A method and system for evaluating the weather resistance of decorative paper

Abstract

The present invention discloses a method and system for evaluating the weather resistance of decorative paper, relating to the technical field of weather resistance evaluation. The method includes: determining the structural composition information of the target decorative paper and the target application space; determining the influencing factors of decorative paper aging; determining the weather resistance evaluation indexes; conducting relationship analysis to establish a weather resistance evaluation model; establishing a closed bias feedback layer and an open bias feedback layer; conducting dynamic characteristic analysis, and inputting the results of the dynamic characteristic analysis into the optimized weather resistance evaluation model for analysis to generate a target weather resistance evaluation result corresponding to the weather resistance evaluation indexes. The present invention solves the technical problem in the prior art that it is difficult to take into account different scenarios and the accuracy is insufficient in the evaluation of the weather resistance of decorative paper, achieves the effect of realizing the accurate evaluation of the weather resistance of decorative paper, and improves the technical effect of the accuracy and reliability of the aging performance evaluation of decorative paper under different application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of weather resistance evaluation, and particularly to a method and system for evaluating the weather resistance of decorative paper. Background Art

[0002] In the current application field of decorative paper, as its usage scenarios become increasingly extensive, ranging from indoor home decoration to outdoor advertising displays and building exterior wall cladding, etc., the consideration of the weather resistance of decorative paper has become increasingly crucial. Traditional decorative paper weather resistance evaluation technologies have many limitations. On the one hand, in terms of setting evaluation indicators, they often only focus on one or a few aspects. For example, only the change in color difference is concerned, ignoring the importance of comprehensively considering multi-dimensional indicators such as mechanical properties, surface damage, and glossiness, resulting in a one-sided assessment of the actual aging state of decorative paper. On the other hand, in terms of incorporating environmental factors, the key factors affecting the aging of decorative paper are not comprehensively covered. For example, only temperature and humidity are considered, while factors such as pollutant exposure, ultraviolet radiation, especially the complex and variable wind speed, are not taken seriously enough, making the evaluation results deviate greatly from the actual aging situation in outdoor or special indoor environments. Moreover, most of the existing technologies lack adaptability to different application scenarios and cannot accurately reflect the true weather resistance performance of decorative paper in different environments, making it difficult to meet the current diverse usage requirements.

[0003] There are technical problems in the prior art that it is difficult to balance different scenarios and the accuracy of the decorative paper weather resistance evaluation is insufficient. Summary of the Invention

[0004] This application provides a method and system for evaluating the weather resistance of decorative paper, aiming to solve the technical problems in the prior art that it is difficult to balance different scenarios and the accuracy of the decorative paper weather resistance evaluation is insufficient.

[0005] In view of the above problems, this application provides a method and system for evaluating the weather resistance of decorative paper.

[0006] In the first aspect of this application, a method for evaluating the weather resistance of decorative paper is provided. The method includes:

[0007] Determine the structural composition information and target application space of the target decorative paper; determine the decorative paper aging influencing factors, where the decorative paper aging influencing factors at least include temperature, humidity, pollutant exposure, ultraviolet radiation, and wind speed; determine the weather resistance evaluation indicators, where the weather resistance evaluation indicators at least include color difference, mechanical properties, surface damage, and glossiness; construct an aging evaluation sample based on the structural composition information, analyze the relationship between the decorative paper aging influencing factors and the weather resistance evaluation indicators, and establish a weather resistance evaluation model; conduct a systematic bias analysis of closed and open scenarios based on the weather resistance evaluation model, establish a closed bias feedback layer and an open bias feedback layer, and optimize the weather resistance evaluation model; conduct a dynamic characteristic analysis of any one of the decorative paper aging influencing factors for the target application space, input the dynamic characteristic analysis results into the optimized weather resistance evaluation model for analysis, and generate target weather resistance evaluation results corresponding to the weather resistance evaluation indicators.

[0008] In the second aspect of the present application, a decorative paper weather resistance evaluation system is provided. The system includes:

[0009] A structural composition information determination module for determining the structural composition information and target application space of the target decorative paper; an aging influencing factor determination module for determining the decorative paper aging influencing factors, where the decorative paper aging influencing factors at least include temperature, humidity, pollutant exposure, ultraviolet radiation, and wind speed; a weather resistance evaluation indicator determination module for determining the weather resistance evaluation indicators, where the weather resistance evaluation indicators at least include color difference, mechanical properties, surface damage, and glossiness; a weather resistance evaluation model establishment module for constructing an aging evaluation sample based on the structural composition information, analyzing the relationship between the decorative paper aging influencing factors and the weather resistance evaluation indicators, and establishing a weather resistance evaluation model; a bias feedback layer establishment module for conducting a systematic bias analysis of closed and open scenarios based on the weather resistance evaluation model, establishing a closed bias feedback layer and an open bias feedback layer, and optimizing the weather resistance evaluation model; a weather resistance evaluation result generation module for conducting a dynamic characteristic analysis of any one of the decorative paper aging influencing factors for the target application space, inputting the dynamic characteristic analysis results into the optimized weather resistance evaluation model for analysis, and generating target weather resistance evaluation results corresponding to the weather resistance evaluation indicators.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Determine the structural composition information and target application space of the target decorative paper; determine the influencing factors of decorative paper aging; determine the weather resistance evaluation index; construct an aging evaluation sample based on the structural composition information, analyze the relationship between the influencing factors of decorative paper aging and the weather resistance evaluation index, and establish a weather resistance evaluation model; based on the weather resistance evaluation model, conduct systematic bias analysis for closed scenarios and open scenarios, establish a closed bias feedback layer and an open bias feedback layer, and optimize the weather resistance evaluation model; conduct dynamic characteristic analysis on any one of the influencing factors of decorative paper aging for the target application space, input the dynamic characteristic analysis results into the optimized weather resistance evaluation model for analysis, and generate a target weather resistance evaluation result corresponding to the weather resistance evaluation index. It achieves the effect of realizing accurate evaluation of the weather resistance of decorative paper, and improves the accuracy and reliability of the aging performance evaluation of decorative paper in different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0013] Figure 1 Schematic flowchart of a method for evaluating the weather resistance of decorative paper provided by an embodiment of the present application;

[0014] Figure 2 Schematic structural diagram of a system for evaluating the weather resistance of decorative paper provided by an embodiment of the present application.

[0015] Description of reference numerals: Structural composition information determination module 10, Aging influencing factor determination module 20, Weather resistance evaluation index determination module 30, Weather resistance evaluation model establishment module 40, Bias feedback layer establishment module 50, Weather resistance evaluation result generation module 60. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The present application provides a method and system for evaluating the weather resistance of decorative paper, which are used to solve the technical problem that it is difficult to take into account different scenarios and the accuracy is insufficient in the evaluation of the weather resistance of decorative paper in the prior art.

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0018] Example 1, as Figure 1 shown, the present application provides a method for evaluating the weather resistance of decorative paper, and the method includes:

[0019] Step S100: Determine the structural composition information of the target decorative paper and the target application space.

[0020] Specifically, determining the structural composition information of the target decorative paper and the target application space is the starting key link in the entire weather resistance evaluation process. For the structural composition information, by observing the fiber structure of the decorative paper through a microscope, understanding the fiber types, thicknesses, and interweaving methods, these factors will affect the paper's resistance to external environmental factors. With the help of a spectroscopic analysis instrument, determine the chemical composition of the surface coating of the decorative paper, and clarify which polymer materials, pigments, or additives the coating is composed of, because the coating plays a key role in moisture-proof, ultraviolet-proof, etc. At the same time, use X-ray diffraction technology to explore whether there are components such as mineral fillers that enhance toughness inside the paper. In determining the target application space, multiple factors need to be considered comprehensively. If it is an indoor application space, further subdivided, like a home living room environment, the daily light intensity needs to be considered, generally a mixture of indirect natural light and indoor lighting, and the lighting time depends on the owner's living habits; the temperature is relatively stable, usually between 18-25 °C, and the humidity is affected by seasons and equipment such as air conditioners, generally between 40%-60%. If it is used in the kitchen, in addition to greater temperature and humidity fluctuations, it also faces oil fume pollution, which contains pollutants such as oil particles and volatile organic compounds. For commercial places such as shopping mall exhibition halls, the light intensity is high and the duration is long, and the frequent flow of people brings more pollutants such as dust, and the temperature and humidity are regulated by the central air conditioner but are relatively stable during business hours. If it is an outdoor application space, such as building exterior wall decorative paper, the characteristics of the local climate zone need to be considered. In tropical regions, it is hot, humid, and has strong ultraviolet radiation, and there are often heavy rain washes; in temperate regions, the four seasons are distinct, the temperature and humidity change greatly, and it may also face ice and snow erosion in winter; while in arid regions, the humidity is low, the sand and wind are strong, and the ultraviolet radiation cannot be underestimated. By carefully investigating these factors, accurately determine the structural composition information of the target decorative paper and the target application space, laying a solid foundation for accurately evaluating its weather resistance in the follow-up.

