BIM-based building exterior wall insulation system thermal performance simulation method

Abstract

The application discloses a BIM-based building external wall thermal insulation system thermal performance simulation method and relates to the technical field of building energy saving.The method comprises the following steps: constructing a multi-factor environment simulation database, collecting experimental data of thermal insulation materials under different temperature, humidity and ultraviolet light combination conditions, recording and classifying the interaction of each group of environmental factors, and obtaining a preliminary environmental influence data set.The BIM-based building external wall thermal insulation system thermal performance simulation method can accurately predict the long-term performance change of the thermal insulation materials, provide targeted suggestions for warehouse management, effectively prolong the service life of the materials, and improve the building energy saving effect.

Description

Technical Field

[0001] This invention relates to the field of building energy conservation technology, specifically to a method for simulating the thermal performance of building exterior wall insulation systems based on BIM. Background Technology

[0002] In the field of building energy conservation, research on the performance of thermal insulation materials is crucial, as it directly relates to building energy consumption, environmental sustainability, and service life. The thermal conductivity and long-term stability of insulation materials in buildings determine their energy-saving effect, while performance degradation during storage significantly impacts their post-construction performance. Currently, industry evaluations of insulation materials largely focus on performance indicators at the time of manufacture, neglecting the influence of storage environment on the long-term performance of materials.

[0003] Existing research and warehousing management methods often focus only on single environmental factors, such as temperature or humidity, lacking comprehensive analysis of the interactions of multiple environmental factors. This approach cannot accurately predict material performance changes under different storage conditions, leading to discrepancies between post-construction thermal performance and expectations, increasing the difficulty of quality control. Furthermore, existing methods struggle to quantify the impact of environmental factors on the material's microstructure when simulating storage environments, thus failing to establish dynamic models of performance degradation. The thermal conductivity and service life of insulation materials are influenced by factors such as temperature, humidity, and ultraviolet radiation in the storage environment, but the interaction mechanisms of these factors remain unclear. Thermal conductivity, as a core indicator of insulation effectiveness, is closely related to the stability of the material's internal pore structure and chemical composition. Long-term exposure to high humidity or ultraviolet radiation can lead to deterioration of the material's internal structure, resulting in increased thermal conductivity and shortened service life. However, current systematic research on how these environmental factors interact and alter the material's microstructure is lacking, making it difficult to accurately establish predictive models for performance degradation. Moreover, the correlation between storage conditions and post-construction thermal performance has not been fully quantified, resulting in a lack of scientific basis for warehousing management and hindering the optimization of material quality control throughout the supply chain. Summary of the Invention

[0004] The purpose of this invention is to provide a BIM-based method for simulating the thermal performance of building exterior wall insulation systems, establish a performance degradation model of insulation materials that comprehensively considers environmental factors such as temperature, humidity, and ultraviolet radiation, quantify their impact on thermal conductivity and service life, and reveal the correlation between storage conditions and post-construction thermal performance.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a BIM-based method for simulating the thermal performance of building exterior wall insulation systems, the method comprising: By constructing a multi-factor environmental simulation database, experimental data of thermal insulation materials under different temperature, humidity and ultraviolet radiation combinations were collected. The interaction of each group of environmental factors was recorded and classified to obtain a preliminary environmental impact dataset. Based on the preliminary environmental impact dataset, the dynamic change curve of the thermal insulation material in the service life prediction is obtained. For the dynamic change curve, the test data of the thermal performance after construction is introduced to verify the model. If the deviation between the verification result and the actual thermal performance exceeds the preset threshold, the model parameters are iteratively optimized to obtain the corrected attenuation model. By using the modified attenuation model, performance attenuation prediction reports are generated under different combinations of storage environment factors, targeted warehouse management suggestions are obtained, and optimized environmental control parameters are determined. Based on the optimized environmental control parameters, an environmental monitoring algorithm is developed to collect and store environmental data in real time. If the monitored value deviates from the preset range, a parameter adjustment command is triggered to obtain a dynamic control scheme.

[0006] Preferably, obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset includes: Based on the preliminary environmental impact dataset, data mining techniques were used to extract features from the correlation between temperature, humidity, ultraviolet radiation, and thermal conductivity trends, and to determine key influencing factors and their weight distribution.

[0007] Preferably, obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset further includes: Based on the key influencing factors and their weight distribution, a performance degradation model based on multivariate regression analysis is constructed to obtain the mapping relationship between model parameters and the trend of thermal conductivity changes, thus obtaining a prediction framework for performance degradation.

[0008] Preferably, obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset further includes: By using a performance degradation prediction framework and combining microscopic scanning data of microstructural changes, we analyze the specific impact of the interaction of environmental factors on the stability of the internal pores and chemical composition of materials, and determine the microstructural degradation law.

[0009] Preferably, obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset further includes: Based on the microstructure degradation law, the parameter settings of the performance degradation model are adjusted, and the results of long-term stability analysis are simulated to obtain the dynamic change curve of the thermal insulation material in the service life prediction.

[0010] Preferably, in the multi-factor environmental simulation database, the temperature simulation range is -20℃ to 60℃, the humidity range is 10% to 95%, and the ultraviolet radiation intensity simulation range is 0.1W / m² to 2.0W / m².

[0011] Preferably, the test data for the thermal performance after construction is verified and analyzed by integrating on-site measurement data from thermal imaging, heat flow meters, and point thermometers through a BIM platform.

[0012] Preferably, the model parameters include the material's initial thermal conductivity, time decay factor, environmental sensitivity coefficient, and structural stability factor, which are jointly adjusted through least squares fitting and gradient descent optimization.

[0013] Preferably, the process of generating a performance degradation prediction report under different combinations of storage environment factors includes: inputting the storage condition parameter set to be evaluated; performing thermal performance prediction calculations using a modified degradation model; plotting a two-dimensional line graph with time as the horizontal axis and thermal conductivity change as the vertical axis to show the performance degradation trend of the material under different conditions; assessing its remaining usable life under current storage conditions by combining the performance degradation rate with the material's design service life; generating environmental control recommendations based on the assessment results, including temperature and humidity setting ranges, shading requirements, and the maximum storage period; and outputting a complete prediction report in a graphical and textual format.

