Curtain wall safety multi-source data intelligent monitoring system

Abstract

The invention relates to the technical field of intelligent early warning, in particular to a curtain wall safety multi-source data intelligent monitoring system. The system comprises a curtain wall structure defect detection module, a material aging detection module, a function defect detection module and a curtain wall safety early warning module. Acquiring vibration data of the glass curtain wall; predicting the glass fracture probability based on the glass curtain wall vibration data; performing curtain wall frame deformation evaluation according to the glass fracture probability to obtain curtain wall frame deformation data; the material aging detection module detects the tensile state of the sealing rubber strip based on the deformation data of the curtain wall frame; calculating a cracking strain value based on the tensile state of the sealing rubber strip; evaluating the aging degree of the sealant according to the cracking strain value; the functional defect detection module is used for performing curtain wall water seepage simulation based on the sealant aging degree to obtain water seepage data; and evaluating the air tightness of the curtain wall based on the sealant aging degree. The safety and maintenance efficiency of the glass curtain wall are improved based on the intelligent early warning technology.

Description

Technical Field

[0001] The present invention relates to the field of intelligent early warning technology, and in particular to a curtain wall safety multi-source data intelligent monitoring system. Background Art

[0002] Glass curtain walls, comprised of high-strength glass and a metal frame, offer excellent transparency and daylighting. Insulated glass or low-emissivity coated glass enhance thermal insulation and energy efficiency. Glass curtain walls are highly resistant to wind pressure, ensuring they resist deformation or damage in high winds or inclement weather. However, traditional intelligent curtain wall security monitoring systems often rely on a single or limited data source, resulting in an incomplete assessment of the curtain wall's overall security status and an inability to promptly identify potential safety hazards. Data collection and processing lack intelligent analytical tools, often requiring manual intervention for data analysis and judgment. This is inefficient and susceptible to human influence, leading to inaccurate or delayed monitoring results. Traditional systems also suffer from poor adaptability. Given the complexity of curtain wall structures and environmental changes, they struggle to dynamically adjust and optimize, unable to respond in real time to changing environmental conditions. Data transmission and storage pose security risks, making them susceptible to data loss or tampering, impacting system reliability and effectiveness. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a curtain wall safety multi-source data intelligent monitoring system to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a curtain wall safety multi-source data intelligent monitoring system is proposed, which includes the following modules:

[0005] The curtain wall structure defect detection module is used to obtain glass curtain wall vibration data; predict the probability of glass breakage based on the glass curtain wall vibration data; and perform curtain wall frame deformation analysis based on the glass breakage probability to obtain curtain wall frame deformation data;

[0006] The material aging detection module is used to detect the tensile state of the sealing strip based on the deformation data of the curtain wall frame; calculate the cracking strain value based on the tensile state of the sealing strip; and evaluate the degree of sealant aging based on the cracking strain value;

[0007] Functional defect detection module, used to simulate curtain wall water seepage based on sealant aging to obtain water seepage data; evaluate curtain wall air tightness based on sealant aging; and detect curtain wall sound insulation based on curtain wall air tightness to obtain curtain wall sound insulation data;

[0008] The curtain wall safety warning module is used to predict the life of the glass curtain wall based on the curtain wall frame deformation data and curtain wall sound insulation data; upload the glass curtain wall life to the glass curtain wall safety intelligent monitoring system, and execute the curtain wall health level 3 warning task to obtain glass curtain wall health warning data.

[0009] The present invention predicts the probability of glass breakage based on glass curtain wall vibration data, thereby detecting existing glass safety hazards in advance and reducing the risk of safety accidents caused by accidental glass breakage. By analyzing the deformation of the curtain wall frame based on the glass breakage probability and obtaining curtain wall frame deformation data, the stability of the curtain wall structure can be more comprehensively analyzed to avoid glass stress concentration and structural failure caused by frame deformation. The beneficial effect of the material aging detection module is that it can detect the tensile state of the sealing strip based on the curtain wall frame deformation data and further calculate the cracking strain value, thereby quantitatively assessing the degree of sealant aging, facilitating maintenance or replacement before the sealant reaches the critical point of failure, thereby improving the durability and sealing of the curtain wall. The beneficial effect of the functional defect detection module is that by simulating curtain wall water seepage based on the degree of sealant aging and obtaining water seepage data, it can prevent water seepage problems before they occur, reducing damage to the curtain wall internal structure and deterioration of the building's internal environment caused by seal failure. In addition, the module can also assess the curtain wall airtightness based on the degree of sealant aging and further detect the curtain wall sound insulation performance, thereby ensuring the comfort of the indoor environment and improving the building's energy efficiency. The beneficial effect of the curtain wall safety warning module is that it predicts the life of the glass curtain wall by combining the curtain wall frame deformation data and the curtain wall sound insulation data, so that maintenance personnel can take measures in advance to prevent safety accidents caused by aging or structural failure of the curtain wall system; this module can upload the glass curtain wall life data to the glass curtain wall safety intelligent monitoring system, and perform the curtain wall health three-level warning task to obtain glass curtain wall health warning data, so that managers can arrange maintenance plans of different degrees according to the warning level, improve the safety, durability and service life of the curtain wall, and at the same time reduce maintenance costs and emergency accident handling costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0011] Figure 1 This is a module diagram of a curtain wall safety multi-source data intelligent monitoring system according to the present invention;

[0012] Figure 2 Detailed functional flow diagram of the curtain wall structure defect detection module in the present invention;

[0013] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0014] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work are within the scope of protection of the present invention.

[0015] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0016] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0017] To achieve this, please refer to Figures 1 to 2 The present invention provides a curtain wall safety multi-source data intelligent monitoring system, which includes the following modules:

[0018] S1: Curtain wall structure defect detection module, used to obtain glass curtain wall vibration data; predict the probability of glass breakage based on the glass curtain wall vibration data; perform curtain wall frame deformation analysis based on the glass breakage probability to obtain curtain wall frame deformation data;

[0019] In this embodiment, the vibration data of the curtain wall is obtained by an acceleration sensor installed on the surface of the glass curtain wall. The acceleration sensor is a high-precision device with an accuracy of 0.01g and a sampling frequency of 500Hz to ensure that tiny vibration changes can be captured. Based on the collected vibration data, the vibration amplitude and vibration frequency of the glass curtain wall are calculated using the time domain analysis method, and these data are compared with the known glass breakage standards. The probability of glass breakage is judged by comparing the vibration amplitude and vibration frequency, using the threshold setting method. When the vibration frequency exceeds 30Hz and the vibration amplitude exceeds 0.05g, it is set as a condition for increased probability of breakage. Then, based on the probability of glass breakage, the deformation analysis of the curtain wall frame is performed, and the finite element analysis method is used to process the data to calculate the maximum deformation value of the frame. By analyzing the relationship between vibration data and deformation data, the frame deformation threshold is set, and a deformation exceeding 0.2mm is used as a structural defect signal.

[0020] S2: Material aging detection module, used to detect the tensile state of the sealing strip based on the deformation data of the curtain wall frame; calculate the cracking strain value based on the tensile state of the sealing strip; and evaluate the degree of sealant aging based on the cracking strain value;

[0021] In this embodiment, based on the frame deformation data, a strain sensor is used to monitor the tensile state of the sealing strip. The strain sensor is installed on the surface of the sealing strip, and a displacement sensor with an accuracy of 0.001mm is used for real-time tensile monitoring. The tensile strain value is calculated by recording the deformation of the sealing strip under different loads. The calculation formula of the tensile strain value is: strain value = (deformation / original length) × 100%. When the threshold value is set to a tensile strain value exceeding 2.0%, it indicates that the sealing strip is cracked. Based on the obtained strain value, the cracking strain value is calculated, and the cracking strain is calculated using the standard stress-strain relationship to determine the degree of aging of the sealant. The specific degree of aging is determined by comparing standard data of different years. When the cracking strain value is greater than 3.5%, the sealant is considered to be in a high aging state.

[0022] S3: Functional defect detection module, used to simulate curtain wall water seepage based on the degree of sealant aging to obtain water seepage data; evaluate curtain wall air tightness based on the degree of sealant aging; and detect curtain wall sound insulation based on curtain wall air tightness to obtain curtain wall sound insulation data;

[0023] In this embodiment, a curtain wall water seepage simulation is performed based on the aging degree of the sealing strip. The water seepage simulation is modeled using CFD (computational fluid dynamics) software, and the aging parameters of the sealant and external environmental conditions (such as precipitation, wind speed, etc.) are input. The path and penetration amount of the curtain wall water seepage are calculated through fluid simulation. After the water seepage simulation, water seepage data is obtained, including water flow velocity, penetration path and penetration depth. Further, based on the water seepage simulation data, the air tightness of the curtain wall is evaluated. The air flow permeability is obtained by simulating the air flow and analyzing the path of the air flow through the sealing strip. When the air flow permeability exceeds 10%, it indicates that the air tightness of the curtain wall has decreased. Then, based on the air tightness evaluation results, the sound insulation performance of the curtain wall is tested. According to the sound insulation test standard ISO140-3, the sound insulation effect of the curtain wall is determined by sound wave propagation test under known environmental noise levels. The sound insulation index of the curtain wall is calculated by measuring the sound wave transmission loss (STL). When the sound insulation index value exceeds 40dB, the curtain wall is considered to have good sound insulation performance. Based on these data, the curtain wall sound insulation data is finally obtained.

[0024] S4: Curtain wall safety warning module, used to predict the life of the glass curtain wall based on the curtain wall frame deformation data and curtain wall sound insulation data; upload the glass curtain wall life to the glass curtain wall safety intelligent monitoring system, and execute the curtain wall health level 3 warning task to obtain glass curtain wall health warning data.

[0025] In this embodiment, the method of predicting the service life is to set the warning life level by fitting the standard life curve with the actual deformation value according to the deformation degree of the frame and the degradation of the sound insulation performance. Specifically, when the deformation of the frame exceeds 0.3mm, combined with the sound insulation index being lower than 35dB, the curtain wall life is predicted to be short-term (less than 5 years). The life data is uploaded to the glass curtain wall safety intelligent monitoring system, and the three-level warning task is automatically executed according to the life level. The three-level warning task is specifically as follows: when the curtain wall life is in a state of slight attenuation (5-7 years), ordinary monitoring is carried out; when the curtain wall life is in a state of moderate attenuation (3-5 years), the drone is started for re-inspection; when the curtain wall life is in a state of high-risk failure (less than 3 years), the emergency warning repair function is triggered. These data are transmitted to the monitoring center through an automated system, providing real-time health warning data and automatically scheduling related repair operations.

[0026] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Schematic diagram of the functional flow of the curtain wall structure defect detection module. In this embodiment, the functions of the curtain wall structure defect detection module include:

[0027] S11: Acquire glass curtain wall vibration data; convert the glass curtain wall vibration data into a vibration spectrum;

[0028] In this embodiment, vibration data is collected by installing multiple acceleration sensors on the glass curtain wall. The sensors are high-precision sensors with a frequency response range of 0.5Hz to 500Hz and a sensitivity of 0.001g, and are ensured to be evenly distributed at multiple locations on the glass curtain wall. The sampling frequency of the sensors is set to 1000Hz to ensure that subtle vibration changes are captured. The collected raw vibration data is filtered by the signal processing module to remove low-frequency noise and high-frequency interference signals. After filtering, the raw vibration data is stored and transmitted at a frequency of 1000 times per second to ensure the real-time and accuracy of the data.