[0021] Step S200: Determine the decorative paper aging influencing factors, where the decorative paper aging influencing factors at least include temperature, humidity, pollutant exposure, ultraviolet radiation, and wind speed.

[0022] Specifically, it is crucial to determine the influencing factors of decorative paper aging. Regarding temperature, data from different regions are collected with the help of a high-precision thermometer and a temperature data acquisition system. In tropical regions, the average summer temperature exceeds 30°C, which can cause the polymer chains in the decorative paper to break, the structure to become loose, and the mechanical properties to decline. In temperate regions, the temperature difference of more than 30°C between winter and summer causes the paper to expand and contract thermally, stress to concentrate, and the paper to crack. Humidity is monitored through a professional humidity sensor network. In coastal areas, the humidity is 70%-90%, which is conducive to the growth of mold, and the enzymes secreted by the mold erode the surface coating of the decorative paper. In arid areas, a humidity of 20%-30% causes the paper to lose water and become brittle, with poor flexibility and tensile resistance. In terms of pollutant exposure, samples in the urban central area are collected with atmospheric composition sampling and analysis equipment. Industrial and vehicle exhaust contain acidic gases, which form acid rain when encountering the moisture on the paper surface and corrode the paper. In the indoor kitchen, it is monitored with an oil particle detection device. The attachment of oil fume particles hinders ventilation and accelerates the aging of the paper. Ultraviolet radiation is monitored with an ultraviolet radiation intensity detector. The intensity in the plateau area is 30%-50% higher than that in the plain area, which can cause the pigment to fade, damage chemical bonds, and reduce the strength of the paper. Long-term cumulative radiation near the window indoors also causes damage. Wind speed is monitored with an anemometer in strong wind areas along the coast, mountain passes, and air ducts for a long time. Strong winds carry sand and dust, and according to the wear principle, they will wear the surface of the decorative paper, make the paper thinner, and damage the coating, exacerbating the aging. Combining these collections and analyses lays the foundation for weather resistance evaluation.

[0023] Step S300: Determine the weather resistance evaluation index, where the weather resistance evaluation index at least includes color difference, mechanical properties, surface damage, and glossiness.

[0024] Specifically, accurately determining the weather resistance evaluation indicators is a key step in evaluating the quality and durability of decorative paper. As a core indicator, color difference is measured using a professional color difference meter. By comparing the color changes of the decorative paper before and after aging, the degree of color difference is quantified. For example, under long-term ultraviolet radiation, high temperature, or pollutant erosion, the molecular structure of the pigments in the decorative paper may change, resulting in the color becoming lighter, darker, or the hue shifting. The color difference indicator can intuitively reflect this visual deterioration degree and provide a basis for users to judge the appearance retention of the decorative paper. Mechanical properties cover multiple aspects such as tensile strength and tear strength. Using a universal material testing machine, mechanical tests such as tensile and tear tests are performed on the decorative paper samples before and after aging. When the decorative paper is affected by changes in humidity and temperature, the internal fiber structure may be damaged and the performance of the polymer material may change. This is reflected in the mechanical properties as a decrease in tensile strength, making the paper easier to break when pulled, a decrease in tear strength, and being more likely to be torn after the appearance of small cracks. The changes in these data accurately show the changes in the physical durability of the decorative paper. The surface damage index is determined through microscopic observation and image analysis techniques. Under the action of aging factors such as mold growth in a high-humidity environment, sandstorm erosion, and pollutant corrosion, the surface of the decorative paper may show damage such as mildew spots, scratches, and holes. By using a high-power microscope to image the surface of the decorative paper and then using image analysis software to identify parameters such as the type, quantity, and area ratio of the damage, the surface damage condition is visually presented, which is related to the aesthetics and protection function of the decorative paper. Glossiness is measured using a glossiness meter, which emits light at a specific angle to irradiate the decorative paper and measures the intensity of the reflected light. If the decorative paper is eroded by acid rain or worn by daily wiping, the microscopic structure of its surface changes, the smoothness decreases, and the glossiness decreases, affecting the decorative effect and texture of the decorative paper. Considering comprehensively these indicators of color difference, mechanical properties, surface damage, and glossiness can comprehensively and accurately evaluate the weather resistance performance of the decorative paper in different environments.

[0025] Step S400: Construct an aging evaluation sample based on the structural composition information, analyze the relationship between the decorative paper aging influencing factors and the weather resistance evaluation indicators, and establish a weather resistance evaluation model.

[0026] Specifically, key operations are started based on the target decorative paper structure composition information obtained in the early stage. First, with this as a constraint, historical data in different environments and different time periods are widely collected, including the application time of historical decorative paper, such as recording the use cycle of a certain decorative paper outdoors for 3 years, and the service data of indoor wallpaper for 5 years, etc.; there are also historical influencing factor characteristic time series for the corresponding time period. For example, in the outdoor environment, temperature and humidity sensors, ultraviolet radiation monitors, air quality collectors, anemometers and other equipment are used to continuously record detailed information such as temperature fluctuations, humidity changes, ultraviolet radiation intensity, pollutant composition and concentration, wind speed, etc. every hour, every day, and even every month. In the indoor environment, the temperature and humidity in different areas such as the living room and the kitchen are also accurately collected with the seasons and usage scenarios; as well as the historical weather resistance evaluation index time series, using tools such as colorimeter, universal material testing machine, microscope combined with image analysis software, gloss meter, etc., to periodically detect the color difference evolution, mechanical property attenuation, surface damage, gloss reduction and other quantitative data during the aging process of decorative paper. These massive and orderly data are integrated to construct an aging evaluation sample. Next, for any influencing factor of the decorative paper aging, such as temperature, and the weather resistance evaluation index, we carried out an in-depth analysis of the influence of temporal variables, and observed how the color difference of the decorative paper changes with the temperature rise and fall when the temperature changes from season to season and from day to night, how the mechanical properties gradually decay, whether the surface damage is accelerated, and whether the glossiness is stable, so as to establish a single factor temporal evaluation channel. At the same time, we conducted a fusion effect analysis on each influencing factor, and combined different combinations of temperature and humidity, ultraviolet radiation and pollutant exposure, and paired any two, three or four of them, and analyzed the superimposed effects of them on the weather resistance evaluation index when they acted together on the decorative paper, such as how much faster the aging rate of decorative paper in a high temperature and high humidity environment is compared with that in a normal temperature and humidity environment, and the degree of deterioration of the mechanical properties of decorative paper when ultraviolet radiation and acid rain erode together, so as to construct an evaluation fusion layer. Finally, we cleverly combined the single factor temporal evaluation channel and the evaluation fusion layer to establish a set of accurate and comprehensive weather resistance evaluation models, which provided the core basis for the subsequent accurate evaluation of the weather resistance of decorative paper.

[0027] Step S500: performing systematic bias analysis of closed scenarios and open scenarios based on the weather resistance evaluation model, establishing a closed bias feedback layer and an open bias feedback layer, and optimizing the weather resistance evaluation model.

