Double-layer window high-altitude cold resistance detection system

Abstract

The invention discloses a double-layer window high-altitude cold resistance detection system, a data acquisition module collects structure parameters and environmental data of a double-layer window in real time to provide accurate original data support for subsequent analysis, a data preprocessing module cleans and classifies the original data to ensure data quality and consistency, and the data quality and consistency are improved. The heat insulation performance detection module, the resistance detection module and the pressure detection module carry out multi-dimensional performance evaluation from the angles of thermal engineering, frost resistance and air pressure adaptability, the accuracy and systematicness of traditional single index detection are improved, the frost resistance and resistance and pressure detection module makes up for the defects of qualitative analysis in a traditional detection method, and the accuracy and systematicness of detection are improved. According to the system, more accurate risk quantification is provided, the feedback module ensures that a user can quickly identify potential risks and perform optimization adjustment through real-time data display and dynamic feedback, and the integrated design of the system not only improves the detection precision, but also greatly enhances the adaptability and long-term reliability of the system in a high-altitude extreme environment.

Description

Technical Field

[0001] The present application relates to the technical field of high-altitude double-layer window detection, and in particular to a high-altitude cold resistance detection system for double-layer windows. Background Art

[0002] With the increasing adoption of green building concepts and the continued advancement of urban development in plateau and mountainous areas, buildings in high-altitude, cold regions are placing higher demands on the energy efficiency and adaptability of doors and windows. As a core component of the building envelope, double-glazed windows, with their excellent thermal insulation properties, have been widely used in cold climates, particularly at high altitudes, where temperatures are low, air pressure is low, and the temperature swings between day and night are large.

[0003] The practical challenges of double-glazed windows in high-altitude applications are numerous, as existing testing methods suffer from numerous shortcomings. First, existing testing methods are fragmented, preventing a unified assessment of double-glazed windows' thermal insulation, frost resistance, and air pressure adaptability. Second, existing data analysis relies heavily on manual interpretation and empirical judgment, lacking systematic modeling and intelligent analysis, making it difficult to accurately reflect the comprehensive performance of double-glazed windows in complex environments. Third, most existing methods suffer from high costs, long cycles, and limited flexibility when simulating real-world high-altitude environments, making them incapable of meeting the demands of large-scale, rapid testing. Summary of the Invention

[0004] The main purpose of this application is to provide a double-layer window high-altitude cold resistance detection system to solve the problems raised by the above background technology.

[0005] To achieve the above objectives, this application provides the following technical solutions: A double-layer window high-altitude cold resistance detection system includes a data acquisition module, a data preprocessing module, a thermal insulation performance detection module, a resistance detection module, a pressure detection module and a feedback module; The data acquisition module is used to collect basic parameters of double-glazed windows and environmental parameters of high-altitude areas; The data preprocessing module is used to preprocess the collected parameters and reorganize them into a thermal insulation performance data group, an antifreeze and anti-frost performance data group, and a pressure performance data group; The thermal insulation performance detection module is used to calculate and generate the thermal insulation performance analysis coefficient GRF and perform data analysis on the thermal insulation performance analysis coefficient GRF; The resistance detection module is used to calculate and generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF; The pressure detection module is used to calculate and generate a pressure analysis coefficient YLF and perform data analysis on the pressure analysis coefficient YLF; The feedback module is used to feed back various parameters and analysis results to the visualization terminal.

[0006] Preferably, the data acquisition module includes a thermal insulation performance acquisition unit, an antifreeze and anti-frost performance acquisition unit, and a pressure performance acquisition unit; The thermal insulation performance acquisition unit is used to collect thermal insulation performance parameters of the double-glazed window, including indoor and outdoor temperature difference, heat flux density, interlayer thickness, glass thermal conductivity, window frame thermal conductivity and inner glass surface temperature; The antifreeze and anti-frost performance collection unit is used to collect antifreeze and cold resistance parameters of the double-glazed window, including the dew point temperature of the hollow layer, argon retention rate, edge linear heat transfer coefficient, low-temperature cycle deformation rate, surface emissivity and condensation lag time; The pressure performance acquisition unit is used to collect the pressure parameters and thermal expansion parameters of the double-layer window, including the absolute pressure of the internal cavity, the internal and external pressure difference gradient, the maximum allowable pressure difference threshold, the linear thermal expansion, the sealant volume change rate and the equivalent stress.

[0007] Preferably, the data preprocessing module includes a cleaning module and a sorting module, the cleaning module is used to preprocess and dimensionlessly convert the multiple parameters collected by the data collection module, and the sorting module is used to sort the processed multiple parameters to generate a thermal insulation performance data group, an antifreeze and anti-frost performance data group, and a pressure performance data group; The thermal insulation performance data set includes the indoor and outdoor temperature difference SWX, heat flux density RLM, interlayer thickness JCH, glass thermal conductivity BDR, window frame thermal conductivity KDR and inner glass surface temperature NBW; The antifreeze and anti-frost performance data set includes the hollow layer dew point temperature LWD, argon retention rate QBL, edge linear heat transfer coefficient BCR, low temperature cycle deformation rate DBL, surface emissivity BFS and condensation lag time LHZ; The pressure performance data set includes the internal cavity absolute pressure KYL, the internal and external pressure difference gradient NWT, the maximum allowable pressure difference threshold MYC, the linear thermal expansion RXP, the sealant volume change rate JBL and the equivalent stress DXY.

[0008] Preferably, the thermal insulation performance detection module includes a thermal insulation performance model and a thermal insulation performance analysis unit; The thermal insulation performance model is used to generate the thermal insulation performance analysis coefficient GRF, and the thermal insulation performance analysis unit is used to analyze the thermal insulation performance analysis coefficient GRF.