[0029] In this embodiment, the fast Fourier transform (FFT) algorithm is applied to convert the time domain signal into a frequency domain signal, thereby obtaining a vibration spectrum. When processing vibration data, the original vibration data is first segmented, and each time it is divided into fixed time windows, for example, every 0.5 seconds is a window. The number of data points in each time window is set to 1024 points, which can ensure that the resolution of the spectrum is high enough while ensuring computational efficiency. In order to reduce the phenomenon of spectrum leakage, the Hanning window is selected to perform windowing processing on the data of each time window. After windowing processing, the FFT transform is applied to the data of each time window to convert it from the time domain to the frequency domain, thereby obtaining the frequency components corresponding to each time window.

[0030] S12: Calculate the power spectrum density based on the vibration spectrum; draw a vibration energy distribution diagram based on the power spectrum density; identify the main vibration frequency of the vibration energy distribution diagram; obtain the natural vibration frequency of the glass curtain wall;

[0031] Specifically, after obtaining the frequency domain data based on step S11, the power spectrum density (PSD) formula is used to process the spectrum. Power spectrum density is a function that describes the power distribution of the signal in the frequency domain. Its calculation formula is: PSD = |FFT(x)|2 / N, where FFT(x) represents the spectrum data after Fourier transform, and N is the number of sampling points in each time window. In this process, the square of the modulus length of FFT(x) represents the energy of the frequency component, and |FFT(x)|2 is the energy of the frequency component. Through this method, the power spectrum density in each frequency interval is calculated, reflecting the energy distribution of different frequency components. Then, the power spectrum density values of each time window are integrated to generate a vibration energy distribution diagram. This diagram shows the vibration energy distribution of each frequency interval and can clearly show the main frequency components and their energy intensity in the vibration signal, especially the frequency peaks therein. These peaks usually represent the main frequencies of system vibration. Through this process, the energy distribution characteristics of the glass curtain wall at different vibration frequencies can be effectively identified, providing important data support for subsequent resonance analysis and structural health monitoring.

[0032] In this embodiment, the dominant frequency is identified by searching for the frequency with the strongest energy peak in the vibration spectrum. This is typically done using a threshold method, where an energy intensity threshold is set. When the energy in a frequency interval exceeds this threshold, that frequency is identified as the dominant frequency. Simultaneously, the natural vibration frequency of the glass curtain wall is determined by consulting the design documentation or conducting experimental tests. The natural frequency is typically between 10 Hz and 100 Hz.

[0033] S13: Perform resonance matching based on the main vibration frequency and the natural vibration frequency of the glass curtain wall to obtain resonance data;

[0034] In this embodiment, the dominant vibration frequency is compared with the natural vibration frequency and analyzed using a resonance matching algorithm. When the dominant vibration frequency approaches the natural vibration frequency, resonance is determined to have occurred. Based on the intensity of the resonance, resonance data is recorded, including the resonance frequency and resonance amplitude.

[0035] S14: measuring the stress of the glass curtain wall based on the resonance data to obtain stress data;

[0036] In this embodiment, strain sensors are installed at key locations of the glass curtain wall to collect strain data generated by resonance. The sensor uses a high-precision strain gauge with an accuracy of 0.001%, and a data acquisition system is used to collect strain data in real time at a sampling frequency of 1kHz. Based on the strain data, the stress value is calculated using the stress-strain relationship formula σ=E×ε, where E is the elastic modulus of the material and ε is the strain value. By monitoring the changes in stress values at the resonant frequency, the stress data of the glass curtain wall at different time nodes is obtained, and the maximum stress value is recorded. The upper limit of stress is usually set at 100MPa. If the stress exceeds this threshold, it means that the glass is under excessive stress and requires further monitoring.

[0037] S15: Evaluate glass fatigue damage based on stress data, wherein the peak stress is set to 50-70 MPa to obtain fatigue damage data;

[0038] In this embodiment, a peak stress of 50-70 MPa is set as the critical range for fatigue damage. In the accumulated stress data, each stress peak is identified, and if the stress value falls within this range, fatigue damage calculation is performed. Evaluation is performed based on the tensile-fatigue curve, and the fatigue damage threshold is set to 0.3. That is, when the glass is subjected to a peak stress of 50-70 MPa for more than 10,000 cycles and the fatigue damage value reaches this threshold, the glass is considered to have been severely damaged. By accumulating stress data and combining it with the fatigue limit of the material, the overall fatigue damage of the glass curtain wall is evaluated, and fatigue damage data is obtained. Based on this data, the probability of glass breakage is further estimated.

[0039] S16: Predicting glass breakage probability based on fatigue damage data;

[0040] In this embodiment, based on the fatigue damage data, a standard fatigue failure model is used to predict the probability of glass breakage. According to the fatigue damage value obtained in the above steps, a fatigue life model, such as the Miner cumulative damage method, is used in combination with experimental data to estimate the probability of glass breakage. The calculation formula for the probability of fatigue breakage is set as: P = 1-exp(-λ×D), where P is the probability of breakage, λ is the damage accumulation rate of the material, D is the cumulative fatigue damage degree, and exp(x) represents the natural exponential function, that is, an exponential function with the mathematical constant e (approximately equal to 2.71828) as the base. Based on historical fatigue damage experimental data, the λ value is set to 0.005. During the calculation, if the fatigue damage value D exceeds 1, the probability of breakage is close to 100%. By combining the cumulative fatigue damage data with the breakage probability formula, the breakage probability of the glass curtain wall is calculated in real time, and compared with the safety warning threshold (such as 90% breakage probability) to determine whether further measures need to be taken.

[0041] S17: Perform curtain wall frame deformation analysis based on the glass breakage probability to obtain curtain wall frame deformation data.

[0042] In this embodiment, a frame deformation analysis model is set up, and the finite element analysis method (FEA) is used to simulate the impact of glass breakage on the curtain wall frame. According to the probability of breakage and the stress-strain data of the glass material, a deformation threshold is set. When the probability of glass breakage is greater than 70%, the frame deformation simulation is started. The maximum deformation of the frame is calculated through simulation, and the threshold of the deformation is set to 0.3mm. If the deformation exceeds this value, the frame is considered to be deformed or damaged. The frame deformation data, including the deformation distribution map and the maximum deformation position, is analyzed, and the monitoring system is updated in a timely manner to obtain the curtain wall frame deformation data, which is then transmitted to the intelligent monitoring platform for real-time monitoring and early warning.

[0043] Obtain the curtain wall area; map the glass breakage probability to the curtain wall area to obtain high breakage probability curtain wall data;

[0044] In this embodiment, each region of a glass curtain wall is identified and the probability of fracture is mapped to a specific curtain wall region to obtain data for curtain walls with high fracture probability. During the specific calculation process, vibration data, stress data, and fatigue damage data of the glass curtain wall are first collected and used as input parameters for stress analysis. The stress data of the glass curtain wall is collected in real time by stress sensors installed on the glass surface. Simultaneously, vibration analysis technology is used to extract spectrum data of the glass curtain wall under different operating conditions and calculate the corresponding stress response. Based on this data, the fracture probability of each region of the glass curtain wall is calculated using the formula: fracture probability = 1-exp(-λ × damage value), where exp(x) represents the natural exponential function, an exponential function with the mathematical constant e (approximately equal to 2.71828) as its base. The damage value is derived from fatigue damage data and calculated using the fatigue cumulative effect. The fracture sensitivity coefficient λ is set between 0.02 and 0.1. The specific value is adjusted based on the mechanical properties of the glass material, the temperature and humidity conditions of the operating environment, and the external load conditions to ensure the applicability of the calculation model. After the calculations are complete, the probability of fracture is assessed for each curtain wall area, and areas with a fracture probability greater than 0.75 are marked as high-probability areas. During the identification of these high-probability areas, not only their specific spatial coordinates are recorded, but also information such as the area's dimensions, the distribution of external forces, and the degree of material degradation. This data is then stored and serves as an important input for subsequent analysis, further used in the overall curtain wall safety assessment and the development of an early warning system to improve the accuracy and reliability of glass curtain wall structural health monitoring.

[0045] Construct curtain wall rupture model using high rupture probability curtain wall data;

[0046] In this embodiment, a detailed stress analysis is performed on each region of the glass curtain wall. This process uses acquired vibration data, stress data, and fatigue damage data as input parameters to ensure the accuracy and reliability of the calculation results. In the specific implementation process, stress data for each curtain wall region is collected in real time by stress sensors installed on the glass surface. The sensors capture the stress conditions of the glass curtain wall under various environmental influences. Combined with vibration analysis technology, the spectrum data of the glass curtain wall is obtained, thereby calculating the stress response of the glass under different working conditions. Based on this data, the probability of fracture of each region of the glass curtain wall is further calculated. The fracture probability calculation formula is P = 1-exp(-λ×D), where P represents the fracture probability, exp(x) is the natural exponential function with the mathematical constant e (approximately 2.71828) as the base, D is the damage value calculated from the fatigue damage data, and λ is the fracture sensitivity coefficient, which is set between 0.02 and 0.1. The value is adjusted according to the mechanical properties of the glass material, the temperature and humidity conditions of the operating environment, and the external load conditions to ensure that the model is suitable for different glass curtain wall types and usage scenarios. During the calculation process, each area of the glass curtain wall is analyzed individually, and local calculations are performed for each specific area. Areas with a probability of fracture exceeding 0.75 are identified and marked as high-probability areas. During the high-probability area identification process, not only the spatial coordinates of the area are recorded, but also key factors such as the specific dimensions, the distribution of external forces, the degree of material aging, and environmental impacts. This data is integrated and serves as an important input for subsequent analysis, further optimizing the curtain wall fracture model to improve the accuracy of fracture predictions and provide more comprehensive support for curtain wall safety monitoring and early warning systems, effectively improving the safety and durability of glass curtain walls.

[0047] In this embodiment, a detailed stress analysis is performed on each glass curtain wall area. This process uses the acquired vibration data, stress data, and fatigue damage data as basic inputs. Specifically, the stress data of each curtain wall area is acquired by stress sensors located on the glass surface and calculated in combination with the spectrum data obtained through vibration analysis. The formula for calculating the probability of fracture for each area is: fracture probability = 1-exp(-λ×damage value), where exp(x) represents the natural exponential function, that is, an exponential function with the mathematical constant e (approximately equal to 2.71828) as the base, where the damage value is calculated from the fatigue damage data, and λ is the sensitivity coefficient for fracture, which is set between 0.02 and 0.1 and adjusted according to the characteristics of the glass material and environmental conditions. By performing local calculations on each area, areas with a fracture probability higher than 0.75 are identified, and these areas are identified as high fracture probability areas. The spatial coordinates, area dimensions, and stress conditions of these high fracture probability areas are recorded and used as input for the next analysis.

