A method and system for predicting loss of lightweight building materials

Abstract

The present application provides a method and system for predicting the loss of lightweight building materials. The method comprises analyzing and processing the long-term changing trends of the environment in which the lightweight building materials are located to obtain analysis results; based on the analysis results, identifying key environmental factors that have a significant impact on the aging of lightweight building materials, and obtaining the potential loss risk level of the building materials; based on the potential loss risk level, and using time series analysis technology to mine the patterns of historical data extracted from maintenance records, user feedback, and historical case libraries, generating a performance degradation prediction curve; and according to the performance degradation prediction curve, comprehensively considering the functional requirements, economic costs, and safety standards of the building, formulating a maintenance plan and replacement schedule for lightweight building materials. The technical solution provided by the present application significantly improves the aging prediction accuracy and maintenance efficiency of lightweight building materials.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of loss prediction, and in particular to a method and system for predicting loss of lightweight building materials. Background Art

[0002] Lightweight building materials are widely used in modern architecture due to their light weight and ease of construction, particularly in high-rise buildings, bridges, and temporary structures. However, the aging of these materials under varying environmental conditions seriously impacts their service life and safety. Therefore, a method that can accurately predict material aging and develop scientific maintenance strategies is urgently needed to ensure the safety and economic efficiency of buildings.

[0003] Currently, aging prediction and maintenance strategies for lightweight building materials rely primarily on laboratory accelerated aging tests and statistical analysis of limited historical data. These methods typically involve subjecting material samples to accelerated aging under extreme conditions, combined with empirical evaluations based on maintenance records and user feedback from existing buildings. Additionally, some studies have attempted to estimate material aging rates using simple mathematical models.

[0004] Existing solutions have the following major flaws: laboratory accelerated aging tests cannot fully simulate the complex conditions in actual use environments, and statistical analysis of historical data often lacks sufficient time span and diversity, resulting in inaccurate prediction results; existing maintenance strategies are mostly static solutions that fail to be updated in real time based on actual usage and the latest performance data, making it difficult to adapt to changing environmental conditions and usage needs; existing methods do not adequately identify the key environmental factors that affect material aging, and cannot effectively distinguish which factors are the main drivers of aging, thus affecting the effectiveness and pertinence of maintenance measures. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for predicting the loss of lightweight building materials, which are used to solve the problems in the prior art of inaccurate aging prediction and inadequate maintenance measures caused by data limitations and lack of dynamic adjustment.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting loss of lightweight building materials, comprising:

[0007] Collecting high-resolution images of environmental parameters surrounding the building and the physical properties and surface conditions of lightweight building materials in the building, and analyzing and processing the long-term change trend of the environment in which the lightweight building materials are located by combining a pre-established geographic information system and a climate model to obtain analysis results;

[0008] Based on the analysis results, an accelerated aging simulation is performed on the aging process of lightweight building materials under different environmental conditions, and a decision tree algorithm in machine learning is applied to calculate the expected aging rate of the lightweight building materials. The contribution of each environmental factor in the analysis results to the aging of the lightweight building materials is analyzed through aging sensitivity analysis technology, and the key influencing factors among the environmental factors are identified to obtain the potential loss risk level;

[0009] Based on the potential loss risk level, combined with the building's maintenance records, user feedback information, and a historical case library of similar building materials during its service life, grey system theory modeling is applied to estimate the long-term performance degradation trend of lightweight building materials under actual use conditions. Time series analysis technology is used to mine historical data patterns extracted from the building's maintenance records, user feedback information, and a historical case library of similar building materials during its service life to generate a performance degradation prediction curve.

[0010] According to the performance degradation prediction curve, and taking into account the functional requirements, economic costs and safety standards of the building, a maintenance plan and replacement schedule for lightweight building materials are formulated.

[0011] Optionally, based on the analysis results, an accelerated aging simulation is performed on the aging process of lightweight building materials under different environmental conditions, and a decision tree algorithm in machine learning is applied to calculate the expected aging rate of the lightweight building materials. The contribution of each environmental factor in the analysis results to the aging of the lightweight building materials is analyzed using an aging sensitivity analysis technique, and key influencing factors among the environmental factors are identified to obtain a potential loss risk level, including:

[0012] Using the analysis results, an accelerated aging simulation is performed on the aging process of lightweight building materials under different environmental conditions to generate aging process data;

[0013] Based on the aging process data, a decision tree algorithm in machine learning is applied to analyze and process the trend of the lightweight building material properties obtained from the aging process data over time to obtain an expected aging rate of the lightweight building material;

[0014] Based on the expected aging rate of the lightweight building materials, the contribution of various environmental factors to the aging of the lightweight building materials is quantified through aging sensitivity analysis technology, key influencing factors are identified, and an aging sensitivity report is generated;

[0015] The aging sensitivity report is used to comprehensively evaluate the aging conditions of lightweight building materials under various environmental conditions in the analysis results to obtain a potential loss risk level.

[0016] Optionally, based on the expected aging rate of the lightweight building material, the contribution of each environmental factor to the aging of the lightweight building material is quantified by using an aging sensitivity analysis technology, key influencing factors are identified, and an aging sensitivity report is generated, including:

[0017] Utilizing the expected aging rate of the lightweight building material, combined with various performance change records collected from lightweight building materials in actual use and aging prediction data of lightweight building materials under various hypothetical conditions, multiple regression analysis or machine learning models are applied to quantify the contribution of each environmental factor to the aging of the lightweight building material, thereby obtaining quantitative results of the impact of environmental factors;

[0018] Based on the quantitative results of the environmental factors, a multi-factor comprehensive evaluation model is established, which takes into account the interaction between the environmental factors and their comprehensive effects on the aging of lightweight building materials, and obtains the output of the multi-factor comprehensive evaluation model;

[0019] Based on the output of the multi-factor comprehensive assessment model, the importance of each environmental factor is analyzed through aging sensitivity analysis technology, key influencing factors that have a significant impact on the aging of lightweight building materials are identified, and a list of key influencing factors is generated;

[0020] By using the list of key influencing factors, integrating all analysis results, compiling a detailed quantitative report on the impact of environmental factors and an analysis of key influencing factors, and forming an aging sensitivity report.

[0021] Optionally, the aging sensitivity report is used to comprehensively evaluate the aging conditions of lightweight building materials under various environmental conditions in the analysis results to obtain a potential loss risk level, including:

[0022] Using the key influencing factors and quantitative results in the aging sensitivity report, an assessment framework is constructed to comprehensively evaluate the aging possibility and degree of lightweight building materials under different environmental conditions, thereby generating an environmental condition aging assessment framework.

[0023] Based on the environmental condition aging assessment framework, multiple possible usage scenarios are created, and the aging performance of lightweight building materials under these scenarios is simulated through computer simulation or experimental verification to obtain scenario simulation analysis results;

[0024] Based on the results of the scenario simulation analysis, a risk assessment model is used to quantitatively assess the aging degree of lightweight building materials under various environmental conditions, and the aging risk is determined by the aging degree to generate an aging risk assessment report;

[0025] The aging risk assessment report is used to optimize and adjust the assessment framework to obtain the potential loss risk level.

[0026] Optionally, based on the potential loss risk level, in combination with the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials, grey system theory modeling is applied to estimate the long-term performance degradation trend of lightweight building materials under actual use conditions, and time series analysis technology is used to mine historical data patterns extracted from the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials to generate a performance degradation prediction curve, including:

[0027] Using the potential loss risk level, maintenance records, user feedback information, and historical case libraries of similar lightweight building materials during the building's lifecycle are integrated to obtain a multi-source information integration report.

[0028] Based on the multi-source information integration report, the grey system theory is applied to model the long-term performance degradation trend of lightweight building materials under actual use conditions to obtain a grey system model;

[0029] Based on the grey system model, the performance degradation of lightweight building materials in the future is estimated and a preliminary performance degradation trend prediction result is obtained;

[0030] Using the preliminary performance degradation trend prediction results and employing time series analysis technology, historical data patterns are mined from the multi-source information integration report to generate a historical data pattern analysis report;

[0031] Based on the comprehensive processing of the historical data regularity analysis report and the estimation results of the grey system model, an estimation curve reflecting the degradation of the performance of lightweight building materials over time is drawn to generate a performance degradation estimation curve.

[0032] Optionally, the method utilizes the preliminary performance degradation trend prediction result and employs time series analysis technology to mine historical data patterns from the multi-source information integration report to generate a historical data pattern analysis report, including:

[0033] Using the preliminary performance degradation trend prediction results, the performance changes of the lightweight building materials in different time periods are organized into an ordered time series data set to obtain a time series data set;

[0034] According to the time series data set, applying statistical methods or machine learning techniques to extract features and preprocess the time series in the time series data set, perform smoothing and remove outliers to obtain a preprocessed time series data set;

[0035] Based on the preprocessed time series data set, time series analysis technology is used to perform pattern recognition and regularity mining on the periodicity, trend and random fluctuation components in the historical data formed after preprocessing, and obtain pattern recognition results.