[0028] Specifically, to further improve the accuracy of the weather resistance evaluation model, a key optimization process is initiated. First, systematic bias analysis is carried out for closed scenarios and open scenarios respectively. For closed scenarios, such as indoor exhibition halls, archives rooms and other environments, using equipment such as temperature and humidity sensors, light intensity detectors, air quality monitors, etc. arranged therein, collect the characteristic values of the actual decorative paper aging influencing factors, accurately record the application time of the decorative paper in this closed environment, and at the same time use color difference meters, mechanical property testing equipment, etc. to obtain the actual weather resistance evaluation index values, and construct the first sample set. Input this sample set into the weather resistance evaluation model for testing. The model will output the corresponding prediction results. Compare the actual value with the prediction value, identify the deviation between the two, and through data statistics and analysis methods, eliminate discrete values, deeply analyze the relationship between the systematic test deviation and the characteristic values of the actual decorative paper aging influencing factors, and then generate a systematic deviation influence curve, and thus construct a closed bias feedback layer. Similarly, in open scenarios, such as outdoor building facades, outdoor billboards and other application scenarios, with the help of various environmental monitoring instruments installed on site, including high-precision thermometers, wind speed and direction sensors, ultraviolet radiation monitoring equipment, and atmospheric pollutant sampling and analysis devices, etc., collect the characteristic values of the actual decorative paper aging influencing factors, combine the accurately recorded application time of the decorative paper, and the actual weather resistance evaluation index values obtained through professional detection means, to form the second sample set. Input the second sample set into the weather resistance evaluation model for testing, and repeat the above deviation analysis process to construct an open bias feedback layer. Finally, connect the closed bias feedback layer and the open bias feedback layer in parallel to the output end of the weather resistance evaluation model, so that in the subsequent operation of the model, according to the input scenario information, it can automatically call the corresponding feedback layer to optimize and adjust the output results, thereby greatly improving the accuracy and reliability of the weather resistance evaluation model for evaluating the weather resistance of decorative papers in different scenarios, and laying a solid foundation for accurately evaluating the durability of decorative papers in various practical applications.

[0029] Step 600: Perform dynamic characteristic analysis on any one of the decorative paper aging influencing factors for the target application space, input the dynamic characteristic analysis result into the optimized weather resistance evaluation model for analysis, and generate a target weather resistance evaluation result corresponding to the weather resistance evaluation index.

[0030] Specifically, it is crucial to accurately grasp the characteristics of the target application space. First, conduct a dynamic characteristic analysis on any one of the aging impact factors of the decorative paper for the target application space. If the target application space is an indoor intelligent office area, for the temperature factor, determine whether there is intelligent temperature control equipment in the space. If there is, connect to the equipment to obtain the real-time adjustment target. For example, the set temperature is 26°C in summer and 22°C in winter. This is the result of the dynamic characteristic analysis of the temperature factor in this space. If there is no temperature control equipment, obtain the geographical location information of the office area and, with the help of local meteorological historical data, statistically analyze the variation law of temperature in this area throughout the year with seasons and day and night. For example, the average daytime temperature in summer is 30°C, the nighttime temperature is 25°C, the daytime temperature in winter is 10°C, and the nighttime temperature is 5°C, etc. Similarly, perform the same operation for other factors such as humidity and ultraviolet radiation. After completing the dynamic characteristic analysis, input the obtained results into the optimized weather resistance evaluation model. Based on the single-factor time-series evaluation channels, evaluation fusion layer, and closed and open bias feedback layers constructed in the early stage, and combined with the input dynamic characteristic analysis results, the model predicts the future weather resistance performance of the decorative paper for the preset evaluation duration, performs complex calculations and in-depth analysis. First, simulate the aging process of the decorative paper under the dynamic changes of each factor at different time periods, and then, by matching the closed type of the target application space, such as the above-mentioned office area being a closed space, automatically call the closed bias feedback layer to optimize and calibrate the preliminary analysis results, and finally generate the target weather resistance evaluation results that accurately correspond to the weather resistance evaluation indicators, including the range of color difference changes, the degree of mechanical property attenuation, the estimated surface damage condition, and the decline amplitude of glossiness of the future decorative paper, providing a key basis for the actual application decision-making of the decorative paper.

[0031] In a possible implementation manner, step S400 further includes:

[0032] Step S410: Constrained by the structural composition information, collect the historical application duration of the decorative paper, the historical characteristic time series of the impact factors, and the historical time series of the weather resistance evaluation indicators, and construct the aging evaluation sample.

[0033] Step S420: Based on the aging evaluation sample, conduct a time-series variable impact analysis on any one of the aging impact factors of the decorative paper and the weather resistance evaluation indicators, and establish a single-factor time-series evaluation channel.

[0034] Step S430: Based on the aging evaluation sample, conduct a fusion effect analysis on each of the aging impact factors of the decorative paper, and construct an evaluation fusion layer.

[0035] Step S440: Combine the single-factor time-series evaluation channel and the evaluation fusion layer to establish the weather resistance evaluation model.

[0036] Specifically, taking the structural composition information of the target decorative paper as the core constraint condition, a comprehensive data collection work is initiated. Deeply explore the historical materials of decorative paper in different past application scenarios, and accurately record the historical application duration of decorative paper. For example, the decorative paper in a certain museum has been used for 8 years, and the decorative paper on an outdoor billboard has gone through 5 seasonal alternations and other specific duration data. At the same time, use a series of professional monitoring equipment, such as temperature and humidity sensors, ultraviolet radiation detectors, air quality collectors, etc., to continuously collect the characteristic time series of historical influencing factors at fixed time intervals (such as every hour, every day). In the outdoor environment, for example, detailed records are made of the fluctuations in temperature, the trends of humidity increase and decrease, the diurnal fluctuations in ultraviolet radiation intensity, and the hourly changes in pollutant concentration throughout the year; in the indoor environment, the dynamic changes in temperature and humidity in areas such as the living room and bedroom with seasons and usage scenarios are also accurately monitored, as well as the possible presence of light and pollutants. Moreover, with the help of tools such as colorimeters, universal material testing machines, microscopes combined with image analysis software, gloss meters, etc., the time series of historical weather resistance evaluation indicators during the aging process of decorative paper are periodically detected, and data such as its color difference evolution, mechanical property attenuation, surface damage conditions, and gloss reduction amplitude are quantified. Integrate and converge these rich and orderly data to carefully construct an aging assessment sample.

[0037] Based on a carefully constructed aging assessment sample, an in-depth time-series variable impact analysis is carried out on the influencing factors of decorative paper aging and the weather resistance evaluation indicators, so as to establish a single-factor time-series evaluation channel. Taking the key influencing factor of temperature as an example, first, long-term temperature data are accurately extracted from the aging assessment sample, covering the specific temperature values in different years, seasons, day and night periods, as well as the corresponding decorative paper application duration information. These data are organized into a structured data set, where the temperature data and application duration are used as independent variables, and the weather resistance evaluation indicators such as color difference, mechanical properties, surface damage, glossiness, etc. are used as dependent variables. Subsequently, the multiple linear regression algorithm is used to attempt to construct a mathematical model between the independent variables and the dependent variables. Through the learning of a large number of data samples by this algorithm, the influence coefficients of each independent variable on the dependent variable are calculated, so as to clearly quantify the influence degree of temperature change and time lapse on the weather resistance indicators of decorative paper. For example, the model may reveal that for every 5°C increase in temperature and with a 1-year increase in application duration, the color difference of the decorative paper will increase by 2 units and the tensile strength will decrease by 10%. At the same time, the ARIMA model in time series analysis is introduced to fully consider the autocorrelation and seasonal fluctuation characteristics of temperature data over time, and further optimize the prediction of the change trend of weather resistance indicators. With the help of this model, not only can the immediate impact of the current temperature change on the decorative paper be accurately captured, but also based on the historical temperature fluctuation law, it can be predicted that in the future period, as the temperature continues to change, the progressive deterioration trend that may occur in the weather resistance indicators of the decorative paper, such as surface damage. By comprehensively applying these advanced machine algorithms, the time-series relationship between the temperature factor and the weather resistance evaluation indicators is deeply analyzed in all aspects, and a single-factor time-series evaluation channel for temperature is established accordingly. Subsequently, similar operations are repeated for other influencing factors such as humidity and ultraviolet radiation, and finally, it provides key support for accurately constructing a weather resistance evaluation model.

