Existing building glass curtain wall operation and maintenance method and system based on digital base

Abstract

The invention relates to an existing building glass curtain wall operation and maintenance method and system based on a digital base. The method comprises the following steps that curtain wall vibration response data, visible light image data and three-dimensional point cloud data are obtained; based on the curtain wall vibration response data, comparing the current modal parameter with the historical modal parameter, and determining the dynamic characteristics of the curtain wall panel; identifying the curtain wall apparent damage type and the corresponding damage degree based on the visible light image data; based on the three-dimensional point cloud data, a curtain wall live-action three-dimensional point cloud model is constructed and mapped into a numerical model, a local stiffness reduction coefficient is calculated based on curtain wall panel dynamic characteristics, curtain wall apparent damage types and corresponding damage degrees, and a local stiffness matrix of a corresponding unit or node in the numerical model is corrected; and performing finite element analysis on the corrected numerical model to realize performance evaluation and deduction of the glass curtain wall. Compared with the prior art, the method has the advantages that the performance change of the glass curtain wall can be accurately and dynamically sensed and predicted, and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of building curtain wall operation and maintenance, and in particular to an operation and maintenance method and system for existing building glass curtain walls based on a digital base. Background Art

[0002] With the continuous development of modern building technology, glass curtain walls, as an important part of building facades, are one of the links in building urban safety and resilient cities. Existing high-rise building curtain walls are threatened by strong dynamic loads such as typhoons and earthquakes during their service life. Traditional curtain wall inspection methods mainly rely on manual inspections, which have problems such as low efficiency, high costs, and a strong dependence on the subjective judgment of inspectors. In addition, the existing evaluation of building glass curtain walls focuses on the analysis of single-modal data, and it is difficult to reveal the performance status of glass curtain walls from multiple dimensions. CN113887091A discloses an assembly building simulation test system and method. The assembly building simulation test system includes: a model assembly unit for assembling assembly components into an assembly building model using equivalent connectors for assembly components; a test unit for carrying the assembly building model and applying loads to the assembly building model; a data acquisition unit for scanning the assembly building model before testing to generate a three-dimensional point cloud building model, and also for collecting sensor data, displacement data, and strain data during the test; a calculation and analysis unit for performing finite element analysis on the three-dimensional point cloud building model to generate a finite element model and a measured model that can reflect the actual state of the model, and also for processing the finite element model and the measured model to obtain a digital twin model, and using the digital twin model to determine the load state of the assembly building model. However, this method is only applicable to assembly buildings, with a limited scope of application and is not suitable for building glass curtain walls; moreover, the collected data and analysis dimensions are single, only focusing on data related to structural performance, lacking the ability to collect and analyze data on other important dimensions such as the appearance state and dynamic response of the building; in addition, this method determines the load state of the assembly building model through finite element analysis and the digital twin model, only involving the overall structural performance evaluation of the model under load, and unable to comprehensively evaluate and deduce the performance of existing building glass curtain walls under different working conditions.

[0003] Therefore, there is currently a lack of a method that can integrate multi-modal data collection, combine the dynamic characteristics evaluation of curtain wall panels and the apparent damage evaluation of curtain walls, achieve comprehensive performance evaluation and deduction of glass curtain walls under different working conditions, and realize the comprehensive operation and maintenance of existing building glass curtain walls. Summary of the Invention

[0004] The object of the present invention is to provide an operation and maintenance method and system for existing building glass curtain walls based on a digital base. Through periodic and high-efficiency inspection methods, multi-modal data is collected. On this basis, through the evaluation of the dynamic characteristics of the curtain wall panels, the evaluation of the apparent damage of the curtain wall, numerical simulation deduction, and visual interaction, a digital base for the glass curtain wall is constructed to realize the full-life cycle monitoring and evaluation of the building glass curtain wall.