[0014] Preferably, the development environment monitoring algorithm includes defining a warehouse environment monitoring point deployment scheme, with monitoring points covering key areas and configured with high-sensitivity temperature and humidity sensors and ultraviolet sensing modules; establishing a data acquisition and preprocessing mechanism, with a sampling period of no more than 10 minutes, removing outliers and performing linear interpolation to complete the data; setting environmental parameter control ranges, and immediately triggering an alarm mechanism and parameter adjustment instructions if any parameter exceeds the set threshold range.

[0015] As can be seen from the above technical solution, the present invention has the following beneficial effects: This BIM-based method for simulating the thermal performance of building exterior wall insulation systems constructs a multi-factor environmental simulation database, collects experimental data on insulation materials under different combinations of temperature, humidity, and ultraviolet radiation, performs data mining and feature extraction, and identifies key influencing factors and their weight distributions. Based on this, a performance degradation model using multivariate regression analysis is constructed. The impact of environmental factors on the internal structure of the material is analyzed by combining microstructural change data to determine the degradation pattern. By introducing actual test data for model validation and iterative optimization, performance degradation prediction reports under different environments are generated, and an environmental monitoring algorithm is developed to achieve dynamic control. This invention can accurately predict the long-term performance changes of insulation materials, provide targeted suggestions for warehouse management, effectively extend the service life of materials, and improve building energy efficiency. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

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

[0018] like Figure 1 As shown, the present invention provides a technical solution: a BIM-based method for simulating the thermal performance of a building exterior wall insulation system, the method comprising: By constructing a multi-factor environmental simulation database, experimental data on thermal insulation materials under different combinations of temperature, humidity, and ultraviolet radiation were collected. The interaction of each group of environmental factors was recorded and classified to obtain a preliminary environmental impact dataset.

[0019] The core technical solution of this method includes steps such as environmental test system construction, test variable setting, thermal performance testing, data acquisition and recording, and interaction identification and classification. Specifically, the first step is to construct a multi-factor environmental simulation database. Using a high-precision climate test system with controllable temperature, humidity, and ultraviolet radiation capabilities, the following three main control factors and their value ranges are set: Temperature parameters: controlled within a range of -20 to 60 degrees Celsius, with 9 gradients: -20, -10, 0, 10, 20, 30, 40, 50, and 60 degrees Celsius; Relative humidity parameters: controlled within a range of 10% to 90%, with 5 gradients: 10%, 30%, 50%, 70%, and 90%; Ultraviolet radiation intensity parameters: controlled within a range of 0 to 1000 watts per square meter, with 5 gradients: 0, 250, 500, 750, and 1000 watts per square meter. A total of 225 environmental combinations (9×5×5) were generated from these three factors. Each condition was tested independently using 3 insulation material samples to obtain average performance indicators, ensuring the representativeness and repeatability of the results.

[0020] The second step involves collecting experimental data on the thermal performance of the insulation material. Thermal performance measurements were performed using a heat flux meter method. The basic test parameters included sample thickness, temperature difference between the two sides, and heat flux density through the sample. Each sample was processed into a cuboid with standard dimensions of 300 mm × 300 mm × 50 mm. The testing process maintained each combination of conditions for at least 48 hours, with thermal conductivity parameters recorded every 4 hours. The thermal conductivity calculation process is as follows: heat flux density data was collected in watts per square meter (W / m²); temperature difference data was collected, i.e., the temperature difference between the two sides of the sample, in degrees Celsius (°C); sample thickness was recorded in meters (m²); the thermal conductivity calculation formula is: heat flux density × sample thickness ÷ temperature difference, with the result in watts per meter·°C; the thermal resistance calculation formula is: sample thickness ÷ thermal conductivity, in meters²·°C / watt. All experimental data were recorded in real-time by sensors and uploaded to the central server of the experimental platform. Each data entry included a timestamp, environmental parameters, sample number, and the two calculated values ​​mentioned above.

[0021] The third step involves recording and classifying the interactions of environmental factors. To reflect the coupling effects between variables, an orthogonal design strategy was employed to supplement the experimental design, with repeated tests added to some representative combinations to assess synergistic effects. The strength of the interaction is expressed as the ratio of the deviation of thermal conductivity under the three-factor combination to the results of a single factor effect. For example, if the thermal conductivity measured at a combination of 40°C, 70% humidity, and 500 watts of UV radiation per square meter is 0.038, while it is 0.034 at 40°C, 0.035 at 70% humidity, and 0.036 at 500 watts of UV radiation per square meter, then the interaction deviation is 0.038 minus the average of the three individual effects (0.035), which is 0.003, resulting in a relative deviation of 8.6%. This value is used to assess the strength of the interaction.

[0022] The fourth step is the initial construction of the environmental impact dataset. After the experiment, all data are stored according to the following field structure: temperature, humidity, UV intensity, sample number, sample thickness, measurement time, heat flux density, temperature difference, thermal conductivity, thermal resistance, cross-tabulation, and environmental label code. The environmental label code is generated in the format "Txx-Hyy-Uzz", where xx, yy, and zz represent the temperature, humidity, and UV gradient numbers, respectively, for subsequent rapid indexing and retrieval.

[0023] Through the above process, this method constructs a multidimensional, structured experimental dataset, which not only contains basic data on material thermal properties but also includes the coupled response relationships of environmental factors, providing data support for building exterior wall thermal performance simulation systems. The constructed database can be directly embedded into the thermal simulation module of BIM software, and corresponding performance data can be retrieved through environmental tag indexes, improving simulation accuracy and reliability.

[0024] This technical solution significantly improves the accuracy and real-world adaptability of thermal performance simulation for insulation materials. By systematically collecting experimental data under various environmental combinations and recording the interactions between different factors, it can more realistically reflect the impact of actual service conditions on material performance, providing scientific and comprehensive data support for thermal performance analysis in BIM systems and enhancing the reliability and efficiency of building energy-saving design.

[0025] Based on the preliminary environmental impact dataset, data mining techniques were used to extract features from the correlation between temperature, humidity, ultraviolet radiation, and thermal conductivity trends, and to determine key influencing factors and their weight distribution.

