication of the synergistic effect of acid rain penetration depth and light irradiation intensity and nonlinear coupling function calculation by using an acid rain erosion response neuron group and a photooxidation degradation response neuron group to obtain an acid rain-light synergistic feature; The target input vector is subjected to coupled calculations of temperature gradient and relative humidity gradient and interaction calculations of pollutant gas concentration and environmental parameters by using a heat-humidity aging response neuron group and a multi-factor interaction response neuron group to obtain a temperature-humidity-pollutant gas coupling feature; An environmental coupling layer output vector is generated according to the acid rain-light synergistic feature and the temperature, humidity and polluted gas coupling feature.

[0039] In this example, the internal structure of the molecular response layer is divided. The molecular response layer includes an acid rain erosion response neuron group, a photooxidative degradation response neuron group, a heat and humidity aging response neuron group, and a multi-factor interaction response neuron group. Each neuron group is designed for a specific environmental aging mechanism and aims to extract deep-level feature information related to that specific mechanism from the target input vector. The target input vector integrates three-dimensional environmental field data, infrared spectral characteristic peak drift data, and pH sensor response time offset data, encompassing the complete response characteristics of the material under the interaction of acid rain erosion, photooxidative degradation, temperature and humidity changes, and pollutant gases. Therefore, a neural network structure maps this complex, multidimensional data into aging characteristics with clear physical meaning, supporting subsequent environmental coupling characteristic analysis and intelligent control parameter generation. During processing, the acid rain erosion response neuron group and the photooxidative degradation response neuron group model the synergistic effect of acid rain penetration depth and light irradiation intensity on the target input vector. The acid rain erosion response neuron group primarily analyzes the impact of changes in acidity distribution on the material surface and internal structure on molecular chain scission and degradation reaction rates, while the photooxidative degradation response neuron group focuses on the induction effect of irradiation intensity and wavelength distribution on polymer molecular bond scission. The two systems work together, multiplying the target input vector by their respective weight matrices through matrix multiplication to extract feature vectors such as acid rain concentration gradient, pH distribution, and light intensity distribution. These extracted features are then combined using a nonlinear coupling function. This nonlinear coupling function captures the interactive effects between acid rain corrosion and photooxidative degradation, simulating the mechanism by which light energy accelerates the breakage rate of polymer chains in a highly acidic environment. This process yields acid rain-light synergistic features, describing the complex molecular structure evolution of materials under the combined effects of acid rain and light. Simultaneously, the heat-humidity aging response neuron group and the multi-factor interaction response neuron group couple the temperature and relative humidity gradients of the target input vector and model the interaction between pollutant gas concentration and environmental parameters. The heat-humidity aging response neuron group focuses on the synergistic effects of increasing temperature and humidity on the material's molecular chain mobility, crosslinking density, and water penetration depth. Matrix multiplication is used to extract a subset of features from the target input vector related to the temperature and humidity distributions. A coupled calculation function is then used to simulate the combined effects of temperature and humidity changes on the material's aging rate. The multi-factor interactive response neuron group extracts high-order interactions between SO2 and NO2 pollutant concentrations and environmental parameters (such as temperature, humidity, and acidity). Using cross-matrix operations, it extracts the synergistic variation between pollutant concentrations and environmental field changes. It then applies complex nonlinear functions to model the coupled acceleration of material degradation reactions by different pollutants under varying environmental conditions. This series of calculations generates a temperature-humidity-pollutant coupling signature, characterizing the material's multiple aging behaviors, including molecular chain breakage, crosslink depolymerization, and surface acidification, under the synergistic effects of high temperature, high humidity, and highly polluted atmospheres.After completing the aforementioned feature extraction and nonlinear coupling modeling, the acid rain-light synergistic features and the temperature-humidity-pollutant gas coupling features are integrated to generate the output vector of the environmental coupling layer. During this integration process, vector concatenation or weighted fusion is used to combine the two types of feature vectors while maintaining their respective physical meanings, ensuring the integrity and identifiability of the features related to different aging mechanisms in subsequent neural network layers. To enhance the model's learning and generalization capabilities, normalization operations and feature importance weighting strategies are introduced during the integration phase to dynamically adjust the contributions of different mechanism features, thereby improving the model's ability to identify and respond to key aging mechanisms in complex, multi-factor environments.