[0036] Utilizing the pattern recognition results, combined with maintenance records, user feedback, and a case library in the multi-source information integration report, the correlation between different factors affecting the performance degradation of lightweight building materials is analyzed to obtain a correlation analysis report;

[0037] Based on the pattern recognition results and correlation analysis report, comprehensive processing is performed to generate a historical data regularity analysis report.

[0038] Optionally, formulating a maintenance plan and replacement schedule for lightweight building materials based on the performance degradation prediction curve and taking into account the functional requirements, economic costs, and safety standards of the building includes:

[0039] Using the performance degradation prediction curve, the requirements for the performance of lightweight building materials at different stages of use of buildings and the safety standards related to lightweight building materials are analyzed, the functional requirements and safety standards of buildings are evaluated, and key performance indicators and safety thresholds are obtained;

[0040] Performing a cost-benefit analysis based on the performance degradation prediction curve and the economic cost of the building during its life cycle to obtain a cost-benefit analysis report;

[0041] Based on the cost-benefit analysis report, develop a preliminary maintenance plan and replacement schedule for different types of lightweight building materials;

[0042] Using the preliminary maintenance plan and replacement schedule, a dynamic adjustment mechanism is designed to allow real-time updates of maintenance strategies based on actual usage and the latest performance data, generating a dynamic adjustment mechanism solution;

[0043] Based on the key performance indicators and safety thresholds, the cost-benefit analysis report, the preliminary maintenance plan and replacement schedule, and the dynamic adjustment mechanism plan, a final maintenance plan and replacement schedule for lightweight building materials is developed.

[0044] In a second aspect, an embodiment of the present application provides a loss prediction system for lightweight building materials, comprising:

[0045] an acquisition module for collecting high-resolution images of environmental parameters surrounding the building and the physical properties and surface conditions of the lightweight building materials in the building, and analyzing and processing the long-term change trend of the environment in which the lightweight building materials are located by combining a pre-established geographic information system and a climate model to obtain analysis results;

[0046] an identification module for performing accelerated aging simulation on the aging process of lightweight building materials under different environmental conditions based on the analysis results, applying a decision tree algorithm in machine learning to calculate the expected aging rate of the lightweight building materials, analyzing the contribution of each environmental factor in the analysis results to the aging of the lightweight building materials through aging sensitivity analysis technology, identifying key influencing factors among the environmental factors, and obtaining a potential loss risk level;

[0047] an estimation module for estimating the long-term performance degradation trend of lightweight building materials under actual use conditions based on the potential loss risk level, in combination with the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials, using grey system theory modeling, and using time series analysis technology to mine historical data patterns extracted from the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials to generate a performance degradation prediction curve;

[0048] A development module is used to develop a maintenance plan and replacement schedule for lightweight building materials based on the performance degradation prediction curve, taking into account the functional requirements, economic costs and safety standards of the building.

[0049] In an embodiment of the present application, high-resolution images of environmental parameters surrounding a building and the physical properties and surface conditions of lightweight building materials in the building are collected, and combined with a pre-established geographic information system and climate model, the long-term change trends of the environment in which the lightweight building materials are located are analyzed and processed to obtain analysis results. Based on the analysis results, the aging process of the lightweight building materials under different environmental conditions is subjected to accelerated aging simulation, and the decision tree algorithm in machine learning is applied to calculate the expected aging rate of the material. Through aging sensitivity analysis technology, the contribution of environmental factors extracted from the analysis results to the aging of lightweight building materials is analyzed, key environmental factors that have a significant impact on the aging of lightweight building materials are identified, and the potential loss risk level of building materials is obtained; based on the potential loss risk level, combined with the maintenance records of the building during its service life, user feedback information and historical case libraries of similar building materials, grey system theory modeling is applied to estimate the long-term performance degradation trend of lightweight building materials under actual use conditions, and time series analysis technology is used to mine the patterns of historical data extracted from maintenance records, user feedback and historical case libraries to generate a performance degradation prediction curve; according to the performance degradation prediction curve, a maintenance plan and replacement schedule for lightweight building materials are formulated taking into account the functional requirements, economic costs and safety standards of the building.

[0050] The technical solution of this application has the following beneficial effects:

[0051] By collecting high-resolution images of the building's surrounding environmental parameters, as well as the physical properties and surface conditions of the materials, and combining them with geographic information systems (GIS) and climate models, the real-world environmental conditions of building materials can be more comprehensively reflected, thereby improving the accuracy of aging process simulations. Accelerated aging simulation can simulate the aging process of materials under various environmental conditions in a short period of time, providing more accurate predicted aging rates. By quantitatively evaluating the environmental factors extracted from the analysis results, key environmental factors with significant impacts on material aging can be identified. This helps focus resources and measures on addressing the most significant influencing factors and optimize maintenance strategies. Aging rates and aging sensitivity analysis results calculated using a decision tree algorithm can accurately assess the potential loss risk level under different environmental conditions, providing a scientific basis for subsequent performance degradation trend prediction and maintenance planning. Applying grey system theory modeling, combined with maintenance records, user feedback information, and historical case libraries of similar materials throughout the building's lifecycle, can more accurately estimate the long-term performance degradation trends of lightweight building materials under actual usage conditions. Time series analysis techniques can be used to mine patterns in historical data and generate performance degradation prediction curves, making long-term performance management more scientific and reasonable. Based on the performance degradation prediction curves, the building's functional characteristics are comprehensively considered. Based on energy demand, economic cost and safety standards, personalized and dynamically adjusted maintenance plans and replacement schedules are formulated to ensure that the maintenance strategy meets the safety and reliability requirements of the building while taking into account economic benefits; a dynamic adjustment mechanism is introduced to allow real-time updating of maintenance strategies based on actual usage and the latest performance data to ensure that the plan can adapt to changing environmental conditions and usage needs; through scientific prediction and optimized maintenance strategies, potential problems can be discovered and dealt with in advance, effectively extending the service life of lightweight building materials and reducing unnecessary replacement and repair costs; comprehensive consideration of the building's safety standards ensures that the maintenance strategy not only focuses on technical feasibility, but also fully considers safety and economic benefits, providing a strong guarantee for the long-term stable operation of the building.

[0052] Furthermore, by utilizing the analysis results to perform accelerated aging simulations and combining them with decision tree algorithms and aging sensitivity analysis techniques from machine learning, this method can generate detailed aging process data, accurately calculate the expected aging rate of materials, quantify the contribution of various environmental factors to the aging of lightweight building materials, identify key influencing factors, and determine the potential loss risk level. This systematic process not only significantly improves the accuracy of aging predictions but also ensures that maintenance strategies can be optimized and adjusted to target the most critical influencing factors. This overcomes the problems of inaccurate aging predictions and inadequate maintenance measures in existing solutions due to data limitations and static maintenance strategies, effectively extending the service life of building materials and improving the safety and economic benefits of buildings.

[0053] Furthermore, by integrating maintenance records, user feedback information, and historical case libraries of similar building materials during the building's life cycle based on the potential loss risk level, and applying grey system theory modeling and time series analysis techniques, this method can generate detailed multi-source information integration reports, construct accurate grey system models, and estimate the long-term performance degradation trends of lightweight building materials under actual use conditions. Furthermore, time series analysis techniques are used to mine the patterns of historical data and generate performance degradation prediction curves. This process not only overcomes the problem of inaccurate long-term performance evaluation caused by data fragmentation and static prediction models in existing solutions, but also provides a scientific basis for dynamic adjustment. The performance degradation prediction curve generated in this way can more accurately reflect the changes in material performance over time, providing a solid foundation for formulating personalized and dynamically adjusted maintenance strategies, significantly improving the safety and economic benefits of buildings, and ensuring that maintenance measures are more scientific, reasonable, timely and effective.

[0054] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flowchart of a method for predicting loss of lightweight building materials provided in an embodiment of the present application;

[0057] Figure 2 A schematic structural diagram of a loss prediction system for lightweight building materials provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0059] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0060] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0061] Figure 1 A flowchart of a method for predicting loss of lightweight building materials is provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0062] 101. Collect high-resolution images of environmental parameters surrounding the building and the physical properties and surface conditions of lightweight building materials in the building, and analyze and process the long-term change trend of the environment in which the lightweight building materials are located by combining a pre-established geographic information system and a climate model to obtain analysis results;

[0063] In this step, high-resolution images of environmental parameters surrounding the building (such as temperature, humidity, and light intensity) are collected, along with the physical properties and surface conditions of the lightweight building materials within the building. This data is combined with pre-established geographic information systems (GIS) and climate models to analyze long-term trends in the environment surrounding the lightweight building materials. The resulting analysis is a dataset documenting the various environmental conditions experienced by the materials over different time periods, along with their changing patterns.