[0005] The object of the present invention can be achieved by the following technical solutions: An operation and maintenance method for existing building glass curtain walls based on a digital base, comprising the following steps: S1, obtaining curtain wall vibration response data, curtain wall visible light image data, and curtain wall three-dimensional point cloud data; S2, based on the curtain wall vibration response data, comparing the current modal parameters with the historical modal parameters to determine the dynamic characteristics of the curtain wall panels; S3, based on the curtain wall visible light image data, through an artificial intelligence vision large model, identifying the types of apparent damage to the curtain wall and the corresponding damage degrees; S4, based on the curtain wall three-dimensional point cloud data, constructing a three-dimensional point cloud model of the curtain wall scene as a digital base, and combining the measured material performance parameters of the curtain wall components, mapping the three-dimensional point cloud model into a numerical model. Based on the dynamic characteristics of the curtain wall panels and the types of apparent damage to the curtain wall and the corresponding damage degrees, calculating the local stiffness reduction coefficient, and correcting the local stiffness matrix of the corresponding elements or nodes in the numerical model; S5, performing finite element analysis on the corrected numerical model to realize the performance evaluation and deduction of the glass curtain wall under static force, wind load, and seismic action.

[0006] For typical specification curtain wall glass panels, the curtain wall vibration response data is collected by an acceleration sensor; for a large number of curtain wall glass panels with similar specifications, the curtain wall vibration response data is collected by a laser vibrometer.

[0007] The specific steps of S2 are as follows: S21, performing modal identification on the curtain wall vibration response data collected by the acceleration sensor by using the stochastic subspace method to obtain the first n modal frequencies of the typical specification curtain wall glass panels; S22, performing spectrum analysis on the curtain wall vibration response data collected by the laser vibrometer, and using the modal identification results in step S21 to exclude interference modes to obtain the first m modal frequencies of the large number of curtain wall glass panels with similar specifications, where m < n; S23, comparing the first m modal frequencies of the large number of curtain wall glass panels with similar specifications with the historical modal frequencies, and judging whether the curtain wall glass panels are abnormal according to the average value of the reduction rate of the first m modal frequencies, and dividing the abnormal degree level.

[0008] The apparent damage types of the curtain wall include glass panel detachment, glass panel self - explosion, deformation of support members, cracks in glass panels, and structural sealant detachment.

[0009] The specific damage degree is as follows: for glass panel detachment, glass panel self - explosion, and deformation of support members, the corresponding apparent damage degree of the curtain wall is severe damage; for cracks in glass panels and structural sealant detachment, calculation and evaluation indexes are used for damage grading. Among them, for cracks in glass panels, the evaluation index is calculated based on the spalling area and / or crack length of the glass panel, and for structural sealant detachment, the evaluation index is calculated based on the detachment length of the structural sealant.

[0010] The mapping of the three - dimensional point cloud model into a numerical model is specifically as follows: The curtain wall panels and support members in the three - dimensional point cloud model are segmented through a point cloud segmentation algorithm to obtain the geometric dimensions and spatial coordinates of the members. The geometric parameters of the members are corrected through a local refined three - dimensional point cloud model, and combined with the measured material property parameters of the curtain wall members, the overall three - dimensional point cloud model of the curtain wall is mapped into a numerical model, where the support members are line elements and the curtain wall panels are plate elements.

[0011] Based on the dynamic characteristics of the curtain wall panels, the apparent damage types of the curtain wall, and the corresponding damage degrees, calculate the local stiffness reduction coefficient to correct the local stiffness matrix of the corresponding elements or nodes in the numerical model. Specifically: Regarding the change in the dynamic characteristics of the curtain wall glass panels, calculate the first local stiffness reduction coefficient according to the mean value of the reduction rate of the first m modal frequencies in the dynamic characteristics of the curtain wall panels : , In the formula, is an empirical coefficient, is the mean value of the reduction rate of the first m modal frequencies; Regarding different types of apparent damage to the curtain wall, determine the second local stiffness reduction coefficient according to the damage degree : , In the formula, is the type of apparent damage to the curtain wall, is the evaluation index of the apparent damage, is the empirical coefficient corresponding to the type of apparent damage to the curtain wall i of, is the evaluation index corresponding to the damage category i of, j where, for glass panel detachment, glass panel self - explosion, and deformation of support members, is taken as 1.

[0012] Based on the first local stiffness reduction coefficient and the second partial stiffness reduction coefficient Modify the local stiffness matrix in the numerical model: , wherein, is the original local stiffness matrix, is the modified local stiffness matrix.