[0038] Based on the existing aging evaluation samples, the key fusion effect analysis is started. First, look at the combination of two influencing factors. Take temperature and humidity as an example. Detailed data in different time periods and application scenarios, as well as the corresponding weather resistance evaluation index values, are accurately screened from the aging evaluation samples. By comparing the changes in decorative paper under high temperature and high humidity and normal temperature and humidity environments, data analysis tools are used to explore whether the color difference increases, whether the mechanical properties decline faster, whether the surface damage is more serious, and the magnitude of the change in gloss. For example, it is found that in the hot and humid summer outdoor environment, the color difference of decorative paper may increase by 5 units in just a few months compared to the normal temperature and humidity environment, and the tensile strength decreases by 15%. Then focus on the combination of ultraviolet radiation and pollutant exposure, extract relevant information from the sample data. When ultraviolet radiation is strong and accompanied by pollutants such as industrial waste gas and automobile exhaust, the surface of the decorative paper is observed with the help of professional analysis methods. It is found that its chemical bond breakage is accelerated, the surface damage quickly develops from fine scratches to obvious corrosion spots, and the glossiness also drops sharply. For multiple factor combinations, such as the synergistic effects of temperature, humidity, and ultraviolet radiation, data modeling technology is used to build a model that reflects their complex interactive relationships based on the historical data of these factors and the corresponding weather resistance evaluation indicators. This model can accurately predict how the mechanical properties of decorative paper decay over time and at what rate the surface damage deteriorates under specific temperature, humidity, and ultraviolet conditions. By integrating the analysis results of these different combinations of influencing factors, an evaluation fusion layer was successfully constructed, which fully demonstrates the comprehensive effect of the fusion of multiple factors on the weather resistance of decorative paper.

[0039] Using the weighted fusion strategy, the weights of the single-factor dynamic model and the evaluation fusion layer model are initialized, and the sum is 1. Based on the validation data set, the prediction accuracy indicators of the two models, such as the root mean square error (RMSE), are calculated in different actual scenarios, such as indoor stable environment (single factor dominant) and outdoor complex climate (multiple factors interact significantly). If the RMSE of the single-factor model is smaller in the indoor scene, indicating that the prediction is more accurate, it is increased by a certain step size (such as 0.05) and reduced accordingly; conversely, if the RMSE of the evaluation fusion layer model is dominant in the outdoor scene, the weight is adjusted to tilt towards it. By repeatedly iterating and optimizing this process on the training set and the validation set, the two are closely combined to build an accurate and reliable weather resistance evaluation model. Once the structural composition of the decorative paper, the environmental parameters of the target application scenario and the expected use time are input, the model can quickly output the detailed evolution of various weather resistance indicators of the decorative paper in the future with the built-in algorithm and the previous massive data learning results, providing strong technical support for the durability evaluation of decorative paper.

[0040] In a possible implementation, step S430 further includes:

[0041] Step S431: Combine any two of the decorative paper aging impact factors, and analyze the co-action superposition relationship based on the aging evaluation samples and the weather resistance evaluation indicators to generate a first superposition relationship.

[0042] Step S432: Combine any three of the decorative paper aging impact factors, and analyze the co-action superposition relationship based on the aging evaluation samples and the weather resistance evaluation indicators to generate a second superposition relationship.

[0043] Step S433: Combine any four of the decorative paper aging impact factors, and analyze the co-action superposition relationship based on the aging evaluation samples and the weather resistance evaluation indicators to generate a third superposition relationship.

[0044] Step S434: Generate the evaluation fusion layer based on the first superposition relationship, the second superposition relationship, and the third superposition relationship.

[0045] Specifically, when analyzing the co-action superposition relationship of any two of the decorative paper aging impact factors to generate the first superposition relationship, take the common combination of temperature and humidity as an example. First, accurately retrieve from the rich aging evaluation sample database the detailed continuous data of temperature and humidity in various historical periods and covering a variety of application scenarios (such as indoor office areas, outdoor billboards, home bedrooms, etc.), as well as the accurate quantitative values of the corresponding weather resistance evaluation indicators. Then, use the analysis of variance algorithm, with different temperature and humidity combination scenarios (normal temperature and normal humidity, high temperature and high humidity, low temperature and high humidity, low temperature and low humidity, etc.) as grouping variables, and weather resistance evaluation indicators such as color difference, mechanical properties (tensile strength, tear strength, etc.), surface damage, glossiness, etc. as dependent variables. Calculate the between-group variance and within-group variance through this algorithm to measure the significant differences in the effects of different temperature and humidity combinations on each weather resistance index. For example, in the comparative analysis between the high temperature and high humidity group and the normal temperature and normal humidity group, the analysis of variance results show that for the color difference index, the between-group variance is significantly greater than the within-group variance, indicating that the combined change of temperature and humidity has a significant impact on the color difference. At the same time, introduce the linear regression algorithm, with temperature values and humidity values as independent variables, and construct a multiple linear regression model with each weather resistance index. Based on the principle of least squares, determine the regression coefficients through iterative training of a large number of sample data. For example, obtain the relationship model between the color difference change of the decorative paper and temperature and humidity in a certain specific application scenario: color difference = a × temperature + b × humidity + c (a, b, c are regression coefficients), accurately quantifying the superposition effect of the two when acting together. Combining the results of the analysis of variance to determine the significance of the impact and the linear regression to quantify the specific relationship clearly presents the first superposition relationship of the two factors of temperature and humidity acting on the decorative paper on each weather resistance index, providing key basic data for the subsequent construction of a comprehensive evaluation fusion layer.

[0046] Taking any three decorative paper aging influencing factors as an example, select a combination of three typical decorative paper aging influencing factors: ultraviolet radiation, temperature, and pollutant exposure. First, extract their detailed data at different time periods and in various environments from the aging assessment samples, including ultraviolet radiation intensity values, temperature values, pollutant components, and concentration data. At the same time, obtain the corresponding weather resistance evaluation index data, such as quantification values like color difference, tensile strength, surface damage degree, glossiness, etc. Then, use the principal component analysis (PCA) algorithm to reduce the dimension of the original high-dimensional data, highlight the key information, and reduce the subsequent calculation amount. After that, use partial least squares regression (PLSR). Take the ultraviolet radiation, temperature, and pollutant-related data after dimension reduction as independent variables, and the weather resistance evaluation index as the dependent variable to construct a regression model. Through multiple iterations and optimizations, solve the regression coefficients to obtain a relationship expression such as "tensile strength attenuation = a × ultraviolet radiation intensity + b × temperature + c × pollutant concentration + d × ultraviolet radiation intensity × temperature + e × ultraviolet radiation intensity × pollutant concentration + f × temperature × pollutant concentration + g" (a - g are regression coefficients), accurately quantify the superimposed influence of the combined action of the three on weather resistance indicators such as tensile strength, and then generate the second superimposed relationship.

[0047] Using a recursive algorithm, we select from the existing set of at least four aging influencing factors, such as temperature, humidity, pollutant exposure, and ultraviolet radiation, step by step. First, we select one factor, then select from the remaining ones, and repeat this process until we have four, such as temperature, humidity, ultraviolet radiation, and pollutant exposure as a group, or temperature, wind speed, ultraviolet radiation, and pollutant exposure in different combinations, and clearly list all the combinations. We establish a close connection with the aging assessment sample data, which are generally stored in the database. At this time, we use a method similar to writing query statements to accurately extract the information of the corresponding time period from the database table based on the specific identification of the combination. For example, for a certain combination, the query statement clearly tells the database to find the application time of each time period under this combination, the temperature, humidity, ultraviolet radiation value, and pollutant exposure value at that time, as well as the corresponding weather resistance evaluation indicators, such as color difference, mechanical properties, surface damage, and gloss values, to quickly obtain the required data set. Finally, a multivariate linear regression model was built, with the four aging influencing factors as independent variables and the weather resistance evaluation index as the dependent variable. By continuously substituting data and using the gradient descent method and other means to adjust the coefficients in the model, the linear relationship between the factors and the index was initially explored. On the other hand, a neural network model was constructed, with the input layer corresponding to the four factors, two hidden layers in the middle, and a suitable activation function. During training, the data obtained earlier was input in batches, allowing the model to learn more complex and deep nonlinear relationships, and finally accurately excavating the intrinsic connection between the four factors and the weather resistance evaluation index when they work together, generating a reliable third superposition relationship.

[0048] When generating an evaluation fusion layer based on the existing first superposition relationship, second superposition relationship and third superposition relationship, a weighted fusion algorithm is used, that is, the frequency ratio of the prediction of the final weather resistance evaluation result corresponding to the feature vector groups based on the first superposition relationship, the second superposition relationship and the third superposition relationship in the historical sample data is first counted respectively, and this is used as the weight W1, W2, and W3, and then the three feature vector groups are weighted and summed according to their respective weights to obtain a fused feature vector group, thereby constructing an evaluation fusion layer that can comprehensively reflect the synergistic effect of all aging influencing factors on the weather resistance of decorative paper.