[0064] In this embodiment, a multi-point sensor network is deployed to collect environmental parameters in real time. High-resolution images are acquired using drones or fixed cameras. This data is then analyzed and processed using GIS and climate models to generate detailed analysis results. This step provides the foundational data for subsequent aging predictions, ensuring the accuracy and reliability of the predictions.

[0065] 102. Based on the analysis results, perform accelerated aging simulation on the aging process of lightweight building materials under different environmental conditions, and apply the decision tree algorithm in machine learning to calculate the expected aging rate of the lightweight building materials. Use aging sensitivity analysis technology to analyze the contribution of each environmental factor in the analysis results to the aging of the lightweight building materials, identify the key influencing factors among the environmental factors, and obtain the potential loss risk level;

[0066] In this step, based on the analysis results, accelerated aging simulations are performed on lightweight building materials under different environmental conditions. The expected aging rate of the material is calculated by applying a decision tree algorithm from machine learning. Aging sensitivity analysis techniques are then used to analyze the contribution of environmental factors extracted from the analysis results to material aging. Key environmental factors with significant impacts on material aging are identified, ultimately deriving the potential loss risk level of the building material.

[0067] In this embodiment, a virtual extreme environment scenario is constructed based on the environmental parameters in the analysis results to simulate the material aging process under these conditions. A decision tree algorithm is used to analyze the changing trends of material properties over time, predict the aging rate, and identify key influencing factors through aging sensitivity analysis to assess the potential loss risk level. This step helps accurately identify the main causes of material aging and guide the development of subsequent maintenance strategies.

[0068] 103. Based on the potential loss risk level, combined with the building's maintenance records, user feedback information, and a historical case library of similar building materials during its service life, apply grey system theory modeling to estimate the long-term performance degradation trend of lightweight building materials under actual use conditions. Utilize time series analysis techniques to mine patterns in historical data extracted from the building's maintenance records, user feedback information, and a historical case library of similar building materials during its service life to generate a performance degradation prediction curve.

[0069] In this step, based on the potential risk level of loss, combined with maintenance records throughout the building's lifecycle, user feedback, and a historical database of similar building materials, grey system theory modeling is applied to estimate the long-term performance degradation trends of lightweight building materials under actual usage conditions. Time series analysis techniques are then used to identify patterns in historical data and generate performance degradation prediction curves. These performance degradation prediction curves reflect the changing trends of material performance over time, providing a scientific basis for subsequent maintenance strategies.

[0070] In this example, we integrated building maintenance records, user feedback, and a historical case library of similar materials, applying grey system theory to modeling to predict the long-term performance degradation trends of lightweight building materials under actual usage conditions. Subsequently, we used time series analysis techniques to identify patterns in the historical data and generate performance degradation prediction curves. This step ensures that maintenance strategies can adapt to changing environmental conditions and usage requirements, improving the accuracy and practicality of predictions.

[0071] 104. Based on the performance degradation prediction curve, and taking into account the functional requirements, economic costs and safety standards of the building, a maintenance plan and replacement schedule for lightweight building materials should be formulated.

[0072] In this step, based on the performance degradation prediction curve, a personalized and dynamically adjusted maintenance plan and replacement schedule for lightweight building materials is developed, taking into account the building's functional requirements, economic costs, and safety standards. This results in an optimized maintenance strategy. This maintenance strategy not only meets the building's safety and reliability requirements but also takes into account economic benefits, ensuring that the plan is both scientifically sound and flexible to adapt to actual conditions.

[0073] In this embodiment, a personalized maintenance plan and replacement schedule is developed based on the performance degradation prediction curve, taking into account the building's functional requirements, economic costs, and safety standards. Furthermore, a dynamic adjustment mechanism is introduced, allowing for real-time updates to maintenance strategies based on actual usage and the latest performance data. This ensures that the plan can adapt to changing environmental conditions and usage requirements, thereby improving the building's safety and economic efficiency.

[0074] In summary, steps 101 to 104 encompass the complete process, from environmental parameter collection to maintenance strategy formulation. This process aims to provide a scientifically sound lightweight building material aging prediction and maintenance management solution that meets the safety and economic efficiency requirements of buildings. Through systematic data collection, precise aging simulation and sensitivity analysis, reliable performance degradation prediction, and dynamically adjusted maintenance strategies, this series of steps significantly improves the safety, reliability, and economic efficiency of buildings, ensuring the effectiveness and timeliness of maintenance measures.

[0075] To address the inaccurate aging prediction issues of existing methods, in some embodiments, in step 102, based on the analysis results, an accelerated aging simulation is performed on the aging process of lightweight building materials under different environmental conditions, and a decision tree algorithm in machine learning is applied to calculate the expected aging rate of the material. Aging sensitivity analysis techniques are used to analyze the contribution of environmental factors extracted from the analysis results to the aging of lightweight building materials, identify key environmental factors that have a significant impact on the aging of lightweight building materials, and obtain the potential loss risk level of the building materials, including:

[0076] Using the analysis results, the aging process of lightweight building materials under different environmental conditions is accelerated by aging simulation to generate aging process data; based on the aging process data, the decision tree algorithm in machine learning is applied to analyze and process the trend of material properties changing over time to obtain the expected aging rate of the material; based on the expected aging rate of the material, the contribution of each environmental factor to material aging is quantified through aging sensitivity analysis technology, the key influencing factors are identified, and an aging sensitivity report is generated; using the aging sensitivity report, the aging conditions of the material under various environmental conditions are comprehensively evaluated to ensure that the selected key influencing factors can accurately reflect the aging characteristics of the material under various conditions and obtain the potential loss risk level.

[0077] In this example, the analysis results include high-resolution images of building environmental parameters (such as temperature, humidity, and light intensity) and the physical properties and surface conditions of lightweight building materials. This data is used in accelerated aging simulations to generate detailed aging process data. This aging process data is then used to train machine learning models to predict material aging rates. An aging sensitivity report summarizes the contribution of various environmental factors to material aging and identifies key influencing factors. Finally, a comprehensive evaluation of material aging under various environmental conditions is performed to determine the potential risk level of material loss.

[0078] In the embodiments of the present application, first, based on the multi-source data in the analysis results, a virtual extreme environment scenario is constructed, accelerated aging simulation is performed, and aging process data is generated; second, these aging process data are used to train the decision tree algorithm in machine learning, analyze the changing trend of material properties over time, and predict the expected aging rate of the material; third, through aging sensitivity analysis technology, the contribution of each environmental factor to material aging is quantified, key influencing factors are identified, and an aging sensitivity report is generated; finally, the aging sensitivity report is used to comprehensively evaluate the aging of the material under various environmental conditions to ensure that the selected key influencing factors can accurately reflect the aging characteristics of the material under various conditions, thereby obtaining the potential loss risk level.

[0079] To address the inaccurate aging prediction issues in existing methods, in some embodiments, based on the expected aging rate of the material, aging sensitivity analysis technology is used to quantify the contribution of various environmental factors to material aging, identify key influencing factors, and generate an aging sensitivity report, including:

[0080] Utilizing the expected aging rate of the lightweight building materials, combined with various performance change records collected from lightweight building materials in actual use and aging prediction data of lightweight building materials under various hypothetical conditions, multiple regression analysis or machine learning models are applied to quantify the contribution of each environmental factor to the aging of lightweight building materials, and obtain quantitative results of environmental factor impacts; based on the quantitative results of environmental factor impacts, a multi-factor comprehensive evaluation model is established, considering the interactions between different environmental factors and their combined effects on material aging, and obtaining the output of the multi-factor comprehensive evaluation model; based on the output of the multi-factor comprehensive evaluation model, the importance of each environmental factor is analyzed through aging sensitivity analysis technology, and the key influencing factors that have a significant impact on material aging are identified, and a list of key influencing factors is generated; using the list of key influencing factors, all analysis results are integrated, and a detailed environmental factor impact quantitative report and key influencing factor analysis are compiled to form an aging sensitivity report.

[0081] In this embodiment, the expected aging rate of the material is based on data obtained from accelerated aging simulation and decision tree algorithm, which is used to evaluate the aging trend of lightweight building materials under different environmental conditions. The quantitative results of the impact of environmental factors are calculated through multiple regression analysis or machine learning models. The input data is based on the expected aging rate of the lightweight building material, and is combined with the various performance change records of lightweight building materials collected in actual use and the aging prediction data of lightweight building materials under various hypothetical conditions, reflecting the specific impact of various environmental factors (such as temperature, humidity, light intensity, etc.) on material aging. The multi-factor comprehensive evaluation model takes into account the interaction between different environmental factors and their combined effects on material aging. The final generated environmental factor impact quantitative report and key influencing factor analysis constitute an aging sensitivity report, which provides a scientific basis to guide the formulation of maintenance strategies.