[0026] This technical solution uses multivariate data mining analysis to extract features and perform weight analysis on the response relationship between various environmental factors and thermal conductivity in the preliminary environmental impact dataset. The detailed implementation process is as follows: First, the experimental data is normalized. Since temperature, humidity, UV intensity, and thermal conductivity have different dimensions, direct comparison will amplify numerical errors. To eliminate the influence of dimensions, a maximum-minimum standardization method is used to uniformly map the values ​​of all variables to the range of zero to one. The processing method is: subtract the minimum value of the variable from each original value, and then divide by the difference between the maximum and minimum values ​​of the variable. Taking temperature as an example, the original range is -20 degrees Celsius to 60 degrees Celsius; the standardized temperature is the original temperature plus 20 and then divided by 80. Second, a multiple regression model is constructed. In this model, thermal conductivity is the dependent variable, and temperature, humidity, and UV intensity are the independent variables. The regression equation is: thermal conductivity equals temperature multiplied by a coefficient one, plus humidity multiplied by a coefficient two, plus UV intensity multiplied by a coefficient three, plus a constant term. To obtain the coefficients, the principle of minimum sum of squared errors is adopted, and all data points are fitted using the least squares method. In the specific implementation, 80% of the total dataset is randomly divided as the training set, and 20% as the test set. The third step is to calculate the Pearson correlation coefficient. This coefficient measures the strength of the linear correlation between thermal conductivity and each environmental factor. Taking temperature as an example, the average values ​​of the temperature and thermal conductivity arrays are calculated separately. The sum of the products of the differences between each item and the average value is then divided by the product of the standard deviation of temperature and the standard deviation of thermal conductivity, multiplied by the number of data points, to obtain the correlation coefficient between temperature and thermal conductivity. This value ranges from -1 to 1; the larger the absolute value, the stronger the correlation. The fourth step is to calculate the feature weights. The standardized regression coefficients of each variable in the regression model are used as the basis for weighting. The regression coefficients of temperature, humidity, and ultraviolet intensity are divided by the sum of their absolute values, multiplied by 100, and converted to percentage form. For example, if the temperature coefficient is 0.56, humidity is 0.31, and ultraviolet radiation is 0.13, the sum is 1, and the converted weights are 56%, 31%, and 13%, indicating that temperature is the main influencing factor. The fifth step is model fit evaluation. The coefficient of determination is used to evaluate the model's goodness of fit. The total variance, regression variance, and residual variance are calculated. Using the coefficient of determination formula, the result value is between 0 and 1; the closer to 1, the stronger the model's explanatory power. When the coefficient of determination is greater than 0.9, it indicates that the model has strong explanatory and predictive capabilities. The sixth step is outputting the analysis results. The characteristic variables, weights, correlation coefficients, coefficients of determination, and errors at each data point are recorded in the results file, and a three-dimensional thermal conductivity response surface plot and variable weight histogram are automatically generated to assist in the subsequent development of thermal performance simulation algorithms and material compatibility analysis. Through the above steps, the main controlling factors affecting the thermal conductivity of insulation materials under multiple environmental combinations are systematically identified, and quantitative weight distribution results are given, providing technical support for data-driven building thermal simulation and material selection.This method has a clear structure, well-defined parameters, and a reproducible calculation process, making it suitable for embedding into various BIM analysis modules and building energy efficiency optimization systems.

[0027] Based on the key influencing factors and their weight distribution, a performance degradation model based on multivariate regression analysis is constructed to obtain the mapping relationship between model parameters and the trend of thermal conductivity changes, thus obtaining a prediction framework for performance degradation.

[0028] This technical solution, based on extracted key environmental impact factors and their weight distribution, establishes a performance degradation prediction model to achieve trend analysis and quantitative prediction of thermal conductivity changes with environmental exposure time. The model employs a multivariate multinomial regression analysis method, integrating four variables—temperature, humidity, ultraviolet radiation intensity, and time—to construct a mathematical description framework for the thermal performance degradation process.

[0029] The first step is to define the variables. Input variables include temperature (degrees Celsius), humidity (percentage), ultraviolet radiation intensity (watts per square meter), and material exposure time (hours). The output variable is thermal conductivity (watts per meter·degrees Celsius). To improve computational stability, all input and output variables are standardized using a max-min linear normalization method, with transformed values ​​ranging from zero to one.

[0030] The second step is to select the model structure. Considering the nonlinear growth characteristic of thermal conductivity under different environmental conditions, the model adopts a multinomial regression structure, including first-order linear terms, second-order squared terms, and interaction terms. The mathematical form of the regression equation is: thermal conductivity equals the constant term plus the first-order term of each input variable, the squared term of each variable, and the weighted sum of the coefficients of the product terms of any two variables, for a total of no less than fifteen terms, to cover the interaction effects of various variables.

[0031] The third step is model training and parameter determination. The model training employs the least squares fitting method, with the objective function being the sum of the squares of the differences between the predicted and actual values ​​for all samples. Training samples are drawn from a previous experimental database, containing no fewer than one thousand standardized data sets, covering all combinations of temperature, humidity, UV intensity, and exposure time. The optimal coefficient set is solved through batch matrix operations, and the model's generalization ability is evaluated using five-fold cross-validation. Coefficient values ​​are automatically generated by the calculation program, determined based on the principle of minimizing error, and all coefficients are rounded to four decimal places.

[0032] The fourth step is to construct a trend mapping relationship for thermal conductivity. By inputting any combination of environmental conditions and usage duration through a regression equation, the model can automatically output the corresponding thermal conductivity value. For example, assuming a temperature of 40 degrees Celsius, humidity of 70%, ultraviolet radiation of 750 watts per square meter, and exposure time of 1000 hours, the corresponding standardized values ​​can be substituted into the regression equation to calculate the thermal conductivity under the current conditions. This value can then be compared with the initial thermal conductivity to determine the degree of performance degradation.

[0033] The fifth step involves establishing a performance degradation prediction framework. The model is encapsulated as a calculation module and integrated into a building thermal performance simulation platform. Users only need to input local meteorological data and the design service life to generate a curve showing the change in thermal conductivity over time under that environment. The output includes a predicted data table, a thermal conductivity curve, numerical indicators of the degradation rate, and the marginal impact of each variable on the final thermal conductivity.