[0049] In a possible implementation, step S500 further includes:

[0050] Step S510: collecting aging evaluation samples, wherein the aging evaluation samples include a first sample set and a second sample set, the first sample set is the characteristic values ​​of actual decorative paper aging influencing factors, decorative paper application time, and actual weather resistance evaluation index values ​​collected in a closed scene, and the second sample set is the characteristic values ​​of actual decorative paper aging influencing factors, decorative paper application time, and actual weather resistance evaluation index values ​​collected in an open scene.

[0051] Step S520: Test the first sample set through the weather resistance evaluation model, identify systematic biases in a closed scenario based on the test results, and construct the closed bias feedback layer.

[0052] Step S530: Test the second sample set through the weather resistance evaluation model, identify systematic biases in an open scenario based on the test results, and construct the open bias feedback layer.

[0053] Step S540: Connect the closed bias feedback layer and the open bias feedback layer in parallel to the output end of the weather resistance evaluation model to complete the optimization of the weather resistance evaluation model.

[0054] Specifically, aging evaluation samples are collected through various pre-built high-precision sensor networks and data acquisition programs. For enclosed scenarios, temperature sensors, humidity sensors, ultraviolet sensors (used to detect trace amounts of ultraviolet light that may penetrate through windows or lighting equipment), pollutant monitors, and anemometers deployed in enclosed environments such as indoor exhibition halls and temperature- and humidity-controlled storage rooms transmit the characteristic values of the aging impact factors of the actual decorative paper's environment in real time to me, including temperature values accurate to one decimal place, such as 22.5°C, humidity maintained at 49.8%, extremely small values corresponding to near-zero wind speed, as well as the detailed quantification of pollutant components and concentrations and weak ultraviolet radiation intensity; at the same time, relying on the built-in clock to accurately record the application time of the decorative paper from the moment of installation and activation, accumulating in milliseconds, and through professional image acquisition and analysis equipment and mechanical property testing devices, measuring the corresponding actual weather resistance evaluation index values, and completely collecting information such as the color difference accurately quantified to a change of 0.1 unit, the specific parameters of the subtle changes in mechanical properties, the scratch depth or damaged area ratio of the surface damage measured in millimeters, and the percentage value of the glossiness attenuation, to construct the first sample set. When facing open scenarios, an enhanced sensor combination distributed in areas such as outdoor billboards and building facades starts to operate. The temperature sensor can adapt to a wide range of fluctuations from -20°C to 40°C, the humidity sensor sensitively captures sudden changes in humidity caused by weather such as rainfall and fog, the high-sensitivity ultraviolet radiation sensor accurately measures the strong and time-varying ultraviolet intensity under direct sunlight, the air pollutant comprehensive monitor analyzes the complex and diverse pollutant components and concentrations in real time, and the wind speed and direction sensor dynamically tracks the wind speed change and direction, quickly feeding back the characteristic values of the aging impact factors faced by the actual decorative paper in the open scenario, such as the temperature reaching 35.2°C during high-temperature periods in summer, the humidity instantly soaring above 90% after heavy rain, the wind speed exceeding 10 m / s during strong winds, and the ultraviolet radiation reaching its peak in the afternoon on sunny days, etc.; combined with the precise time synchronization module to record the duration of the decorative paper's exposure to the complex outdoor environment, subdividing it into hours based on days; and then using outdoor-specific weather resistance detection equipment to obtain the corresponding actual weather resistance evaluation index values, accurately recording the real-time changes in color difference, mechanical properties, surface damage, and glossiness under the changing outdoor conditions, and aggregating them into the second sample set.

[0055] For the first sample set, the characteristic values ​​of the actual decorative paper aging influencing factors in the closed scene, including accurate temperature, humidity, ultraviolet radiation intensity, pollutant concentration data, and application time down to milliseconds, are strictly arranged and input according to the established format of the weather resistance evaluation model. The hybrid algorithm of the built-in multiple linear regression and neural network is quickly operated. The multiple linear regression initially builds a linear prediction framework, and gives basic predictions with aging factors as independent variables and weather resistance evaluation indicators as dependent variables. The neural network uses nonlinear fitting capabilities to dig deep into complex relationships for optimization and outputs the predicted weather resistance evaluation index values. Then, the clustering algorithm is used to compare the predicted values ​​with the actual values, and similar deviation sample subsets are found by grouping according to the deviation size, direction, and trend. Subsequently, the Pearson correlation coefficient algorithm is used to analyze the correlation between the deviation and the characteristic value of the aging factor in each subset, such as the change law of the color difference prediction deviation when the indoor temperature fluctuates, and the deviation distribution model is constructed to complete the identification of the systematic bias of the closed scene, convert the relationship between the deviation and the factor function into a digital adjustment parameter, integrate it into the algorithm architecture, and finally construct a closed bias feedback layer.

[0056] Similarly, for the second sample set, the processing flow is similar. The characteristic values ​​of aging influencing factors in open scenarios, such as variable outdoor temperature, unstable humidity, strong ultraviolet rays, complex pollutants, and uncertain wind speed, are accurately entered into the model together with the application time. After the model calculates and outputs the predicted value, it is compared with the actual value. At this time, considering the complex and variable nature of the outdoor environment, adaptive algorithms such as chaos theory analysis and wavelet transform are used to mine deviation characteristics. Because factors such as outdoor temperature and wind speed often change nonlinearly, these algorithms are used to identify complex fluctuation patterns such as mechanical performance deviations during high temperature and strong wind periods, and an open scenario deviation distribution model is constructed to complete systematic bias identification. The relevant rules are then converted into adjustment parameters to construct an open bias feedback layer, laying the foundation for subsequent model optimization.

[0057] To optimize the weather resistance evaluation model, the closed bias feedback layer runs based on the built-in high-precision algorithm and rich local database. When the model outputs the weather resistance prediction results of the decorative paper under the set environmental parameters, such as the values ​​of various indicators after being placed in a certain temperature, humidity, and ultraviolet intensity scene for a certain period of time, it quickly retrieves the historical data of similar conditions, uses algorithms such as mean square error to compare the deviation, and generates a digital coded feedback signal according to the correction rule when the threshold is exceeded. At the same time, the open bias feedback layer builds a multi-element acquisition fusion system, relying on the outdoor sensor network to capture the state of the decorative paper in the natural climate in real time, using image recognition and MEMS sensors to quantify damage and monitor the environment; the machine learning algorithms used include Bayesian networks and decision tree algorithms to generate optimization instructions. Finally, through the parallel interface and synchronization mechanism, the two are connected in parallel to the model output for collaborative optimization.

[0058] In a possible implementation, step S520 further includes:

[0059] Step S521: Test the first sample set through the weather resistance evaluation model to establish a systematic test deviation set.

[0060] Step S522: After removing discrete values from the systematic test deviation set, analyze the relationship between the systematic test deviation and the characteristic values of the actual decorative paper aging influencing factors to generate a systematic deviation influence curve.

[0061] Step S523: Construct the closed-loop bias feedback layer based on the systematic deviation influence curve.

[0062] Specifically, first, organize the multi-dimensional data corresponding to each sample in the first sample set, including the material category of the decorative paper (identified by specific codes for different paper types, plastic materials, etc.), the environmental parameters (temperature and humidity recorded as precise measurement values by sensors, ultraviolet radiation intensity quantified according to professional radiation measurement, and pollutant concentration based on chemical analysis instrument data), and information such as the preset usage duration, into a vector form suitable for the input format of the model. Then, input these vectors into the weather resistance evaluation model in sequence. The model is based on an internal deep learning architecture, such as a multi-layer perceptron (MLP), whose neuron nodes perform complex operations according to the decorative paper aging rules learned from a large amount of past training data. For each input sample, the model outputs the predicted values of its corresponding weather resistance indicators, including the predicted value of color difference (quantified value of color change in the simulated environment obtained according to the color space conversion algorithm), the prediction of mechanical property attenuation (calculation of changes in tensile strength, tear strength, etc. through stress-strain simulation algorithms), the estimation of surface damage degree (quantification of scratches, wear, etc. judged by combining image recognition and texture analysis algorithms), and the speculation of glossiness change (calculation of the reduction value of gloss units using a light reflection model). At the same time, calculate the difference between each predicted value output by the model and the actual weather resistance indicator values accurately measured and stored in the sample set one by one according to the mean square error (MSE) algorithm, and arrange the obtained deviation values in order according to the sample numbers. Finally, a systematic test deviation set is successfully established, accurately reflecting the overall picture of the current deviation of the model's test on the first sample set.