[0082] In the embodiments of the present application, the process first integrates three core input data types: the expected aging rate of lightweight building materials (usually predicted based on accelerated aging simulations and decision tree algorithms, reflecting the theoretical aging trend of materials under different environments); records of various performance changes of lightweight building materials collected during actual use (this is key empirical data in real-life service environments, recording the degradation of physical, chemical, or mechanical properties of materials under actual exposure to environmental factors such as temperature, humidity, light, and pollutants, such as strength loss, color change, and cracking); and aging prediction data of lightweight building materials under various hypothetical conditions (using simulation models to simulate the possible performance evolution of materials under different environmental combinations or extreme conditions). Based on this set of multidimensional data that integrates theoretical predictions, actual observations, and simulation deductions, multiple regression analysis or machine learning models (such as linear / nonlinear regression, random forest, gradient boosting tree, or neural network) are applied for modeling and analysis; the model uses various environmental factors (such as temperature, humidity, ultraviolet radiation, salt spray concentration, acid rain pH, etc.) as independent variables (features) and the aging degree or rate of the material as the dependent variable (target). The core goal of model training is to quantify the contribution of various environmental factors to the aging of lightweight building materials. This involves analyzing the relative impact of each factor, both individually and in interaction with other factors, on the aging outcomes (e.g., measured through regression coefficients, feature importance scores, SHAP values, and other metrics). Ultimately, through analytical calculations, the model yields quantitative results of the environmental factors' impact. These results, expressed in numerical form (e.g., weights, percentages, and rankings), clearly demonstrate the specific role of different environmental factors in driving the aging of lightweight building materials. This provides a precise data foundation for subsequently identifying key influencing factors and developing targeted protective strategies.

[0083] Secondly, based on these quantitative results, a multi-factor comprehensive evaluation model is established, taking into account the interactions between different environmental factors and their combined effects on material aging, and obtaining the output of the multi-factor comprehensive evaluation model; thirdly, based on the output of the multi-factor comprehensive evaluation model, the importance of each environmental factor is analyzed through aging sensitivity analysis technology, and the key influencing factors that have a significant impact on material aging are identified, and a list of key influencing factors is generated; finally, using the list of key influencing factors, all analysis results are integrated, and a detailed environmental factor impact quantitative report and key influencing factor analysis are compiled to form an aging sensitivity report.

[0084] Through the above steps, this embodiment not only improves the understanding of the aging process of lightweight building materials, but also can accurately identify key environmental factors that have a significant impact on material aging, providing a scientific basis for subsequent maintenance strategies to ensure the safety and economic benefits of the warehouse.

[0085] To address the issue of inaccurate aging prediction in existing methods, in some embodiments, the aging sensitivity report is used to comprehensively evaluate the aging of materials under various environmental conditions, ensuring that the selected key influencing factors can accurately reflect the aging characteristics of the material under various conditions, thereby obtaining a potential loss risk level, including:

[0086] Using the key influencing factors and quantitative results in the aging sensitivity report, an evaluation framework is constructed to comprehensively evaluate the aging possibility and degree of materials under different environmental conditions, and generate an environmental condition aging evaluation framework; based on the environmental condition aging evaluation framework, multiple possible usage scenarios are created, and the aging performance of materials under these scenarios is simulated through computer simulation or experimental verification to obtain scenario simulation analysis results; based on the scenario simulation analysis results, a risk assessment model is applied to quantitatively evaluate the aging of materials under each environmental condition, determine the level of aging risk, and generate an aging risk assessment report; using the aging risk assessment report, the evaluation framework is optimized and adjusted to ensure its applicability to a wider range of environmental conditions, and obtain the potential loss risk level.

[0087] In this embodiment, the aging sensitivity report contains the identified key influencing factors and their quantitative results, which are used to construct an evaluation framework. The evaluation framework is designed to systematically evaluate the possibility and degree of material aging under different environmental conditions and generate an environmental condition aging evaluation framework. The scenario simulation analysis results are obtained through computer simulation or experimental verification, reflecting the aging performance of the material under different usage scenarios. The aging risk assessment report quantitatively evaluates the material aging risk under each environmental condition by applying a risk assessment model, and ultimately generates a potential loss risk level to ensure the effectiveness and scientific nature of the maintenance strategy.

[0088] In the embodiments of the present application, first, a comprehensive evaluation framework is constructed using the key influencing factors and quantitative results in the aging sensitivity report to systematically evaluate the possibility and degree of aging of materials under different environmental conditions, thereby generating an environmental condition aging evaluation framework; second, based on this evaluation framework, multiple possible usage scenarios are created, and the aging performance of materials under these scenarios is simulated through computer simulation or experimental verification to obtain scenario simulation analysis results; third, based on these scenario simulation analysis results, a risk assessment model is applied to quantitatively evaluate the aging of materials under each environmental condition, determine the level of aging risk, and generate an aging risk assessment report; finally, the aging risk assessment report is used to optimize and adjust the evaluation framework to ensure that it is applicable to a wider range of environmental conditions, thereby obtaining the potential loss risk level.

[0089] Through the above steps, this embodiment not only improves the understanding of the aging process of lightweight building materials, but also can accurately identify the aging risks of materials under different environmental conditions, providing a scientific basis for subsequent maintenance strategies and ensuring the long-term safe and reliable operation of rail transit projects.

[0090] This application takes into account that in the prior art, due to the lack of sufficient preprocessing and refined analysis of the analysis result data, the prediction of the material aging rate is not accurate enough, making it difficult to effectively guide the formulation of maintenance strategies. In addition, the traditional method is relatively abrupt in the transition from aging rate prediction to maintenance plan, and fails to fully utilize the advantages of machine learning algorithms for dynamic adjustment. Therefore, the embodiment of the present invention proposes this optional solution, which ensures the accuracy and representativeness of the input data by introducing preprocessing steps such as data cleaning, feature engineering, and model initialization; applies the decision tree algorithm to calculate the expected aging rate of the material, and generates a node selection score to guide the formulation of the maintenance strategy, thereby achieving a smooth transition from aging rate prediction to maintenance plan, and solving the problems of inaccurate prediction and inflexible maintenance strategy in the prior art.

[0091] Optionally, applying a decision tree algorithm in machine learning based on the aging process data to analyze and process the trend of material properties changing over time to obtain the expected aging rate of the material includes:

[0092] When calculating the expected aging rate of a material Before this, the data in the analysis results needs to be preprocessed, including data cleaning, feature engineering, and model initialization, to ensure the accuracy and representativeness of the input data and lay the foundation for aging rate prediction;

[0093] ;

[0094] Indicates the expected aging rate of lightweight building materials; represents the initial material performance coefficient; represents the time decay factor; Indicates time; represents the temperature sensitivity coefficient; Indicates the basic value of environmental conditions; represents the temperature influence weight; Indicates the actual temperature; represents the humidity response coefficient; Represents humidity change frequency factor; Indicates actual humidity; Indicates relative humidity; represents the humidity square term adjustment coefficient; represents the interaction coefficient between humidity and temperature; represents the comprehensive adjustment factor; represents the time sensitivity factor; represents the average aging rate;

[0095] Calculation completed Finally, the decision tree node selection score is generated through the steps of aging rate correction, feature importance evaluation and node selection scoring. , to guide the development of maintenance strategies and ensure a smooth transition from aging rate prediction to maintenance planning;

[0096] ;

[0097] Represents the decision tree node selection score; represents the node selection preference coefficient; represents the nonlinear adjustment coefficient; Indicates the expected aging rate of lightweight building materials; Indicates the sensitivity coefficient of the property change rate of lightweight building materials; represents the average aging rate; represents the characteristic fluctuation coefficient; Indicates the convergence speed of leaf nodes; represents the feature importance score; Represents the decision tree depth adjustment factor; represents the data distribution smoothing coefficient; Indicates the basic value of the data set; Represents the data feature weight; Represents the data feature value;

[0098] Calculation completed Finally, through additional steps such as comprehensive evaluation, designing dynamic adjustment mechanisms and compiling optimized maintenance strategy documents, the expected aging rate of the materials and a scientific and reasonable maintenance strategy are obtained to ensure the safety and economic benefits of the building.

[0099] The formula aims to more accurately predict the aging rate of lightweight building materials and optimize maintenance strategies. This solution introduces two key formulas: the expected aging rate of the material and decision tree node selection score These two formulas are designed to comprehensively consider multiple environmental factors (such as temperature and humidity) and their interactions, as well as the influence of time factors, to provide a scientific and reasonable aging rate prediction model, and evaluate the effectiveness of different maintenance strategies through a decision tree algorithm to ensure the safety and economic benefits of buildings.