[0009] Preferably, the thermal insulation performance model extracts parameters from the thermal insulation performance data group and the pressure performance data group, including interlayer thickness JCH, indoor and outdoor temperature difference SWX, heat flux RLM, glass thermal conductivity BDR, window frame thermal conductivity KDR, internal and external pressure gradient NWT, internal cavity absolute pressure KYL, linear thermal expansion RXP, and sealant volume change rate JBL. The extracted multiple parameters are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and generate a thermal insulation performance analysis coefficient GRF. The specific calculation method is as follows:

[0010] Where: JCH is the interlayer thickness, SWX is the indoor-outdoor temperature difference, RLM is the heat flux, BDR is the thermal conductivity of the glass, KDR is the thermal conductivity of the window frame, NWT is the internal-external pressure gradient, KYL is the internal cavity absolute pressure, RXP is the linear thermal expansion, JBL is the volume change rate of the sealant, and A1 is an empirical constant with a value range of [1,2]. The specific value of A1 is adjusted by the user.

[0011] Preferably, the thermal insulation performance analysis unit is used to analyze the thermal insulation performance analysis coefficient GRF, and according to the analysis result, determine whether the current double-glazed window meets the thermal insulation requirements of high altitude areas. The specific analysis method is as follows: when When , it means that the current double-glazed windows meet the thermal insulation needs of high-altitude areas; when It means that the current double-glazed windows cannot meet the thermal insulation needs of high-altitude areas.

[0012] Preferably, the resistance detection module includes a resistance detection model and a heat resistance performance analysis unit; The resistance detection model is used to generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and the heat resistance analysis unit is used to perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF.

[0013] Preferably, the resistance detection model extracts parameters from the antifreeze and antifrost information data group and the pressure performance data group, including the air layer dew point temperature LWD, the argon retention rate QBL, the edge linear heat transfer coefficient BCR, the low-temperature cyclic deformation rate DBL, the surface emissivity BFS, the condensation lag time LHZ, the linear thermal expansion RXP, the equivalent stress DXY, the internal cavity absolute pressure KYL, the maximum allowable pressure difference threshold MYC, and the internal and external pressure difference gradient NWT. The extracted parameters are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF. The specific calculation methods are as follows:

[0014]

[0015] Where: LWD is the dew point temperature of the air layer, QBL is the argon retention rate, BCR is the edge linear heat transfer coefficient, DBL is the low-temperature cyclic deformation rate, BFS is the surface emissivity, LHZ is the condensation lag time, RXP is the linear thermal expansion, DXY is the equivalent stress, KYL is the internal cavity absolute pressure, MYC is the maximum allowable pressure difference threshold, and NWT is the internal and external pressure difference gradient; A2 and A3 are empirical constants. The value range of A2 is [0.5.1.5], and the value range of A3 is [0.1,0.5]. The values ​​of A2 and A3 are adjusted by the user.

[0016] Preferably, the model integration unit is used to perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and determine whether the current double-glazed window meets the cold and frost resistance requirements of high-altitude areas based on the analysis results. The specific analysis method is as follows: when When, it means that the current double-glazed windows meet the cold resistance needs of high-altitude areas; when When , it means that the current double-glazed windows do not meet the cold resistance needs of high altitude areas; when When , it means that the current double-glazed windows meet the frost resistance requirements of high altitude areas; when It means that the current double-glazed windows do not meet the frost resistance requirements of high-altitude areas.

[0017] Preferably, the mechanical response analysis module includes a simulation control unit and a response index extraction unit; The simulation control unit is used to generate a pressure analysis coefficient YLF, and the 52 is used to analyze the pressure analysis coefficient YLF.

[0018] The simulation control unit is used to input the parameters under the pressure information data group, including the internal cavity absolute pressure KYL, the internal and external pressure difference gradient NWT, the maximum allowable pressure difference threshold MYC, the linear thermal expansion RXP, the sealant volume change rate JBL and the equivalent stress DXY, into the pre-trained deep learning framework, and perform feature fusion through a multi-layer neural network to calculate and generate the pressure analysis coefficient YLF. The specific calculation method is as follows;

[0019] Where: KYL is the absolute pressure of the internal cavity, NWT is the internal and external pressure gradient, MYC is the maximum allowable pressure difference threshold, RXP is the linear thermal expansion, JBL is the volume change rate of the sealant, DXY is the equivalent stress, CL is the maximum equivalent stress, which is provided by the laboratory or manufacturer, and A4 is an empirical constant with a value range of [0.1, 0.3]. The specific value is adjusted by the user. The response index extraction unit is used to perform data analysis on the pressure analysis coefficient YLF generated by the calculation to determine whether the current commercial double-layer window meets the pressure change requirements of high altitude areas. The specific analysis method is as follows: When , it means that the current double-glazed windows meet the pressure change requirements of high altitude areas; when When , it means that the current double-glazed windows cannot meet the pressure change requirements of high altitude areas.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. By integrating multi-module data acquisition and processing technologies, the system provides a comprehensive solution for evaluating the performance of double-glazed windows in high-altitude areas. The data acquisition module collects structural parameters and environmental data of double-glazed windows in real time, providing accurate raw data for subsequent analysis. The data preprocessing module cleans and organizes the raw data to ensure data quality and consistency, providing a reliable foundation for performance analysis. The thermal insulation performance testing module, the resistance testing module, and the pressure testing module conduct multi-dimensional performance evaluation from the perspectives of thermal performance, frost resistance, and air pressure adaptability, respectively. In particular, the use of multi-parameter integrated modeling improves the accuracy and systematicity of traditional single-metric testing. The frost resistance and pressure testing modules address the shortcomings of traditional qualitative analysis methods, providing more precise risk quantification. The feedback module, through real-time data display and dynamic feedback, enables users to quickly identify potential risks and make optimization adjustments. The system's integrated design not only improves detection accuracy but also greatly enhances its adaptability and long-term reliability in extreme high-altitude environments, driving the development of double-glazed window technology towards higher performance and intelligentization.