[0040] In one example, the step of adjusting and testing the test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data includes: Generate xenon lamp irradiation system control instructions, acid rain spray system control instructions and polluted gas injection system control instructions according to the intelligent control parameter combination; Adjusting the irradiation intensity of the xenon lamp irradiation system in the aging detection platform based on the xenon lamp irradiation system control instruction, adjusting the acidity value of the acid rain spraying system in the aging detection platform based on the acid rain spraying system control instruction, and controlling the flow rate of the polluted gas injection system in the aging detection platform based on the polluted gas injection system control instruction; During the test parameter adjustment, the water-based waterproof material sample in the aging detection platform is continuously monitored and multi-parameter synchronously recorded for compensation of the synergistic hysteresis effect of acid corrosion and photooxidation to obtain second aging response data.

[0041] In this example, a xenon lamp irradiation system control instruction is generated based on the xenon lamp irradiation intensity adjustment value. The control instruction includes information such as target irradiation intensity setting, irradiation time scheduling, and irradiation band correction, ensuring that the irradiation intensity output by the xenon lamp system is between 100 and 1200W / m 2Dynamically adjust on demand within the specified range, with a spectral distribution covering 280 to 800nm, simulating natural sunlight environments or accelerated aging conditions. Based on the acid rain pH adjustment, control instructions for the acid rain spray system are generated. These instructions include setting the target pH value, adjusting the acid solution ratio, and regulating the spray flow rate. The pH value of the acid rain solution is stabilized within the range of 2.5 to 6.5, with flow rate fluctuations controlled within ±2%, ensuring spray uniformity and acid corrosion efficiency. Based on the sulfur dioxide and nitrogen dioxide flow adjustment values, control instructions for the polluted gas injection system are generated, specifying the flow rate settings for each gas source, with target concentrations adjusted within the ranges of 0.1 to 5.0 ppm and 0.05 to 2.0 ppm, respectively. Simultaneously, the gas mixing unit is coordinated to achieve a mixing uniformity exceeding 95%, ensuring the stability and reproducibility of the polluted atmosphere. After each control instruction is generated, it is transmitted to the aging testing platform's central control module, which drives the xenon lamp irradiation system, acid rain spray system, and polluted gas injection system through a real-time communication interface to adjust parameters. The xenon lamp irradiation system automatically adjusts the light source output power according to control instructions. Through frequency conversion dimming and filter switching, it precisely controls the irradiation intensity and spectral characteristics to ensure that the surface of the water-based waterproof material receives a light dose that meets the set conditions. The acid rain spray system adjusts the acid preparation unit according to instructions, automatically adjusts the acid mixing ratio and monitors and corrects the pH value in real time. It implements uniform spraying through an array of precision atomizing nozzles to ensure that the acid corrosion effect on the material surface is similar to that in a natural environment. The polluted gas injection system adjusts the gas flow controller according to instructions, precisely controls the injection rate of SO2 and NO2 gases, and achieves a highly uniform atmosphere distribution through the gas mixing chamber, ensuring the authenticity and repeatability of the aging reaction of the material surface in a stable polluted environment. After completing the parameter adjustment of each subsystem of the aging detection platform, it enters the continuous monitoring and multi-parameter synchronous recording stage. Considering the significant hysteresis in the response of water-based waterproofing materials to the combined effects of acid attack and photooxidation, particularly during the process of molecular chain breakage, crosslinking changes, and hydrogen bond network rearrangement, this hysteresis effect causes a time delay between macroscopic performance changes and environmental changes. Therefore, a compensation mechanism for the synergistic hysteresis effect of acid attack and photooxidation was introduced into the monitoring process. A Fourier transform infrared spectrometer equipped with an ATR accessory was used to detect in situ changes in the molecular structure of the material surface. Wavenumber and intensity changes of characteristic absorption peaks such as CO, C=O, and OH were collected in real time. Through continuous scanning and data accumulation, hysteresis phenomena in microscopic molecular reactions were captured. Simultaneously, a highly sensitive pH sensor array was incorporated to record surface pH changes, monitoring the progression of acid rain attack and surface reaction dynamics, complementing the material's environmental adaptability. To achieve high temporal resolution and coordinated multi-parameter recording, the monitoring system employed a high-frequency sampling strategy with a 10-minute sampling period to ensure data continuity and integrity.Environmental parameters of each subsystem, including radiation intensity, acid rain pH value, SO2 and NO2 concentrations, temperature and humidity, are collected and recorded synchronously to form a multidimensional data set with time stamps, ensuring that changes in the aging environment and changes in material responses correspond to each other on the time axis. To further improve the accuracy of hysteresis effect compensation, a time alignment technology based on a dynamic time warping algorithm is introduced to dynamically match the environmental change curve with the material response curve, calculate the response lag time window, and perform time axis reconstruction and lag correction on the detection data. Through this compensation strategy, the time deviation between environmental changes and material performance responses is effectively eliminated, improving the accuracy of aging mechanism analysis and the reliability of aging process prediction. During the continuous monitoring and synchronous recording process, real-time data is stored in a central database to form a second aging response data set.