[0034] Step 6: Evaluate the model's accuracy. Three metrics are used to evaluate the results: coefficient of determination, mean absolute error, and maximum relative error. The coefficient of determination should be higher than 0.95, the mean absolute error should be lower than 3%, and the maximum relative error should not exceed 5%. If the evaluation results are unsatisfactory, the sample size or variable combination structure needs to be readjusted for retraining.

[0035] Through the specific implementation process described above, this solution constructs a complete, reproducible, and highly accurate predictive model for thermal performance degradation. This model not only reflects the combined effects of environmental variables on insulation performance but also provides long-term performance forecasting capabilities for building energy-saving systems, effectively supporting building design, material selection, and maintenance cycle planning. The model possesses strong engineering adaptability and platform integration, making it suitable for most BIM modeling software and energy consumption simulation systems.

[0036] By using a performance degradation prediction framework and combining microscopic scanning data of microstructural changes, we analyze the specific impact of the interaction of environmental factors on the stability of the internal pores and chemical composition of materials, and determine the microstructural degradation law.

[0037] Building upon the previous framework for predicting thermal performance degradation, this technical solution introduces image analysis and compositional analysis of microstructures to establish a quantitative assessment system for the impact of multiple environmental factors on the stability of the material's internal pore structure and main chemical components, thus clarifying the evolution path of microstructure deterioration. The analysis encompasses porosity calculation, extraction of elemental content change ratios, regression model construction, and multi-factor interactive response evaluation, ultimately forming a microstructure deterioration prediction model.

[0038] Step 1: Sample Preparation and Processing. Material samples that had undergone different environmental combinations of exposure cycles in the performance degradation test were selected. Three samples were chosen for each environmental combination, and each sample was processed to a standard size of 10 mm × 10 mm × 5 mm. The surface of each sample was sanded and ultrasonically cleaned, and then dried for scanning electron microscopy and energy dispersive spectroscopy (EDS) analysis.

[0039] Step 2: Microscopic Image Acquisition and Parameter Measurement. A field emission scanning electron microscope with a resolution better than 5 nanometers was used. The accelerating voltage was set to 15 kV, the working distance to 8 mm, and the magnifications were 1000x, 5000x, and 10000x. The image acquisition area was selected in the middle of the sample, 5 mm off the edge, and at least three sets of images were acquired for pore identification.

[0040] Step 3: Porosity Extraction. The image is imported into image analysis software. Pixels with a grayscale value below 50 are identified as porosity regions using a threshold segmentation method. The ratio of the total number of porosity pixels to the total number of pixels in the image is the porosity, expressed as a percentage. Each image is processed three times, and the average value is taken. The resulting porosity value is accurate to two decimal places. For example, if an image has 1,000,000 pixels, and 175,000 are porosity pixels, then the porosity is 17.5%.

[0041] Step 4: Chemical stability assessment. Elemental analysis was performed on the image areas using an energy dispersive spectroscopy (EDS) instrument, detecting the main constituent elements including carbon, oxygen, silicon, calcium, and aluminum. For each element, five measurements were taken in each sample group, recording the mass percentage and calculating the average. Assuming the initial mass percentage of carbon was 23% and decreased to 19% after exposure, the change was calculated as the initial value minus the current value divided by the initial value, yielding a decay rate of 17.4%, representing the degree to which the structural stability of carbon is affected by the environment.

[0042] Step 5: Establish a microstructure regression model. Temperature, humidity, UV radiation intensity, and exposure time are used as input variables, while porosity changes and the decay rates of each major element are used as response variables. A multinomial nonlinear regression model is constructed. Each variable includes a first-order linear term, a squared term, and a cross-product term. The model fitting uses the least squares method, and the training sample size is no less than 1000 sets. The model outputs the regression coefficients of each variable, which are then normalized to convert them into influence weights to identify the dominant degradation factors.

[0043] Step 6, Interaction Analysis. Based on the model parameters, a 3D response surface plot is constructed to show the evolution trends of porosity and elemental stability under different temperature, humidity, and UV combinations. Using sensitivity analysis, with other variables kept constant and only one variable changed, the magnitude of change in the response variable is observed to determine the marginal contribution of single factors and interaction factors to the microscopic degradation of the material.

[0044] Step 7: Extraction of microstructural degradation patterns. Plot porosity and element decay rates separately on a time axis, analyzing their slopes, accelerations, and limiting characteristics. If porosity increases from 8% to 25% within 1000 hours, with a growth rate of 1.75% per 100 hours, it indicates accelerated material structural degradation. If the decay rate of a certain element is low in the first 500 hours of exposure and then significantly increases, it indicates the presence of a critical point for structural failure in the environmental combination.

[0045] Step 8: Results Output and Model Integration. All model parameters, image analysis results, chemical change data, and prediction curves are integrated to form a microstructural degradation database. This module is then connected to a BIM platform or building operation and maintenance system to achieve visualized prediction of structural degradation based on real environmental conditions.

[0046] Through the above eight steps, this scheme extends from macroscopic thermal performance degradation to microscopic structural evolution, realizing quantitative modeling and prediction of the entire process of material performance loss and structural failure under complex environmental conditions, providing theoretical basis and practical tools for the design and durability evaluation of building energy-saving materials.

[0047] Based on the microstructure degradation law, the parameter settings of the performance degradation model are adjusted, and the results of long-term stability analysis are simulated to obtain the dynamic change curve of the thermal insulation material in the service life prediction.

[0048] This technical solution integrates microstructural degradation data with macroscopic thermal conductivity evolution trends, optimizes the variable structure and regression parameter configuration of the performance degradation model, and achieves dynamic prediction and visualization curve output of the thermal performance of insulation materials over long-term service life. This process, based on the evolution values ​​of micropores and the rate of change of major chemical components, forms a coupled structural thermal performance prediction system through mathematical modeling and data fitting.

[0049] Step 1: Parameter Preparation and Dataset Construction. Based on existing microstructure analysis data, four time points were selected: 100 hours, 300 hours, 600 hours, and 1000 hours, corresponding to porosities of 8.5%, 12.7%, 18.4%, and 25.1%, respectively; and carbon mass percentages of 23.0%, 21.1%, 19.3%, and 18.0%, respectively. Porosity was designated as variable P, and element content as variable C, serving as additional input variables for subsequent modeling.