[0021] 2. The thermal insulation performance model integrates multiple key parameters from the thermal insulation and pressure performance data sets to establish a multi-input feature vector. This includes thermal parameters such as interlayer thickness JCH, indoor-outdoor temperature difference SWX, heat flux RLM, glass thermal conductivity BDR, and window frame thermal conductivity KDR, as well as pressure-related parameters such as internal cavity absolute pressure KYL, internal-external pressure gradient NWT, linear thermal expansion RXP, and sealant volume change rate JBL. By introducing a pre-trained deep learning neural network architecture, this multi-dimensional data is subjected to feature fusion and nonlinear mapping to calculate a thermal insulation performance factor (GRF) representing the overall thermal insulation capacity. The thermal insulation performance analysis unit sets a threshold based on the calculated GRF. When GRF ≥ 0.8, the double-glazed window is deemed to meet the insulation requirements for high-altitude areas. When GRF < 0.8, the double-glazed window is deemed to have insufficient thermal insulation capacity and requires design optimization or material adjustment. This threshold-based decision mechanism enhances the system's intelligent decision-making capabilities and is suitable for integration into real-time feedback modules to efficiently screen out substandard samples.

[0022] 3. In plateaus and severely cold regions, window structures are frequently exposed to multiple climatic influences, including rapid temperature swings between day and night, low-pressure expansion, and condensation. The resistance detection module systematically integrates influencing factors related to thermal stress and water vapor transmission pathways to effectively evaluate the stress-deformation resistance and frost-delay performance of window material systems. This provides a scientific basis for the design of window structures, airtight construction, and filling processes, helping ensure the stable operation of window products in extreme environments. Resistance detection model 41 introduces two independent performance indicators—the frost resistance analysis coefficient (KDF) and the frost resistance analysis coefficient (KHF)—to reflect the double-glazed window's resistance to extreme low-temperature deformation and frost resistance, respectively. This addresses the inability of traditional assessment methods to distinguish between different low-temperature impact mechanisms, improving the model's relevance and analytical resolution. The thermal performance analysis unit applies thresholds to the frost resistance coefficients (KDF) and frost resistance coefficients (KHF) output by the model: KDF ≥ 0.6 indicates good frost resistance, while KHF ≥ 0.7 indicates good frost resistance. Values ​​below the thresholds indicate failure to meet performance requirements. This mechanism establishes quantifiable automatic judgment standards, improves the efficiency and intelligence of the system in window structure screening in high-altitude and cold areas, and is suitable for quality control and material optimization processes in actual projects.

[0023] 4. In high-altitude environments, the air is thin, the pressure changes frequently, and the temperature difference is drastic, which will cause continuous loads and periodic deformations to the internal cavity of the double-layer window. Traditional static strength testing methods are difficult to cope with this complex load field. The mechanical response analysis module embeds the thermal-mechanical-pressure multi-physics field coupling analysis into the learning model, which can more realistically and comprehensively reflect the force response of the window during actual use. The pressure analysis coefficient YLF, as a comprehensive indicator of structural mechanical properties, has the advantages of strong uniformity, high degree of quantification, and fast dynamic response. It is suitable as an input reference variable for subsequent window intelligent control modules such as automatic air pressure balancing devices or thermal deformation adjustment structures, realizing an integrated closed-loop system from "detection-evaluation-feedback" and improving the ability to identify structural pressure resistance risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is the flow chart of the application system.

[0025] In the figure: 1. Data acquisition module; 11. Thermal insulation performance acquisition unit; 12. Antifreeze and anti-frost performance acquisition unit; 13. Pressure performance acquisition unit; 2. Data preprocessing module; 21. Cleaning module; 22. Sorting module; 3. Thermal insulation performance detection module; 31. Thermal insulation performance model; 32. Thermal insulation performance analysis unit; 4. Resistance detection module; 41. Resistance detection model; 42. Heat resistance performance analysis unit; 5. Pressure detection module; 51. Pressure detection model; 52. Pressure performance analysis unit; 6. Feedback module. DETAILED DESCRIPTION

[0026] 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 ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified as "first," "second," or "third" may explicitly or implicitly include at least one of such features. In the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are intended only to illustrate the relative positional relationships and movement of components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly. Furthermore, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements and may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to such process, method, product, or apparatus.

[0028] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0029] Example 1: Please refer to Figure 1 , a double-layer window high altitude cold resistance detection system, including a data acquisition module 1, a data preprocessing module 2, a thermal insulation performance detection module 3, a resistance detection module 4, a pressure detection module 5 and a feedback module 6; The data acquisition module 1 is used to collect basic parameters of double-layer windows and environmental parameters of high altitude areas; The data preprocessing module 2 is used to preprocess the collected parameters and reorganize them into a thermal insulation performance data group, an antifreeze and anti-frost performance data group, and a pressure performance data group; The thermal insulation performance detection module 3 is used to calculate and generate the thermal insulation performance analysis coefficient GRF and perform data analysis on the thermal insulation performance analysis coefficient GRF; The resistance detection module 4 is used to calculate and generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF; The pressure detection module 5 is used to calculate and generate the pressure analysis coefficient YLF and perform data analysis on the pressure analysis coefficient YLF; The feedback module 6 is used to feed back various parameters and analysis results to the visualization terminal.

[0030] In this embodiment, data acquisition module 1 is used to collect real-time data on double-glazed window structural parameters, including glass thickness, interlayer spacing, and sealing, as well as high-altitude environmental parameters such as temperature, humidity, wind speed, and air pressure. By integrating multiple types of high-precision sensors and environmental monitoring devices, this module comprehensively and accurately captures key input variables that influence double-glazed window performance. This module overcomes the issues of low accuracy, high data latency, and insufficient environmental simulation associated with traditional manual sampling, providing authentic and complete data support for subsequent performance analysis.

[0031] Data preprocessing module 2 cleans, filters, and categorizes the raw data acquired by acquisition module 1, removing noise and outliers. It then divides the data into thermal insulation performance, antifreeze and frost resistance, and pressure performance data groups based on functional requirements. This module effectively improves data quality and structure, ensuring the accuracy and consistency of input data. This provides a stable and reliable foundation for indicator calculation and performance analysis in subsequent modules, overcoming the technical shortcomings of existing systems, such as data redundancy and unclear classification.