[0048] Obtain wind data, input the wind data into the curtain wall rupture model, and perform wind-induced dynamic response simulation to obtain wind-induced dynamic response data;

[0049] In this embodiment, the wind data is input into the curtain wall rupture model, and a wind-induced dynamic response simulation is performed. After the wind speed data is obtained, it needs to be input into the established curtain wall rupture model. The simulation process uses numerical simulation technology to analyze the effect of wind on the curtain wall. First, the wind speed data is input into the model in a time series, and the corresponding external load is applied to the curtain wall according to the instantaneous change of wind speed and the change of wind direction. In the simulation, the wind load model used is based on the relationship between wind speed and wind pressure. The calculation formula of wind pressure is: wind pressure = 0.5*ρ*v 2 , where ρ is the air density and v is the wind speed. Wind-induced dynamic response simulations typically employ dynamic analysis methods. This involves simulating the structural response to wind forces through time-domain analysis, calculating dynamic parameters such as displacement, acceleration, and velocity at each time point. The simulation time step is set to 0.1 seconds to accurately capture the details of wind speed variations, thereby generating detailed wind-induced dynamic response data.

[0050] The deformation of the curtain wall frame of the curtain wall rupture model is calculated according to the wind-induced dynamic response data, and the deformation displacement field of the curtain wall frame is constructed based on the deformation of the curtain wall frame;

[0051] In this embodiment, wind-induced dynamic response data primarily includes acceleration, velocity, and wind pressure data. Acceleration and velocity data serve as input variables, and numerical integration methods are used to calculate the displacement of the curtain wall frame. Specifically, during the calculation process, the acceleration data is first numerically integrated to obtain velocity data, which is then quadratically integrated to obtain displacement data. Mathematically, the displacement calculation for each time step t follows the following formula: Displacement(t) = Initial Displacement + ∫ Acceleration(t)dt + ∫ Velocity(t)dt, where the initial displacement refers to the displacement state of the curtain wall frame at the initial moment of calculation, and the integration operation is used to calculate the cumulative change in displacement during the dynamic response. During the numerical calculation, a numerical integration method is used for discretization, enabling efficient iteration within a limited time step, thereby ensuring the accuracy and stability of the curtain wall frame deformation calculation. For each time step, displacement data for each measuring point is continuously updated and calculated, and spatial interpolation is performed across the entire curtain wall frame, ensuring that the displacement data fully covers all key structural components of the curtain wall frame. Finally, the overall deformation displacement field of the curtain wall frame is constructed based on these displacement data. The deformation displacement field of the curtain wall frame not only includes the displacement data of each measuring point at different time steps, but also can show the deformation trend and local strain characteristics of the curtain wall frame under wind-induced loads. In the actual analysis, special attention is paid to the rate of change of displacement, maximum displacement and cumulative displacement to ensure that the deformation characteristics of the curtain wall frame under long-term wind loads can be accurately identified, and the safety of the curtain wall frame is further evaluated in combination with structural stability analysis. In addition, high displacement gradient areas, that is, areas with a high rate of displacement change, will be marked to assist in identifying possible local weaknesses in the curtain wall frame, providing key data support for subsequent structural optimization, maintenance strategy formulation and safety warnings, thereby effectively improving the long-term reliability and safety of the curtain wall structure.

[0052] Locate the maximum deformation area based on the deformation displacement field of the curtain wall frame;

[0053] In this embodiment, the deformation displacement field of the entire curtain wall is post-processed to analyze the displacement size of each area. During the calculation process, by comparing the displacement values of each area, the area with the most serious deformation is selected as the maximum deformation area. Specifically, for each area, the average displacement of all points in the area is first calculated, and then compared with other areas, and the position with the largest displacement value is selected as the maximum deformation area. To ensure accuracy, a displacement threshold needs to be set. For example, an area with a displacement greater than 0.5mm is considered to be a significant deformation area. The identification of the maximum deformation area must not only consider the absolute value of the displacement, but also the changes in the deformation rate and deformation direction. Through post-processing visualization software, such as ANSYS or Abaqus, the displacement field data can be displayed in the form of a heat map, which further helps to locate the maximum deformation area.

[0054] Extract curtain wall frame deformation data based on the maximum deformation area.

[0055] In this embodiment, after selecting the maximum deformation area, detailed data of this area is extracted from the deformation displacement field. This data includes the displacement values, deformation rates, and differences with adjacent areas of each point within the area. By further analyzing the displacement data of this area, the maximum deformation amount, deformation direction, and deformation changes over time of the area are obtained. The extracted data includes information such as the displacement value, deformation rate, and deformation direction of each point. In addition, the stress concentration in this area needs to be calculated, and by comparing it with the surrounding areas, the structural weaknesses of this area are analyzed. Ultimately, the extracted data is used for subsequent safety assessments and the formulation of repair plans.

[0056] Preferably, the material aging detection module includes the following functions:

[0057] Locate the sealing strip position based on the curtain wall frame deformation data;

[0058] In this embodiment, the location of the sealing strip in the curtain wall frame is identified based on the acquired deformation data. The overall deformation of the curtain wall under the influence of factors such as wind load and temperature difference is analyzed through the deformation displacement field. The sealing strip is usually installed on the contact surface between the curtain wall glass and the frame. It is necessary to analyze the deformation of the curtain wall frame to find the places where the deformation is uneven in the local area. Usually, these areas correspond to the location of the strip. Specifically, a displacement sensor is used to obtain the displacement data between the glass and the frame, and the data is analyzed in combination with the local stress distribution. According to the experimental results, a displacement change threshold is set. For example, the position where the displacement difference between the glass and the frame is greater than 0.2 mm is the area where the strip is located. Through cluster analysis of the deformation data, the specific position of the sealing strip can be accurately located.

[0059] Identify the displacement of the sealing strip according to the position of the sealing strip and obtain displacement data;

[0060] In this embodiment, the displacement of the sealing strip is identified based on the position of the sealing strip. After the sealing strip is located, the displacement change of the strip under the action of load is continuously tracked in real time through a displacement sensor or an optical sensor. To this end, a displacement sensor is used to accurately measure the relative displacement of the area where the sealing strip is located. The sampling frequency is set to 1Hz, and the displacement data at each moment is recorded in real time. The measurement accuracy of the displacement needs to reach 0.1mm to ensure that the slight deformation of the strip can be accurately captured. Multiple displacement sensors are arranged around the sealing strip area to capture the displacement changes of the local area and calculate the deformation of the sealing strip at each moment. According to the data provided by the sensor, precise positioning is performed in combination with the installation position to obtain the displacement data of the sealing strip.

[0061] Determine the force point of the rubber strip based on the displacement data;

[0062] In this embodiment, a mechanical model of the stress point is established based on the position and displacement data of the sealing strip. Using a mechanical formula such as F=k×Δx, where F is force, k is the elastic coefficient, and Δx is the displacement, the size of the stress point is inferred from the displacement data. In this model, the k value is set based on parameters such as the material properties, size, and pre-tension of the sealing strip. For example, the elastic modulus of the strip can be set to 0.3MPa. The stress point is generally located where the curtain wall frame and the glass are in closest contact. By analyzing the displacement data and combining it with known load conditions, the stress point of the strip can be accurately calculated.

[0063] Calculate the tensile strain of the rubber strip at the stress point;

[0064] In this embodiment, the tensile strain of the rubber strip at the stress point of the rubber strip is calculated. After the stress point of the sealing strip is known, the tensile strain is calculated using Hooke's law. The tensile strain (ε) is calculated based on the stress (σ) and the Young's modulus (E) of the material, and the formula is: ε = σ / E, where σ is the stress at the stress point and E is the Young's modulus of the material. The stress value σ can be obtained by the displacement data of the stress point and the elastic model. In order to ensure accurate calculations, the Young's modulus E of the material is set as the material property value of the sealing strip, such as the Young's modulus of silicone is approximately 0.5 MPa. In the process of stress calculation, the dimensional parameters such as the thickness and width of the rubber strip are taken into account, and by combining with the mechanical model, the tensile strain of the sealing strip is finally obtained.

[0065] Determine the tensile state of the sealing strip according to the tensile strain of the strip;

[0066] In this embodiment, the tensile state of the sealing strip is determined based on the tensile strain of the strip. After the tensile strain is calculated, the tensile state of the sealing strip is determined by comparing it with the tensile limit value of the material. The tensile limit value of the strip is obtained by laboratory testing. For example, for silicone, its tensile limit is usually 10%. When the calculated tensile strain exceeds this limit value, it indicates that the sealing strip is already in the tensile limit state. According to the calculated strain, a threshold is set. If the tensile strain is greater than 5%, it means that the tensile state of the strip is abnormal. This threshold is used to judge the current tensile state of the strip, and then to determine whether there is over-stretching.

[0067] Calculate the cracking strain value based on the tensile state of the sealing strip;

[0068] In this embodiment, the cracking strain value is calculated based on the tensile state of the sealing strip. After determining the tensile state of the strip, the cracking strain value is calculated in combination with the tensile properties of the material. The cracking strain value usually depends on factors such as the material of the sealing strip, the tensile strain rate, and the stress time. A standard cracking strain value is set. For example, for silicone materials, the cracking strain value is generally 12%. By real-time monitoring of the strain data of the strip during the stretching process, its cracking strain value is calculated. If the cracking strain value exceeds 12%, the sealing strip is in a dangerous state and is prone to cracking. The calculation of the cracking strain value depends on the material properties, experimental data, and real-time monitoring data of the sealing strip, and based on this, it is further evaluated whether the strip needs to be replaced or repaired.

[0069] The degree of sealant aging is evaluated based on the cracking strain value.

[0070] In this example, the cracking strain value of the sealing strip was obtained and compared with a standard aging curve. The standard aging curve is obtained through multiple experiments and long-term aging tests. It is usually obtained by subjecting the sealing strip to accelerated aging under different conditions (such as temperature changes, UV exposure, repeated stress, etc.), measuring its strain characteristics, and then plotting a curve. This curve describes the change trend of the cracking strain value of the sealing strip over time under different service years or stress cycles. For each type of sealing strip, the specific shape of the aging curve can be determined based on the material properties (such as silicone, polyurethane, etc.) and its exposure to the operating environment (such as humidity, temperature changes, UV exposure, etc.). The cracking strain value of the sealing strip is monitored in real time using a sensor. This strain value reflects the deformation of the strip under the influence of multiple factors such as wind pressure, temperature differences, and mechanical stress. After the strain value is calculated, it is compared with the standard aging curve to analyze the aging process of the strip. The aging curve typically includes several time periods, such as 0 to 1 year, 1 to 3 years, and 3 to 5 years, which correspond to the changing properties of the strip material. The cracking strain value gradually increases during each time period of the aging curve, with the rate of increase varying from stage to stage. When the cracking strain value approaches or exceeds the maximum value of a particular segment of the aging curve, it indicates that the sealant has entered a critical aging stage and is losing its original sealing performance. For example, suppose the standard aging curve shows that the cracking strain value of the sealant reaches a maximum of 8% after three years of use. If the actual monitored cracking strain value of the sealant reaches or exceeds this value, it can be inferred that the sealant has entered a high-risk aging period and its sealing performance has seriously deteriorated. To ensure the stability and safety of the curtain wall system, comparison with the aging curve can be used to assess whether the sealant needs to be replaced. The specific strain value and the comparison results can be used to determine the maintenance or replacement cycle of the sealant, ensuring that necessary repair or replacement measures are implemented before the sealant fails. As the cracking strain value increases, the sealant's sealing ability gradually decreases, which not only affects the curtain wall's airtightness and watertightness but also allows air or moisture infiltration, affecting the overall performance of the building. Therefore, the comparative analysis of cracking strain values and standard aging curves can not only evaluate the aging degree of the rubber strips, but also provide a scientific basis for subsequent maintenance, ensuring the long-term safety and reliability of the curtain wall system.