[0050] Step 2: Data Standardization. To avoid inconsistencies in the dimensions of variables, all variables are normalized using maximum and minimum values. Taking porosity as an example, with a maximum value of 25.1% and a minimum value of 8.5%, the standardized value is equal to the current value minus 8.5, divided by 16.6. For example, with 18.4%, the standardized P-value is 0.598. All porosity and carbon content change rate data are processed in the same way to form a dimensionless data input set.

[0051] Step 3: Expand the variable set of the regression model. Add two input variables to the original model: standardized porosity P and standardized elemental stability index C. The original model had four input variables: temperature, humidity, ultraviolet radiation, and time. Adding the new variables brings the total to six. Construct a multinomial regression model containing first-order terms, squared terms, and pairwise interaction terms, with a total of at least 20 variables.

[0052] Step 4: Model Training and Coefficient Calculation. A least squares error optimization algorithm is used to iteratively adjust the coefficients of each variable across 1500 sets of sample data to ensure minimal prediction error. Taking the linear coefficient of porosity P as an example, assuming a regression solution yields a value of 0.1347, this means that for every 0.1 standardized unit increase in P, thermal conductivity will increase by approximately 0.0135 watts per meter·degree Celsius. Each coefficient is accurate to four decimal places to ensure model stability and accuracy.

[0053] Step 5: Simulate long-term environmental conditions. A typical service environment is set: temperature 40 degrees Celsius, humidity 70%, and UV intensity 750 watts per square meter. Exposure time ranges from 100 hours to 10,000 hours, with a step size of 100 hours. P and C values ​​are interpolated and predicted using existing micro-degradation curves, constructing a combination of six input variables for each time point.

[0054] Step 6: Dynamically predict thermal conductivity. Substitute each set of input variables into the regression model to calculate the corresponding thermal conductivity value at each time point. For example, assuming the P value is 0.72 and the C value is 0.63 at 2000 hours, and other variables are taken as standard values ​​and substituted into the model, the predicted thermal conductivity value is 0.0427 watts per meter·degree Celsius. This process is repeated to obtain the complete thermal conductivity variation sequence.

[0055] Step 7: Lifetime Assessment and Curve Generation. Assume the maximum permissible thermal conductivity of the material is 0.045 W / m·°C. The time point at which the dynamic curve first reaches this value is considered the lifetime boundary. If the thermal conductivity first exceeds this value at 8200 hours, the expected service life of the material under this environment is recorded as 8200 hours. Generate a thermal conductivity versus time curve from all data. The curve clearly distinguishes the stable phase, the accelerated degradation phase, and the critical point.

[0056] Step 8: Output Report and System Integration. The system automatically outputs: a complete time-series thermal conductivity data table, standardized P and C evolution curves, thermal conductivity variation graphs, lifetime boundary judgment values, and variable influence weighting analysis results. This model can be packaged as a component and integrated into a BIM energy consumption simulation platform for comparative analysis of material stability in different building projects.

[0057] Through the above eight steps, this solution reconstructs a performance degradation prediction model based on microstructure degradation data, enabling refined and dynamic prediction of material thermal conductivity. The model has clear logic, well-defined parameters, and a traceable path, making it suitable for long-term stability analysis of various insulation materials and environmental scenarios. It provides quantifiable decision-making support for engineering design, material selection, and lifespan assessment.

[0058] In the multi-factor environmental simulation database, the temperature simulation range is -20℃ to 60℃, the humidity range is 10% to 95%, and the ultraviolet radiation intensity simulation range is 0.1w / ㎡ to 2.0w / ㎡.

[0059] The core principle of this method lies in constructing a high-precision, multi-variable controllable environmental simulation database during the BIM-based simulation of the thermal performance of building exterior wall insulation systems. This database supports research on the thermal performance response of materials in actual use environments. The database uses three key environmental factors as basic variables: temperature, humidity, and ultraviolet radiation intensity, with the following ranges set: Temperature simulation range: -20℃ to 60℃: This range covers extreme temperature conditions that may occur in different seasons across most climate zones in my country, from the lowest winter temperatures in frigid regions (such as parts of Heilongjiang) to the peak summer building surface temperatures in tropical regions (such as cities on Hainan Island), comprehensively reflecting the thermal conductivity response characteristics of insulation materials under conditions such as low-temperature thermal shrinkage and high-temperature aging. Humidity simulation range: 10% to 95%: By setting environmental conditions from extremely dry to extremely humid, the weakening effect of dry air on the thermal conductivity of materials, the trend of decreasing thermal resistance after moisture adsorption, and the impact of possible material structural expansion and increased moisture content on thermal performance under high humidity conditions can be effectively simulated. The simulated ultraviolet (UV) irradiation intensity ranges from 0.1 watts per square meter to 2.0 watts per square meter. UV radiation is a key external factor affecting the surface stability and structural integrity of thermal insulation materials. The simulated intensity covers the background UV level in the indoor diffused environment to the maximum radiation value under strong sunlight conditions on a south-facing outdoor facade in summer, which can be used to analyze issues such as surface aging, color changes, and changes in interfacial thermal resistance. The above three environmental factors are independently controlled through a high-precision environmental chamber and combined to form a complete cross-environmental variable matrix. Under each set of variables, the response data of the corresponding thermal insulation material sample in terms of thermal conductivity, thermal diffusivity, and thermal resistance are recorded, while the cumulative influence trend of environmental variables on material performance is also recorded. The data recording not only covers the instantaneous thermal performance parameters under static conditions but also includes the decay process of thermal performance over time during long-term exposure, which facilitates the subsequent construction of a three-dimensional fitting model of time-environment-thermal performance for dynamic prediction modules in building thermal energy simulation. This setting provides basic support for realizing refined modeling of exterior wall thermal performance based on real environmental boundary conditions within the BIM system, making the simulation results closer to actual building operation and more meaningful for engineering guidance.

[0060] The test data of thermal performance after construction are verified and analyzed by integrating on-site measurement data from thermal imaging, heat flow meters, and point thermometers through the BIM platform.

[0061] The core principle of this technical solution is to integrate multi-source thermal performance field test data into the BIM platform to conduct on-site verification and analysis of the thermal performance of the building exterior wall insulation system after construction, thereby verifying the prediction accuracy of the simulation model and dynamically correcting its parameters, thus improving the credibility and prediction accuracy of the BIM model in actual engineering applications.