[0100] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0101] ;

[0102] :This item is used to describe the exponential decay characteristics of material properties over time, where Reflects the influence of the initial state of the material, The time decay factor refers to the decay rate of the aging rate over time. By introducing an exponential function, we can accurately capture the gradual decline in material performance over long-term use, providing a basis for aging rate prediction.

[0103] :This sub-item takes into account the logarithmic effect of temperature on the aging rate of materials, where is the temperature sensitivity coefficient, is the basic value of environmental conditions, is the temperature influence weight, The logarithmic function can effectively simulate the nonlinear effect of temperature changes on material aging, especially the accelerated aging effect when the temperature approaches the critical value.

[0104] :This sub-item is used to describe the periodic effect of humidity on the aging rate of materials, where is the humidity response coefficient, is the humidity change frequency factor, The sine function captures the periodic effects of humidity fluctuations (such as seasonal changes) on material aging and reflects the changing characteristics of humidity over different time periods.

[0105] :This sub-item considers the effect of the interaction between humidity and temperature on the aging rate of materials, among which is the relative humidity, is the humidity square adjustment coefficient, is the interaction coefficient between humidity and temperature. By using the square term, it enhances the combined effect of humidity and temperature. This is particularly applicable to environments where humidity and temperature significantly influence each other.

[0106] :This item is used to adjust the overall aging rate, where is the comprehensive adjustment factor, is the time sensitivity factor, is the average aging rate. Through the exponential function, the aging rate can be dynamically adjusted to ensure the adaptability and accuracy of the model at different time scales, especially for aging prediction over long time spans.

[0107] The following is a brief introduction to how to obtain the parameters of this formula:

[0108] Initial material performance coefficient Obtained from laboratory testing; time decay factor Derived from historical data analysis; time Recorded by the system; temperature sensitivity coefficient Determined by experiment; basic value of environmental conditions Set based on historical data; temperature influence weight Determined by statistical analysis; actual temperature Collected in real time by environmental monitoring equipment; humidity response coefficient Determined by experiment; humidity change frequency factor According to historical data statistics; actual humidity Collected in real time by environmental monitoring equipment; relative humidity Directly measured by humidity sensor; humidity square term adjustment coefficient Through experimental optimization; the interaction coefficient between humidity and temperature Determined by multivariate regression analysis; comprehensive adjustment factor Adjusted through model training; time sensitivity factor Through historical data analysis; average aging rate Calculated through long-term monitoring data;

[0109] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0110]

[0111] :This item is used to evaluate the node selection preference, where is the node selection preference coefficient, is the nonlinear adjustment coefficient, is the expected aging rate of the material. The sine function can capture the periodic changes in the aging rate, especially when selecting key nodes in the decision tree algorithm, ensuring that the node selection has a certain periodicity and regularity.

[0112] : This sub-item is used to measure the sensitivity of the material property change rate, where is the material property change rate sensitivity coefficient, is the expected aging rate of the material, is the average aging rate. The square root function can amplify the degree to which the material aging rate deviates from the average, highlighting abnormal aging conditions and increasing the model's sensitivity to abnormal data.

[0113] :This sub-item is used to evaluate the impact of feature fluctuations on the score, where is the characteristic fluctuation coefficient, is the node selection preference coefficient, is the leaf node convergence speed. The cosine function can capture the periodic changes of feature fluctuations, especially in the decision tree node selection process, ensuring that the model can adapt to the periodic changes of features and improve the robustness of the model.

[0114] :This item is used to adjust the depth of the decision tree, where is the decision tree depth adjustment factor, is the leaf node convergence speed, is the feature importance score. Through the exponential function, the depth of the decision tree can be dynamically adjusted to ensure the adaptability and accuracy of the model under different complexities, especially when processing high-dimensional data, to prevent overfitting or underfitting.

[0115] :This item is used to smooth the data distribution, where is the data distribution smoothing coefficient, is the basic value of the dataset, is the data feature weight, is the data eigenvalue. The logarithmic function can effectively smooth the data distribution, especially when dealing with extreme values ​​or sparse data, ensuring the stability and reliability of the model and improving prediction accuracy.

[0116] The following is a brief introduction to how to obtain the parameters of this formula:

[0117] Node selection preference coefficient Through experimental optimization; nonlinear adjustment coefficient Adjusted through model training; expected aging rate of the material According to the above formula: Calculated; material property change rate sensitivity coefficient Determined by experiment; average aging rate Calculated from long-term monitoring data; characteristic fluctuation coefficient Through experimental optimization; leaf node convergence speed Output from the decision tree model; feature importance score Calculated by feature selection algorithm; decision tree depth adjustment factor Adjust through model training; data distribution smoothing coefficient Optimize through experiments; basic value of data set Set based on historical data; data feature weights Determined by feature selection algorithm; data eigenvalue Obtained from actual data;

[0118] Assume that for a construction project, the initial material performance coefficient is , time decay factor , time is Year, temperature sensitivity coefficient , basic value of environmental conditions , temperature influence weight , actual temperature , humidity response coefficient , humidity change frequency factor , actual humidity , relative humidity , humidity square adjustment coefficient , the interaction coefficient between humidity and temperature , comprehensive adjustment factor , time sensitivity factor , average aging rate ; Substitute into the formula to calculate .

[0119] Assume that for a certain construction project, the node selection preference coefficient , nonlinear adjustment coefficient , the expected aging rate of the material , material property change rate sensitivity coefficient , average aging rate , characteristic fluctuation coefficient , leaf node convergence speed , feature importance score , decision tree depth adjustment factor , data distribution smoothing coefficient , the basic value of the dataset , data feature weight , data characteristic value ; Substitute into the formula to calculate .

[0120] Through the above steps, this embodiment not only improves the prediction accuracy of the expected aging rate of the material, but also can identify the key factors affecting material aging, providing a scientific basis for subsequent maintenance strategies. In particular, by introducing the decision tree node selection score , achieving a smooth transition from aging rate prediction to maintenance plan, ensuring that the maintenance strategy is both scientific and reasonable and flexible to actual conditions. Assuming the threshold is set to , due to the results It is greater than the set threshold, indicating that the proposed maintenance strategy has high reliability and effectiveness, and significantly improves the safety and economic benefits of the building.

[0121] To address the problem of inaccurate long-term performance degradation trend prediction in existing methods, in some embodiments, the method of step 103 uses grey system theory modeling to estimate the long-term performance degradation trend of lightweight building materials under actual usage conditions based on the potential loss risk level, combined with the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials. Time series analysis technology is then used to mine the patterns of historical data extracted from the maintenance records, user feedback, and historical case library to generate a performance degradation prediction curve, including:

[0122] Using the potential loss risk level, the maintenance records, user feedback information and historical case libraries of similar materials during the building's use cycle are integrated to obtain a multi-source information integration report; based on the multi-source information integration report, the grey system theory is applied to model the long-term performance degradation trend of lightweight building materials under actual use conditions to obtain a grey system model; based on the grey system model, the performance degradation of lightweight building materials in a future period of time is estimated to obtain preliminary performance degradation trend prediction results; using the preliminary performance degradation trend prediction results, time series analysis technology is used to mine historical data patterns from the multi-source information integration report to generate a historical data pattern analysis report; based on the historical data pattern analysis report and the estimation results of the grey system model, comprehensive processing is performed to draw an estimation curve reflecting the decline of material performance over time to generate a performance decline estimation curve.

[0123] In this embodiment, the potential loss risk level is derived from an aging sensitivity analysis of environmental factors and is used to assess the aging risk of materials under different environmental conditions. The multi-source information integration report includes maintenance records of the building during its lifecycle, user feedback, and data from a historical case library of similar building materials. This data is used to construct a gray system model. The gray system model is used to predict the long-term performance degradation trends of lightweight building materials under actual usage conditions, while time series analysis techniques are used to discover patterns in historical data, ultimately generating a performance degradation prediction curve that provides a scientific basis for maintenance strategies.

[0124] In an embodiment of the present application, first, the potential loss risk level is used to integrate maintenance records, user feedback information and historical case libraries of similar materials during the building's use cycle to form a multi-source information integration report; second, based on the multi-source information integration report, gray system theory is applied to model the long-term performance degradation trend of lightweight building materials under actual use conditions to obtain a gray system model; third, based on the gray system model, the material performance degradation in a future period is estimated to obtain a preliminary performance degradation trend prediction result; then, using the preliminary performance degradation trend prediction result, time series analysis technology is used to mine historical data patterns from the multi-source information integration report to generate a historical data pattern analysis report; finally, based on the historical data pattern analysis report and the estimation results of the gray system model, comprehensive processing is performed to draw an estimation curve reflecting the decline of material performance over time to generate a performance decline estimation curve.