[0071] Preferably, the evaluation of the sealant aging degree includes:

[0072] The cracking state of the sealant is divided into the plastic state and the brittle fracture state according to the cracking strain value;

[0073] In this embodiment, the cracking strain of the sealing strip is monitored in real time by a strain gauge or an optical fiber sensor. It is assumed that the cracking strain value ranges from 0% to 10%. By collecting data, if the cracking strain value is lower than 3%, it is determined that the sealant is in a plastic state. At this time, the sealant is deformable, elastic, and not easy to break; if the cracking strain value is higher than 3%, it is considered that the sealant has entered a brittle fracture state. At this time, the sealant is easy to break and exhibits brittle characteristics. The basis for dividing the cracking strain value is the deformation ability of the sealant under the action of external force. The use of a strain sensor can obtain this data more accurately. The threshold value is set to 3%, which is determined based on a large amount of experimental data and the typical characteristics of the sealant. During each operation, the deformation state of the sealant is judged based on the real-time measurement value.

[0074] UV exposure simulation is performed based on the plastic state to obtain UV irradiation data;

[0075] In this embodiment, a standard ultraviolet radiation source is selected. A commonly used ultraviolet lamp has a wavelength of 365nm. The sealant sample in a plastic state is placed in an ultraviolet irradiation environment and the ultraviolet intensity is controlled to be 20mW / cm 2 The exposure time is set to 200 hours. This UV exposure process simulates the effects of UV radiation that the sealant would be exposed to in a long-term outdoor environment. During the exposure process, UV intensity, exposure time, and temperature changes require real-time monitoring, typically using a UV radiometer and temperature sensor. All data is stored in the control system for subsequent analysis.

[0076] Predict sealant hardness based on UV exposure data;

[0077] In this embodiment, in the UV exposure experiment, the sealant samples will be exposed to a UV environment with a specific wavelength and intensity to simulate the aging effects of long-term sunlight exposure. During the experiment, the UV wavelength is usually set between 280nm and 400nm, corresponding to the two main aging ranges of ultraviolet B (UVB) and ultraviolet A (UVA), and the UV irradiance is controlled at 50W / m 2 Up to 200W / m 2The exposure time is adjusted to ensure it effectively simulates the UV intensity experienced in a real-world environment. The exposure time is adjusted based on the sealant's intended use environment, typically ranging from hundreds to thousands of hours, to observe long-term aging trends. After UV exposure, sealant samples undergo hardness testing to quantify the impact of UV radiation on the sealant's mechanical properties. Hardness testing is typically performed using a Shore A durometer, which presses a probe vertically into the sealant surface and applies a constant force (typically 1N). The hardness value after the probe rebounds is recorded. The Shore A durometer typically measures between 0 and 100, and for sealant materials, the typical hardness range is between 30 and 90. Higher values indicate greater hardness, less elasticity, and greater resistance to deformation. During testing, measurements are taken at multiple test points and averaged to reduce measurement error and improve data reliability. By comparing hardness data before and after UV exposure, it is possible to analyze the changes in the sealant's physical properties under UV irradiation. Typically, an increase in hardness indicates a hardening of the sealant material, indicating increased aging, a gradual loss of flexibility, and a potential decline in sealing performance. To improve prediction accuracy, a mathematical model will be established that correlates sealant hardness changes with UV exposure parameters. This model uses UV exposure time, intensity, and wavelength as input variables and is fitted with hardness test data to predict sealant hardness. Based on this model, in actual engineering applications, the hardness change trend of the sealant can be predicted in advance based on exposure conditions in different UV environments, and its aging state can be assessed. This provides a scientific basis for curtain wall maintenance, material selection, and replacement strategies, thereby improving the long-term reliability and durability of curtain wall sealing systems.

[0078] Raman spectroscopy irradiation based on brittle fracture state;

[0079] In this embodiment, a monochromatic laser with a wavelength of 532 nm was selected as the irradiation light source. This wavelength can effectively excite the molecular vibration modes within the sealant and generate Raman scattering signals. The 532 nm wavelength was chosen because it is within the visible light range and matches the molecular absorption characteristics of the sealant material, thereby improving the detection sensitivity of the Raman signal and reducing background fluorescence interference. In the experiment, the monochromatic laser was focused onto the surface of the sealant sample in a brittle fracture state, ensuring that the spot diameter was between 1 μm and 5 μm to achieve local high-precision detection. The irradiation time was set to 10 minutes to ensure that the molecular structure of the sealant sample was fully excited and a stable Raman scattering signal was generated. During the irradiation process, the laser power was controlled between 5 mW and 50 mW to prevent local temperature increases in the sample due to photothermal effects, which could affect the measurement results. Subsequently, a Raman spectrometer was used to measure the Raman spectrum of the irradiated sealant sample. The scattered light was collected by the spectral detection system and its frequency changes were analyzed to obtain information about the molecular vibration of the sealant. The basic principle of Raman spectroscopy is that when incident photons interact with sealant molecules, a portion of the photons are inelastically scattered by molecular vibrations, a phenomenon known as Raman scattering. The frequency shift of the scattered light (i.e., the Raman shift) reflects the molecular vibrational modes, thereby revealing the chemical bond structure and intermolecular interactions within the material. In the experiment, a high-resolution spectrometer was used to collect and analyze the Raman scattering signals, focusing on characteristic parameters such as the Raman peak shift, peak intensity, and half-maximum width of key chemical bonds such as CH, C=C, Si-O, and Si-C. These Raman spectral features can be used to assess molecular structural changes in the sealant during brittle fracture, such as chemical bond breakage, molecular chain degradation, and changes in cross-link density. To enhance data reliability, repeated measurements were performed at multiple different fracture sites, and the results were statistically analyzed to reduce experimental error. Ultimately, by comparing Raman spectral changes, the molecular structural damage mechanism of the sealant during brittle fracture can be revealed, providing a scientific basis for performance optimization, aging assessment, and durability analysis of sealant materials.

[0080] Identify short-chain molecules based on Raman spectra and obtain short-chain molecule data;

[0081] In this embodiment, short-chain molecules are identified based on Raman spectroscopy. The collected Raman spectra are processed using Raman spectroscopy data analysis software to find characteristic absorption peaks associated with short-chain molecules. These characteristic absorption peaks are usually located at 1600 cm -1By comparing the spectra with a standard Raman spectral library, short-chain molecules were identified. Short-chain molecules are molecular fragments that break during sealant aging and are often found in sealants that exhibit brittle fracture. Identifying short-chain molecules allows for further analysis of the extent of molecular chain breakage in the sealant. Spectral analysis accuracy and software algorithms are crucial in this process, enabling efficient identification of the number and types of short-chain molecules based on experimental data and theoretical models.

[0082] Determine molecular chain breakage based on short-chain molecular data to obtain molecular chain breakage data;

[0083] In this example, the number and distribution of short-chain molecules obtained using Raman spectroscopy data were compared with the theoretical fracture model used in experimental studies to determine whether the sealant molecular chains had broken. Generally, the number of short-chain molecules is an important indicator. Short-chain molecules are formed by molecular chain breakage and are the product of molecular fracture. As the sealant material ages, the molecular chains gradually break, forming an increasing number of short-chain molecules. Therefore, the number and distribution of short-chain molecules are closely related, with a higher number indicating a greater degree of molecular chain breakage. For each sealant sample, when collecting data using a Raman spectrometer, the vibrational characteristics of the short-chain molecules are analyzed within specific spectral peak regions to further confirm their number and type. Based on the experimental data and model, the concentration of short-chain molecules is quantified, and a critical value is set. When the number of short-chain molecules exceeds this value, it indicates that the molecular chain has clearly broken. During the experimental process, specialized software is usually required for data processing, counting the number of short-chain molecules, and comparative analysis is used to determine the distribution of molecular chain breakage. Analyzing the number and distribution of molecular chain breakage provides important evidence for subsequent assessments of brittle strength and material durability. Furthermore, by comparing this data with known fracture models, the degree of sealant molecular chain fracture can be determined. This molecular chain fracture data provides a theoretical basis for subsequent aging assessment and strength analysis, enabling precise identification of sealant performance changes at different aging stages.

[0084] Evaluate the brittle strength of the brittle fracture state based on molecular chain breakage data;

[0085] In this embodiment, a tensile test is performed on a sealant sample using a tensile testing machine. During the test, the sealant sample is subjected to a gradually increasing tensile force at a constant speed until the sample breaks. During the test, the stress data of the sealant at different strain values is recorded in real time to generate a stress-strain curve. The stress-strain curve is a key data point for evaluating the mechanical properties of a material. By analyzing the changes in the curve, the point at which the sealant breaks during the tensile process can be identified, which is crucial for the subsequent brittle strength calculation. During the tensile test, the cross-sectional area of the sealant sample must be accurately measured as this will affect the calculation of the brittle strength. During the test, ensure that the applied tensile speed is constant, typically selecting a speed range of 1mm / min to 5mm / min. This helps to simulate the stress loading conditions in actual use. During the tensile process, the stress value at the point where the sample reaches the maximum breaking point is the maximum breaking stress. By recording this maximum stress value and combining it with the cross-sectional area of the sealant, the brittle strength is calculated using the brittle strength calculation formula: Brittle Strength = Maximum Breaking Stress ÷ Cross-sectional Area of Sealant. By comparing this value with the brittle strength value of a known standard, the brittle fracture strength of the sealant can be assessed. During the experiment, cross-sectional area measurements are typically performed using a precision caliper or laser measuring instrument to ensure the accuracy of the cross-sectional area data. Furthermore, the experimental temperature and humidity must be controlled during the test, as these factors affect the strength of the sealant. Combined with the molecular chain fracture data, the correlation between the sealant's brittle fracture state and molecular chain fracture can be further analyzed. When the degree of molecular chain fracture is high, the brittle strength is typically low, indicating that the material is more fragile and more susceptible to brittle fracture. The combination of experimental mechanical test data and molecular chain fracture data provides a scientific basis for subsequent evaluations of the sealant's durability, aging state, and replacement cycle.

[0086] Assess the degree of sealant aging based on sealant hardness and brittleness strength.