[0062] The specific implementation process is as follows: Step 1, after construction is completed, thermal performance testing equipment is deployed on the building site, including three types of equipment: thermal imagers, heat flow meters, and spot thermometers. Thermal imagers are used to acquire two-dimensional thermal distribution images of the exterior wall surface, enabling rapid identification of areas that may cause abnormal thermal performance, such as thermal bridges, insulation layer delamination, and material detachment. Their operating parameters are set to a resolution of no less than 640 pixels × 480 pixels, a temperature detection accuracy of ±0.5℃, and a measurement distance of 3 to 10 meters. Heat flow meters are used to measure the actual heat flux density of the insulation system wall surface, sampling every 5 seconds for at least 48 hours to obtain heat flux changes under diurnal temperature variations. Spot thermometers are used to obtain the temperature difference between indoor and outdoor wall surfaces, calculating local thermal resistance by simultaneously recording the indoor and outdoor surface temperatures and combining them with heat flux density data. Step 2, after data acquisition, the original measurement data from the three types of equipment are uniformly numbered according to timestamps and imported into the BIM platform data module through a data interface. The platform features data fusion capabilities, enabling the structured processing of image and numerical data and its binding with wall information within the BIM building component model. Step 3 compares the output values ​​of the BIM simulation model with on-site data. The system automatically extracts the predicted heat flux density value from the model and calculates the error point-by-point between the predicted and actual measured values. The error calculation method is model value minus on-site value divided by the on-site value, converted into a relative error percentage. For example, if the heat flux density of a wall segment model is 18.2 W / m², and the on-site measurement is 19.0 W / m², the error is -4.21%. Step 4 generates a difference assessment report. For wall components with errors exceeding a set threshold (e.g., ±5%), the system automatically marks them as high-difference areas and visualizes them in the BIM platform using color layers, assisting designers in determining whether model adjustments are needed. Step 5 provides feedback to correct simulation parameters. For identified error areas, the system reverse-engineers material thermal resistance, thickness, or construction deviation parameters and recalculates the corresponding thermal performance parameters. The platform can automatically call thermal data from other similar components in the database for parameter substitution analysis and reconstruct the simulation results.

[0063] Through the above five steps, a closed-loop thermal performance evaluation mechanism was constructed, encompassing on-site testing, BIM model verification, and parameter callback. This method not only ensures the authenticity of the actual performance of the insulation system but also enables the BIM model to have dynamic correction and data-driven optimization capabilities, providing a solid technical foundation for subsequent building energy efficiency analysis and operation and maintenance management.

[0064] The model parameters include the initial thermal conductivity of the material, the time decay factor, the environmental sensitivity coefficient, and the structural stability factor, which are adjusted jointly by least squares fitting and gradient descent optimization.

[0065] The core principle of this technical solution lies in using a parametric thermal performance model in the BIM-based simulation of the thermal performance of building exterior wall insulation systems. This model combines on-site data with historical experimental data, and employs mathematical fitting and optimization algorithms to jointly adjust model parameter values, thereby improving simulation accuracy and prediction stability. The model parameters used include: the initial thermal conductivity of the material, the time decay factor, the environmental sensitivity coefficient, and the structural stability factor. Each parameter has a physical meaning and can be determined through regression calculations using actual data.

[0066] The specific implementation process is as follows: Step 1, parameter setting and explanation. Initial thermal conductivity represents the thermal conductivity of the material under standard conditions (i.e., room temperature, dry environment, and no ultraviolet radiation) without the influence of aging, and the unit is watts per meter·degree Celsius; the time decay factor is used to quantify the rate at which thermal conductivity increases with the increase of usage time, reflecting the thermal aging effect of the material; the environmental sensitivity coefficient is used to describe the degree of response of external environment such as temperature, humidity, and ultraviolet radiation to changes in thermal conductivity, and the larger the value, the higher the sensitivity; the structural stability factor represents the ability of the material structure to maintain its original performance state under multi-factor environment, reflecting its resistance to deformation, delamination, and pore expansion. Step 2, constructing a thermal performance simulation function. The above four parameters are used as input variables to construct a nonlinear thermal conductivity evolution function, with time and environmental variables as input dimensions, and outputting the predicted value of thermal conductivity of the insulation material under specific conditions. The model structure includes first-order linear terms, quadratic terms, and parameter interaction terms to ensure sufficient expressive power. Step 3, collecting a training dataset. The measured values ​​of thermal conductivity at different time points and under different environmental combinations are extracted from the experimental database and field test data to form a training dataset. Each data point includes the actual thermal conductivity value and corresponding time and environmental variable information, with a total sample size of no less than 1500 sets. Step 4: Preliminary parameter fitting using the least squares method. Using the actual measured thermal conductivity as the target value, the sum of squared residuals between the model's predicted value and the target value is minimized to solve for the initial parameter estimates. Base values ​​for the four parameters are calculated using linear regression or nonlinear least squares methods, serving as initial values ​​for subsequent optimization. Step 5: Introducing the gradient descent optimization algorithm. Using the least squares fitting results as initial values, a loss function is constructed, and the gradient direction for each parameter is calculated. The parameter values ​​are iteratively updated according to the set learning rate. After each iteration, the change in prediction error is calculated. If the error decreases by less than 0.01% or there is no significant optimization after 10 consecutive iterations, the iteration is terminated, and the optimal parameter set is output. Step 6: Model evaluation and parameter output. The final parameter values ​​are substituted into the thermal conductivity prediction model, and error analysis is performed on the test dataset. The model's average absolute error should be controlled within 5%, and the coefficient of determination should be no less than 0.95, ensuring that the fitting accuracy meets engineering requirements. Ultimately, the four parameter values ​​are stored in a structured form in the BIM model database, which can be reused among different building components.

[0067] Through the above process, this scheme achieves automated, data-driven updates of key parameters for thermal performance simulation by jointly optimizing least-squares fitting and gradient descent, ensuring the model has stronger adaptability and generalization ability. Furthermore, the parameter system's setting method and fitting mechanism are physically interpretable, facilitating modular application and engineering implementation in different projects.