[0013] An existing building glass curtain wall operation and maintenance system based on a digital base, used to implement the described method, the system includes: Multimodal data acquisition module: including acceleration sensors, laser vibrometers, UAV platforms, 3D laser scanners. An optical camera and an RTK lidar are carried on the UAV platform. The acceleration sensors and laser vibrometers are used to collect and obtain the curtain wall vibration response data. The optical camera is used to collect the visible light image data of the curtain wall. The RTK lidar and 3D laser scanners are used to collect the 3D point cloud data of the curtain wall; Dynamic characteristic evaluation module: connected to the multimodal data acquisition module, used to compare the current modal parameters with the historical modal parameters based on the curtain wall vibration response data to determine the dynamic characteristics of the curtain wall panel; Apparent damage identification module: connected to the multimodal data acquisition module, used to identify the type and corresponding damage degree of the apparent damage of the curtain wall through an artificial intelligence vision large model based on the visible light image data of the curtain wall; Numerical simulation deduction module: connected to the multimodal data acquisition module, dynamic characteristic evaluation module and apparent damage identification module, used to construct a real - scene 3D point cloud model of the curtain wall as a digital base based on the 3D point cloud data of the curtain wall, and combine the measured material performance parameters of the curtain wall components to map the 3D point cloud model into a numerical model. Calculate the local stiffness reduction coefficient based on the dynamic characteristics of the curtain wall panel and the type and corresponding damage degree of the apparent damage of the curtain wall, and modify the local stiffness matrix of the corresponding elements or nodes in the numerical model; perform finite - element analysis on the modified numerical model to realize the performance evaluation and deduction of the glass curtain wall under static force, wind load and seismic action.

[0014] The system further includes a visualization interaction module: used to support roaming the digital model of the glass curtain wall at any angle and visualize the performance evaluation and deduction results of the building glass curtain wall.

[0015] The user interacts with the system through the visualization interaction module. When the user performs 3D real - scene roaming, the viewing angle, distance and viewing range can be freely adjusted through the interaction to view the damaged parts, and different - state glass panels are distinguished and displayed using different - colored markings.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention focuses on the operation and maintenance management of existing building glass curtain walls. In view of the usage characteristics and practical problems faced by existing building glass curtain walls, a dedicated operation and maintenance method is provided, which can accurately evaluate and deduce the performance status of existing building glass curtain walls, providing a more targeted decision-making basis for operation and maintenance work.

[0017] (2) The present invention comprehensively collects curtain wall vibration response data, curtain wall visible light image data, and curtain wall three-dimensional point cloud data, covering multiple dimensions such as the dynamic response, appearance state, and overall geometric shape of the curtain wall. The data collection is more comprehensive and can more comprehensively reflect the actual state of the curtain wall.

[0018] (3) The present invention accurately determines its dynamic characteristics by comparing the current modal parameters of the curtain wall panel with the historical modal parameters; uses an artificial intelligence vision large model to identify the apparent damage of the curtain wall, and can quickly and accurately identify the type and degree of damage; and combines the measured material performance parameters to convert the real-scene three-dimensional point cloud model constructed from three-dimensional point cloud data into a numerical model, making the data processing more in-depth and accurate, and providing a higher-quality data basis for subsequent numerical simulations and performance evaluations.

[0019] (4) The present invention calculates the local stiffness reduction coefficient and modifies the model, enabling the numerical model to more realistically reflect the actual performance status of the curtain wall, improving the accuracy of performance evaluation and deduction, helping to discover potential problems in advance, formulate a more scientific and reasonable operation and maintenance plan, and reduce the safety risks during the use of the curtain wall. After modifying the local stiffness matrix of the numerical model, finite element analysis is carried out to realize the performance evaluation and deduction of the glass curtain wall under various working conditions such as static force, wind load, and seismic action. It not only pays attention to the performance of the curtain wall in the current state, but also simulates and deduces possible future complex working conditions, providing more comprehensive and in-depth support for operation and maintenance decision-making.