[0063] The box plot method in statistics is used to identify and eliminate discrete values. Taking the deviation values of each weather resistance index as the data basis, a box plot is drawn. The normal data interval is determined based on the positions of the upper and lower edges of the box and the upper and lower lines. Data points outside the interval are discrete values, and these abnormal points are removed from the deviation set to ensure the reliability of the subsequent analysis data. After the elimination of discrete values, for each actual decorative paper aging influencing factor, such as temperature, the deviation data of the corresponding weather resistance index under different temperature conditions is extracted from the deviation set. The linear regression analysis method is used to explore the potential linear relationship between the two, and observe whether the deviations of color difference, mechanical properties, etc. show a linear increase or a more complex change trend when the temperature rises; the same operation is performed for factors such as humidity and ultraviolet radiation. After comprehensively analyzing the relationship between each factor and the deviation, a suitable data fitting algorithm is selected. If it is found that most relationships are close to the quadratic function form, quadratic polynomial fitting is used. Taking the values of each factor as the independent variable and the systematic deviation of the corresponding weather resistance index as the dependent variable, the polynomial coefficients are accurately determined through a large number of calculations, and then a systematic deviation influence curve that can intuitively show the dynamic change of the systematic test deviation with the characteristic values of the actual aging influencing factors is generated, providing a key guidance for the subsequent model optimization.

[0064] When constructing the closed - loop offset feedback layer, key information is first extracted from the systematic deviation influence curve. According to the change trends of the weather resistance index deviations corresponding to different aging influencing factors (such as temperature, humidity, ultraviolet radiation, etc.) presented by the curve, the initial weight distribution inside the feedback layer is determined. For example, if it is found that the color difference deviation of the decorative paper increases sharply when the temperature rises, in the closed - loop offset feedback layer, a higher initial weight is set for the adjustment module corresponding to the temperature factor, making it more sensitive to the color difference problem caused by temperature in the subsequent feedback adjustment process. At the same time, according to the deviation changes caused by the synergistic effect between each factor reflected by the curve, a complex internal correlation structure is constructed. For example, when the ultraviolet radiation and temperature rise simultaneously, resulting in a non - linear increase in the mechanical property deviation, a special synergistic adjustment link is set inside the feedback layer to accurately handle this combined effect.

[0065] When constructing an open - bias feedback layer, the second sample set, that is, the data carefully collected in an open scenario covering the characteristic values of the actual decorative paper aging influencing factors, the application time of the decorative paper, and the actual weather resistance evaluation index values, is input into the weather resistance evaluation model for testing. The model outputs a prediction result according to the built - in algorithm. By comparing the predicted value with the actual weather resistance evaluation index value in the sample set, the deviation is calculated and a systematic test deviation set is established. Subsequently, due to the complexity of the open - scenario data, means such as the box - plot method combined with data smoothing processing are used to accurately eliminate the discrete values in the systematic test deviation set caused by environmental mutations, measurement accidental errors, etc., to ensure the reliability of the data. Then, the data value is deeply explored, and the relationship between the systematic test deviation and the characteristic values of the actual decorative paper aging influencing factors is analyzed. At this time, since the second sample set comes from a real open scenario, such as outdoor billboards being affected by complex factors such as strong winds, heavy rains, and long - term sunlight exposure, when analyzing, the relationship between these dynamic and sudden situations and the weather resistance index deviation can be comprehensively considered. The multiple linear regression or the random forest algorithm in machine learning is used to explore the laws under the synergistic action of multiple factors. Furthermore, based on the results of these in - depth analyzes, data fitting technology is used to generate a systematic deviation influence curve, which truthfully reflects the deviation change trend under the interweaving of various factors in the real open scenario. Finally, an open - bias feedback layer is constructed based on this systematic deviation influence curve rich in real - world information. The key nodes and change trends of the influence of different environmental factors on the deviation are extracted from the curve and transformed into the dynamic weight setting and adaptive adjustment rules inside the feedback layer.

[0066] In a possible implementation manner, step S600 further includes:

[0067] Step S610: Determine whether there is a device in the target application space that can adjust any of the factors.

[0068] Step S620: If so, connect to the device to obtain the adjustment target, and determine the dynamic characteristic analysis result according to the adjustment target.

[0069] Step S630: If not, obtain the geographical location where the target application space is located, and perform historical dynamic statistics on any of the factors in the decorative paper aging influencing factors based on the geographical location to generate the dynamic characteristic analysis result.

[0070] Specifically, conduct a comprehensive and detailed investigation of the target application space to determine whether there is any equipment that can adjust any factor among the factors affecting the aging of decorative paper. The target application space mentioned here has a wide range, covering both indoor areas with high environmental control requirements such as museum exhibition halls and libraries, and outdoor places such as bus stop billboards and building exterior wall decoration areas exposed to the natural environment. For each factor that may affect the aging of decorative paper, such as temperature, humidity, ultraviolet radiation, pollutant concentration, etc., verify whether there is a corresponding adjustment facility one by one. For example, in a museum exhibition hall, check whether there is a temperature and humidity adjustment system, air purification and filtration devices to control temperature and humidity and reduce harmful pollutants.

[0071] Once it is determined that such adjustment equipment is indeed included in the target application space, quickly establish a connection channel with these devices and obtain the adjustment target information from them. Taking an indoor office space as an example, if connected to a central air-conditioning control system and it is found that the set temperature adjustment target is to maintain between 22-26°C throughout the year and the humidity is stable in the range of 40%-60%, this indicates that this space has clear regulatory requirements for the two key aging factors of temperature and humidity. Based on these obtained adjustment targets, combined with the existing knowledge system related to the aging of decorative paper and the data model accumulated in the early stage, deeply analyze the dynamic characteristics under such precise control, and accurately determine the dynamic characteristic analysis results such as the change of the aging rate of decorative paper over time and the evolution trend of various weather resistance indicators in this environment, providing strong support for accurately predicting the life and performance changes of decorative paper.

[0072] If the judgment result is negative, that is, the target application space is not equipped with equipment to adjust any aging factor, immediately lock the geographical location of the target application space. This geographical location information contains rich environmental connotations. For example, being located in a low-latitude tropical region means high temperature, strong ultraviolet radiation and high humidity all year round; being located in a high-latitude cold region faces environmental characteristics such as cold temperature and large seasonal differences in sunshine time. Relying on the Geographic Information System (GIS) and a large amount of historical environmental data resources, conduct an in-depth historical dynamic statistics on any factor among the factors affecting the aging of decorative paper. For example, for the outdoor decorative paper application scenario in the coastal area, conduct a historical retrospective on the humidity factor, and statistically analyze the humidity change curves in different seasons and different time periods in the past few years or even decades, as well as the actual aging records of decorative paper in this area. Through data analysis and fitting techniques, generate dynamic characteristic analysis results covering how humidity fluctuations affect the aging process of decorative paper and the decline laws of weather resistance indicators of decorative paper under different humidity extremes, which also provides key basis for formulating subsequent decorative paper maintenance and replacement strategies.

[0073] In a possible implementation manner, step S600 further includes:

[0074] Step S640: Identify the enclosure type of the target application space.

[0075] Step S650: Based on the enclosure type, match the target feedback layer in the closed - type bias feedback layer and the open - type bias feedback layer.

[0076] Step S660: After analyzing the dynamic characteristic analysis result and the preset evaluation duration with the weather resistance evaluation model, perform output feedback optimization through the target feedback layer to generate the target weather resistance evaluation result corresponding to the preset evaluation duration.

[0077] Specifically, identifying the enclosure type of the target application space becomes a key task. The target application spaces vary in form, and the enclosure types cover a wide range, from a completely enclosed indoor precision laboratory environment where the surrounding walls, ceiling, and floor are specially sealed, and air exchange depends on a professional purification ventilation system, which can isolate the interference of external temperature, humidity, and pollutants to the greatest extent; to a relatively enclosed ordinary office space where, although there are doors and windows for ventilation, the temperature and humidity are generally in a stable state under artificial control on a daily basis; then to semi - open areas such as shopping mall entrances and street - side store windows, where one or more sides of such spaces are directly connected to the external open environment, and the temperature, humidity, light, and air composition fluctuate greatly with the external environment; until the completely open outdoor building exterior walls and the spaces where large outdoor billboards are located, which are exposed to natural climate and various environmental factors without any shelter. Through multi - aspect means such as on - site investigation, sensor monitoring, and combining with space design drawings, accurately determine which enclosure type the target application space belongs to.