[0087] In this embodiment, the degree of sealant aging is evaluated based on the sealant hardness and brittle strength, and the hardness and brittle strength values obtained previously are input into a preset sealant aging model. This model usually defines the relationship between hardness and brittle strength through the accumulation and fitting of a large amount of experimental data. According to the results of the model calculation, the degree of sealant aging will be presented in numerical form, usually expressed as an aging index. By comparing the actual hardness and brittle strength of the sealant, combined with the standard aging curve, the degree of sealant aging can be accurately evaluated. If the aging index exceeds the set threshold (for example, 80%), it indicates that the sealant is approaching the end of its service life and needs to be replaced.

[0088] Preferably, the curtain wall water seepage simulation in the functional defect detection module includes:

[0089] Construct a 3D curtain wall model based on the sealant aging degree;

[0090] In this embodiment, it is necessary to collect aging data of the sealant, which generally include the hardness, tensile strength, bonding strength, tear strength, etc. of the sealant. These data can be obtained by conducting accelerated aging tests (such as ultraviolet rays, temperature changes, etc.) in the laboratory. These experimental results are processed and input into a three-dimensional modeling software (such as AutoCAD, Revit or SolidWorks) in combination with structural design parameters (such as the geometry of the curtain wall, the type of sealant and the application location, etc.). During modeling, the degree of sealant aging will affect the material properties of each area. Specifically, by mapping the aging data of each area (such as hardness change, strength attenuation) to the model, a three-dimensional structural diagram representing the aging distribution of the sealant is formed. Areas with high aging degrees need to be distinguished by different colors or marking methods to ensure that the aging data can be accurately reflected in the model, thereby providing a basis for subsequent simulation analysis.

[0091] Rainwater flow simulation was performed based on the curtain wall 3D model, with the rainfall intensity range set to 10-150mm and the raindrop particle size to 0.1-5mm, to obtain rainwater flow data;

[0092] In the present embodiment, when carrying out rainwater flow simulation, it is necessary to set the rainfall intensity and raindrop particle size range. The rainfall intensity is set between 10mm / h and 150mm / h, based on the statistical analysis of meteorological station data and rainfall intensity. This range covers situations from light rainfall to heavy rain. The raindrop particle size range is set to 0.1mm to 5mm, depending on the actual particle size distribution during natural rainfall. Computational fluid dynamics (CFD) software (such as ANSYS Fluent, OpenFOAM, COMSOL) is used to simulate. The input rainfall intensity and raindrop particle size will affect the distribution of water flow on the curtain wall. During the simulation process, it is necessary to input the rainfall intensity and raindrops of different particle sizes into the simulation. The software simulates the flow behavior of rainwater according to the principles of fluid mechanics, taking into account factors such as gravity, surface tension, flow velocity, flow path, and obtains the flow path, flow velocity distribution and water accumulation area of rainwater on the curtain wall surface. These simulation data provide important references for subsequent analysis.

[0093] Identify stormwater flow paths based on stormwater flow data;

[0094] In this embodiment, rainwater flow data is collected, which can be obtained through on-site rainfall experiments, fluid sensor monitoring or numerical simulation methods. When using computational fluid dynamics (CFD) software to simulate rainwater flow, a three-dimensional geometric model of the curtain wall needs to be established, and reasonable boundary conditions are set according to parameters such as actual rainfall intensity, wind speed, and curtain wall surface material properties. During the numerical simulation process, the Euler-Lagrange method or the VOF (Volume of Fluid) method is used to track the motion trajectory of liquid water, and the flow field is solved by combining the Reynolds time-averaged Navier-Stokes equations (RANS) or large eddy simulation (LES) to calculate the velocity, pressure distribution, and water film thickness of the rainwater. After the simulation calculation is completed, the flow path data is extracted and analyzed using the post-processing module provided by the CFD software, or using professional data processing software (such as ParaView, Tecplot, MATLAB, etc.). During the data processing process, the simulation results are first smoothed to reduce the influence of numerical errors, and the flow path is visualized, and the movement of rainwater on the curtain wall surface is intuitively displayed through streamlines, vector field maps, pressure cloud maps, etc. The team then further analyzed key characteristics of water flow along the curtain wall, including stagnant zones, low-velocity zones, and areas of concentrated water flow. Stagnant zones are often located at edges where the curtain wall structure changes suddenly, such as joints or frame connections. The water velocity in these areas is close to zero, prone to localized water accumulation, which accelerates sealant aging. Areas of low velocity typically exhibit a slow-moving water film, with water remaining on the sealant surface for extended periods, exposing it to moisture and potentially causing softening, degradation, and mold growth. Areas of concentrated water flow may experience significant hydrodynamic erosion, impacting the sealant's adhesion and durability. By accurately extracting the water flow path and combining it with rainwater flow data analysis, it is possible to identify areas of the curtain wall surface most affected by rainfall, further identifying locations at risk of water accumulation, leakage, or accelerated sealant aging. These identification results provide valuable data support for sealant material selection, curtain wall drainage design optimization, and long-term durability assessment.

[0095] Determine the sealant aging area according to the sealant aging degree;

[0096] In this example, the degree of aging in different areas is marked on the three-dimensional model. Areas with more severe aging can be identified by the attenuation values of hardness and strength, the specific values of which can be obtained through experimental measurements. For example, an area with a hardness reduction of more than 10% or a tensile strength reduction of more than 20% is considered severely aged. The rainwater flow path is then superimposed on the aged areas, focusing on areas that are both severely aged and located in the rainwater flow path, as these areas are often most susceptible to hydrolysis reactions. In this way, areas of the curtain wall with high aging and susceptible to water flow can be accurately identified, providing precise target areas for subsequent analysis.

[0097] The hydrolysis reaction of the sealant aging area is analyzed based on the rainwater flow path to obtain the hydrolysis reaction data;

[0098] In this embodiment, the hydrolysis reaction generally refers to the chemical reaction between water and the molecular chain after entering the sealant material, resulting in the molecular chain breaking, thereby changing the performance of the material. According to the simulated rainwater flow path, the sealant aging area through which the water flows is analyzed, and the probability and degree of the occurrence of the hydrolysis reaction are further inferred. The impact of the hydrolysis reaction depends on the intensity of the water flow, the residence time and the degree of aging of the sealant. The model of the hydrolysis reaction can be obtained through laboratory research. Common hydrolysis reaction data include hydrolysis rate, reaction duration, etc. In the analysis process, the time information of the rainwater flow path is combined to calculate the time that the water flow stays on the sealant surface, and then the occurrence of the hydrolysis reaction is inferred, and the corresponding hydrolysis reaction data are obtained.

[0099] Sealant failure data based on hydrolysis reaction data, where the bond strength reduction is ≥50%, the tear strength reduction is ≥40%, and the material crack length is ≥5mm, are obtained;

[0100] In this embodiment, the standard for sealant failure is generally set as a decrease in bond strength of ≥50%, a decrease in tear strength of ≥40%, and a material crack length of ≥5mm. Physical performance data of the sealant are obtained through tensile tests, tear tests, and cracking tests. When the bond strength, tear strength, and crack length of the sealant exceed the set failure threshold, it is determined to be failed. Specifically, by applying a standardized tensile force or tearing force and comparing the test results according to the failure standard, it is determined which areas of the sealant have failed. Failure data can be obtained through multiple experiments and can be verified by combining the model with experimental data.

[0101] Determine sealant failure areas based on sealant failure data;

[0102] In this embodiment, by analyzing the failure conditions of each area, the specific failure area is identified. In this process, it is necessary to combine the physical property data, aging data and hydrolysis reaction results of the sealant to identify the most serious failure area. In order to perform water seepage simulation, computational fluid dynamics simulation software (such as ANSYS Fluent, COMSOL) is usually used for analysis to simulate the water penetration of the failure area. In the water seepage simulation, it is necessary to set specific parameters, such as the bonding strength, tear strength, crack length, etc. of the failure area, to simulate the speed, depth and penetration path of the water seepage to the interior. By further analyzing the water seepage results, the potential impact of water on the curtain wall structure can be evaluated to determine whether it is necessary to repair or replace it.

[0103] Based on the rainwater flow path, curtain wall seepage simulation is performed in the sealant failure area to obtain seepage data.

[0104] In this embodiment, detailed data on the failure area is obtained, including physical properties such as bond strength, tear strength, and crack length. This data can be obtained through previous experimental tests, such as tensile tests and shear tests. Next, computational fluid dynamics (CFD) software, such as ANSYS Fluent or COMSOL, is used to perform a water seepage simulation. The geometric model of the curtain wall and rainwater flow path data, which is derived from a previous rainwater flow simulation, are input. Using factors such as rainfall intensity, raindrop size, and rainfall duration as boundary conditions, a time-domain simulation is performed to simulate rainwater flow through the failure area. The water seepage simulation considers the physical properties of the failure area, such as bond strength, tear strength, and crack length, as these properties affect the rate and depth of water penetration. When the bond strength of the failure area is low or cracks appear, water will more easily flow through these areas into the curtain wall, causing water seepage. A rainfall intensity range (e.g., 10 mm / h to 150 mm / h) and a raindrop size range (e.g., 0.1 mm to 5 mm) are input as initial conditions, and the rainwater flow path is set to ensure that the simulation results match actual rainfall conditions. Based on fluid mechanics equations, the software simulates the water's penetration path across and within the curtain wall surface by taking into account the flow velocity, raindrop size, rainfall intensity, and the properties of the failure zone. During the simulation, appropriate physical boundary conditions must be set, such as the initial humidity and water saturation at the interface between the curtain wall surface and the sealant. The degree of water seepage is directly related to the size and properties of the failure zone. When the bond strength of the failure zone is low, water can easily penetrate the curtain wall's internal structure and gradually expand. Through continuous iterative simulations, the software can provide real-time information on the depth of the seepage area, the penetration path, and the flow rate.

[0105] Preferably, the evaluation of curtain wall air tightness in the functional defect detection module includes:

[0106] Detect sealant microstructure based on sealant aging degree;

[0107] In this embodiment, a sealant sample is scanned and observed using a scanning electron microscope (SEM) or a transmission electron microscope (TEM) to obtain microstructural images of the sealant surface and interior. The scanned images are processed and analyzed using image processing software, such as ImageJ, to extract detailed information such as microscopic pores, cracks, and particle distribution. Based on this information, combined with sealant aging data, the microstructural changes of the sample are analyzed. For example, as the degree of aging increases, larger pores or cracks are found, and the microstructure of the sealant changes.

[0108] Calculate the sealant porosity based on the sealant microstructure;

[0109] In this embodiment, porosity refers to the ratio of the pore volume in the sealant to the total sample volume. In a specific implementation, the pore area in the SEM or TEM image is binarized using image analysis software to distinguish the pores from the sealant matrix. The porosity value is obtained by calculating the ratio of the pores to the total area in the image. The calculation formula for porosity is: porosity = (pore area / total area) × 100%. If a more accurate measurement is required, three-dimensional imaging technology can be used to obtain the three-dimensional structure of the sample through CT scanning, and then calculate the porosity of the sample.