[0032] Thermal Insulation Performance Testing Module 3, based on the thermal insulation performance data set, calculates the thermal insulation performance analysis factor (GRF) by establishing a correlation model between parameters such as thermal resistance, heat flux, and temperature gradient, and then performs statistical and trend analysis on the GRF. This module effectively reflects the thermal conductivity and insulation performance of double-glazed windows in high-altitude, low-temperature environments. By comprehensively modeling multiple parameters, this module overcomes the traditional reliance on a single indicator in thermal engineering testing and significantly improves the systematicity and accuracy of thermal insulation performance assessments.

[0033] Based on the antifreeze and antifrost performance data set, the resistance detection module 4 calculates the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, respectively. It then comprehensively evaluates the antifreeze and antifrost properties based on key indicators such as the window surface temperature difference, frost formation time, and humidity change rate. This module quantifies the risk of cracking or frosting of windows in extremely cold environments, providing a scientific and reliable reference for optimizing double-glazed window structural design. This module overcomes the shortcomings of existing technologies, which primarily rely on visual observation and lack quantitative analysis methods, and enhances the detection system's in-depth assessment of its ability to adapt to extreme climates.

[0034] Pressure Testing Module 5 simulates the low-pressure conditions found in high-altitude areas, assessing the responsiveness and structural stability of double-layer window cavities under varying pressure differentials. By measuring parameters such as the pressure difference between the inside and outside of the window, the rate of change in sealing, and material deformation characteristics, it generates a pressure analysis coefficient (YLF) and conducts dynamic performance analysis. This module effectively identifies potential issues such as pressure imbalance and material fatigue that may arise during long-term use, helping to ensure reliable long-term operation at high altitudes. Compared to traditional static airtightness testing methods, this module offers enhanced dynamic adaptability and environmental simulation capabilities.

[0035] Feedback Module 6 presents key indicators, such as the thermal insulation performance analysis coefficient GRF, the antifreeze analysis coefficient KDF, the frost resistance analysis coefficient KHF, and the pressure analysis coefficient YLF, along with their analysis results, to a visualization terminal in the form of charts and reports. Users can intuitively view the changing trends and comprehensive evaluation conclusions of each performance indicator, and utilize the system's built-in early warning mechanisms to promptly identify potential risks. This module enables real-time display of data analysis results and human-computer interaction, effectively improving the system's practicality and user-friendliness, and addressing the delayed feedback and difficult interpretation issues associated with traditional testing methods.

[0036] By integrating multi-module data acquisition and processing technologies, the system provides a comprehensive solution for evaluating the performance of double-glazed windows in high-altitude areas. Data Acquisition Module 1 collects structural parameters and environmental data of double-glazed windows in real time, providing accurate raw data for subsequent analysis. Data Preprocessing Module 2 cleans and organizes the raw data to ensure data quality and consistency, providing a reliable foundation for performance analysis. Thermal Insulation Testing Module 3, Resistance Testing Module 4, and Pressure Testing Module 5 conduct multi-dimensional performance assessments from the perspectives of thermal performance, frost and frost resistance, and air pressure adaptability, respectively. In particular, the use of multi-parameter integrated modeling improves the accuracy and systematicity of traditional single-metric testing. The frost and frost resistance and pressure testing modules address the shortcomings of traditional qualitative analysis methods, providing more precise risk quantification. Feedback Module 6, through real-time data display and dynamic feedback, enables users to quickly identify potential risks and make optimization adjustments. The system's integrated design not only improves detection accuracy but also significantly enhances its adaptability and long-term reliability in extreme high-altitude environments, driving the development of double-glazed window technology towards higher performance and intelligent capabilities.

[0037] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a thermal insulation performance acquisition unit 11, an antifreeze and anti-frost performance acquisition unit 12 and a pressure performance acquisition unit 13; The thermal insulation performance acquisition unit 11 is used to collect thermal insulation performance parameters of the double-layer window, including indoor and outdoor temperature difference, heat flux density, interlayer thickness, glass thermal conductivity, window frame thermal conductivity and inner glass surface temperature; The antifreeze and anti-frost performance collection unit 12 is used to collect the antifreeze and cold resistance parameters of the double-glazed window, including the dew point temperature of the hollow layer, the argon retention rate, the edge linear heat transfer coefficient, the low-temperature cycle deformation rate, the surface emissivity and the condensation lag time; The pressure performance acquisition unit 13 is used to collect the pressure parameters and thermal expansion parameters of the double-layer window, including the absolute pressure of the internal cavity, the internal and external pressure difference gradient, the maximum allowable pressure difference threshold, the linear thermal expansion, the volume change rate of the sealant and the equivalent stress.

[0038] The data preprocessing module 2 includes a cleaning module 21 and a sorting module 22. The cleaning module 21 is used to preprocess and dimensionlessly convert the multiple parameters collected by the data acquisition module 1. The sorting module 22 is used to sort the processed multiple parameters to generate a thermal insulation performance data set, an antifreeze and anti-frost performance data set, and a pressure performance data set. The thermal insulation performance data set includes the indoor and outdoor temperature difference SWX, heat flux density RLM, interlayer thickness JCH, glass thermal conductivity BDR, window frame thermal conductivity KDR and inner glass surface temperature NBW; The antifreeze and anti-frost performance data set includes the hollow layer dew point temperature LWD, argon retention rate QBL, edge linear heat transfer coefficient BCR, low temperature cycle deformation rate DBL, surface emissivity BFS and condensation lag time LHZ; The pressure performance data set includes the internal cavity absolute pressure KYL, the internal and external pressure difference gradient NWT, the maximum allowable pressure difference threshold MYC, the linear thermal expansion RXP, the sealant volume change rate JBL and the equivalent stress DXY.