[0068] The process of generating a performance degradation prediction report under different combinations of storage environment factors includes inputting the storage condition parameter set to be evaluated, performing thermal performance prediction calculations using a modified degradation model, plotting a two-dimensional line graph with time as the horizontal axis and thermal conductivity change as the vertical axis to show the performance degradation trend of the material under different conditions, assessing its remaining usable life under current storage conditions by combining the performance degradation rate with the material's design service life, generating environmental control recommendations based on the assessment results, including temperature and humidity setting ranges, shading requirements, and the maximum storage period, and outputting a complete prediction report in a graphical and textual format.

[0069] This technical solution is based on an established thermal performance degradation prediction model. Combined with specific storage environment parameters input by the user, it uses a BIM platform to simulate the thermal performance changes of insulation materials during storage, thereby achieving quantitative prediction and visualization of performance degradation trends. Based on the calculation results, it outputs the remaining usable life and environmental control suggestions to support refined management and scientific decision-making of materials during the storage stage.

[0070] The specific implementation process includes the following steps: Step 1, Input storage condition parameter set. The user inputs the storage environment information to be evaluated through the BIM platform. The parameters include: ambient temperature (in degrees Celsius, supported range: -20 to 60), relative humidity (in percentage, supported range: 10% to 95%), ultraviolet radiation intensity (in watts per square meter, supported range: 0.1 to 2.0), and planned storage period (in days or hours). The platform verifies the validity of the input parameters to ensure they fall within the model's calculable range. Step 2, Thermal performance prediction calculation. The system automatically calls the performance degradation model obtained in the previous stage through least squares fitting and gradient descent optimization, uses the user-input storage parameters as input conditions, and sets a time series variable as the simulation time axis (e.g., 0 days to 360 days, with a step size of 1 day), calculating the change in thermal conductivity of the material under the storage conditions on a daily basis, in watts per meter·degree Celsius. The system generates a data array based on the relationship between thermal conductivity and time for subsequent analysis and chart plotting. Step 3, Plot performance degradation trend graph. The system generates a two-dimensional line graph with time (in days) on the x-axis and thermal conductivity (in watts per meter·degree Celsius) on the y-axis, visually reflecting the deterioration trend of the material's thermal performance over time under the current storage environment. The graph displays the predicted thermal conductivity change as a curve, automatically marking the initial value, inflection point, and critical time point for reaching the performance limit. Step 4: Assess remaining usable lifespan. The system pre-stores the material's design thermal conductivity upper limit threshold (e.g., 0.045 watts per meter·degree Celsius) for lifespan determination. The system finds the time point in the predicted data corresponding to the first time the thermal conductivity reaches or exceeds this threshold; this is the material's remaining usable lifespan under the current storage conditions. If the threshold is not reached within the set storage period, "Safe to store" is output; if the threshold is exceeded, "Risk of exceeding the expiration date" is displayed. Step 5: Generate environmental control recommendations. Based on the material's sensitivity to different environmental parameters and the main influencing factors in the current forecast, the system outputs customized storage recommendations, specifically including: recommended temperature range (e.g., 25 to 28 degrees Celsius); recommended relative humidity range (e.g., 50% to 65%); shading requirements (e.g., "recommended shading rate not less than 90%)"; and maximum recommended storage period (e.g., "not exceeding 180 days"). Step 6: Output a graphical forecast report. The system integrates the above input parameters, forecast data, attenuation curves, lifetime assessment conclusions, and environmental recommendations into a complete graphical forecast report, which can be exported as PDF or image format for project archiving, management reporting, or decision-making reference. The report has a clear structure and includes: a basic information summary, a description of the forecast model, detailed parameter settings, a display of calculation results, and recommended solutions and technical basis explanations.

[0071] Through the above six steps, this solution effectively simulates and assesses the thermal performance of insulation materials throughout the entire storage process. Its working principle, with clear parameter definitions, standardized calculation methods, and a clear graphical output structure, ensures that the assessment conclusions are scientifically sound, and the results are visible and controllable, meeting the quality control requirements for building energy-saving materials during storage and distribution.

[0072] The development of the environmental monitoring algorithm includes defining a warehouse environment monitoring point deployment scheme, with monitoring points covering key areas and equipped with high-sensitivity temperature and humidity sensors and ultraviolet sensing modules; establishing a data acquisition and preprocessing mechanism, with a sampling period of no more than 10 minutes, removing outliers and performing linear interpolation to complete the data; setting environmental parameter control ranges, and immediately triggering an alarm mechanism and parameter adjustment instructions if any parameter exceeds the set threshold range.

[0073] This technical solution integrates environmental monitoring algorithms into the BIM platform to continuously and dynamically monitor the temperature, humidity, and ultraviolet radiation of the environment in which building exterior wall insulation materials are stored. It enables real-time acquisition, anomaly identification, automatic intervention, and historical record tracing of key environmental parameters, thereby ensuring that the insulation materials maintain stable performance and do not deteriorate during storage, and providing thermal performance assurance for subsequent construction and application.

[0074] The specific implementation process is as follows: Step 1, define the layout plan for warehouse environmental monitoring points. Within the warehouse, based on factors such as material stacking areas, ventilation flow, and natural lighting conditions, plan the locations of environmental monitoring points. The general principle is: one basic monitoring point is set up for every 200 square meters, with independent monitoring units set up in warehouse corners, doorways, near windows, and densely stacked areas to ensure that changes in spatial heat and humidity distribution can be detected throughout the entire area. The monitoring point configuration includes: a high-sensitivity temperature and humidity sensor with a temperature accuracy of ±0.2℃ and a humidity accuracy of ±2%; and an ultraviolet sensing module with a response range of 0.1 to 2.5 watts per square meter and an accuracy of ±0.05 watts per square meter.

[0075] Step 2: Establish a data acquisition and preprocessing mechanism. Three types of environmental data are collected at each monitoring point: temperature, relative humidity, and ultraviolet irradiance. The sampling period is set to no more than 10 minutes, with a default sampling interval of 5 minutes. All data is recorded with timestamps. To improve data quality, the system automatically performs the following processing: outlier identification; if a single-point collected value deviates from the values ​​of the preceding and following sets by more than a set ratio (default ±30%), it is judged as an anomaly and removed; missing values ​​are filled using linear interpolation to ensure continuous data for subsequent calculations and analysis; all data is synchronously uploaded to the BIM database and bound to the warehouse component nodes to achieve model association.