[0020] (5) Based on the construction and modification of the digital base and numerical model, the present invention realizes the digital management of the glass curtain wall, enabling operation and maintenance personnel to intuitively understand the performance status and damage conditions of the curtain wall through the virtual model, facilitating remote monitoring and data analysis, and improving the intelligent level and decision-making efficiency of operation and maintenance management. Brief Description of the Drawings

[0021] Figure 1 is the flowchart of the method of the present invention; Figure 2 is the system structure diagram of the present invention. Detailed Embodiments

[0022] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives the detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0023] Embodiment 1 This embodiment provides an operation and maintenance method for existing building glass curtain walls based on a digital base, as Figure 1 shown, including the following steps: S1. Obtain curtain wall vibration response data, curtain wall visible light image data, and curtain wall three-dimensional point cloud data.

[0024] In this embodiment, a periodic and rapid curtain wall inspection mechanism is used to continuously collect data from the glass curtain wall, and dynamic data on the performance of the glass curtain wall in the time dimension is obtained.

[0025] Before implementing multi-modal data collection, a sensor network is arranged inside the building, the typical specification panels of the glass curtain wall are counted, and dense acceleration sensors are arranged. An unmanned aerial vehicle (UAV) airport is deployed outside the building, and an UAV equipped with an ultra-high-definition optical camera and an RTK lidar device is deployed inside the airport. A laser scanner is deployed in a typical curtain wall area to collect three-dimensional point cloud data of the curtain wall area and construct a local refined model.

[0026] Specifically, 16 acceleration sensors are evenly arranged on the typical specification panels, and the acceleration sensors are used to collect the vibration response data of the typical size glass panels; single-point laser vibration meters are evenly arranged on a large number of panels with similar specifications, and the laser vibration meters are used to collect the vibration response data of a large number of glass panels with similar specifications. The UAV platform is equipped with an optical camera to regularly collect high-precision visible light image data of the curtain wall according to a preset customized cruise route for the evaluation of the apparent damage of the curtain wall. The UAV platform is equipped with an RTK lidar to regularly collect the overall three-dimensional point cloud data of the glass curtain wall according to a preset flight route for the reconstruction of the overall point cloud model of the curtain wall. The laser scanner is used to collect the three-dimensional point cloud data of the curtain wall area for the construction of a local refined model to obtain high-precision geometric parameters of local curtain wall components.

[0027] During the normal service process of the glass curtain wall, multi-modal data collection is implemented every 3-6 months, and the frequency of multi-modal data collection is increased after extreme weather, including typhoons and earthquakes.

[0028] S2. Based on the curtain wall vibration response data, compare the current modal parameters with the historical modal parameters to determine the dynamic characteristics of the curtain wall panels.

[0029] S2 specifically includes the following steps: S21, using the random subspace method to perform modal identification on the curtain wall vibration response data collected by the acceleration sensor, and obtaining the first n modal frequencies of the curtain wall glass panels of typical specifications; S22, performing spectrum analysis on the curtain wall vibration response data collected by the laser vibrometer, eliminating interference modes using the modal identification result in step S21, and obtaining the first three modal frequencies of a large number of curtain wall glass panels with similar specifications; S23, compare the first three modal frequencies of curtain wall glass panels of similar specifications in large quantities with the historical modal frequencies, and judge whether the curtain wall glass panels are abnormal according to the average of the reduction rate of the first three modal frequencies, and divide the abnormality level into no abnormality, slight abnormality, moderate abnormality, and severe abnormality. In one embodiment, the average of the reduction rate of the first three modal frequencies is 0-5% for no abnormality, 5%-15% for slight abnormality, 15%-30% for moderate abnormality, and more than 30% for severe abnormality.

[0030] S3, based on the visible light image data of the curtain wall, uses the artificial intelligence visual big model to identify the curtain wall surface damage type and the corresponding damage degree.

[0031] In this embodiment, a pre-trained visual large model based on the Transformer architecture is used, and fine-tuning technology is used to fine-tune the glass curtain wall damage dataset. The fine-tuned model identifies and classifies curtain wall damage, and grades the damage based on the evaluation index to obtain the degree of damage. The details are as follows: Preprocess and annotate high-definition curtain wall image data, including abnormal image removal and image size adjustment. Further, use image annotation tools to annotate different types of curtain wall damage with pixel-level masks to accurately mark the location and shape of the damaged area. Curtain wall damage includes glass panel detachment, glass panel self-explosion, support component deformation, glass panel cracks, and structural adhesive detachment.