[0042] In one example, the calculating of the spectral attenuation index and grading of the aging degree based on the second aging response data, and outputting the aging state assessment result of the water-based waterproof material sample, includes: Extracting characteristic parameters and configuring weighting coefficients on the second aging response data to obtain a spectral attenuation index; Based on the spectral attenuation index, a weighted comprehensive operation is performed on the characteristic peak intensity attenuation rate and the peak position drift, and the spectral attenuation index ADI value is calculated to obtain comprehensive spectral attenuation data; Performing threshold judgment and aging degree classification based on the comprehensive spectral attenuation data to obtain an aging degree grade; Combined with the aging degree grade, the second aging response data is input into a life prediction algorithm based on the Arrhenius-pH coupled aging model to perform a multivariate nonlinear regression calculation to obtain an aging state assessment result.

[0043] In this example, feature extraction is performed on the second aging response data. Based on Fourier transform infrared spectroscopy data, the wavenumber positions and absorbance intensities of the absorption peaks corresponding to key vibrational modes, such as CO, C=O, and OH, are extracted. The initial wavenumber, initial intensity, current wavenumber, and current intensity of each characteristic peak during the aging process are recorded, and the characteristic peak intensity decay rate and wavenumber drift are calculated. The intensity decay rate characterizes changes in the material's functional group content and reflects chemical structural degradation processes such as molecular chain breakage and changes in crosslink density. The wavenumber drift reveals the microscopic mechanisms of changes in polymer molecular chain bond energy and hydrogen bond network rearrangement. To ensure a unified weighting system for different characteristic parameters in subsequent calculations, the extracted parameters are standardized, and weighting coefficients are set based on their sensitivity to the material's aging behavior. Changes in the C=O peak are most sensitive to the aging process and are therefore assigned a higher weight, such as 0.4. Changes in the CO peak are second most sensitive, with a weight of 0.3. Changes in the OH peak, sensitive to hydrogen bond network rearrangement, are assigned a weight of 0.2. The overall peak position drift is assigned a weight of 0.1. This weighting coefficient configuration strategy highlights the dominant role of key response parameters in the overall aging index calculation, while also taking into account other secondary changes, ensuring the comprehensiveness and scientific nature of the index. After extracting characteristic parameters and configuring weighting coefficients, the spectral attenuation index is calculated based on these characteristic parameters and weighting coefficients. The intensity attenuation rate of each characteristic peak is multiplied by its corresponding weight to obtain a weighted intensity attenuation value. The wavenumber shift of each characteristic peak is then multiplied by its corresponding weight to obtain a weighted wavenumber shift value. The weighted intensity attenuation value and the weighted wavenumber shift value are added together to form the median value of the spectral attenuation index, which is then normalized to obtain the final ADI value. The ADI value comprehensively reflects the degree of degradation of the micromolecular structure of water-based waterproofing materials during aging, encompassing both the reduction in functional group content and structural evolution such as molecular chain breakage, crosslinking changes, and hydrogen bond rearrangements. Through statistical analysis and regression validation of multiple aging data sets, the ADI value has been proven to accurately reflect the material's aging degree and lifespan trends, making it a crucial input for subsequent aging assessment and lifespan prediction. Based on the calculated ADI value, threshold judgment and aging degree classification are performed. According to the established aging classification standards, the ADI value is divided into five grade intervals, corresponding to slight aging, mild aging, moderate aging, severe aging and severe aging. Specifically, when the ADI value is less than 0.1, it is judged as slight aging, and the material basically maintains its initial performance; when the ADI value is between 0.1 and 0.3, it is judged as slight aging, and the material performance begins to show signs of decline; when the ADI value is between 0.3 and 0.5, it is judged as moderate aging, and the material performance has obviously degraded; when the ADI value is between 0.5 and 0.7, it is judged as severe aging, and the material performance has dropped significantly; when the ADI value is greater than 0.7, it is judged as severe aging, and the material has basically failed.After determining the aging severity level, the second aging response data and the corresponding ADI value are input into a life prediction algorithm based on the Arrhenius-pH coupled aging model. This model builds on the traditional Arrhenius thermally accelerated aging theory and incorporates the aging acceleration effect of pH in acidic environments. Through multivariate nonlinear regression, a quantitative relationship is established between the aging life and ambient temperature, pH, SO₂ concentration, NO₂ concentration, and the spectral attenuation index (ADI). In this model, temperature influences the reaction rate through the Arrhenius term, pH modulates the corrosion rate through the acidic acceleration term, and pollutant gas concentration modifies the material's molecular chain breakage rate. The ADI value, as a proxy for microstructural degradation, participates in the regression fitting of the life function. The specific calculation process utilizes nonlinear least squares multivariate regression, combining experimental data with environmental record data. Model parameters are then iteratively optimized to ensure good consistency and reliability between the predicted results and actual aging behavior. The model outputs the predicted material life and, combined with the aging severity level, a comprehensive aging status assessment result. The assessment results include the remaining useful life of the material under the current environmental conditions, as well as aging rate trend analysis, environmental sensitivity assessment and possible failure mode inference.