[0039] In this embodiment, the data acquisition module 1 integrates a thermal insulation performance acquisition unit 11, an antifreeze and frost performance acquisition unit 12, and a pressure performance acquisition unit 13, enabling comprehensive collection of multi-dimensional key performance parameters of double-glazed windows. The thermal insulation performance acquisition unit 11 collects thermal parameters such as the indoor and outdoor temperature difference, heat flux, interlayer thickness, glass thermal conductivity, window frame thermal conductivity, and inner glass surface temperature. The antifreeze and frost performance acquisition unit 12 collects weathering parameters such as the dew point temperature of the hollow layer, argon retention rate, edge linear heat transfer coefficient, low-temperature cyclic deformation rate, surface emissivity, and condensation lag time. The pressure performance acquisition unit 13 collects structural and environmental adaptability parameters such as the absolute pressure of the internal cavity, the internal and external pressure differential gradient, the maximum allowable pressure differential threshold, linear thermal expansion, sealant volume change rate, and equivalent stress. Compared with traditional systems, this module has stronger data integrity and adaptability, significantly improving the systematicness and accuracy of performance evaluation. The parameters collected by each performance acquisition unit 11 to 13 are all core indicators that affect the performance of double-glazed windows in actual environments. Through structured and quantitative parameter division, it facilitates subsequent performance degradation identification and cause analysis, further improving the system's engineering applicability and targeted evaluation.

[0040] The data preprocessing module 2 includes a cleaning module 21 and a sorting module 22. The cleaning module 21 is used to preprocess and dimensionlessly transform the raw data obtained in the data acquisition module 1, eliminating scale differences and abnormal interference between different physical quantities; the sorting module 22 classifies and aggregates the cleaned parameters to form a thermal insulation performance data group, an antifreeze and anti-frost performance data group, and a pressure performance data group, respectively, providing standardized data input for subsequent modeling and analysis modules, ensuring model accuracy and stability through modular design and standardized data structure.

[0041] Example 3: Please refer to Figure 1 , the thermal insulation performance detection module 3 includes a thermal insulation performance model 31 and a thermal insulation performance analysis unit 32; The thermal insulation performance model 31 is used to generate a thermal insulation performance analysis coefficient GRF, and the thermal insulation performance analysis unit 32 is used to analyze the thermal insulation performance analysis coefficient GRF.

[0042] Thermal insulation performance model 31 extracts parameters from the thermal insulation performance data set and the pressure performance data set, including interlayer thickness JCH, indoor and outdoor temperature difference SWX, heat flux RLM, glass thermal conductivity BDR, window frame thermal conductivity KDR, internal and external pressure gradient NWT, internal cavity absolute pressure KYL, linear thermal expansion RXP, and sealant volume change rate JBL. The extracted parameters are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate the thermal insulation performance analysis coefficient GRF. The specific calculation method is as follows:

[0043] Where: JCH is the interlayer thickness, SWX is the indoor-outdoor temperature difference, RLM is the heat flux, BDR is the thermal conductivity of the glass, KDR is the thermal conductivity of the window frame, NWT is the internal-external pressure gradient, KYL is the internal cavity absolute pressure, RXP is the linear thermal expansion, JBL is the volume change rate of the sealant, and A1 is an empirical constant with a value range of [1,2]. The specific value of A1 is adjusted by the user.

[0044] The thermal insulation performance analysis unit 32 is used to analyze the thermal insulation performance analysis coefficient GRF and, based on the analysis results, determine whether the current double-glazed window meets the thermal insulation requirements of high-altitude areas. The specific analysis method is as follows: when When , it means that the current double-glazed windows meet the thermal insulation needs of high-altitude areas; when It means that the current double-glazed windows cannot meet the thermal insulation needs of high-altitude areas.

[0045] In this embodiment, because high altitude areas are often accompanied by significant air pressure changes and large temperature differences between day and night, traditional thermal insulation performance evaluation methods are difficult to accurately reflect their comprehensive thermal performance. Thermal insulation performance detection module 3 incorporates pressure parameters into the thermal insulation performance evaluation logic, which is more physically consistent with the actual working state of the window in the plateau environment, thereby improving the authenticity and engineering reliability of the evaluation results. Thermal insulation performance model 31 establishes a multi-input feature vector by integrating multiple key parameters from the thermal insulation performance data set and the pressure performance data set. These features include thermal parameters such as interlayer thickness JCH, indoor and outdoor temperature difference SWX, heat flux density RLM, glass thermal conductivity BDR, and window frame thermal conductivity KDR, as well as pressure-related parameters such as internal cavity absolute pressure KYL, internal and external pressure gradient NWT, linear thermal expansion RXP, and sealant volume change rate JBL. By introducing a pre-trained deep learning neural network architecture, the above multi-dimensional data is subjected to feature fusion and nonlinear mapping to calculate the thermal insulation performance analysis coefficient GRF, which represents the comprehensive thermal insulation capacity. This modeling method breaks through the traditional single-factor determination mechanism of thermal resistance or heat flux, and for the first time realizes the quantitative evaluation of thermal insulation capacity under the structure-environment-material coupling relationship, significantly improving the accuracy of model predictions and the breadth of application scenarios.

[0046] The calculation formula for the thermal insulation performance analysis coefficient GRF not only reflects the combined effect of thermal resistance and air pressure, but also introduces the empirical constant A1 as an adjustment item, allowing users to customize parameters based on actual application scenarios. This further enhances the model's applicability and engineering promotion value in different regions and window types.

[0047] Thermal insulation performance analysis unit 32 sets a threshold based on the calculated thermal insulation performance factor (GRF). When GRF ≥ 0.8, the double-glazed window is deemed to meet the thermal insulation requirements of high-altitude areas. When GRF < 0.8, the window's thermal insulation capacity is deemed insufficient, requiring design optimization or material adjustments. This threshold-based decision mechanism enhances the system's intelligent decision-making capabilities and is suitable for integration into the real-time feedback module to efficiently screen out substandard samples.

[0048] Example 4: Please refer to Figure 1 , the resistance detection module 4 includes a resistance detection model 41 and a heat resistance performance analysis unit 42; The resistance detection model 41 is used to generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and the heat resistance performance analysis unit 42 is used to perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF.