[0110] Perform air flow simulation based on the sealant porosity to obtain air flow data;

[0111] In this embodiment, CFD (computational fluid dynamics) software, such as ANSYS Fluent, is used to perform airflow simulation. First, a geometric model of the sealant is constructed, and the porosity data is embedded in the model. According to the actual situation, the initial conditions of the airflow are set, including gas flow rate, temperature, and pressure. In the simulation, the flow process of the airflow through the sealant is set, and the fluid mechanics equations are solved to obtain the flow data of the airflow in the pores, including information such as flow rate, pressure field, and streamlines. Through multiple iterative simulations, the flow of the airflow inside the sealant is obtained.

[0112] Identify the air flow penetration path based on the air flow data, where the penetration path determination criteria are set as flow rate gradient change ≥ 10%, air pressure drop ≥ 50 Pa, and pore connectivity ≥ 40%;

[0113] In this embodiment, a flow rate gradient change of 10% or greater is set as one of the criteria for the permeation path. Secondly, a change in the pressure drop when the airflow passes through the sealant is greater than or equal to 50Pa, indicating that the airflow experiences significant flow resistance in this area, and therefore can also be used as one of the criteria for the permeation path. Finally, through the analysis of pore connectivity, a pore connectivity of 40% or greater is set as the standard for determining the permeation path. Pore connectivity refers to the proportion of pores that can be connected to each other. By calculating the connectivity between pores, it is determined whether there is a connected permeation channel.

[0114] Calculate gas permeability based on the air flow permeation path;

[0115] In this example, the gas permeability formula is used to calculate the gas permeability of the sealant. Gas permeability is closely related to factors such as porosity, pore size distribution, and airflow resistance. The gas permeability of the sealant under a certain pressure difference is calculated using data from airflow simulations combined with the physical properties of the gas (such as gas molecular weight, temperature, and pressure). The formula is as follows:

[0116]

[0117] Where P is the gas permeability, Q is the gas flow rate, μ is the gas viscosity, L is the path length of the gas flow, A is the cross-sectional area of the gas channel, and ΔP is the pressure difference of the gas flow. By combining simulation calculations and experimental data, accurate gas permeability values are obtained.

[0118] Perform gas diffusion analysis based on gas permeability to obtain gas diffusion data;

[0119] In this embodiment, the pore structure, airflow path and flow velocity distribution inside the sealant are obtained through airflow simulation. These data provide a basis for gas diffusion analysis. The diffusion coefficient (Diffusion Coefficient, usually represented by D) is used to describe the diffusion behavior of gas molecules in the sealant. The diffusion coefficient is an important parameter to measure the diffusion ability of gas in the medium. In order to obtain an accurate diffusion coefficient, it is first necessary to analyze it based on factors such as porosity, pore size distribution and airflow velocity. In this step, the Brownian motion theory of gas molecules is applied to determine the diffusion coefficient in combination with the pressure field and temperature field data in the airflow simulation. The diffusion behavior of gas molecules in the medium is usually affected by multiple factors such as temperature, pressure, gas type, pore structure, etc., so these physical properties need to be considered in the calculation. For example, at room temperature, the diffusion coefficient of air molecules is about 10^-5m 2 The diffusion coefficient is calculated based on the flow simulation results using the following formula: D = (k*T) / (6*π*η*r), where k is the Boltzmann constant, T is the temperature, η is the viscosity of the gas, and r is the pore radius. This formula is derived based on the properties of gas molecules and their movement within the pores. After the calculation, the gas diffusion rate in different pore regions can be further analyzed based on the magnitude of the diffusion coefficient. The gas diffusion rate can be calculated using the following formula: J = -D*(ΔC / Δx), where J represents the gas diffusion rate, ΔC is the change in gas concentration, Δx is the distance over which the concentration changes, and D is the diffusion coefficient, which represents the gas's ability to diffuse in the medium (a negative sign indicates that diffusion always proceeds in the direction of decreasing concentration (i.e., from high to low concentration)). Based on the concentration changes within different pore regions, the gas diffusion rate data in the sealant is obtained. During this process, it is necessary to compare experimental data with simulation results to ensure the accuracy of the calculated diffusion coefficient. After the gas diffusion data calculation is completed, the diffusion behavior of gas molecules in different pore areas in the sealant can be obtained, thus providing a basis for evaluating the air tightness of the curtain wall.

[0120] Evaluate curtain wall airtightness based on gas diffusion data.

[0121] In this embodiment, the core goal of the airtightness assessment is to analyze the performance of the sealant after aging by comparing the gas permeability and gas diffusion data, and to determine whether it can continue to effectively isolate gas penetration, thereby evaluating the overall airtightness performance of the curtain wall system. First, a comparative analysis is performed based on the gas permeability data obtained in the previous step. Gas permeability refers to the ratio between the flow rate of gas passing through the sealant and the gas concentration gradient per unit time, usually in units of (m 2 / s). A higher permeability indicates that the sealing performance of the sealant has been seriously affected, and the gas can easily pass through the sealant layer into the curtain wall system. In order to further determine the effectiveness of the sealant, the gas diffusion data, especially the diffusion coefficient (Diffusion Coefficient), are combined to compare the changes in the performance of the sealant before and after aging. A sealant with a larger diffusion coefficient value means that the gas diffuses faster inside it, indicating that the microstructure of the sealant has changed, resulting in a decrease in its barrier ability to gas. In this process, it is necessary to clearly set some judgment criteria, such as the gas permeability is higher than a certain set threshold (for example, 0.1m 2 / s), it indicates that the airtightness of the sealant fails; at the same time, if the diffusion coefficient is greater than a certain value (such as 10^-4m 2 If the sealant's airtightness data exceeds the design requirements, the sealant's air permeability or diffusion coefficient may exceed the specified value, indicating that the sealant has aged and failed, no longer meeting the curtain wall's airtightness requirements. In this case, the sealant needs to be replaced, reinforced, or repaired to ensure the curtain wall system's airtightness is maintained, preventing the ingress of air and moisture, and maintaining a stable and comfortable interior environment.

[0122] Preferably, the detection of curtain wall sound insulation in the functional defect detection module includes:

[0123] Identify airtightness weak areas based on curtain wall airtightness;

[0124] In this embodiment, by conducting a comprehensive air tightness test on the curtain wall, based on the gas permeability and gas diffusion data, combined with the degree of aging, porosity, air flow path and other structural characteristics, the areas with weak air tightness are identified. The identification criteria for weak air tightness areas include: gas permeability exceeding 0.1m 2 / s, the diffusion coefficient is greater than 10^-4m 2 / s, or local airtightness failure during multiple tests. Through the changes in these parameters, the curtain wall parts with severe damage or aging are identified, which usually become key areas with weak airtightness.

[0125] Perform acoustic wave propagation simulation on the airtight weak area to obtain acoustic wave propagation data;

[0126] In this embodiment, the sound wave propagation equation is used to calculate the propagation characteristics of sound waves in different areas based on known curtain wall material properties (such as density, elastic modulus, thickness, porosity, etc.) and structural characteristics. The input sound wave frequency range is generally set to 125Hz to 4000Hz to simulate the noise impact in daily environments. The airtight weak area is finely modeled using numerical simulation software (such as COMSOL or Ansys) to obtain sound wave propagation data in this area, including sound wave velocity, propagation path, and attenuation.

[0127] Calculate the sound wave transmission loss based on the sound wave propagation data;

[0128] In this embodiment, the sound wave transmission loss is obtained by comparing the difference in the intensity of the sound wave in the weak area and the normal area, and is usually calculated using the following formula:

[0129]

[0130] Among them, I0 is the sound wave intensity when there is no obstruction, I t The intensity of the sound wave after passing through the weak area. This process requires processing the sound wave intensity data calculated in the simulation to determine the intensity variation at each point along the sound wave propagation path, thereby obtaining the sound wave transmission loss value for each weak area. A higher transmission loss value indicates a stronger sound wave blocking ability and better sound insulation performance of the curtain wall.

[0131] Calculate the curtain wall sound insulation index based on sound wave transmission loss;

[0132] In this embodiment, the sound transmission loss data is used to further calculate the curtain wall's sound insulation index (STC). STC is a standard indicator used to evaluate the sound insulation performance of structures such as walls, doors and windows. It is usually calculated by taking the weighted average of the sound transmission losses of different frequencies. The formula for calculating the sound insulation index is:

[0133] STC=∑[TL(f)×W(f)];

[0134] Where TL(f) is the transmission loss at frequency f, and W(f) is the weighting factor for each frequency. The curtain wall's sound insulation index is calculated by multiplying the transmission loss at each frequency band by the weighting factor and summing the results across all frequency bands. A higher STC value indicates a curtain wall with greater sound insulation performance.

[0135] Calculate the noise reduction factor based on the sound wave transmission loss;

[0136] In this embodiment, the Noise Reduction Coefficient (NRC) is another indicator that measures the noise isolation capability of a curtain wall, particularly useful for evaluating performance in noisy environments. This coefficient is calculated by calculating the reflection and absorption of sound waves at different frequencies and comparing them with the sound transmission loss. The calculation process uses the following formula:

[0137]

[0138] Where A(f) is the sound absorption coefficient at each frequency band. The noise reduction factor is calculated by weighting the sound transmission loss and the sound absorption coefficient at each frequency band. A higher value for this factor indicates a greater ability of the curtain wall to reduce external noise.

[0139] The curtain wall sound insulation performance is evaluated according to the curtain wall sound insulation index and noise reduction coefficient to obtain the curtain wall sound insulation data.

[0140] In this embodiment, after obtaining the sound insulation index (STC) and noise reduction coefficient (NRC) of the curtain wall, a further sound insulation performance analysis is performed based on a standardized evaluation system. The STC value is determined by taking a weighted average of the transmission loss at different frequencies, where an STC value of more than 45 generally indicates good sound insulation performance, and an STC value of less than 40 indicates poor sound insulation. The calculation of the NRC value takes into account the sound wave absorption capacity of different frequency bands. Generally, for the sound insulation performance of a building, the closer the NRC value is to 1.0, the stronger the sound insulation performance. This value is often used to evaluate the noise suppression effect of the curtain wall within a specific frequency range. By comprehensively analyzing the STC and NRC values, if the STC value is low or the NRC value cannot meet the standard, it means that the curtain wall has deficiencies in sound insulation performance and cannot effectively isolate external noise. Therefore, it is necessary to take appropriate measures based on the evaluation results, such as improving the curtain wall design, enhancing the sound insulation of the material, or replacing components such as sealants to improve the sound insulation performance. During this process, the specific numerical requirements of STC and NRC are based on national or industry standards, and are adjusted and optimized accordingly according to the specific use environment and noise requirements of the building.