[0078] The precise matching target feedback layer is a crucial part of optimizing the weather resistance assessment. After determining the enclosure type of the target application space, a choice needs to be made between the closed - type offset feedback layer and the open - type offset feedback layer. If the target application space is fully enclosed, for example, a dust - free production workshop with tight seals all around, where temperature and humidity are constantly controlled by precision air conditioners, and the air purification level is extremely high, being almost completely isolated from the external environment. In this case, the closed - type offset feedback layer is undoubtedly the best match. It relies on an internally solidified high - precision model, and the model parameters are derived from a large number of rigorous experimental data on the aging of decorative papers in similar closed environments. It can meticulously adjust the preliminary results output by the weather resistance assessment model in a stable and precise manner. For example, when the assessment model predicts the color difference change of the decorative paper in the workshop over a certain period in the future, the closed - type feedback layer corrects possible minor deviations based on the aging trajectory of decorative papers stored internally in an environment with the same temperature, humidity, and low pollution, making the result more in line with the actual situation. On the contrary, if the target application space is open, such as a large outdoor bus stop advertising display area directly facing natural factors like sunlight, wind, rain, and sand and dust without any shelter, the environmental changes are drastic and unpredictable. At this time, the open - type offset feedback layer is selected. Through multi - functional environmental sensors deployed around, it can collect real - time data such as sudden temperature changes, soaring humidity, and peak ultraviolet intensity immediately, enabling the finally feedback result to fully reflect the rapidly changing aging trend of decorative papers in the real open environment, ensuring that the matching target feedback layer and the enclosure type complement each other, and providing a guarantee for generating accurate weather resistance evaluation results in the follow - up.

[0079] The weather resistance assessment model starts to receive the results of dynamic characteristic analysis, covering all - round environmental information of the target application space. From the daily fluctuations of temperature, seasonal changes in humidity, to the diurnal differences in ultraviolet radiation, and the environmental responses of pollutant concentrations, all are presented in precise data. At the same time, combined with the preset evaluation duration, the complex algorithm trained by the model based on a large amount of aging data is quickly launched, deeply analyzing the aging trajectory of the decorative paper within a given time span, and predicting the trends of key weather resistance indicators such as the range of color difference gradual change, the attenuation amplitude of tensile strength, and the development of surface damage degree. Immediately afterwards, the target feedback layer plays a key role. If it is a closed - type offset feedback layer, it verifies each output of the model against the standard aging mode of the closed space. Once a color difference deviation caused by humidity, for example, is found, it precisely corrects the deviation according to the internal rules to ensure compliance with the characteristics of the closed environment; if it is an open - type offset feedback layer, it immediately captures real - time changing data such as sudden heavy rain and strong wind outdoors, and flexibly optimizes the model results based on this, making it keep up with the changes in the open environment, and finally generating a target weather resistance evaluation result that precisely corresponds to the preset evaluation duration, providing a solid decision - making foundation for the full - life - cycle management of decorative papers.

[0080] Example 2, based on the same inventive concept as a method for evaluating the weather resistance of a decorative paper in the foregoing example, as Figure 2As shown in the figure, the present application provides a weather resistance evaluation system for decorative paper. The system and method embodiments in the present application are based on the same inventive concept. Among them, the system includes:

[0081] A structural composition information determination module 10 for determining the structural composition information and target application space of the target decorative paper.

[0082] An aging influence factor determination module 20 for determining the aging influence factors of the decorative paper. Among them, the aging influence factors of the decorative paper at least include temperature, humidity, pollutant exposure, ultraviolet radiation, and wind speed.

[0083] A weather resistance evaluation index determination module 30 for determining the weather resistance evaluation indexes. Among them, the weather resistance evaluation indexes at least include color difference, mechanical properties, surface damage, and glossiness.

[0084] A weather resistance evaluation model establishment module 40 constructs an aging evaluation sample based on the structural composition information, analyzes the relationship between the aging influence factors of the decorative paper and the weather resistance evaluation indexes, and establishes a weather resistance evaluation model.

[0085] A bias feedback layer establishment module 50 conducts a systematic bias analysis of closed - loop scenarios and open - loop scenarios based on the weather resistance evaluation model, establishes a closed - loop bias feedback layer and an open - loop bias feedback layer, and optimizes the weather resistance evaluation model.

[0086] A weather resistance evaluation result generation module 60 conducts dynamic characteristic analysis on any one of the aging influence factors of the decorative paper for the target application space, inputs the dynamic characteristic analysis result into the optimized weather resistance evaluation model for analysis, and generates a target weather resistance evaluation result corresponding to the weather resistance evaluation indexes.

[0087] Furthermore, the weather resistance evaluation model establishment module 40 further includes:

[0088] An aging evaluation sample construction unit for collecting the historical application duration of the decorative paper, the historical influence factor characteristic time series, and the historical weather resistance evaluation index time series with the structural composition information as a constraint, and constructing the aging evaluation sample.

[0089] A single - factor time - series evaluation channel establishment unit conducts time - series variable influence analysis on any one of the aging influence factors of the decorative paper and the weather resistance evaluation indexes based on the aging evaluation sample, and establishes a single - factor time - series evaluation channel.

[0090] An evaluation fusion layer construction unit conducts fusion effect analysis on each of the aging influence factors of the decorative paper based on the aging evaluation sample, and constructs an evaluation fusion layer.

[0091] A weather resistance evaluation model establishment unit, which is used to establish the weather resistance evaluation model in combination with the single-factor time-series evaluation channel and the evaluation fusion layer.

[0092] Furthermore, the evaluation fusion layer construction unit further includes:

[0093] A first superposition relationship generation unit, which is used to combine any two factors in the decorative paper aging influence factors, and perform a common action superposition relationship analysis based on the aging evaluation sample and the weather resistance evaluation index to generate a first superposition relationship.

[0094] A second superposition relationship generation unit, which is used to combine any three factors in the decorative paper aging influence factors, and perform a common action superposition relationship analysis based on the aging evaluation sample and the weather resistance evaluation index to generate a second superposition relationship.

[0095] A third superposition relationship generation unit, which is used to combine any four factors in the decorative paper aging influence factors, and perform a common action superposition relationship analysis based on the aging evaluation sample and the weather resistance evaluation index to generate a third superposition relationship.

[0096] An evaluation fusion layer generation unit, which generates the evaluation fusion layer based on the first superposition relationship, the second superposition relationship, and the third superposition relationship.

[0097] Furthermore, the bias feedback layer establishment module 50 further includes:

[0098] An aging evaluation sample collection unit, which is used to collect aging evaluation samples. Among them, the aging evaluation samples include a first sample set and a second sample set. The first sample set is the characteristic values of the actual decorative paper aging influence factors, the decorative paper application time, and the actual weather resistance evaluation index values collected in a closed scenario, and the second sample set is the characteristic values of the actual decorative paper aging influence factors, the decorative paper application time, and the actual weather resistance evaluation index values collected in an open scenario.

[0099] A closed bias feedback layer construction unit, which is used to test the first sample set through the weather resistance evaluation model, identify systematic biases in the closed scenario according to the test results, and construct the closed bias feedback layer.

[0100] An open bias feedback layer construction unit, which is used to test the second sample set through the weather resistance evaluation model, identify systematic biases in the open scenario according to the test results, and construct the open bias feedback layer.

[0101] The weather resistance evaluation model optimization unit is used to connect the closed bias feedback layer and the open bias feedback layer in parallel to the output end of the weather resistance evaluation model to complete the optimization of the weather resistance evaluation model.

[0102] Furthermore, the closed bias feedback layer construction unit further includes:

[0103] The systematic test deviation set establishment unit is used to test the first sample set through the weather resistance evaluation model to establish a systematic test deviation set.

[0104] The systematic deviation influence curve generation unit is used to analyze the relationship between the systematic test deviation and the characteristic value of the actual decorative paper aging influence factor after removing discrete values from the systematic test deviation set, and generate a systematic deviation influence curve.