[0125] Through the above steps, this embodiment not only improves the accuracy of predicting the long-term performance degradation trend of lightweight building materials, but also can identify the key factors affecting material performance, providing a solid foundation for formulating scientific and reasonable maintenance strategies, and ensuring the safety and economic benefits of commercial complexes.

[0126] To address the problem of insufficient historical data pattern mining in existing methods, in some embodiments, the preliminary performance degradation trend prediction results are utilized to employ time series analysis technology to mine historical data patterns from the multi-source information integration report and generate a historical data pattern analysis report, including:

[0127] Using the preliminary performance degradation trend prediction results, the performance changes of lightweight building materials in different time periods are organized into an ordered time series data set to obtain a time series data set; based on the time series data set, statistical methods or machine learning techniques are applied to extract features and preprocess the time series, remove outliers and perform smoothing to obtain a preprocessed time series data set; based on the preprocessed time series data set, time series analysis technology is used to perform pattern recognition and regularity mining on the periodicity, trend and random fluctuation components in the historical data to obtain pattern recognition results; using the pattern recognition results, combined with the maintenance records, user feedback and case library in the multi-source information integration report, the correlation between different factors is analyzed to obtain a correlation analysis report; based on the pattern recognition results and the correlation analysis report, comprehensive processing is performed to generate a historical data regularity analysis report.

[0128] In this embodiment, the preliminary performance degradation trend prediction result is the data obtained by estimating the material performance degradation in the future period through the gray system model. The time series data set contains the performance changes of lightweight building materials in different time periods. These data are sorted and used for subsequent time series analysis. The quality and reliability of the pre-processed time series data set are improved by removing outliers and smoothing. The pattern recognition result is the periodicity, trend and random fluctuation components in the historical data identified from the pre-processed data. The correlation analysis report analyzes the correlation between different factors by combining the maintenance records, user feedback and case library in the multi-source information integration report, and finally generates a historical data law analysis report to provide a scientific basis for subsequent maintenance strategies.

[0129] In an embodiment of the present application, first, the performance changes of lightweight building materials in different time periods are organized into an ordered time series data set using preliminary performance degradation trend prediction results to obtain a time series data set; secondly, based on the time series data set, statistical methods or machine learning techniques are applied to perform feature extraction and preprocessing on the time series, remove outliers and perform smoothing to obtain a preprocessed time series data set; thirdly, based on the preprocessed time series data set, time series analysis techniques are used to perform pattern recognition and regularity mining on the periodicity, trend and random fluctuation components in the historical data to obtain pattern recognition results; then, the pattern recognition results are used, combined with the maintenance records, user feedback and case library in the multi-source information integration report, to analyze the correlation between different factors to obtain a correlation analysis report; finally, based on the pattern recognition results and the correlation analysis report, comprehensive processing is performed to generate a historical data regularity analysis report.

[0130] Through the above steps, this embodiment not only improves the understanding of the performance variation patterns of lightweight building materials, but also accurately identifies the key factors affecting material performance, providing a solid foundation for formulating scientific and reasonable maintenance strategies, and ensuring the safety and economic benefits of airport terminals.

[0131] This application takes into account that in the existing technology, due to the lack of comprehensive integration and refined modeling of multi-source information, the prediction of the long-term performance degradation trend of lightweight building materials is not accurate enough, making it difficult to effectively guide the formulation of maintenance strategies. In addition, the traditional method is relatively abrupt in the transition from degradation trend prediction to rate correction, and fails to fully utilize the advantages of gray system theory for dynamic adjustment. Therefore, the embodiment of the present invention proposes this optional solution, which ensures the comprehensiveness and representativeness of the input data by introducing pre-analysis steps such as data integration and preprocessing, environmental factor modeling, initial condition setting and multi-factor comprehensive evaluation; and applies gray system theory modeling to calculate the long-term performance degradation trend. , and generate a performance degradation rate correction value , thereby achieving a smooth transition from degradation trend prediction to rate correction, solving the problems of inaccurate prediction and inflexible maintenance strategy in existing technologies.

[0132] Optionally, the multi-source information integration report is used to apply grey system theory modeling to model the long-term performance degradation trend of lightweight building materials under actual use conditions to obtain a grey system model, including:

[0133] Calculating long-term performance degradation trends Prior to this, preliminary analysis such as data integration and preprocessing, environmental factor modeling, initial condition setting, and multi-factor comprehensive evaluation is required to ensure the comprehensiveness and representativeness of the input data, laying the foundation for accurate prediction of material degradation trends;

[0134] ;

[0135] Indicates the long-term performance degradation trend of lightweight building materials under actual use conditions; represents the initial performance level coefficient; represents the time growth factor; Indicates time; represents the time power adjustment factor; Indicates the frequency sensitivity coefficient; Indicates the basic value of environmental conditions; Indicates the usage frequency influence weight; Indicates frequency of use; represents external stress; represents the stress influence weight; represents relative stress; represents the stress power adjustment factor; represents the comprehensive adjustment factor; represents the nonlinear adjustment coefficient; Indicates the modulus of lightweight building materials;

[0136] Calculation completed After that, the performance degradation rate correction value is generated through transition steps such as degradation trend correction, feature importance evaluation and node selection scoring. , ensuring a smooth transition from degradation trend prediction to rate correction, and guiding the formulation of subsequent maintenance strategies;

[0137] ;

[0138] Indicates the performance degradation rate correction value; represents the rate-corrected preference coefficient; represents the nonlinear adjustment coefficient; Indicates long-term performance degradation trend; represents the characteristic fluctuation coefficient; represents the average degradation trend; Represents the data feature weight; Represents the data feature value; represents the model depth adjustment factor; represents the nonlinear adjustment coefficient; Indicates the wear rate of lightweight building materials; represents the comprehensive adjustment factor; represents the average degradation trend;

[0139] Calculation completed Finally, through additional steps of model validation and optimization, design of dynamic adjustment mechanism, and model integration and application, a complete grey system model is obtained, which provides a scientific basis for aging prediction of lightweight building materials.

[0140] Calculation completed Finally, through additional steps such as model verification and optimization, design of dynamic adjustment mechanism, and model integration and application, a complete grey system model is obtained to ensure that its prediction results are reliable and practical, providing a scientific basis for the aging prediction of lightweight building materials.

[0141] This formula is designed to more accurately predict the long-term performance degradation trend of lightweight building materials under actual use conditions and optimize maintenance strategies. This solution introduces two key formulas: long-term performance degradation trend and performance degradation rate correction These two formulas are designed to comprehensively consider multiple environmental factors (such as time, frequency of use, external stress) and their interactions, provide a scientific and reasonable degradation trend prediction model, and evaluate the effectiveness of different maintenance strategies through rate correction values ​​to ensure the safety and economic benefits of buildings.

[0142] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0143] ;

[0144] This sub-item describes the nonlinear growth or decay of the initial performance of lightweight building materials over time. By introducing a power function, it captures the long-term evolution of material properties, particularly their rapid initial changes and gradual stabilization over time. This provides a foundation for predicting material degradation over the entire lifecycle.

[0145] This sub-item considers the impact of usage frequency on material degradation trends. The hyperbolic tangent function effectively models the nonlinear effects of usage frequency on material properties, particularly the dramatic changes in material properties under high-frequency usage. This helps more accurately reflect material degradation under actual usage conditions.

[0146] This sub-item evaluates the impact of external stress and its interaction with relative stress on material degradation trends. By adjusting the power function, it enhances the representation of the combined effects of external and relative stresses, making it particularly suitable for environments where stress significantly affects material properties. This ensures that the model fully accounts for the influence of stress factors.

[0147] : This sub-item is used to capture the cyclical effects of material modulus variations. The sine function can reflect the fluctuations in material properties (such as modulus) over time, particularly the accelerated aging effects associated with cyclical material properties. This helps increase the model's sensitivity to cyclical material variations.

[0148] The following is a brief introduction to how to obtain the parameters of this formula:

[0149] Initial performance level coefficient Obtained from laboratory testing; time growth factor Derived from historical data analysis; time Recorded by the system; time power adjustment factor Determined by experimental optimization; using frequency sensitivity coefficient Determined by experiment; basic value of environmental conditions Set based on historical data; frequency of use affects weight Determined through statistical analysis; frequency of use Real-time collection of usage records; external stress Measured by stress sensors; stress influences weighting Determined by experimental optimization; relative stress Direct measurement via stress sensor; stress power adjustment factor Determined through experimental optimization; comprehensive adjustment factor Adjust through model training; nonlinear adjustment coefficient Determined through experimental optimization; Material modulus Obtained through material testing;

[0150] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0151] ;

[0152] : This component adjusts for nonlinear changes in degradation trends. The exponential function dynamically adjusts the degradation rate, particularly when the degradation trend approaches its extreme value. This allows the model to maintain high prediction accuracy across different degradation stages, ensuring a smooth transition from degradation trend prediction to rate correction.