[0141] Preferably, the curtain wall safety warning module includes the following functions:

[0142] Calculate the glass displacement according to the curtain wall frame deformation data to obtain the glass displacement data;

[0143] In this embodiment, strain sensors installed on the curtain wall frame collect real-time deformation data of the frame. Strain sensors are located at various locations on the frame, enabling precise measurement of strain at each location. These strain values reflect the deformation of the frame under varying external conditions. To determine the overall deformation of the frame, the strain values can be processed using either a differential or integral method to infer the displacement or deformation of the frame. The differential method calculates deformation based on the strain differences between adjacent sensors, while the integral method integrates the strain values along the length of the frame to obtain deformation data. This deformation data is then input into a finite element analysis (FEA) model for more complex calculations. The FEA method decomposes the curtain wall frame into multiple small units to simulate its response under external loads. During the analysis, multiple parameters are required, such as the frame's material properties, including elastic modulus and Poisson's ratio, which determine its stiffness and deformation behavior. External load data, such as wind force and temperature fluctuations, also need to be set based on actual conditions to simulate the loads experienced in a real-world environment. The geometry of the glass panel (e.g., length, width, height, thickness, etc.) is also a necessary input parameter, as the displacement of the glass depends not only on the deformation of the frame but also on the geometric properties of the glass. Using this data, the finite element model calculates the deformation of the glass panel under different loads, including the amount of glass displacement and deformation distribution. This displacement data provides the basis for subsequent calculations of sealant deformation, helping to evaluate the overall performance of the glass curtain wall under load.

[0144] Identify deformation points of the curtain wall frame based on the deformation data of the curtain wall frame, and identify deformation boundaries of the sealant based on the deformation points of the curtain wall frame;

[0145] In this embodiment, high-precision strain sensors are installed at various locations on the curtain wall frame to obtain strain data under different stress conditions. These sensors can monitor the deformation of the curtain wall frame in real time under external loads such as wind pressure, temperature changes, earthquakes, or the structure's own weight, and reflect the stress distribution of the frame in the form of strain values. After data acquisition is completed, the strain data is input into finite element analysis (FEA) software, and a structural mechanics model of the curtain wall frame is established to accurately simulate the frame's deformation. This model must comprehensively consider factors such as the frame's geometry, material properties (such as elastic modulus, Poisson's ratio, yield strength, etc.), connection method, and external load conditions to ensure the accuracy of the calculation results. During the FEA simulation process, the stress distribution at different locations on the frame is first calculated based on the strain data, and the degree and deformation pattern of the frame's deformation are further inferred. By refining the grid division, the calculation accuracy is improved, making the identification of deformation points more precise. The calculated curtain wall frame deformation data can be visualized using contour maps or cloud maps to identify the areas of the curtain wall frame with the most significant deformation, namely the deformation points. These deformation points are typically located at the connection nodes of the curtain wall frame, areas of concentrated stress, or regions with large variations in material thickness. They are key factors influencing sealant deformation. Next, to identify the sealant's deformation boundaries, the deformation data of the curtain wall frame are overlaid with the stress distribution map for analysis, focusing on the impact of frame deformation on the sealant's location. Since sealant is typically applied at the joint between the curtain wall frame and the glass, frame deformation causes stress concentration in the sealant, ultimately leading to localized tensile, compressive, or shear deformation. Further analysis of these stress conditions identifies the areas of greatest sealant stress, which are typically concentrated at the edges or corners of the curtain wall frame-glass interface. Subsequently, by developing a material mechanics model for the sealant and incorporating its nonlinear deformation properties (such as viscoelasticity and stress relaxation), the sealant's response to various frame deformation conditions is simulated to determine the sealant's deformation boundaries. The identified sealant deformation boundaries are presented as high-strain regions and visualized through data visualization tools (such as strain and displacement field distribution plots). This analysis result can not only help identify the parts of the curtain wall structure where the sealant may fail, but also provide important data support for subsequent sealant material optimization, construction process improvement and durability evaluation.

[0146] Calculate the tensile deformation based on the deformation boundary of the sealant; calculate the shear deformation based on the deformation boundary of the sealant; calculate the compressive deformation based on the deformation boundary of the sealant; integrate the tensile deformation, shear deformation and compressive deformation to obtain the maximum strain value of the sealant;

[0147] In this embodiment, the tensile deformation represents the deformation of the sealant in a certain direction under the action of an external force. According to the identified sealant deformation boundary, combined with the material properties of the sealant, especially the elastic modulus and stress value, the tensile stress-strain relationship is used for calculation. The specific calculation process is: the stress data of the sealant deformation boundary area is input into the tensile stress-strain formula, which usually shows that the stress and strain are in a linear relationship. It should be noted that the calculation of the tensile deformation must take into account the initial physical properties of the sealant. Parameters such as the tensile modulus and Poisson's ratio must be set according to the known material properties to ensure the accuracy of the calculation results. For example, if the elastic modulus of the sealant is E and the Poisson's ratio is ν, the tensile deformation (ΔL) can be calculated by the formula ΔL = (F×L) / (A×E), where F is the external force, L is the original length, and A is the cross-sectional area. Furthermore, the actual load conditions of the sealant (such as wind pressure, temperature changes, etc.) will affect these calculation results. Calculate the shear deformation of the sealant. In this process, it is first necessary to calculate based on the shear stress of the sealant deformation boundary. Shear deformation refers to the deformation of the sealant under shear force, typically occurring in the area of contact between the sealant and the frame, particularly in areas subject to greater stress. To calculate shear deformation, the shear modulus (G) and strain data at the deformation boundary are used, using the shear stress-strain relationship. The formula for calculating shear stress is τ = G × γ, where τ is the shear stress, G is the shear modulus, and γ is the strain angle. The shear modulus G can typically be inferred from the tensile or compression modulus of the sealant or obtained experimentally. By inputting the shear modulus and boundary strain data, the shear deformation can be accurately determined. Calculating the sealant's compression deformation. Compression deformation represents the deformation of the sealant under compression. To calculate compression deformation, the sealant's compression modulus and strain data at the deformation boundary are required. The compression stress-strain formula is typically σ = E × ε, where σ is the compression stress, E is the compression modulus, and ε is the strain. In order to calculate the compression deformation, you first need to input the compression modulus of the sealant, which is usually obtained through experiments or inferred from the relevant physical properties of the sealant. Similar to the calculation of tensile and shear deformation, external loads (such as external pressure, temperature changes, etc.) and environmental factors must be taken into account to ensure the accuracy of the calculation results. Integrate the tensile deformation, shear deformation and compression deformation to obtain the maximum strain value of the sealant. The maximum strain value is usually obtained by combining the deformations of the three according to the specific stress and strain relationship. Taking into account the impact of different types of deformation on the sealant structure, the final calculated maximum strain value is a combination of each deformation, which is usually completed by engineering software or structural mechanics analysis tools. This maximum strain value is used to further analyze the deformation limit of the sealant under different conditions and provide a basis for subsequent life prediction.

[0148] Predict the life of glass curtain walls based on glass displacement data and maximum sealant strain value;

[0149] In this embodiment, the tensile, shear, and compressive deformations of the sealant need to be synthesized to obtain the sealant's maximum strain value. In this embodiment, strain superposition theory is used to vector-synthesize the three types of deformation data. Specifically, tensile, shear, and compressive deformations are calculated independently, representing the deformation of the sealant in different directions under external forces. Because these deformation types occur in different directions and with different forces acting in different directions, their deformations must be integrated using vector superposition. Each deformation type can be converted into corresponding strain data using the stress-strain relationship, and this data can be represented in a three-dimensional coordinate system. By synthesizing the strain values of tensile, shear, and compressive deformations according to their direction and magnitude, a comprehensive strain value is obtained, typically expressed as total strain or maximum strain. This maximum strain value reflects the maximum degree of deformation of the sealant during use and is directly related to its durability and performance. Once the maximum strain value of the sealant is obtained, it is combined with glass displacement data for life prediction. Glass displacement data reflects the movement of the glass panel under external loads, including deformation of the frame, deformation of the glass, and the interaction between the frame and glass. Glass displacement data is typically collected by strain sensors mounted on the curtain wall frame and obtained through deformation analysis. The amount of glass displacement is closely related to the stress on the sealant, which must withstand the stress generated by the relative displacement between the glass and the frame. Therefore, glass displacement data provides fundamental data for evaluating the stress on the sealant. Combining this data with the sealant's maximum strain, the sealant's service life can be further predicted using material fatigue performance models (such as fatigue life prediction based on the SN curve). The maximum stress of the sealant under actual operating conditions is calculated using this data. Next, a life prediction is performed based on the material fatigue performance model (such as the Goodman or Miner method) and the maximum stress data. The life prediction formula typically involves parameters such as the sealant's fatigue limit, stress amplitude, and strain amplitude, which can be obtained from experimental data or theoretical calculations. Finally, based on these calculation results, the sealant's expected service life under different operating conditions is determined, further assessing the overall durability of the glass curtain wall.

[0150] The glass curtain wall life is uploaded to the glass curtain wall safety intelligent monitoring system, and the curtain wall health level 3 warning task is executed to obtain the glass curtain wall health warning data.

[0151] In this embodiment, the service life of the glass curtain wall is estimated by a life prediction model using glass displacement data and the maximum strain value of the sealant. This model is usually based on material fatigue theory and combines factors such as historical loads, external environmental changes, and temperature fluctuations to evaluate the degree of fatigue damage to the glass and sealant. First, it is necessary to input the physical properties of the glass (such as elastic modulus and flexural strength) and the fatigue properties of the sealant (such as yield strength and durability). Then, the fatigue damage accumulation of the glass and sealant under cyclic loads is calculated. Ultimately, the fatigue life model is used to predict the overall life of the glass curtain wall and provide relevant life estimation data.

[0152] Preferably, the three-level warning tasks for curtain wall health include:

[0153] Upload the glass curtain wall life to the glass curtain wall safety intelligent monitoring system, and divide the glass curtain wall warning life level into the following levels: obtain the slight attenuation warning life data, the moderate attenuation warning life data and the high-risk failure warning life data;

[0154] In this embodiment, the life of the curtain wall is graded according to the life prediction data of the glass curtain wall, combined with the fatigue curve of the glass curtain wall material, environmental factors (such as temperature, humidity, etc.) and external loads (such as wind pressure, earthquake, etc.) through the set life assessment standard. According to the predicted remaining service life, the threshold of the warning level is set. For example, a remaining life of less than 5 years is a high-risk failure warning level, 5 to 10 years is a moderate attenuation warning level, and more than 10 years is a slight attenuation warning level. Based on these thresholds, the system uploads the glass curtain wall life data to the intelligent monitoring system and automatically divides it into three warning life levels. The specific setting of each level depends on the working environment of the curtain wall and the designed service life.

[0155] Automatically mark curtain wall warning monitoring points based on slight attenuation warning life data;

[0156] In this embodiment, based on the slight attenuation warning life data, the intelligent monitoring system will identify the warning area and mark it as a warning monitoring point. These warning monitoring points are located in key positions of the glass curtain wall, such as joints, support points, and areas around sealants, which are usually prone to structural changes. During the specific implementation process, the system determines the location of the monitoring point based on the position information and life data of the glass curtain wall, combined with sensor data (such as strain sensors, temperature and humidity sensors, etc.). Through the algorithm, combined with life data and historical monitoring records, the system will automatically update the location of the monitoring point and set it as the warning object. The location and status of the monitoring point will be continuously tracked, and the monitoring results will be updated through real-time data transmission.