[0076] Step 3: Set environmental parameter control thresholds. Based on material technical manuals and experimental data, the system presets recommended environmental parameter control ranges for different materials. Taking commonly used polystyrene insulation boards as an example: temperature control range is 15℃ to 30℃; humidity control range is 40% to 70%; ultraviolet irradiance upper limit is no higher than 0.8 watts per square meter. If the user inputs the material model, the system automatically loads its matching parameter threshold group. The thresholds are set based on the following principles: the temperature threshold is determined according to the "starting point of significant increase in performance degradation rate" in the material's thermal aging test data, with the upper limit set not exceeding this starting temperature; the humidity threshold is determined based on both the material's moisture absorption critical point and the inflection point of thermal resistance decrease, ensuring that high humidity environments do not cause a sudden increase in thermal conductivity; the ultraviolet irradiance upper limit is conservatively set at 20% lower than the "initiation intensity of surface structure deformation".

[0077] Step 4: Trigger the alarm mechanism and control commands. The system performs data analysis every 5 minutes. If any parameter at any monitoring point exceeds the set threshold, the following mechanisms are immediately triggered: the abnormal point is highlighted in red at the corresponding location on the BIM interface; an alarm message is automatically pushed to the duty terminal and the mobile devices of management personnel; if the system is connected to environmental control equipment (such as fans, humidifiers, and sunshades), control commands are automatically sent to respond and adjust, such as starting the sunshade roller blinds, turning on the dehumidifier, or cooling the air conditioner; at the same time, the abnormal data and the entire response action process are recorded and a log is generated for subsequent traceability analysis.

[0078] Through the aforementioned complete process, the environmental monitoring algorithm constructs a full-link, visualized, and automated closed-loop environmental quality control system. This ensures that the environment for insulation materials remains within a controllable range during storage, preventing unplanned thermal performance degradation and laying a reliable foundation for the later performance of the building insulation system. This mechanism boasts advantages such as strong real-time performance, scientifically sound thresholds, and high integration, making it suitable for operation and maintenance management scenarios in storage facilities of varying sizes.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A BIM-based method for simulating the thermal performance of building exterior wall insulation systems, characterized in that, The method includes: By constructing a multi-factor environmental simulation database, experimental data of thermal insulation materials under different temperature, humidity and ultraviolet radiation combinations were collected. The interaction of each group of environmental factors was recorded and classified to obtain a preliminary environmental impact dataset. Based on the preliminary environmental impact dataset, the dynamic change curve of the thermal insulation material in the service life prediction is obtained. For the dynamic change curve, the test data of the thermal performance after construction is introduced to verify the model. If the deviation between the verification result and the actual thermal performance exceeds the preset threshold, the model parameters are iteratively optimized to obtain the corrected attenuation model. By using the modified attenuation model, performance attenuation prediction reports are generated under different combinations of storage environment factors, targeted warehouse management suggestions are obtained, and optimized environmental control parameters are determined. Based on the optimized environmental control parameters, an environmental monitoring algorithm is developed to collect and store environmental data in real time. If the monitored value deviates from the preset range, a parameter adjustment command is triggered to obtain a dynamic control scheme.

2. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 1, characterized in that: The process of obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset includes: Based on the preliminary environmental impact dataset, data mining techniques were used to extract features from the correlation between temperature, humidity, ultraviolet radiation, and thermal conductivity trends, and to determine key influencing factors and their weight distribution.

3. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 2, characterized in that: The step of obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset also includes: Based on the key influencing factors and their weight distribution, a performance degradation model based on multivariate regression analysis is constructed to obtain the mapping relationship between model parameters and the trend of thermal conductivity changes, thus obtaining a prediction framework for performance degradation.

4. The method for simulating the thermal performance of a BIM-based building exterior wall insulation system according to claim 3, characterized in that: The step of obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset also includes: By using a performance degradation prediction framework and combining microscopic scanning data of microstructural changes, we analyze the specific impact of the interaction of environmental factors on the stability of the internal pores and chemical composition of materials, and determine the microstructural degradation law.

5. The method for simulating the thermal performance of a BIM-based building exterior wall insulation system according to claim 4, characterized in that: The step of obtaining the dynamic change curve of the thermal insulation material in the service life prediction based on the preliminary environmental impact dataset also includes: Based on the microstructure degradation law, the parameter settings of the performance degradation model are adjusted, and the results of long-term stability analysis are simulated to obtain the dynamic change curve of the thermal insulation material in the service life prediction.

6. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 1, characterized in that: The multi-factor environmental simulation database includes a temperature simulation range of -20℃ to 60℃, a humidity range of 10% to 95%, and an ultraviolet radiation intensity simulation range of 0.1w / ㎡ to 2.0w / ㎡.

7. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 1, characterized in that: The test data of the thermal performance after construction are verified and analyzed by integrating on-site measurement data from thermal imaging, heat flow meters, and spot thermometers through the BIM platform.

8. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 1, characterized in that: The model parameters include the material's initial thermal conductivity, time decay factor, environmental sensitivity coefficient, and structural stability factor, which are adjusted jointly through least squares fitting and gradient descent optimization.

9. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 1, characterized in that: The process of generating a performance degradation prediction report under different combinations of storage environment factors includes inputting the storage condition parameter set to be evaluated, using the modified degradation model to perform thermal performance prediction calculations, and drawing a two-dimensional line graph with time axis as the horizontal axis and thermal conductivity change as the vertical axis to show the performance degradation trend of the material under different conditions. By combining the performance degradation rate with the material's design service life, the remaining usable life under current storage conditions is assessed; based on the assessment results, environmental control recommendations are generated, including temperature and humidity setting ranges, shading requirements, and maximum storage period, and a complete prediction report is output in graphic and textual form.

10. The method for simulating the thermal performance of a building exterior wall insulation system based on BIM according to claim 1, characterized in that: The developed environment monitoring algorithm includes defining a warehouse environment monitoring point deployment scheme, with monitoring points covering key areas and configured with high-sensitivity temperature and humidity sensors and ultraviolet sensing modules; establishing a data acquisition and preprocessing mechanism, with a sampling period of no more than 10 minutes, removing outliers and performing linear interpolation to complete the data; setting environmental parameter control ranges, and immediately triggering an alarm mechanism and parameter adjustment instructions if any parameter exceeds the set threshold range.

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