[0049] The resistance detection model 41 extracts parameters from the antifreeze and antifrost information data set and the pressure performance data set, including the air layer dew point temperature LWD, argon retention rate QBL, edge linear heat transfer coefficient BCR, low-temperature cyclic deformation rate DBL, surface emissivity BFS, condensation lag time LHZ, linear thermal expansion RXP, equivalent stress DXY, internal cavity absolute pressure KYL, maximum allowable pressure difference threshold MYC, and internal and external pressure difference gradient NWT. The extracted parameters are input into a pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and generate the antifreeze analysis coefficient KDF and antifrost analysis coefficient KHF. The specific calculation methods are as follows:

[0050]

[0051] Where: LWD is the dew point temperature of the air layer, QBL is the argon retention rate, BCR is the edge linear heat transfer coefficient, DBL is the low-temperature cyclic deformation rate, BFS is the surface emissivity, LHZ is the condensation lag time, RXP is the linear thermal expansion, DXY is the equivalent stress, KYL is the internal cavity absolute pressure, MYC is the maximum allowable pressure difference threshold, and NWT is the internal and external pressure difference gradient; A2 and A3 are empirical constants. The value range of A2 is [0.5.1.5], and the value range of A3 is [0.1,0.5]. The values ​​of A2 and A3 are adjusted by the user.

[0052] The model integration unit 42 is used to perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and to determine whether the current double-glazed window meets the cold and frost resistance requirements of high-altitude areas based on the analysis results. The specific analysis method is as follows: when When, it means that the current double-glazed windows meet the cold resistance needs of high-altitude areas; when When , it means that the current double-glazed windows do not meet the cold resistance needs of high altitude areas; when When , it means that the current double-glazed windows meet the frost resistance requirements of high altitude areas; when It means that the current double-glazed windows do not meet the frost resistance requirements of high-altitude areas.

[0053] In this embodiment, window structures in plateaus and severely cold regions are frequently exposed to multiple climatic influences, including rapid temperature swings between day and night, low-pressure expansion, and condensation. Resistance Detection Module 4 systematically integrates factors affecting thermal stress and moisture transmission paths to effectively assess the stress-deformation resistance and frost-retardation performance of the window material system. This provides a scientific basis for the design of window structures, airtight construction, and filling processes, ensuring stable operation of window products in extreme environments.

[0054] Resistance detection model 41 introduces two independent performance indicators - the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, which respectively reflect the resistance of double-glazed windows to extreme low-temperature deformation antifreeze and surface frosting trend antifrost. It solves the problem that traditional evaluation methods cannot distinguish different low-temperature impact mechanisms, and improves the model's targetedness and analysis resolution.

[0055] The model extracts multiple highly correlated parameters from the antifreeze and antifrost performance data set and the pressure performance data set, covering key antifreeze and antifrost indicators such as argon retention rate QBL, edge linear heat transfer coefficient BCR, low-temperature cyclic deformation rate DBL, air layer dew point temperature LWD, condensation lag time LHZ, and surface emissivity BFS. It further introduces parameters reflecting structural stability and stress behavior, such as equivalent stress DXY, linear thermal expansion RXP, internal and external pressure difference gradient NWT, maximum pressure difference threshold MYC, and cavity absolute pressure KYL. The characteristics of each parameter are fused and modeled through a multi-layer neural network, which improves the model's adaptability and prediction accuracy to complex climate change conditions at high altitudes.

[0056] The calculation formulas for the freeze resistance coefficient (KDF) and the frost resistance coefficient (KHF) are based on physical correlations and incorporate adjustable empirical constants A2 and A3, enabling the model to maintain physical interpretability while also possessing adaptive capabilities. These empirical parameters can be optimized for different regions and material systems to meet the individual needs of project implementation, enhancing the system's practicality and portability.

[0057] Thermal performance analysis unit 42 applies threshold values ​​to the model's outputs: the antifreeze coefficient (KDF) and antifrost coefficient (KHF): KDF ≥ 0.6 indicates good antifreeze performance, while KHF ≥ 0.7 indicates good frost performance. Values ​​below these thresholds trigger a warning indicating that performance requirements are not met. This mechanism establishes a quantifiable, automated judgment standard, improving the system's efficiency and intelligence in selecting window structures in high-altitude cold regions, making it suitable for quality control and material optimization processes in real-world projects.

[0058] Example 5: Please refer to Figure 1 , the mechanical response analysis module 5 includes a simulation control unit 51 and a response index extraction unit 52; The simulation control unit 51 is used to generate the pressure analysis coefficient YLF, and 52 is used to analyze the pressure analysis coefficient YLF.

[0059] The simulation control unit 51 is used to input the parameters of the pressure information data set, including the internal cavity absolute pressure KYL, the internal and external pressure difference gradient NWT, the maximum allowable pressure difference threshold MYC, the linear thermal expansion RXP, the sealant volume change rate JBL, and the equivalent stress DXY, into a pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and thus calculate and generate the pressure analysis coefficient YLF. The specific calculation method is as follows;

[0060] Where: KYL is the absolute pressure of the internal cavity, NWT is the internal and external pressure gradient, MYC is the maximum allowable pressure difference threshold, RXP is the linear thermal expansion, JBL is the volume change rate of the sealant, DXY is the equivalent stress, CL is the maximum equivalent stress, which is provided by the laboratory or manufacturer, and A4 is an empirical constant with a value range of [0.1, 0.3]. The specific value is adjusted by the user. The response index extraction unit 52 is used to perform data analysis on the pressure analysis coefficient YLF generated by the calculation to determine whether the current commercial double-layer window meets the pressure change requirements of high altitude areas. The specific analysis method is as follows: When , it means that the current double-glazed windows meet the pressure change requirements of high altitude areas; when When , it means that the current double-glazed windows cannot meet the pressure change requirements of high altitude areas.