[0032] The pre-trained model based on the Transformer architecture can use large visual models such as ViT and DinoV2, and adapt to the curtain wall surface damage dataset through LoRA fine-tuning technology. and Parameter matrix, the LoRA module fine-tuning process is as follows: Fixed pre-trained model weight matrix ; Initialize the dimension reduction matrix with random Gaussian distribution , zero matrix initializes the dimension-raising matrix ,in ; The parameter update matrix is ​​calculated as: , Calculate the forward propagation output : , The number of matrix parameters updated by LoRA fine-tuning technology is much smaller than that of pre-trained model parameters, thereby reducing the parameter update amount during the training of the large visual model, improving the efficiency of model fine-tuning and reducing the cost of model fine-tuning. When the large visual model is inferred, the curtain wall image data is input into the large model to obtain the curtain wall damage type and the corresponding damage mask.

[0033] The types of apparent damage to the curtain wall classified by the pre-trained visual large model include glass panel falling off, glass panel self-explosion, supporting component deformation, glass panel cracks and structural adhesive falling off.

[0034] For glass panel falling off, glass panel self-explosion and supporting component deformation, the corresponding apparent damage degree of the curtain wall is severe damage; for glass panel cracks and structural adhesive falling off, the evaluation index is calculated to classify the damage into slight damage, moderate damage and severe damage. For glass panel cracks, the evaluation index is calculated based on the glass panel peeling area and / or glass panel crack length, and for structural adhesive falling off, the evaluation index is calculated based on the structural adhesive falling length. Specifically: Evaluation index 1: The ratio of the potential peeling area of ​​the glass panel and / or the closed area of ​​the glass panel crack to the area of ​​the glass panel; Evaluation indicator 2: The ratio of the length of the crack in the glass panel to the circumference of the glass panel; Evaluation indicator 3: The ratio of the length of structural adhesive peeling off to the total length of structural adhesive on the glass panel.

[0035] In one embodiment, an evaluation index of 0-10% indicates slight injury, 10%-30% indicates moderate injury, and above 30% indicates severe injury.

[0036] S4, based on the curtain wall 3D point cloud data, construct a real-scene 3D point cloud model of the curtain wall as a digital base, and combine the measured material performance parameters of the curtain wall components to map the 3D point cloud model into a numerical model. Based on the dynamic characteristics of the curtain wall panel and the apparent damage type and corresponding damage degree of the curtain wall, calculate the local stiffness reduction factor, and correct the local stiffness matrix of the corresponding unit or node in the numerical model.

[0037] S41, establish a 3D point cloud model based on the curtain wall 3D point cloud data as the platform basic model and carrier for the operation and maintenance of the existing building glass curtain wall.

[0038] Perform filtering and denoising operations on RTK lidar data to remove noise and outliers from the point cloud data, thereby improving the clarity and accuracy of the data. Subsequently, perform a simplification operation based on the voxel downsampling algorithm, which is achieved by converting to a voxel grid and adjusting the resolution to simplify, reduce the complexity and storage volume of the point cloud data, improve the efficiency of data processing and transmission, and simultaneously maintain the geometric features of the data. Finally, perform a stitching operation based on the iterative closest point algorithm. By iteratively optimizing the alignment of the point cloud data, minimize the overlap between different point clouds, stitch together point cloud data obtained from multiple different positions or angles into a complete model, and eliminate the overlap and gaps between different point clouds.

[0039] In this embodiment, control point piercing optimization is adopted to improve the accuracy of model construction. When laying out control points, both artificial control points and natural control points are used, which are laid on the structural surface and the surrounding ground. The total station is used to measure the three-dimensional coordinates to ensure the accuracy of three-dimensional point cloud model reconstruction. When performing three-dimensional model reconstruction, computer vision algorithms are adopted to analyze the geometric features and texture information of the model, delete suspended objects and render the model to optimize the quality and visual effect of the three-dimensional model.

[0040] S42, map the three-dimensional point cloud model into a numerical model.