[0105] The closed bias feedback layer establishment unit is used to construct the closed bias feedback layer with the systematic deviation influence curve.

[0106] Furthermore, the weather resistance evaluation result generation module 60 further includes:

[0107] The device determination unit is used to determine whether there is a device in the target application space that adjusts any of the factors.

[0108] The dynamic characteristic analysis result determination unit is used to, if so, connect to the device to obtain an adjustment target, and determine the dynamic characteristic analysis result according to the adjustment target.

[0109] The dynamic characteristic analysis result generation unit is used to, if not, obtain the geographical location where the target application space is located, and perform historical dynamic statistics on any of the decorative paper aging influence factors based on the geographical location to generate the dynamic characteristic analysis result.

[0110] Furthermore, the weather resistance evaluation result generation module 60 further includes:

[0111] The closed type identification unit is used to identify the closed type of the target application space.

[0112] The target feedback layer matching unit matches the target feedback layer in the closed bias feedback layer and the open bias feedback layer based on the closed type.

[0113] The feedback optimization output unit is used to analyze the dynamic characteristic analysis result and the preset evaluation duration through the weather resistance evaluation model, and then perform output feedback optimization through the target feedback layer to generate the target weather resistance evaluation result corresponding to the preset evaluation duration.

[0114] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described. Moreover, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0116] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for evaluating weather resistance of decorative paper, characterized in that: include: Determine the structural composition information and target application space of the target decorative paper; Determining factors affecting aging of the decorative paper, wherein the factors affecting aging of the decorative paper include at least temperature, humidity, pollutant exposure, ultraviolet radiation, and wind speed; Determining weather resistance evaluation indicators, wherein the weather resistance evaluation indicators at least include color difference, mechanical properties, surface damage and glossiness; constructing an aging evaluation sample based on the structural composition information, performing a relationship analysis on the decorative paper aging influencing factor and the weather resistance evaluation index, and establishing a weather resistance evaluation model; Based on the weather resistance evaluation model, a systematic bias analysis of closed scenarios and open scenarios is performed, a closed bias feedback layer and an open bias feedback layer are established, and the weather resistance evaluation model is optimized; Performing a dynamic characteristic analysis on any factor of the decorative paper aging influencing factors for the target application space, inputting the dynamic characteristic analysis result into the optimized weather resistance evaluation model for analysis, and generating a target weather resistance evaluation result corresponding to the weather resistance evaluation index; Performing a dynamic characteristic analysis on any factor of the decorative paper aging influencing factors for the target application space includes: Determining whether the target application space includes a device for adjusting any of the factors; If yes, connect the device to obtain the adjustment target, and determine the dynamic characteristic analysis result according to the adjustment target; If not, obtaining the geographical location of the target application space, and performing historical dynamic statistics on any factor of the decorative paper aging influencing factors based on the geographical location to generate the dynamic characteristic analysis result; The dynamic characteristic analysis results are input into the optimized weather resistance evaluation model for analysis to generate target weather resistance evaluation results corresponding to the weather resistance evaluation index, including: Identifying the closure type of the target application space; matching a target feedback layer in the closed bias feedback layer and the open bias feedback layer based on the closed type; After analyzing the dynamic characteristic analysis result and the preset evaluation time by the weather resistance evaluation model, the output feedback optimization is performed through the target feedback layer to generate the target weather resistance evaluation result corresponding to the preset evaluation time.

2. A method for evaluating weather resistance of decorative paper according to claim 1, characterized in that: An aging evaluation sample is constructed based on the structural composition information, a relationship analysis is performed on the decorative paper aging influencing factor and the weather resistance evaluation index, and a weather resistance evaluation model is established, including: Taking the structural composition information as a constraint, collecting the historical decorative paper application time, historical influencing factor characteristic time series and historical weather resistance evaluation index time series to construct the aging evaluation sample; Based on the aging evaluation sample, a temporal variable impact analysis is performed on any influencing factor of the decorative paper aging influencing factor and the weather resistance evaluation index to establish a single factor temporal evaluation channel; Based on the aging evaluation sample, performing fusion effect analysis on each influencing factor of the decorative paper aging influencing factor to construct an evaluation fusion layer; The weather resistance evaluation model is established by combining the single factor temporal evaluation channel and the evaluation fusion layer.

3. A method for evaluating weather resistance of decorative paper according to claim 2, characterized in that: Based on the aging evaluation sample, a fusion effect analysis is performed on each influencing factor of the decorative paper aging influencing factor to construct an evaluation fusion layer, including: Combining any two factors of the decorative paper aging influencing factors, and performing a joint action superposition relationship analysis based on the aging evaluation sample and the weather resistance evaluation index to generate a first superposition relationship; Combining any three factors among the decorative paper aging influencing factors, and performing a joint action superposition relationship analysis based on the aging evaluation sample and the weather resistance evaluation index to generate a second superposition relationship; Combining any four factors of the decorative paper aging influencing factors, and performing a joint action superposition relationship analysis based on the aging evaluation sample and the weather resistance evaluation index to generate a third superposition relationship; The evaluation fusion layer is generated based on the first superposition relationship, the second superposition relationship and the third superposition relationship.

4. A method for evaluating weather resistance of decorative paper according to claim 1, characterized in that: Based on the weather resistance evaluation model, a systematic bias analysis of closed scenarios and open scenarios is performed, a closed bias feedback layer and an open bias feedback layer are established, and the weather resistance evaluation model is optimized, including: Collecting aging evaluation samples, wherein the aging evaluation samples include a first sample set and a second sample set, the first sample set is the characteristic values ​​of actual decorative paper aging influencing factors, decorative paper application time, and actual weather resistance evaluation index values ​​collected in a closed scene, and the second sample set is the characteristic values ​​of actual decorative paper aging influencing factors, decorative paper application time, and actual weather resistance evaluation index values ​​collected in an open scene; Testing the first sample set by using the weather resistance evaluation model, identifying the systematic bias in a closed scenario according to the test results, and constructing the closed bias feedback layer; Testing the second sample set by using the weather resistance evaluation model, identifying the systematic bias in the open scene according to the test results, and constructing the open bias feedback layer; The closed bias feedback layer and the open bias feedback layer are connected in parallel to the output end of the weather resistance evaluation model to complete the optimization of the weather resistance evaluation model.

5. A method for evaluating weather resistance of decorative paper according to claim 4, characterized in that: The first sample set is tested by the weather resistance evaluation model, and systematic bias identification is performed in a closed scenario according to the test results, so as to construct the closed bias feedback layer, including: Testing the first sample set using the weather resistance evaluation model to establish a systematic test deviation set; After removing discrete values ​​from the systematic test deviation set, the relationship between the systematic test deviation and the characteristic value of the actual decorative paper aging influencing factor is analyzed to generate a systematic deviation influence curve; The closed bias feedback layer is constructed with the systematic deviation influence curve.

6. A decorative paper weather resistance evaluation system, characterized in that: The system is used to implement a decorative paper weather resistance evaluation method according to any one of claims 1 to 5, and the system comprises: A structural composition information determination module, used to determine the structural composition information and target application space of the target decorative paper; An aging influence factor determination module, used to determine the aging influence factors of the decorative paper, wherein the aging influence factors of the decorative paper at least include temperature, humidity, pollutant exposure, ultraviolet radiation and wind speed; A weather resistance evaluation index determination module, used to determine weather resistance evaluation indexes, wherein the weather resistance evaluation indexes at least include color difference, mechanical properties, surface damage and glossiness; A weather resistance evaluation model establishment module is used to construct an aging evaluation sample based on the structural composition information, analyze the relationship between the decorative paper aging influencing factor and the weather resistance evaluation index, and establish a weather resistance evaluation model; A bias feedback layer establishment module performs a systematic bias analysis of closed scenarios and open scenarios based on the weather resistance evaluation model, establishes a closed bias feedback layer and an open bias feedback layer, and optimizes the weather resistance evaluation model; The weather resistance evaluation result generation module is used to perform dynamic characteristic analysis on any factor of the decorative paper aging influencing factors for the target application space, input the dynamic characteristic analysis result into the optimized weather resistance evaluation model for analysis, and generate a target weather resistance evaluation result corresponding to the weather resistance evaluation index.

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