[0044] Reference Figure 2 This embodiment provides a water-based waterproof material xenon lamp simulated environment aging detection system, including: The acquisition module 21 is used to test the water-based waterproof material sample in the aging detection platform and collect the first aging response data; A solution module 22 is configured to calculate a characteristic spectrum drift parameter combination based on the first aging response data and solve a multi-factor aging dynamics equation group to obtain three-dimensional environmental field data; Collaborative optimization module 23, configured to input the three-dimensional environmental field data into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination; a parameter adjustment module 24 for adjusting and detecting test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; The output module 25 is configured to calculate the spectral attenuation index and classify the aging degree based on the second aging response data, and output an aging status assessment result of the water-based waterproof material sample.

[0045] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0046] In an embodiment of the present invention, the present invention realizes the coordinated simulation of acid rain erosion, pollutant gas corrosion and photooxidative degradation through the coupling configuration of the acid rain spray system, the polluted gas injection system and the xenon lamp irradiation system, overcoming the limitations of the traditional single light intensity simulation and significantly improving the environmental authenticity of the aging test. At the same time, the Fourier transform infrared spectroscopy detection system is used to realize the real-time, continuous and non-destructive monitoring of the polymer molecular chain breakage process, breaking through the technical bottleneck of the traditional periodic sampling detection. By extracting the characteristic spectral drift parameters of the carbon-oxygen bond, carbonyl and hydroxyl stretching vibration peaks, a quantitative mapping relationship between the material molecular response and the environmental parameters is established, and an innovative inversion algorithm for inferring the distribution of environmental parameters from the material response is realized. In addition, the present invention has developed a back propagation neural network controller specifically for the molecular response mechanism of water-based waterproof materials. Compared with the manual parameter adjustment method of the traditional controller, it realizes adaptive parameter optimization based on the material molecular response. The multi-layer neural network architecture effectively handles the complex nonlinear relationship such as the synergistic effect of acid rain erosion and photooxidation, significantly improving the coordinated control accuracy of multi-factor environmental parameters and effectively compensating for the synergistic hysteresis effect between acid erosion and photooxidation. The present invention establishes a weighted comprehensive evaluation system based on the spectral attenuation index and a five-level evaluation grading system, integrates a life prediction algorithm based on a coupled aging model, and realizes a multi-dimensional quantitative characterization of the aging state of water-based waterproof materials and a scientific prediction of their service life in an acidic pollution environment.

[0047] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, system, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, system, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, system, article, or method comprising the element.