[0041] Segment the curtain wall panels and supporting members in the three-dimensional point cloud model through the random sample consensus algorithm and obtain the geometric dimensions and spatial coordinates of the members. Correct the geometric parameters of the members through local refinement of the three-dimensional point cloud model, and combine the measured material performance parameters of the curtain wall members to map the overall three-dimensional point cloud model of the curtain wall into a numerical model, where the supporting members are line elements and the curtain wall panels are plate elements.

[0042] S43, map the analysis results of steps S2 and S3 into the numerical model in the form of stiffness reduction to correct the numerical model.

[0043] Specifically, calculate the local stiffness reduction coefficient based on the dynamic characteristics of the curtain wall panels, the type of curtain wall apparent damage and the corresponding damage degree, and correct the local stiffness matrix of the corresponding elements or nodes in the numerical model.

[0044] For the change in the dynamic characteristics of the curtain wall glass panels, calculate the first local stiffness reduction coefficient according to the mean value of the reduction rate of the first m modal frequencies in the dynamic characteristics of the curtain wall panels : , In the formula, is the empirical coefficient, is the mean value of the reduction rate of the first m modal frequencies; For different types of curtain wall apparent damage, determine the second local stiffness reduction coefficient according to the damage degree : , In the formula, is the type of apparent damage to the curtain wall, is the evaluation index of the apparent damage, is the empirical coefficient corresponding to the type of apparent damage to the curtain wall i , is the evaluation index corresponding to the damage category i , and the value of j Among them, for the glass panel detachment, glass panel self - explosion and deformation of the support member, is taken as 1.

[0045] Based on the first local stiffness reduction coefficient and the second local stiffness reduction coefficient correct the local stiffness matrix in the numerical model: , In the formula, is the original local stiffness matrix, is the corrected local stiffness matrix.

[0046] S5. Conduct finite - element analysis on the corrected numerical model to realize the performance evaluation and deduction of the glass curtain wall under static force, wind load and seismic action.

[0047] Example 2 This example provides an operation and maintenance system for existing building glass curtain walls based on a digital base, which is used to implement the method of Example 1. As Figure 2 shown, the system includes: (1) Multimodal data acquisition module: including acceleration sensors, laser vibrometers, UAV platforms, 3D laser scanners. An optical camera and an RTK lidar are carried on the UAV platform. The acceleration sensors and laser vibrometers are used to collect the vibration response data of the curtain wall. The optical camera is used to collect the visible - light image data of the curtain wall. The RTK lidar and 3D laser scanners are used to collect the 3D point - cloud data of the curtain wall; (2) Dynamic characteristic evaluation module: connected to the multimodal data acquisition module, and used to compare the current modal parameters with the historical modal parameters based on the vibration response data of the curtain wall to determine the dynamic characteristics of the curtain wall panel; (3) Apparent damage identification module: connected to the multimodal data acquisition module, and used to identify the type of apparent damage to the curtain wall and the corresponding damage degree through an artificial - intelligence vision large - model based on the visible - light image data of the curtain wall; (4)Numerical Simulation Deduction Module: Connecting the multi-modal data acquisition module, dynamic characteristic evaluation module, and apparent damage identification module, it is used to construct a three-dimensional point cloud model of the curtain wall scene as a digital base based on the three-dimensional point cloud data of the curtain wall, and combine the measured material property parameters of the curtain wall components to map the three-dimensional point cloud model into a numerical model. Based on the dynamic characteristics of the curtain wall panel, the type of apparent damage to the curtain wall, and the corresponding damage degree, calculate the local stiffness reduction coefficient, and modify the local stiffness matrix of the corresponding elements or nodes in the numerical model; perform finite element analysis on the modified numerical model to realize the performance evaluation and deduction of the glass curtain wall under static force, wind load, and seismic action. (5)Visualization Interaction Module: It is used to support roaming the digital model of the glass curtain wall at any angle and visualize the performance evaluation and deduction results of the building glass curtain wall.

[0048] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the multi-modal data acquisition module, dynamic characteristic evaluation module, apparent damage identification module, and numerical simulation deduction module can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0049] For the visualization interaction module, a digital model of the glass curtain wall that can be roamed (including fixed-track roaming and free roaming) is constructed based on the Unreal Engine. Users interact with the system through the visualization interaction module. When performing three-dimensional real-scene roaming, the viewing angle, distance, and viewing range can be freely adjusted through the interaction to view the damaged parts. It includes: Mark and prompt the damage characteristics of the building facade, such as cracks, peeling, and deformation. Different colors are used to distinguish and display glass panels in different states. Green indicates normal, orange indicates abnormal, and red indicates severely abnormal.