[0153] This sub-item measures the impact of feature fluctuations on the degradation rate correction. The logarithmic function can amplify the impact of feature fluctuations and highlight anomalous data points. This is particularly important when dealing with complex and changing data features. This ensures the model is highly sensitive to feature fluctuations and improves prediction accuracy.

[0154] This sub-item evaluates the cyclical variation of material wear rate. The cosine function captures the periodic fluctuations in material wear rate, particularly the effects of aging when the wear rate exhibits cyclical variations. This helps improve the model's sensitivity to material wear and ensures the scientific and effective nature of maintenance strategies.

[0155] This sub-item is again used to assess the cyclical variation of material wear rate. The cosine function captures the cyclical fluctuations in material wear rate, particularly the effects of aging when the wear rate exhibits cyclical variations. This helps improve the model's sensitivity to material wear and ensures the scientific and effective maintenance strategy.

[0156] The following is a brief introduction to how to obtain the parameters of this formula:

[0157] Rate-corrected preference coefficient Through experimental optimization; nonlinear adjustment coefficient Adjust through model training; long-term performance degradation trend Calculated by; characteristic fluctuation coefficient Optimized by experiments; average degradation trend Calculated from long-term monitoring data; data feature weight Determined by feature selection algorithm; data eigenvalue Obtained from actual data; model depth adjustment factor Adjust through model training; nonlinear adjustment coefficient Optimize through experiments; material wear rate Obtained through wear testing; comprehensive adjustment factor Adjusted by model training; average degradation trend Calculated from long-term monitoring data;

[0158] Assume that for a bridge project, the initial performance level coefficient , time growth factor , time is Year, time power adjustment factor , using the frequency sensitivity coefficient , basic value of environmental conditions , frequency of use affects weight , frequency of use times / day, external stress , stress influence weight , relative stress , stress power adjustment factor , comprehensive adjustment factor , nonlinear adjustment coefficient , material modulus ; Substitute into the formula to calculate .

[0159] Assume that for a bridge project, the rate-corrected preference coefficient , nonlinear adjustment coefficient , long-term performance degradation trend , characteristic fluctuation coefficient , average degradation trend , data feature weight , data characteristic value , model depth adjustment factor , nonlinear adjustment coefficient , material wear rate , comprehensive adjustment factor , average degradation trend ; Substitute into the formula to calculate .

[0160] Through the above steps, this embodiment not only improves the accuracy of the prediction of the long-term performance degradation trend of lightweight building materials, but also can identify the key factors affecting material degradation, providing a scientific basis for subsequent maintenance strategies. In particular, by introducing the performance degradation rate correction value , achieving a smooth transition from degradation trend prediction to rate correction, ensuring that the maintenance strategy is both scientific and reasonable and flexible to actual conditions. Assuming the threshold is set to , due to the results It is greater than the set threshold, indicating that the proposed maintenance strategy has high reliability and effectiveness, and significantly improves the safety and economic benefits of the building.

[0161] To address the problem of insufficient personalization and dynamic adjustment of maintenance strategies in existing methods, in some embodiments, step 104 formulates a maintenance plan and replacement schedule for lightweight building materials based on the performance degradation prediction curve, taking into account the functional requirements, economic costs, and safety standards of the building, including:

[0162] The performance degradation prediction curve is used to analyze the material performance requirements and related safety standards of the building at different stages of use, evaluate the building's functional requirements and safety standards, and obtain key performance indicators and safety thresholds; based on the performance degradation prediction curve, combined with the economic cost during the building's life cycle, a cost-benefit analysis is performed to obtain a cost-benefit analysis report; based on the cost-benefit analysis report, a preliminary maintenance plan and replacement schedule are formulated for different types of lightweight building materials; using the preliminary maintenance plan and replacement schedule, a dynamic adjustment mechanism is designed to allow real-time updating of maintenance strategies based on actual usage and the latest performance data, and to generate a dynamic adjustment mechanism solution; based on the key performance indicators and safety thresholds, the cost-benefit analysis report, the preliminary maintenance plan and replacement schedule, and the dynamic adjustment mechanism solution, a final maintenance plan and replacement schedule for the lightweight building materials is formulated.

[0163] In this embodiment, the performance degradation prediction curve is data generated by time series analysis technology, which reflects the performance trend of lightweight building materials over time. Key performance indicators and safety thresholds are used to evaluate the building's requirements for material performance and related safety standards at different stages of use, ensuring that maintenance strategies meet functional and safety requirements. The cost-benefit analysis report combines the economic costs over the building's life cycle to evaluate the cost-effectiveness of different maintenance measures. The preliminary maintenance plan and replacement schedule are formulated based on the cost-benefit analysis report, covering specific maintenance arrangements for different types of lightweight building materials. The dynamic adjustment mechanism solution allows for real-time updates to maintenance strategies based on actual usage and the latest performance data, ensuring its flexibility and adaptability. Ultimately, the optimized maintenance strategy document, that is, the final maintenance plan and replacement schedule for lightweight building materials, integrates all of the above information and provides scientific and reasonable maintenance guidance.

[0164] In an embodiment of the present application, first, a performance degradation prediction curve is used to analyze the material performance requirements and related safety standards of a building at different stages of use, evaluate the building's functional requirements and safety standards, and obtain key performance indicators and safety thresholds; second, a cost-benefit analysis is performed based on the performance degradation prediction curve, combined with the economic cost of the building during its life cycle, to obtain a cost-benefit analysis report; third, based on the cost-benefit analysis report, a preliminary maintenance plan and replacement schedule are formulated for different types of lightweight building materials; then, a dynamic adjustment mechanism is designed using the preliminary maintenance plan and replacement schedule, allowing the maintenance strategy to be updated in real time based on actual usage and the latest performance data, and a dynamic adjustment mechanism solution is generated; finally, based on the key performance indicators and safety thresholds, the cost-benefit analysis report, the preliminary maintenance plan and replacement schedule, and the dynamic adjustment mechanism solution, comprehensive processing is performed to compile a detailed optimized maintenance strategy document to obtain the optimized maintenance strategy, that is, the final maintenance plan and replacement schedule for lightweight building materials.

[0165] Through the above steps, this embodiment not only improves the personalization and dynamic adjustment capabilities of the maintenance strategy, but also ensures that the building maximizes economic benefits while meeting functional requirements and safety standards, providing a scientific and reasonable maintenance guidance plan.

[0166] Figure 2 The present invention provides a schematic diagram of a lightweight building material loss prediction system. Figure 2 As shown, the device includes:

[0167] Acquisition module 21 is used to collect environmental parameters around the building, obtain high-resolution images of the physical properties and surface conditions of lightweight building materials, and analyze and process the long-term changing trends of the micro-environment in which the lightweight building materials are located in combination with geographic information systems and climate models to obtain analysis results that cover the entire life cycle of the lightweight building materials;

[0168] Identification module 22 is configured to perform accelerated aging simulation on the aging process of lightweight building materials under different environmental conditions based on the analysis results, calculate the expected aging rate of the materials using a decision tree algorithm in machine learning, analyze the contribution of various environmental factors to material aging using aging sensitivity analysis technology, identify key influencing factors, and obtain a potential loss risk level;

[0169] An estimation module 23 is configured to estimate the long-term performance degradation trend of lightweight building materials under actual use conditions based on the potential loss risk level, combined with maintenance records, user feedback information, and a historical case library of similar materials during the building's use cycle, using grey system theory modeling, and using time series analysis technology to mine patterns in historical data to generate a performance degradation prediction curve;

[0170] The formulation module 24 is used to formulate a personalized and dynamically adjusted lightweight building material maintenance plan and replacement schedule based on the performance degradation prediction curve, taking into account the functional requirements, economic costs and safety standards of the building, to obtain an optimized maintenance strategy.

[0171] Figure 2 The loss prediction system for lightweight building materials can be executed Figure 1 The implementation principles and technical effects of the lightweight building material loss prediction method described in the illustrated embodiment will not be elaborated on here. The specific manner in which the various modules and units in the lightweight building material loss prediction system described in the above embodiment perform their operations has been described in detail in the relevant embodiments of the method and will not be elaborated on here.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting loss of lightweight building materials, characterized in that: include: Collecting high-resolution images of environmental parameters surrounding the building and the physical properties and surface conditions of lightweight building materials in the building, and analyzing and processing the long-term change trend of the environment in which the lightweight building materials are located by combining a pre-established geographic information system and a climate model to obtain analysis results; Based on the analysis results, an accelerated aging simulation is performed on the aging process of lightweight building materials under different environmental conditions, and a decision tree algorithm in machine learning is applied to calculate the expected aging rate of the lightweight building materials. The contribution of each environmental factor in the analysis results to the aging of the lightweight building materials is analyzed through aging sensitivity analysis technology, and the key influencing factors among the environmental factors are identified to obtain the potential loss risk level; Based on the potential loss risk level, combined with the building's maintenance records, user feedback information, and a historical case library of similar building materials during its service life, grey system theory modeling is applied to estimate the long-term performance degradation trend of lightweight building materials under actual use conditions. Time series analysis technology is used to mine historical data patterns extracted from the building's maintenance records, user feedback information, and a historical case library of similar building materials during its service life to generate a performance degradation prediction curve. According to the performance degradation prediction curve, and taking into account the functional requirements, economic costs and safety standards of the building, a maintenance plan and replacement schedule for lightweight building materials are formulated.