[0061] In this embodiment, simulation control unit 51 constructs a pressure analysis model based on key parameters of the pressure performance data set. These include core variables such as internal cavity absolute pressure KYL, internal / external pressure gradient NWT, maximum allowable pressure differential threshold MYC, linear thermal expansion RXP, sealant volume change rate JBL, and equivalent stress DXY. This model reflects the comprehensive mechanical behavior of the window under the combined effects of air pressure, thermal expansion and contraction, and material deformation. These parameters are input into a pre-trained deep learning framework, where nonlinear features are integrated using a multi-layer neural network structure. The resulting pressure analysis coefficient YLF is calculated and generated, providing a quantitative reference for mechanical safety.

[0062] The calculation formula for the pressure analysis coefficient YLF integrates the coupling relationship between multiple mechanical influencing factors, such as the air pressure-pressure difference ratio, the thermal expansion-strain influencing factor, the material's ultimate bearing capacity ratio, etc., and introduces the maximum equivalent stress constant CL and the adjustable empirical constant A4, making the model both physically interpretable and capable of adaptive parameter adjustment, effectively adapting to different window types and different application scenarios.

[0063] At high altitudes, the air is thin, pressure fluctuates frequently, and the temperature difference is drastic, causing continuous loads and periodic deformations on the internal cavity of double-layer windows. Traditional static strength testing methods are difficult to handle this complex load field. The mechanical response analysis module 5 embeds the thermal-mechanical-pressure multi-physics field coupling analysis into the learning model, which can more realistically and comprehensively reflect the force response of the window during actual use. The pressure analysis coefficient YLF, as a comprehensive indicator of structural mechanical performance, has the advantages of strong uniformity, high quantification, and fast dynamic response. It is suitable as an input reference variable for subsequent window intelligent control modules such as automatic air pressure balancing devices or thermal deformation adjustment structures, realizing an integrated closed-loop system from "detection-evaluation-feedback" and improving the ability to identify structural pressure resistance risks.

[0064] The response index extraction unit 52 automatically determines whether the current double-glazed window has the structural response capability to cope with pressure fluctuations in high-altitude areas by setting a reasonable threshold. If the pressure analysis coefficient YLF is lower than the threshold, it indicates that there is a structural stress risk or insufficient material pressure resistance, which helps to achieve rapid screening, problem tracing and targeted improvement before the product leaves the factory or at the construction site.

[0065] In addition, the functional units in the various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the description and drawings of this application, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

[0066] The above detailed description of the specific embodiments of the invention is intended only as an example, and the present application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions of the invention are also within the scope of the present application. Therefore, equivalent changes, modifications, and improvements made without departing from the spirit and scope of the present application should be included within the scope of the present application.

Claims

1. A double-layer window high altitude cold resistance detection system, characterized by: It includes a data acquisition module (1), a data preprocessing module (2), a thermal insulation performance detection module (3), a resistance detection module (4), a pressure detection module (5) and a feedback module (6); The data acquisition module (1) is used to collect basic parameters of double-layer windows and environmental parameters of high-altitude areas; The data preprocessing module (2) is used to preprocess the collected parameters and reorganize them into a thermal insulation performance data group, an antifreeze and anti-frost performance data group, and a pressure performance data group; The thermal insulation performance detection module (3) is used to calculate and generate the thermal insulation performance analysis coefficient GRF, and perform data analysis on the thermal insulation performance analysis coefficient GRF; The resistance detection module (4) is used to calculate and generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF; The pressure detection module (5) is used to calculate and generate a pressure analysis coefficient YLF, and perform data analysis on the pressure analysis coefficient YLF; The feedback module (6) is used to feed back various parameters and analysis results to the visualization terminal.

2. The double-glazed window high altitude cold resistance detection system according to claim 1, characterized in that: The data acquisition module (1) comprises a thermal insulation performance acquisition unit (11), an antifreeze and anti-frost performance acquisition unit (12), and a pressure performance acquisition unit (13); The thermal insulation performance acquisition unit (11) is used to collect thermal insulation performance parameters of the double-layer window, including indoor and outdoor temperature difference, heat flux density, interlayer thickness, glass thermal conductivity, window frame thermal conductivity and inner glass surface temperature; The antifreeze and anti-frost performance acquisition unit (12) is used to collect antifreeze and cold resistance parameters of the double-layer window, including the dew point temperature of the hollow layer, the argon retention rate, the edge linear heat transfer coefficient, the low-temperature cycle deformation rate, the surface emissivity and the condensation lag time; The pressure performance acquisition unit (13) is used to acquire the pressure parameters and thermal expansion parameters of the double-layer window, including the absolute pressure of the internal cavity, the internal and external pressure difference gradient, the maximum allowable pressure difference threshold, the linear thermal expansion, the volume change rate of the sealant, and the equivalent stress.

3. The double-glazed window high altitude cold resistance detection system according to claim 2, characterized in that: The data preprocessing module (2) includes a cleaning module (21) and a sorting module (22), wherein the cleaning module (21) is used to preprocess and dimensionlessly convert the multiple parameters collected by the data collection module (1), and the sorting module (22) is used to sort the processed multiple parameters, thereby generating a thermal insulation performance data group, an antifreeze and anti-frost performance data group, and a pressure performance data group; The thermal insulation performance data set includes the indoor and outdoor temperature difference SWX, heat flux density RLM, interlayer thickness JCH, glass thermal conductivity BDR, window frame thermal conductivity KDR and inner glass surface temperature NBW; The antifreeze and anti-frost performance data set includes the hollow layer dew point temperature LWD, argon retention rate QBL, edge linear heat transfer coefficient BCR, low temperature cycle deformation rate DBL, surface emissivity BFS and condensation lag time LHZ; The pressure performance data set includes the internal cavity absolute pressure KYL, the internal and external pressure difference gradient NWT, the maximum allowable pressure difference threshold MYC, the linear thermal expansion RXP, the sealant volume change rate JBL and the equivalent stress DXY.