[0050] Display the historical monitoring data, structural vibration response, and historical changes in the defects of the building facade, compare the damage changes during multiple inspections, and predict the development of damage indicators.

[0051] The UE platform has high-fidelity real-time rendering capabilities, can support complex lighting effects and material performances, and provides a more realistic visual experience for the visualization interaction of the glass curtain wall.

[0052] Under the UE engine, display the historical vibration response of the curtain wall and the historical changes in apparent damage, compare the damage changes during multiple inspections through the built-in fine-tuned visual large model, and predict the development of damage indicators through the LSTM algorithm.

[0053] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative efforts. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art shall fall within the protection scope determined by the claims.

Claims

1. An operation and maintenance method for existing building glass curtain walls based on a digital base, characterized in that The following steps are involved: S1, obtaining curtain wall vibration response data, curtain wall visible light image data and curtain wall three-dimensional point cloud data; S2, based on the curtain wall vibration response data, comparing the current modal parameters with the historical modal parameters to determine the dynamic characteristics of the curtain wall panel; S3, based on the visible light image data of the curtain wall, identifying the type of apparent damage of the curtain wall and the corresponding degree of damage through an artificial intelligence visual big model; S4, based on the curtain wall three-dimensional point cloud data, construct a curtain wall real-scene three-dimensional point cloud model as a digital base, and map the three-dimensional point cloud model into a numerical model in combination with the measured material performance parameters of the curtain wall components, calculate the local stiffness reduction coefficient based on the dynamic characteristics of the curtain wall panel and the curtain wall apparent damage type and the corresponding damage degree, and correct the local stiffness matrix of the corresponding unit or node in the numerical model; S5, finite element analysis is performed on the modified numerical model to evaluate and deduce the performance of the glass curtain wall under static, wind load and earthquake action.

2. The operation and maintenance method of the existing building glass curtain wall based on a digital base according to claim 1, characterized in that, For curtain wall glass panels of typical specifications, the curtain wall vibration response data is collected by using an acceleration sensor; for curtain wall glass panels of similar specifications in large quantities, the curtain wall vibration response data is collected by using a laser vibrometer.

3. The operation and maintenance method of the existing building glass curtain wall based on the digital base according to claim 2, characterized in that, The S2 specifically includes the following steps: S21, using the random subspace method to perform modal identification on the curtain wall vibration response data collected by the acceleration sensor, and obtaining the first n modal frequencies of the curtain wall glass panels of typical specifications; S22, performing spectrum analysis on the curtain wall vibration response data collected by the laser vibrometer, eliminating interference modes using the modal identification result in step S21, and obtaining the first m modal frequencies of a large number of curtain wall glass panels with similar specifications, where m < n; S23, comparing the first m modal frequencies of a large batch of curtain wall glass panels with similar specifications with the historical modal frequencies, judging whether the curtain wall glass panels are abnormal based on the average of the reduction rates of the first m modal frequencies, and classifying the degree of abnormality.

4. A method for operation and maintenance of existing building glass curtain walls based on a digital base according to claim 1, characterized in that The types of apparent damage to the curtain wall include falling off of glass panels, self-explosion of glass panels, deformation of supporting components, cracks in glass panels and falling off of structural adhesives.

5. A method for the operation and maintenance of existing building glass curtain walls based on a digital base according to claim 4, characterized in that, The damage degree is specifically as follows: for glass panel falling off, glass panel self-explosion and supporting component deformation, the corresponding apparent damage degree of the curtain wall is severe damage; for glass panel cracks and structural adhesive falling off, evaluation indicators are calculated to perform damage classification, wherein, for glass panel cracks, the evaluation indicator is calculated based on the glass panel peeling area and / or the glass panel crack length, and for structural adhesive falling off, the evaluation indicator is calculated based on the structural adhesive falling length.