[0048] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for detecting aging of water-based waterproof materials using a xenon lamp in a simulated environment, characterized in that: include: Testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data; Calculating a characteristic spectrum drift parameter combination in combination with the first aging response data, and solving a multi-factor aging kinetic equation group to obtain three-dimensional environmental field data; Inputting the three-dimensional environmental field data into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination; adjusting and testing test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; Spectral attenuation index calculation and aging degree classification are performed based on the second aging response data, and an aging status evaluation result of the water-based waterproof material sample is output.

2. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 1, characterized in that: The step of testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data includes: The multi-factor coupling test parameters of the xenon lamp irradiation system, acid rain spray system and polluted gas injection system in the aging detection platform were set to obtain composite environmental exposure conditions; Based on the composite environmental exposure conditions, collaborative parameter setting is performed on the temperature control module, the humidity control module, and the pressure control module in the aging detection platform to obtain an environmental parameter control scheme; According to the environmental parameter control scheme, the water-based waterproof material sample is respectively subjected to initial stage exposure, accelerated stage exposure and stable stage exposure, and the first aging response data is collected at the same time.

3. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 1, characterized in that: The calculation of characteristic spectrum drift parameter combinations in combination with the first aging response data and solving a multi-factor aging dynamics equation group to obtain three-dimensional environmental field data includes: Performing Fourier transform infrared spectroscopy detection based on the first aging response data to obtain a characteristic peak detection interval of the polymer molecular chain; Based on the characteristic peak detection interval of the polymer molecular chain, the surface of the water-based waterproof material sample is subjected to in-situ detection by an ATR accessory and synchronous monitoring by a pH response sensor array to obtain original detection data; performing signal operation on the original detection data to obtain spectrum extraction parameters; Calculating the spectrum drift amount and the intensity attenuation rate according to the spectrum extraction parameters to obtain a characteristic spectrum drift parameter combination; The multi-factor aging dynamics equations are numerically solved based on the characteristic spectrum drift parameter combination to obtain three-dimensional environmental field data.

4. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 3, characterized in that: The method of numerically solving the multi-factor aging dynamics equations based on the characteristic spectrum drift parameter combination to obtain three-dimensional environmental field data includes: Establish a multi-factor aging kinetics equation system including the acid rain penetration equation, the photooxidative degradation equation, and the heat and moisture coupled transfer equation; Inputting the characteristic spectrum drift parameter combination into the multi-factor aging kinetic equation group to analyze the mapping relationship between molecular response characteristics and environmental parameters to obtain a polymer molecular response coefficient matrix; Iteratively numerically solving the multi-factor aging kinetics equations based on the polymer molecular response coefficient matrix to obtain kinetic parameter solution results; Gradient calculation and time evolution law analysis are performed based on the solution results of the kinetic parameters to obtain three-dimensional environmental field data, which includes sulfur dioxide concentration distribution gradient, nitrogen dioxide concentration distribution gradient and acidity value distribution gradient.

5. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 4, characterized in that: The characteristic spectrum drift parameter combination is input into the multi-factor aging kinetic equation group to perform a mapping relationship analysis between molecular response characteristics and environmental parameters to obtain a polymer molecular response coefficient matrix, including: Classifying the molecular response types based on the characteristic spectral drift parameter combination to obtain a classification spectral response parameter set; Based on the classification spectral response parameter set, a quantitative correlation analysis is performed on the polymer main chain rupture mechanism, the molecular chain crosslinking density change mechanism and the molecular chain rearrangement mechanism to obtain a molecular response sensitivity function combination; Performing numerical coupling calculations on the molecular response sensitivity function combination and the multi-factor aging kinetic equations for the environmental node gradient values ​​and the polymer molecular response characteristics to obtain environmental-molecule coupling correlation data; According to the environment-molecule coupling correlation data, reverse numerical solution and coefficient matrix construction are performed to obtain the polymer molecule response coefficient matrix.

6. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 1, characterized in that: The three-dimensional environmental field data is input into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination, including: fusing the three-dimensional environmental field data with infrared spectrum characteristic peak drift data and pH sensor response time offset data to obtain a target input vector; Inputting the target input vector into the molecular response layer of the back propagation neural network for linear transformation to obtain a molecular response layer output vector; Inputting the output vector of the molecular response layer into the environmental coupling layer of the back propagation neural network to perform weighted linear transformation and Sigmoid activation function calculation of the synergistic effect of acid rain erosion and photooxidation, thermal aging and moisture-heat coupling, and multi-factor interaction to obtain an output vector of the environmental coupling layer; The output vector of the environmental coupling layer is input into the output layer of the back propagation neural network for linear transformation and parameter analysis to obtain an intelligent control parameter combination, which includes a xenon lamp irradiation intensity adjustment value, an acid rain pH adjustment value, a sulfur dioxide flow adjustment value, a nitrogen dioxide flow adjustment value, and a temperature and humidity adjustment value.

7. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 6, characterized in that: The step of inputting the target input vector into the molecular response layer of the back propagation neural network for linear transformation to obtain a molecular response layer output vector comprises: Inputting the target input vector into a molecular response layer of a back propagation neural network, wherein the molecular response layer includes an acid rain erosion response neuron group, a photooxidation degradation response neuron group, a heat and humidity aging response neuron group, and a multi-factor interaction response neuron group; The target input vector is subjected to matrix multiplication of the synergistic effect of acid rain penetration depth and light irradiation intensity and nonlinear coupling function calculation by using an acid rain erosion response neuron group and a photooxidation degradation response neuron group to obtain an acid rain-light synergistic feature; The target input vector is subjected to coupled calculations of temperature gradient and relative humidity gradient and interaction calculations of pollutant gas concentration and environmental parameters by using a heat-humidity aging response neuron group and a multi-factor interaction response neuron group to obtain a temperature-humidity-pollutant gas coupling feature; An environmental coupling layer output vector is generated according to the acid rain-light synergistic feature and the temperature, humidity and polluted gas coupling feature.

8. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 1, characterized in that: The step of adjusting and testing the test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data includes: Generate xenon lamp irradiation system control instructions, acid rain spray system control instructions and polluted gas injection system control instructions according to the intelligent control parameter combination; Adjusting the irradiation intensity of the xenon lamp irradiation system in the aging detection platform based on the xenon lamp irradiation system control instruction, adjusting the acidity value of the acid rain spraying system in the aging detection platform based on the acid rain spraying system control instruction, and controlling the flow rate of the polluted gas injection system in the aging detection platform based on the polluted gas injection system control instruction; During the test parameter adjustment, the water-based waterproof material sample in the aging detection platform is continuously monitored and multi-parameter synchronously recorded for compensation of the synergistic hysteresis effect of acid corrosion and photooxidation to obtain second aging response data.

9. The xenon lamp simulated environment aging detection method for water-based waterproof materials according to claim 1, characterized in that: The calculating of the spectral attenuation index and grading of the aging degree based on the second aging response data, and outputting the aging state assessment result of the water-based waterproof material sample, includes: Extracting characteristic parameters and configuring weighting coefficients on the second aging response data to obtain a spectral attenuation index; Based on the spectral attenuation index, a weighted comprehensive operation is performed on the characteristic peak intensity attenuation rate and the peak position drift, and the spectral attenuation index ADI value is calculated to obtain comprehensive spectral attenuation data; Performing threshold judgment and aging degree classification based on the comprehensive spectral attenuation data to obtain an aging degree grade; Combined with the aging degree grade, the second aging response data is input into a life prediction algorithm based on the Arrhenius-pH coupled aging model to perform a multivariate nonlinear regression calculation to obtain an aging state assessment result.

10. A water-based waterproof material xenon lamp simulated environment aging detection system, characterized in that: The steps for implementing the method for detecting aging of water-based waterproof materials using a xenon lamp in a simulated environment according to any one of claims 1 to 9 are as follows: An acquisition module, configured to test a water-based waterproof material sample in an aging detection platform and acquire first aging response data; a solution module, configured to calculate a characteristic spectrum drift parameter combination based on the first aging response data, and solve a multi-factor aging kinetic equation group to obtain three-dimensional environmental field data; A collaborative optimization module is used to input the three-dimensional environmental field data into a back propagation neural network to perform collaborative optimization of multi-factor environmental parameters to obtain an intelligent control parameter combination; a parameter adjustment module, configured to adjust and detect test parameters of the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; An output module is used to calculate the spectral attenuation index and classify the aging degree based on the second aging response data, and output an aging status evaluation result of the water-based waterproof material sample.

Images