4. The double-glazed window high altitude cold resistance detection system according to claim 3, characterized in that: The thermal insulation performance detection module (3) includes a thermal insulation performance model (31) and a thermal insulation performance analysis unit (32); The thermal insulation performance model (31) is used to generate the thermal insulation performance analysis coefficient GRF, and the thermal insulation performance analysis unit (32) is used to analyze the thermal insulation performance analysis coefficient GRF.

5. The double-glazed window high altitude cold resistance detection system according to claim 4, characterized in that: The thermal insulation performance model (31) extracts parameters from the thermal insulation performance data set and the pressure performance data set, including interlayer thickness JCH, indoor and outdoor temperature difference SWX, heat flux density RLM, glass thermal conductivity BDR, window frame thermal conductivity KDR, internal and external pressure gradient NWT, internal cavity absolute pressure KYL, linear thermal expansion RXP and sealant volume change rate JBL. The extracted multiple parameters are input into the pre-trained deep learning framework, and feature fusion is performed through a multi-layer neural network to calculate and generate the thermal insulation performance analysis coefficient GRF. The specific calculation method is as follows: Where: JCH is the interlayer thickness, SWX is the indoor-outdoor temperature difference, RLM is the heat flux, BDR is the thermal conductivity of the glass, KDR is the thermal conductivity of the window frame, NWT is the internal-external pressure gradient, KYL is the internal cavity absolute pressure, RXP is the linear thermal expansion, JBL is the volume change rate of the sealant, and A1 is an empirical constant with a value range of [1,2]. The specific value of A1 is adjusted by the user.

6. The double-glazed window high altitude cold resistance detection system according to claim 5, characterized in that: The thermal insulation performance analysis unit (32) is used to analyze the thermal insulation performance analysis coefficient GRF, and according to the analysis result, determine whether the current double-layer window meets the thermal insulation requirements of high altitude areas. The specific analysis method is as follows: when When , it means that the current double-glazed windows meet the thermal insulation needs of high-altitude areas; when It means that the current double-glazed windows cannot meet the thermal insulation needs of high-altitude areas.

7. The double-glazed window high altitude cold resistance detection system according to claim 6, characterized in that: The resistance detection module (4) includes a resistance detection model (41) and a heat resistance performance analysis unit (42); The resistance detection model (41) is used to generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and the heat resistance performance analysis unit (42) is used to perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF.

8. The double-glazed window high altitude cold resistance detection system according to claim 7, characterized in that: The resistance detection model (41) extracts parameters from the antifreeze and antifrost information data set and the pressure performance data set, including the air layer dew point temperature LWD, the argon retention rate QBL, the edge linear heat transfer coefficient BCR, the low temperature cycle deformation rate DBL, the surface emissivity BFS, the condensation lag time LHZ, the linear thermal expansion RXP, the equivalent stress DXY, the internal cavity absolute pressure KYL, the maximum allowable pressure difference threshold MYC, and the internal and external pressure difference gradient NWT. The extracted parameters are input into the pre-trained deep learning framework, and the features are fused through the multi-layer neural network to calculate and generate the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF. The specific calculation methods are as follows: Where: LWD is the dew point temperature of the air layer, QBL is the argon retention rate, BCR is the edge linear heat transfer coefficient, DBL is the low-temperature cyclic deformation rate, BFS is the surface emissivity, LHZ is the condensation lag time, RXP is the linear thermal expansion, DXY is the equivalent stress, KYL is the internal cavity absolute pressure, MYC is the maximum allowable pressure difference threshold, and NWT is the internal and external pressure difference gradient; A2 and A3 are empirical constants. The value range of A2 is [0.5.1.5], and the value range of A3 is [0.1,0.5]. The values ​​of A2 and A3 are adjusted by the user.

9. The double-glazed window high altitude cold resistance detection system according to claim 8, characterized in that: The model integration unit (42) is used to perform data analysis on the antifreeze analysis coefficient KDF and the antifrost analysis coefficient KHF, and to determine whether the current double-layer window meets the cold and frost resistance requirements of high-altitude areas based on the analysis results. The specific analysis method is as follows: when When, it means that the current double-glazed windows meet the cold resistance needs of high-altitude areas; when When, it means that the current double-glazed windows do not meet the cold resistance needs of high-altitude areas; when When , it means that the current double-glazed windows meet the frost resistance requirements of high altitude areas; when It means that the current double-glazed windows do not meet the frost resistance requirements of high-altitude areas.

10. The double-glazed window high altitude cold resistance detection system according to claim 9, characterized in that: The mechanical response analysis module (5) includes a simulation control unit (51) and a response index extraction unit (52); The simulation control unit (51) is used to generate a pressure analysis coefficient YLF, and the 52 is used to analyze the pressure analysis coefficient YLF. The simulation control unit (51) is used to input the parameters under the pressure information data group, including the internal cavity absolute pressure KYL, the internal and external pressure difference gradient NWT, the maximum allowable pressure difference threshold MYC, the linear thermal expansion RXP, the sealant volume change rate JBL and the equivalent stress DXY, into the pre-trained deep learning framework, perform feature fusion through a multi-layer neural network, and thus calculate and generate the pressure analysis coefficient YLF. The specific calculation method is as follows; Where: KYL is the absolute pressure of the internal cavity, NWT is the internal and external pressure gradient, MYC is the maximum allowable pressure difference threshold, RXP is the linear thermal expansion, JBL is the volume change rate of the sealant, DXY is the equivalent stress, CL is the maximum equivalent stress, which is provided by the laboratory or manufacturer, and A4 is an empirical constant with a value range of [0.1, 0.3]. The specific value is adjusted by the user. The response index extraction unit (52) is used to perform data analysis on the pressure analysis coefficient YLF generated by calculation to determine whether the current commercial double-layer window meets the pressure change requirements of high altitude areas. The specific analysis method is as follows: When , it means that the current double-glazed windows meet the pressure change requirements of high altitude areas; when When , it means that the current double-glazed windows cannot meet the pressure change requirements of high altitude areas.

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