6. The operation and maintenance method of the existing building glass curtain wall based on a digital base according to claim 1, characterized in that The mapping of the three-dimensional point cloud model into a numerical model is specifically as follows: The curtain wall panels and supporting components in the 3D point cloud model are segmented through the point cloud segmentation algorithm, and the geometric dimensions and spatial coordinates of the components are obtained. The component geometric parameters are corrected by locally refining the 3D point cloud model, and combined with the measured material performance parameters of the curtain wall components, the overall 3D point cloud model of the curtain wall is mapped into a numerical model, in which the supporting components are line units and the curtain wall panels are plate units.

7. A method for the operation and maintenance of existing building glass curtain walls based on a digital base according to claim 1, characterized in that, Calculate the local stiffness reduction coefficient based on the dynamic characteristics of the curtain wall panel, the type of apparent damage to the curtain wall, and the corresponding damage degree, and correct the local stiffness matrix of the corresponding elements or nodes in the numerical model. Specifically: According to the change in the dynamic characteristics of the curtain wall glass panel, the first local stiffness reduction coefficient is calculated based on the average value of the reduction rate of the first m modal frequencies in the dynamic characteristics of the curtain wall panel. : ， In the formula, is the empirical coefficient, is the average reduction rate of the first m natural frequencies; For different types of curtain wall appearance damages, determine the second local stiffness reduction coefficient according to the damage degree : ， In the formula, is the apparent damage type of the curtain wall. is an evaluation index of apparent damage. Corresponding to the curtain wall apparent damage type i The empirical coefficient of Corresponding to the damage category i Evaluation Metrics j The value of, among which, for glass panel falling off, glass panel self-explosion and support member deformation, Take it as 1; Based on the first local stiffness reduction coefficient and the second local stiffness reduction coefficient Modify the local stiffness matrix in the numerical model: ， In the formula, is the original local stiffness matrix, is the modified local stiffness matrix.

8. An operation and maintenance system for existing building glass curtain walls based on a digital base, characterized in that, For implementing the method according to any one of claims 1-7, the system includes: Multi-modal data acquisition module: including acceleration sensors, laser vibration meters, UAV platforms, and 3D laser scanners. An optical camera and an RTK lidar are carried on the UAV platform. The acceleration sensors and laser vibration meters are used to collect and obtain the vibration response data of the curtain wall. The optical camera is used to collect the visible light image data of the curtain wall. The RTK lidar and 3D laser scanner are used to collect the 3D point cloud data of the curtain wall; Dynamic characteristic evaluation module: connected to the multi-modal data acquisition module, and used to determine the dynamic characteristics of the curtain wall panel based on the vibration response data of the curtain wall and compare the current modal parameters with the historical modal parameters; Apparent damage identification module: connected to the multi-modal data acquisition module, and used to identify the type of apparent damage to the curtain wall and the corresponding damage degree through an artificial intelligence vision large model based on the visible light image data of the curtain wall; Numerical simulation deduction module: connected to the multi-modal data acquisition module, dynamic characteristic evaluation module, and apparent damage identification module, and used to construct a 3D point cloud model of the curtain wall scene as a digital base based on the 3D point cloud data of the curtain wall, and combine the measured material performance parameters of the curtain wall components to map the 3D point cloud model into a numerical model. Calculate the local stiffness reduction coefficient based on the dynamic characteristics of the curtain wall panel, the type of apparent damage to the curtain wall, and the corresponding damage degree, and correct the local stiffness matrix of the corresponding elements or nodes in the numerical model; perform finite element analysis on the corrected numerical model to realize the performance evaluation and deduction of the glass curtain wall under static force, wind load, and seismic action.

9. The operation and maintenance system for the existing building glass curtain wall based on a digital base according to claim 8, characterized in that, The system further includes a visualization interaction module: used to support roaming the digital model of the glass curtain wall at any angle and visualize the performance evaluation and deduction results of the building glass curtain wall.

10. The operation and maintenance system for existing building glass curtain walls based on a digital base according to claim 9, characterized in that, The user interacts with the system through the visualization interaction module. When the user performs 3D real-scene roaming, the viewing angle, distance, and viewing range can be freely adjusted through the interaction to view the damaged parts, and different color markings are used to distinguish and display the glass panels in different states.

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