[0157] Start the drone warning re-inspection function based on the moderate attenuation warning life data;

[0158] In this embodiment, when the moderate attenuation warning life data reaches the set threshold (for example, the remaining life is less than 10 years and greater than 5 years), the system automatically starts the drone warning re-inspection function. The drone is equipped with high-definition cameras, lidar and other equipment to perform high-precision scanning of the surface of the glass curtain wall. The drone's flight trajectory and mission will be planned in the intelligent monitoring system according to the specific location and status of the moderate attenuation area. In specific operations, the drone will cover all calibrated monitoring points according to the predetermined flight route, and conduct a detailed inspection of the glass curtain wall, take high-definition images, and record potential problems such as wall deformation, cracks, and falling off. The captured images and data will be uploaded to the intelligent monitoring system in real time, analyzed through image recognition technology, verify the actual condition of the moderate attenuation area, and further determine whether maintenance measures are needed.

[0159] Trigger emergency warning and repair functions based on high-risk failure warning life data;

[0160] In this embodiment, when the system detects that the remaining life of the glass curtain wall is less than 5 years, it enters a high-risk failure warning state and triggers the emergency warning repair function. In specific implementation, the system automatically identifies the areas that need immediate attention based on the life data and monitoring point status of the high-risk failure area, and prioritizes the initiation of the repair plan. The emergency warning repair function includes automatic notification of relevant maintenance personnel and generation of a repair task order. The repair task order includes information such as the specific repair location, repair plan, and required materials to ensure that maintenance work can be started quickly and carried out efficiently. The system will also track the repair progress in real time and record the status after the repair for subsequent evaluation.

[0161] Integrate curtain wall early warning monitoring points, drone early warning re-inspection functions, and emergency early warning repair functions to obtain glass curtain wall health early warning data.

[0162] In this embodiment, the intelligent monitoring system first collects data from early warning monitoring points. These points are typically equipped with strain sensors, temperature and humidity sensors, pressure sensors, and other equipment to monitor the glass curtain wall's stress, deformation, and temperature and humidity changes in real time. The location, monitoring data, and early warning level of each monitoring point are recorded by the system and synchronized to the data analysis platform for processing. Furthermore, the system automatically initiates drone re-inspection based on early warning life data indicating moderate degradation or high-risk failure. During a mission, the drone scans the early warning area along a pre-set flight path, capturing high-definition images or using equipment such as LiDAR for 3D modeling to generate detailed image data. The image data collected by the drone is analyzed using image recognition technology to detect cracks, spalling, or other damage. The analysis results are transmitted to the intelligent monitoring system in real time for updating. Simultaneously, for areas where emergency repairs are triggered, the system automatically generates repair tasks, specifies the specific repair location and plan, and dispatches professional maintenance personnel for on-site processing. The system also records repair progress in real time, including the use of repair materials and the time of completion. All data from early warning monitoring points, drone re-inspections, and repair tasks is integrated into the system's backend for summary, analysis, and evaluation. This data analysis generates a comprehensive glass curtain wall health early warning report. This report details the current health of each monitoring point, displays its warning level, lists the latest imagery and analysis results from drone re-inspections, and indicates the progress of repair tasks. The data and analysis results in the report provide building managers with comprehensive information on the current condition of the glass curtain wall, helping to guide subsequent maintenance, upkeep, and further risk assessment decisions.

[0163] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0164] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A curtain wall safety multi-source data intelligent monitoring system, characterized in that: Includes the following modules: The curtain wall structure defect detection module is used to obtain glass curtain wall vibration data; predict the probability of glass breakage based on the glass curtain wall vibration data; and perform curtain wall frame deformation analysis based on the glass breakage probability to obtain curtain wall frame deformation data; The material aging detection module is used to detect the tensile state of the sealing strip based on the deformation data of the curtain wall frame; calculate the cracking strain value based on the tensile state of the sealing strip; and evaluate the degree of sealant aging based on the cracking strain value; Functional defect detection module, used to simulate curtain wall water seepage based on the degree of sealant aging and obtain water seepage data; Evaluate curtain wall air tightness based on sealant aging; test curtain wall sound insulation based on curtain wall air tightness to obtain curtain wall sound insulation data; The curtain wall safety warning module is used to predict the life of the glass curtain wall based on the curtain wall frame deformation data and curtain wall sound insulation data; upload the glass curtain wall life to the glass curtain wall safety intelligent monitoring system, and execute the curtain wall health level 3 warning task to obtain glass curtain wall health warning data.

2. The curtain wall safety multi-source data intelligent monitoring system according to claim 1 is characterized in that: The curtain wall structure defect detection module includes the following functions: Obtaining glass curtain wall vibration data; converting the glass curtain wall vibration data into a vibration spectrum; Calculate the power spectrum density based on the vibration spectrum; draw a vibration energy distribution diagram based on the power spectrum density; identify the main vibration frequency of the vibration energy distribution diagram; obtain the natural vibration frequency of the glass curtain wall; Resonance matching is performed based on the main vibration frequency and the natural vibration frequency of the glass curtain wall to obtain resonance data; Measure the stress of the glass curtain wall based on the resonance data to obtain stress data; Evaluate glass fatigue damage based on stress data, where the peak stress is set at 50-70 MPa to obtain fatigue damage data; Predicting glass breakage probability based on fatigue damage data; The deformation of the curtain wall frame is analyzed based on the probability of glass breakage to obtain the deformation data of the curtain wall frame.

3. The curtain wall safety multi-source data intelligent monitoring system according to claim 2 is characterized in that: The curtain wall frame deformation analysis includes: Obtain the curtain wall area; map the glass breakage probability to the curtain wall area to obtain high breakage probability curtain wall data; Construct curtain wall rupture model using high rupture probability curtain wall data; Obtain wind data; Input wind data into the curtain wall rupture model and perform wind-induced dynamic response simulation to obtain wind-induced dynamic response data; The deformation of the curtain wall frame of the curtain wall rupture model is calculated according to the wind-induced dynamic response data, and the deformation displacement field of the curtain wall frame is constructed based on the deformation of the curtain wall frame; Locate the maximum deformation area based on the deformation displacement field of the curtain wall frame; Extract curtain wall frame deformation data based on the maximum deformation area.

4. The curtain wall safety multi-source data intelligent monitoring system according to claim 1 is characterized in that: The material aging detection module includes the following functions: Locate the sealing strip position based on the curtain wall frame deformation data; Identify the displacement of the sealing strip according to the position of the sealing strip and obtain displacement data; Determine the force point of the rubber strip based on the displacement data; Calculate the tensile strain of the rubber strip at the stress point; Determine the tensile state of the sealing strip according to the tensile strain of the strip; Calculate the cracking strain value based on the tensile state of the sealing strip; The degree of sealant aging is evaluated based on the cracking strain value.

5. The curtain wall safety multi-source data intelligent monitoring system according to claim 4 is characterized in that: The evaluation of sealant aging degree includes: The cracking state of the sealant is divided into the plastic state and the brittle fracture state according to the cracking strain value; UV exposure simulation is performed based on the plastic state to obtain UV irradiation data; Predict sealant hardness based on UV exposure data; Raman spectroscopy irradiation based on brittle fracture state; Identify short-chain molecules based on Raman spectra and obtain short-chain molecule data; Determine molecular chain breakage based on short-chain molecular data to obtain molecular chain breakage data; Evaluate the brittle strength of the brittle fracture state based on molecular chain breakage data; Assess the degree of sealant aging based on sealant hardness and brittleness strength.

6. The curtain wall safety multi-source data intelligent monitoring system according to claim 1 is characterized in that: The curtain wall water seepage simulation in the functional defect detection module includes: Construct a 3D curtain wall model based on the sealant aging degree; Rainwater flow simulation was performed based on the curtain wall 3D model, with the rainfall intensity range set to 10-150mm and the raindrop particle size to 0.1-5mm, to obtain rainwater flow data; Identify stormwater flow paths based on stormwater flow data; Determine the sealant aging area according to the sealant aging degree; The hydrolysis reaction of the sealant aging area is analyzed based on the rainwater flow path to obtain the hydrolysis reaction data; Sealant failure data based on hydrolysis reaction data, where the bond strength reduction is ≥50%, the tear strength reduction is ≥40%, and the material crack length is ≥5mm, are obtained; Determine sealant failure areas based on sealant failure data; Based on the rainwater flow path, curtain wall seepage simulation is performed in the sealant failure area to obtain seepage data.

7. The curtain wall safety multi-source data intelligent monitoring system according to claim 1 is characterized in that: The evaluation of curtain wall air tightness in the functional defect detection module includes: Detect sealant microstructure based on sealant aging degree; Calculate the sealant porosity based on the sealant microstructure; Perform air flow simulation based on the sealant porosity to obtain air flow data; Identify the air flow penetration path based on the air flow data, where the penetration path determination criteria are set as flow rate gradient change ≥ 10%, air pressure drop ≥ 50 Pa, and pore connectivity ≥ 40%; Calculate gas permeability based on the air flow permeation path; Perform gas diffusion analysis based on gas permeability to obtain gas diffusion data; Evaluate curtain wall airtightness based on gas diffusion data.

8. The curtain wall safety multi-source data intelligent monitoring system according to claim 1 is characterized in that: The detection of curtain wall sound insulation in the functional defect detection module includes: Identify airtightness weak areas based on curtain wall airtightness; Perform acoustic wave propagation simulation on the airtight weak area to obtain acoustic wave propagation data; Calculate the sound wave transmission loss based on the sound wave propagation data; Calculate the curtain wall sound insulation index based on the sound wave transmission loss; Calculate the noise reduction factor based on the sound wave transmission loss; The curtain wall sound insulation performance is evaluated according to the curtain wall sound insulation index and noise reduction coefficient to obtain the curtain wall sound insulation data.

9. The curtain wall safety multi-source data intelligent monitoring system according to claim 1 is characterized in that: The curtain wall safety warning module includes the following functions: Calculate the glass displacement according to the curtain wall frame deformation data to obtain the glass displacement data; Identify deformation points of the curtain wall frame based on the deformation data of the curtain wall frame, and identify deformation boundaries of the sealant based on the deformation points of the curtain wall frame; Calculate the tensile deformation according to the deformation boundary of the sealant; Calculate the shear deformation based on the deformation boundary of the sealant; Calculate the compression deformation based on the deformation boundary of the sealant; integrate the tensile deformation, shear deformation and compression deformation to obtain the maximum strain value of the sealant; Predict the life of glass curtain walls based on glass displacement data and maximum sealant strain value; The glass curtain wall life is uploaded to the glass curtain wall safety intelligent monitoring system, and the curtain wall health level 3 warning task is executed to obtain the glass curtain wall health warning data.

10. The curtain wall safety multi-source data intelligent monitoring system according to claim 9, characterized in that: The three-level early warning tasks for curtain wall health include: Upload the glass curtain wall life to the glass curtain wall safety intelligent monitoring system, and divide the glass curtain wall warning life level into the following levels: obtain the slight attenuation warning life data, the moderate attenuation warning life data and the high-risk failure warning life data; Automatically mark curtain wall warning monitoring points based on slight attenuation warning life data; Start the drone warning re-inspection function based on the moderate attenuation warning life data; Trigger emergency warning and repair functions based on high-risk failure warning life data; Integrate curtain wall early warning monitoring points, drone early warning re-inspection functions, and emergency early warning repair functions to obtain glass curtain wall health early warning data.

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