2. The method for predicting loss of lightweight building materials according to claim 1, characterized in that: According to the analysis results, the aging process of lightweight building materials under different environmental conditions is subjected to accelerated aging simulation, and the decision tree algorithm in machine learning is applied to calculate the expected aging rate of lightweight building materials. The contribution of each environmental factor in the analysis results to the aging of lightweight building materials is analyzed through aging sensitivity analysis technology, and the key influencing factors among the environmental factors are identified to obtain the potential loss risk level, including: Using the analysis results, an accelerated aging simulation is performed on the aging process of lightweight building materials under different environmental conditions to generate aging process data; Based on the aging process data, a decision tree algorithm in machine learning is applied to analyze and process the trend of the lightweight building material properties obtained from the aging process data over time to obtain an expected aging rate of the lightweight building material; Based on the expected aging rate of the lightweight building materials, the contribution of various environmental factors to the aging of the lightweight building materials is quantified through aging sensitivity analysis technology, key influencing factors are identified, and an aging sensitivity report is generated; The aging sensitivity report is used to comprehensively evaluate the aging conditions of lightweight building materials under various environmental conditions in the analysis results to obtain a potential loss risk level.

3. The method for predicting loss of lightweight building materials according to claim 2, characterized in that: Based on the expected aging rate of the lightweight building materials, the contribution of various environmental factors to the aging of the lightweight building materials is quantified through aging sensitivity analysis technology, key influencing factors are identified, and an aging sensitivity report is generated, including: Utilizing the expected aging rate of the lightweight building material, combined with various performance change records collected from lightweight building materials in actual use and aging prediction data of lightweight building materials under various hypothetical conditions, multiple regression analysis or machine learning models are applied to quantify the contribution of each environmental factor to the aging of the lightweight building material, thereby obtaining quantitative results of the impact of environmental factors; Based on the quantitative results of the environmental factors, a multi-factor comprehensive evaluation model is established, which takes into account the interaction between the environmental factors and their comprehensive effects on the aging of lightweight building materials, and obtains the output of the multi-factor comprehensive evaluation model; Based on the output of the multi-factor comprehensive assessment model, the importance of each environmental factor is analyzed through aging sensitivity analysis technology, key influencing factors that have a significant impact on the aging of lightweight building materials are identified, and a list of key influencing factors is generated; By using the list of key influencing factors, integrating all analysis results, compiling a detailed quantitative report on the impact of environmental factors and an analysis of key influencing factors, and forming an aging sensitivity report.

4. The method for predicting loss of lightweight building materials according to claim 2, characterized in that: The aging sensitivity report is used to comprehensively evaluate the aging conditions of lightweight building materials under various environmental conditions in the analysis results to obtain a potential loss risk level, including: Using the key influencing factors and quantitative results in the aging sensitivity report, an assessment framework is constructed to comprehensively evaluate the aging possibility and degree of lightweight building materials under different environmental conditions, thereby generating an environmental condition aging assessment framework. Based on the environmental condition aging assessment framework, multiple possible usage scenarios are created, and the aging performance of lightweight building materials under these scenarios is simulated through computer simulation or experimental verification to obtain scenario simulation analysis results; Based on the results of the scenario simulation analysis, a risk assessment model is used to quantitatively assess the aging degree of lightweight building materials under various environmental conditions, and the aging risk is determined by the aging degree to generate an aging risk assessment report; The aging risk assessment report is used to optimize and adjust the assessment framework to obtain the potential loss risk level.

5. The method for predicting loss of lightweight building materials according to claim 1, characterized in that: Based on the potential loss risk level, combined with the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials, the long-term performance degradation trend of lightweight building materials under actual use conditions is estimated using grey system theory modeling. Time series analysis technology is used to mine historical data patterns extracted from the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials to generate a performance degradation prediction curve, including: Using the potential loss risk level, maintenance records, user feedback information, and historical case libraries of similar lightweight building materials during the building's lifecycle are integrated to obtain a multi-source information integration report. Based on the multi-source information integration report, the grey system theory is applied to model the long-term performance degradation trend of lightweight building materials under actual use conditions to obtain a grey system model; Based on the grey system model, the performance degradation of lightweight building materials in the future is estimated and a preliminary performance degradation trend prediction result is obtained; Using the preliminary performance degradation trend prediction results and employing time series analysis technology, historical data patterns are mined from the multi-source information integration report to generate a historical data pattern analysis report; Based on the comprehensive processing of the historical data regularity analysis report and the estimation results of the grey system model, an estimation curve reflecting the degradation of the performance of lightweight building materials over time is drawn to generate a performance degradation estimation curve.

6. The method for predicting loss of lightweight building materials according to claim 5, characterized in that: The method utilizes the preliminary performance degradation trend prediction results and employs time series analysis technology to mine historical data patterns from the multi-source information integration report and generate a historical data pattern analysis report, including: Using the preliminary performance degradation trend prediction results, the performance changes of the lightweight building materials in different time periods are organized into an ordered time series data set to obtain a time series data set; According to the time series data set, applying statistical methods or machine learning techniques to extract features and preprocess the time series in the time series data set, perform smoothing and remove outliers to obtain a preprocessed time series data set; Based on the preprocessed time series data set, time series analysis technology is used to perform pattern recognition and regularity mining on the periodicity, trend and random fluctuation components in the historical data formed after preprocessing, and obtain pattern recognition results. Utilizing the pattern recognition results, combined with maintenance records, user feedback, and a case library in the multi-source information integration report, the correlation between different factors affecting the performance degradation of lightweight building materials is analyzed to obtain a correlation analysis report; Based on the pattern recognition results and correlation analysis report, comprehensive processing is performed to generate a historical data regularity analysis report.

7. The method for predicting loss of lightweight building materials according to claim 1, characterized in that: According to the performance degradation prediction curve, taking into account the functional requirements, economic costs and safety standards of the building, a maintenance plan and replacement schedule for lightweight building materials are formulated, including: Using the performance degradation prediction curve, the requirements for the performance of lightweight building materials at different stages of use of buildings and the safety standards related to lightweight building materials are analyzed, the functional requirements and safety standards of buildings are evaluated, and key performance indicators and safety thresholds are obtained; Performing a cost-benefit analysis based on the performance degradation prediction curve and the economic cost of the building during its life cycle to obtain a cost-benefit analysis report; Based on the cost-benefit analysis report, develop a preliminary maintenance plan and replacement schedule for different types of lightweight building materials; Using the preliminary maintenance plan and replacement schedule, a dynamic adjustment mechanism is designed to allow real-time updates of maintenance strategies based on actual usage and the latest performance data, generating a dynamic adjustment mechanism solution; Based on the key performance indicators and safety thresholds, the cost-benefit analysis report, the preliminary maintenance plan and replacement schedule, and the dynamic adjustment mechanism plan, a final maintenance plan and replacement schedule for lightweight building materials is developed.

8. A loss prediction system for lightweight building materials, characterized in that: include: an acquisition module for collecting high-resolution images of environmental parameters surrounding the building and the physical properties and surface conditions of the lightweight building materials in the building, and analyzing and processing the long-term change trend of the environment in which the lightweight building materials are located by combining a pre-established geographic information system and a climate model to obtain analysis results; an identification module for performing accelerated aging simulation on the aging process of lightweight building materials under different environmental conditions based on the analysis results, applying a decision tree algorithm in machine learning to calculate the expected aging rate of the lightweight building materials, analyzing the contribution of each environmental factor in the analysis results to the aging of the lightweight building materials through aging sensitivity analysis technology, identifying key influencing factors among the environmental factors, and obtaining a potential loss risk level; an estimation module for estimating the long-term performance degradation trend of lightweight building materials under actual use conditions based on the potential loss risk level, in combination with the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials, using grey system theory modeling, and using time series analysis technology to mine historical data patterns extracted from the maintenance records of the building during its service life, user feedback information, and a historical case library of similar building materials to generate a performance degradation prediction curve; A development module is used to develop a maintenance plan and replacement schedule for lightweight building materials based on the performance degradation prediction curve, taking into account the functional requirements, economic costs and safety standards of the building.

Images